[Please cite the version of this manuscript published in the Journal of Progress in Planning since this output is only intended to ensure research reproducibility. Likewise, the accompanying preprint text is included to provide context to the chunks of r script.]
Together, globalisation and urbanisation are accelerating the densification of cities while disruptive technologies such as micro-mobility and ride-hailing are transforming urban mobility. Amidst this change, urban planning officials and practitioners typically remain constrained to the same urban footprint, left to grapple with earlier car-oriented development, and yet must accommodate a growing population and variety of travel modes operating within the same space. Further, they must operate alongside government officials whose re-election could depend upon appeasing suburban residents that are unable or unwilling to relocate along active transport corridors, near public transit nodes, or forgo the flexibility and comfort of private automobiles. As a result, private automobiles can become necessary for traversing urban forms already enlarged by parking, driveways, roads, highways, and flyovers. Likewise, alternatives such as public and active transport can become impractical and dangerous within urban forms that are fragmented by congestion or fast traffic. Given that urban mobility research typically focuses on keeping our pre-existing modal choices moving rather than side-effects, daily commutes have remained unchanged for decades, and planners are better equipped to continually accommodate rather than influence our modal choices. This volume of Progress in Planning aims to strengthen the evidence base for influencing modal choice by developing a comparative framework of urban mobility, and by examining how parking policy has influenced modal choice within the three largest Australian cities: Brisbane, Sydney, and Melbourne. In addition, it provides reproducible methods for estimating parking supply using land use audits, parking demand using a population census, and geo-statistical modelling for determining whether and where parking policy can explain more sustainable modal choices. As such, this volume sets a research agenda for metropolitan-scale examination and coordination of transport and land use planning for sustainable rather than temporary urban mobility.
library(spgwr)
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my.palette <- RColorBrewer::brewer.pal(9, 'Set1')
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map.diameter <- 60000Land use planning—particularly parking—can be a potent and prescriptive tool for influencing our modal choices given that land use determines when, where, and whether our modal choices are convenient and cost-effective (Manville 2017a). Given that metropolitan land use planning authorities are typically decentralised and fragmented between multiple local governments, policy inequalities can emerge when the land use planning approaches are poorly coordinated. In contrast, metropolitan transport planning authority is typically the domain of a singular state government but is relegated to the more ‘responsive’ role of increasing road capacity, prioritising lanes for our modal choices, and increasing public transit services according to our modal prescribed by land use planning (Barter 2015). In this sense, it is the lower tiers of government that are prescribing the urban mobility agenda yet not necessarily accountable when policy inequalities emerge.
An example of policy inequality could be an inner-city local government introducing policies that increase the inner-city parking supply to attract businesses and shoppers during the same period that an outer-city local government introduces policies that encourage using ‘Park ‘n’ Ride’ facilities (PnR) to reduce congestion. A further, outer-city local government may introduce Transit Orientated Development (TOD) policies to reduce auto-dependence. Within this hypothetical scenario, an antagonistic relationship could emerge between the three local governments since abundant inner-city parking could cause outer-city residents to disregard their PnR, or motorists that reside beyond the walkable-range of TOD zone may decide to utilize the improved rapid transit services and begin to clog the streets and exhaust the local parking supply by treating the TOD as an informal PnR. Notably, the local government that increases their parking supply that reaps the greatest rewards with an expanded customer base while it is typically the state government that will need to accommodate the heightening demand for road capacity (Young & Miles 2015; McCahill & Garrick 2014). As such, there is positive reinforcement for local governments that provide parking and positive feedback loop for parking supply and demand that is typically termed the ‘induced demand problem’ within transport research (Hansen 1995).
Given the potential for policy inequality, induced demand, and reactive rather than prescriptive transport planning, research and policy should arguably broaden focus beyond amenity (e.g. speed and convenience) to include the dis-amenities and side-effects (e.g. sprawl, expenses, and danger). As Manville (2017b: 29) argues, ‘The travel and built environment literature yields weak and sometimes contradictory results because it emphasizes what is less important and ignores what is more important. This is not a formula for powerful and relevant research.’, which could explain why technical solutions for supplying enough parking, eliminating traffic congestion, and reducing daily commute times remain so elusive for researchers and practitioners.
Button (2006) estimates that private automobiles are in motion just five percent of the time and this is a particularly important finding given the swathes of urban space dedicated to storing private automobiles throughout the periods they are stationary, unproductive, and displacing people and more productive land use types. Indeed, researchers and practitioners that prioritise private automobiles are not staying abreast of contemporary urban mobility trends. For instance, driving appears to be losing its appeal and personal significance throughout OECD countries where young adults are delaying when they start driving (Goodwin & van Dender 2013). Further, as urbanisation continues to concentrate populations closer towards inner cities, the private automobiles are losing utility relative to Active and more Sustainable Transport modes (AST; e.g. walking, cycling and public transit) that operate more effectively within higher urban densities. Closely related, technologies such as ride-hailing, micromobility (e.g. e-scooters and bike-sharing) and Autonomous Vehicles (AVs) are emerging and improving the appeal of public transit by solving the first mile problem of traveling from home to a public transit node and the last mile problem of travelling from a public transit node to the workplace or destination without requiring a private automobile or long walk (Sipe & Pojani 2018). Lastly, Mobility as a Service (MaaS) is emerging that can route and coordinate complex journeys (e.g. ride-hailing, ride-sharing, and public transit), reserve parking bays, and integrated payment across various commercial and public services thus further reducing the utility of owning a private automobile. With the peak demand for road and parking capacity passing by within OECD countries, it may be time to slow or cease the supply to avoid burdening future generations with sprawling cities that are blanketed by artificial surfaces (Steele 2018). For instance, researchers and practitioners can begin by identifying where urban space can be reclaimed and repurposed for docking-stations, protected bike lanes, and automobile drop-off lanes.
Within this volume of Progress in Planning, our initial aim is to clarify and integrate urban mobility research and planning approaches by developing a comparative framework of urban mobility. Following, this comparative framework and template analysis are employed to explore parking planning policy and practice throughout the three largest Australian cities: Brisbane, Sydney, and Melbourne. This exploration will determine whether best practice is occurring by identifying the planning approaches in place. Given that insufficient parking data typically limits investigations but is necessary for determining baseline parking supply and calibrating parking requirements, the analysis to follow will draw on disparate data sources for estimating parking supply within each inner-city and draw on population censuses of residents and workers for estimating parking demand. The final analysis employs spatial modelling to determine whether and where modal choices are explained by land use planning while taking neighbourhood demographics into account. The article concludes by charting a way forward for researchers and practitioners that cam stay abreast of contemporary mobility trends. While Australian cities are examined solely within this volume, the comparison of these three independent cities ensures external validity and generalisability to cities world-wide. As such, we can contend that the study findings will interest urban planners, policy makers, and transport engineers working towards improving consolidation and improving urban mobility.
When planning practitioners and policy makers attempt to improve urban mobility and influence modal choices through parking supply, they typically adopt one of three broad yet distinct planning approaches. While the terms for these planning approaches vary throughout the literature, the terms ‘predict and provide’, ‘multimodalism’, and ‘demand management’ are used throughout this volume and the emphasis is on the distinctions between the planning approaches that arguably can be arranged along a continuum between pragmatist- or radical -leanings (Table 1).
Table 1. A comparative framework of urban mobility organised by planning orientation, approach, and intended modal choice
knitr::include_graphics("./tab1.png")The predict and provide planning approach is situated the closest towards the pragmatists far end of the three planning approaches. It attempts to accommodate motorists’ parking demands by predicting their parking demand and providing this amount. This planning approach is pragmatic by acknowledging that some commuters already reside within auto-dependent locations (i.e. where a private automobile is a necessity), and therefore will require enough parking both at their home origin and their various destinations to ensure that all arriving motorists during peak-demand periods can park on-site, which is a principle termed ‘parking self-containment’. By ensuring that parking is self-contained, this minimises the occurrence of parking overflow that can be a source of social conflict when it exhausts nearby on-street and off-street parking. Further, ensuring that parking is self-contained also minimises the period that motorists spend cruising for parking that slows all trailing traffic.
The multimodalism planning approach is located towards the centre of the pragmatic-radical continuum. For instance, this planning approach is both pragmatic by acknowledging that some commuters already reside within auto-dependent locations but also radical by attempting to shift their demands for road and parking capacity away from the inner-city where urban land is expensive and could be put to more productive use. Multimodalism planning approaches can include supplying PnR parking bays, kiss ‘n’ ride drop-off bays at Rapid Transit Nodes (RTN), or feeder transit services such as public buses that deliver commuters to RTN rather than their destination. Notably, RTN are a critical component of multimodalism given that rapid transit (e.g. trains, ferries, or Bus Rapid Transit) can make multimodal journeys appealing by bypassing traffic congestion unlike direct transit (e.g. busses and trams) and private automobiles.
Last, the demand management planning approach is the most radical of the three planning approaches. It regards parking demand—and road capacity demand—as potentially limitless. This is because parking that is easy to locate and short walk from destinations can induce further driving (i.e. induce demand) and can consequently abstract commuters from AST modal choices. Demand management planning approaches can include restricting or charging for parking at locations near RTN (i.e. Transit Orientated Development or TOD) or within inner cities where commuters can feasibly substitute driving for AST (i.e. 24-hour cities where dwellings and workplaces are co-present). With the distinctions between these planning approaches clarified, each can now be explored in greater depth.
The predict and provide planning approach has dominated land use and transport planning and policy for at least fifty years, and parking is no exception (Goulden, Ryley, & Dingwall 2014). Its overarching aim as it relates to parking is to maximise parking supply to minimise the cruising time spent by motorists searching for parking, and consequently travelling at cruising speeds that slows all trailing traffic (Seibert 2008). Barter (2015) argues that parking supply ‘mindsets’ vary according to whether parking supply is regarded a market good or infrastructure, and whether it is orientated towards a district or a particular site (i.e. parking self-containment; Table 2). The ‘responsive’ mindset expects supply to follow demand because supplying parking can be profitable, and this mindset particularly applies towards commercial parking. In contrast, the ‘area management’ mindset regards parking supply as a market failure and thus requires government regulation to ensure sufficient parking supply and avoid commercial monopolies. As such, this mindset particularly relates towards on-street parking and public parking lots. Last, the ‘conventional’ mindset is primarily concerned with on-site supply and therefore off-street parking with the overarching aim of mitigating parking overspill that can be a source of local conflict.
Table 2. Three parking-based mindsets (Barter 2015)
knitr::include_graphics("./tab2.png")These mindsets emerge through policy and typically in the form of parking minimums that can vary according land use type to contain all motorists expected to arrive during the peak demand periods (Shoup 1999). Given that peak-demand will vary according to land use type, parking minimums are calibrated according to each observed land use type (McCahill & Garrick 2014). For example, the 2014 Brisbane City Plan (Brisbane City Council 2014a) requires: bars to have at least 6 parking bays per 100 m2 gross floor area; hospitals at least 0.5 parking bays per bed and 0.8 per staff member; and district parks to have between 10 and 20 parking bays.
While it is challenging to perfectly calibrate parking supply according to anticipated parking demand, generous parking supply has multiple benefits. Parking provides spot accessibility to customers arriving by car and in so doing, enlarges the customer catchment for merchants thus explaining why parking is sometimes regarded the ‘life blood’ of businesses (Box 2000). It is well-established that parking supply is a critical determinant of customers’ destination of choice and particularly grocery customers (Recker & Kostyniuk 1978; Shobeirinejad et al. 2013). Generous parking supply can also be used as a tool for downtown economic development as demonstrated by the City of Baltimore that increased its downtown parking supply, which revitalised its downtown district and raised trade (Meyer & Mc Shane 1983).
Aside from the economic benefits of generous parking allocation, parking can also improve urban safety. For instance, on-street parking often functions as a protective buffer between colliding automobiles, pedestrians, and cyclists, and can restrict where pedestrians cross thus reducing the visual complexity of roads for motorists, which could explain why both pedestrians and motorists report feeling more secure and relaxed where on-street parking occurs (Dumbaugh & Gattis 2005; Marshall, Garrick, & Hansen 2008; De Cerreño 2004; Lerner-lam et al. 1992; Szplett & Sale 1997; Ossenbruggen et al. 2001). In addition, since both cruising for parking and manoeuvring into parking can slow trailing traffic, on-street parking can be an effective passive traffic calming tool based upon the premise that ‘slower road is safer road’ (Gattis & Watts 1999; Gattis 2000; Gårder et al. 2002).
While theories for the benefits of parking are plentiful, theories against can be found in similar measure (Marshall, Garrick, & Hansen 2008). Critics argue that the parking minimums used typically lack a rigorous evidence base (Andersson 2016; Shoup 1999; Millard-Ball 2015) or are little more than the ad nauseam reproduction of the Institute of Transportation Engineers’ 1987 Trip Generation Manual that was calibrated for low density suburban development (Shoup 2005). In an attempt to predict peak parking demand, this manual was calibrated for 87 distinct land use types but could not account for site specifics such as store popularity, merchandise type, and adjacent land use types (Shoup 2010; Inci 2015; Rowe, Bae, & Shen 2010). As a result, the manual was calibrated to assume that the maximum number of typical visitors for a given land type will arrive by private automobile, and the parking bays are scaled to accommodate the maximum domestic vehicle size thus generating a parking oversupply. In reality, visitors often arrive by various modes that include auto-pooling, smaller automobiles, public transit, and active transport, and particularly when their destinations are located within the higher density of TODs and inner cities (Gabbe & Pierce 2017; Marsden 2006). Further, more recent research findings suggest that the manual overestimates trip generation by 55 percent when checked against household survey results (Millard-Ball 2015) thus Shoup (2005) could have good reason to dismiss parking minimums as a baseless ‘pseudoscience’.
Shoup (2005) further argues that having site-specific parking minimums causes practitioners to overlook other potential arrangements such as reciprocal parking arrangements between adjacent land use types that have distinct peak demand periods e.g. a bar, a church, and a school. In addition to the road space required for rapid driving between parking spaces, cities typically contain at least three parking spaces per registered private automobile, which includes at least one within an area where land is highly valued such as the inner-city (Jakle & Sculle 2004; Cullinane, Smith, & Green 2004). Given that generous parking provision adversely impacts both urban land use and mobility (Shoup 2005), it is necessary to keep in mind that every motorist’s land use footprint greatly exceeds the physical dimensions of their private automobile.
Critics routinely position motorists in opposition to commuters choosing AST and this is unfounded. For instance, Shoup (2005) stresses throughout their The High Cost of Free Parking that everyone pays for free parking since: developers must embed the marginal costs of parking into property sales and rents; merchants into goods and services; and government departments into rates, registration, and taxes. Manville (2017b) employs the term ‘shadow market’ to describe how parking minimums hide the marginal costs of providing parking, and draws parallels to the rent control problem (Arnott 1995) since providers are unable to shed the marginal costs of parking and also unable to pass the savings on to their customers or their voting constituents. Further, Manville (2017b) argues that parking minimums inflate parking demand and create price ceilings since parking stretches the urban form so that private automobiles become more of a necessity.
To provide a sense of the marginal costs of parking, Shoup (2014) estimates that parking minimums inflate Seattle residential construction by between US10,000 and US14,000, and Los Angeles shopping centre construction by 67 percent when parking is constructed above ground and 97 percent when constructed below ground. Similarly, Andersson and colleagues (2016) estimate that parking minimums inflate Stockholm residential construction costs by 10 percent and reduce the residential stock by 1.2 percent. Notably, renters are not immune to these marginal costs given that Gabbe and Pierce (2017) estimate that parking minimums inflate rent by 10 percent on average throughout the US, and Marsden (2006) emphasises that these parking minimums do not discriminate given that even households choosing to live auto-free or unable to afford a private automobile must pay for the expense of residential parking. This could explain why parking-share services are on the rise such as Parkhound and Kerb that enable car-free households to rent their surplus parking to motorists, and should come as welcome news given that having a surplus parking space triples the likelihood of automobile purchases (Christiansen et al. 2017b) thus inducing further parking and road capacity demand.
Manville (2017b: 30) points the finger squarely at government departments when writing that ‘Governments give drivers free land; people as a result drive more than they otherwise would. That’s it. The rest is commentary’, and is not alone in this accusation since an expanding body of theoretical and empirical research suggests that parking supply induces driving and is a Band-Aid solution to win votes (Weinberger 2012; McCahill & Garrick 2014; Shoup 2005). Interestingly, Shoup (2005) finds that parking restrictions can be made more palatable for the public when the revenue is spent directly on uplifting the surrounding area. Manville (2017b) further argues that public parking is a misallocation of resources given that parking can increase how often motorist take short, low-value trips, and therefore are impeding motorists travelling longer, high-value trips. Interestingly, Timmermans (1982), Shobeirinejad and colleagues (2013) identified that a goods-wise variation in parking preferences given that on-street parking is key determinant of choosing where to purchase non-daily goods but less so for daily goods. This is an important finding given that parking minimums do not typically take the supply of on-street parking into account. Motorists also typically have a strong preference for on-street parking and particularly when it is free or under-priced relative to commercial providers. As a result, they may spend excessive time cruising for free parking or doubling back for cheaper parking (Shoup 2006; Weinberger 2012; Marshall, Garrick, & Hansen 2008; Spiliopoulou & Antoniou 2012). A further consequence is that increasing off-street parking has less impact once the expectation for on-street parking has developed (Adiv & Wang 1987). Indeed, because on-street parking is typically publicly provided and intended for outside visitors, it is prone to abuse and over exploitation. For instance, residents may regard nearby on-street parking as more convenient than their private off-street parking. Alternatively, they may have already repurposed their off-street parking for storage or living space or own more vehicles than off-street parking thus the on-street parking becomes a convenient overspill space. In one extreme case in Brisbane, Australia, this residential overspill included six cars, a campervan, a caravan, two boats, and a jet ski, which was a source of frustration for: motorists with their sightlines blocked by a row of large vehicles; and the local council that were powerless to move any vehicle shorter than 7.5 metres long from unregulated on-street parking (Lim 2014).
Interestingly, many of the benefits of parking have been framed as detriments by other authors. For instance, Shoup (2006) estimates that up to 30 percent of inner city traffic is motorists cruising for parking, and simulations reveal similar associations (Sykes et al. 2010) but should these findings be interpreted as demoting the mobility of the road or traffic calming? Further research suggests that because on-street parking narrows roads, it reduces road stream speed and capacity (Chiguma 2007; Rudjanakanoknad 2010; Chen et al. 2017; Box 2004; Cao, Yang, & Zuo 2017; Edquist, Rudin-Brown, & Lenné 2012; Kladeftiras & Antoniou 2013), and that frequent parking and unparking manoeuvres generate congestion and frustrate motorists (Yousif 2004). Similarly, critics argue that on-street parking increases the visual complexity rather than decreases the visual complexity within the road environment thus increasing reaction times and reducing travel speeds and pedestrian safety (Gitelman et al. 2012; Loukaitou-Sideris, Liggett, & Sung 2007; Biswas, Chandra, & Ghosh 2017). Further, Biswas and colleagues (2017) highlight the last point by revealing that between 13 and 17 percent of British pedestrian causalities are a result of pedestrians stepping out from between parked automobiles although arguably this interpretation does not take into account exposure (e.g. are more pedestrians crossing near cars) nor controls (e.g. does this rate differ from roads without on-street parking).
The final set of predict and provide criticisms consider the strategic value of this planning approach. For instance, estimates typically suggest that if all on-street and off-street parking was laid flat, it would blanket at least 10 percent of its urban form (Scharnhorst 2018) or as much as 30 percent for Los Angeles County (Chester et al 2015). This raises broader concerns about whether parking is a productive use of urban space; particularly given estimates that private automobiles remain parked and therefore unproductive 95 percent of the time (Button 2006). A further concern is that the cost of fulfilling parking minimums is incentivising developers to choose locations closer to the urban fringe where land is cheaper, and thus effectively facilitating urban sprawl (Shoup 2014). This creates a vicious cycle, since sending public transit to these peripheral areas is expensive and traffic engineers must expand the road capacity for auto-dependent residents leaving and returning to these areas (Shoup 1999). Finally, there are concerns that by developing and scaling cities towards automobiles, practitioners and policy makers are embedding auto-dependency into the urban form through road crossings and expansive parking lots that reduce active modes (e.g. walking and cycling) and cruising motorists and traffic congestion that reduce the appeal of public transit (Shoup 2005, 2011; McCahill & Garrick 2014). This could explain why practitioners and policy makers are increasingly turning towards multimodalism and demand management planning approaches to improve the urban mobility.
While the predict and provide planning approach focuses on accommodating motorists’ demands, the multimodalism planning approach instead focuses on reducing their driving route redundancy, which reduces Vehicle Kilometres Travelled (VKT), road requirements, and parking requirements (Kimpton et al. 2020). This typically entails developing or expanding Park ‘n’ Ride (PnR) facilities for convenient transfer from private automobiles to rapid transit, or Kiss ‘n’ Ride drop offs from ride-sharing, ride-hailing, or feeder transit services (Ison & Mulley 2014; Parkhurst & Meek 2014; Meek et al. 2015). Further, PnR are in effect dispersing parking demand away from the inner-city and TOD locations towards areas where land is relatively inexpensive (Kimpton et al. 2020). In theory, multimodal infrastructure needs to be placed downstream and within the general route of motorists so that this modal choice does not introduce extra driving and capacity should be scaled to reflect the catchment population residing within commuter or PnR sheds (Horner & Grubesic 2001; Bolger et al. 1992; Kimpton et al. 2020; Figure 1). Further, it should be placed before the catchment populations’ Break-Even Distance where the benefits of multimodalism such as rapid transit are still outweighed by the transfer costs (Holguin-Veras et al. 2012; Kimpton et al. 2020).
knitr::include_graphics("./fig1.png")While less common within the planning literature, the transport geography literature employs the term ‘transmodal’ to describe modal choices that entail transitioning from private and public transit (Rodrigue, Comtois, & Slack 2006), which could include parking at a station and continuing by train (i.e. using PnR), or riding as a private automobile or taxi passenger to the station and continuing by train (i.e. kissing and riding) . As such, a transmodal planning approach typically entails developing PnR facilities at train terminals (i.e. Heavy Rail Transit or HRT) where the operating costs are relatively low but could also entail developing PnR facilities at Bus Rapid Transit (BRT) or Light Rail Transit (LRT) terminals (Falconer & Mason 2014). A clear and early example of this approach is the HRT developed in Detroit during the 1930s (Noel 1988).
Likewise, transport geography employs the term ‘intermodal’ to describe modal choices that entail transitioning between multiple vehicles within the same transport system (Rodrigue, Comtois, & Slack 2006). This could include switching trains within a metropolitan rail network, riding a feeder bus to the station and continuing by train, or direct bus service that shares the road with private automobiles for a portion of the journey and completes the remainder with BRT busways. An early and opportunistic example of the intermodal planning approach could include the 1914 East Side Trolley Tunnel in Providence, Rhode Island that was repurposed as a BRT in 1948. The BRT approach gained popularity following the 1974 development of the Rede Integrada de Transporte (i.e. integrated transportation network) in Curitiba, Brazil, and has now spread to at least 172 cities worldwide (BRTdata.org 2020).
While the intermodalism planning approach continues to spread and is expected to accelerate as MaaS becomes more commonplace, each private automobile passing a vacant PnR bay reaffirms that transmodalism lacks universal appeal. Notably, a key distinction between intermodalism and transmodalism is that PnR facilities introduces greater uncertainty into urban travel (Kimpton et al. 2020; Figure 2). Specifically, there are at least three types of unreliability that motorists may consider when deciding whether to use PnR. Capacity unreliability is when motorists anticipate that they may arrive at PnR lot that is already fully occupied, or when they secure parking but their public transit does not stop due to already operating at peak capacity. Connectivity unreliability is when motorists anticipate that road delays could cause them to miss their connecting service or their connecting service arrives late causing them to miss further connections. Last, travel time unreliability is when motorists are uncertain about the duration of their commute so they may need to leave early or prefer their private automobiles since these can be re-routed around traffic congestion. A further challenge for multimodalism and particularly transmodalism is that is particularly vulnerable to policy inequalities. For instance, an inner-city council may develop free or cheap parking for downtown economic development, which as a result undermines the PnR facilities developed by an outer-city council or state transport department (Young & Miles 2015). There are concerns about whether it is a sustainable solution given that the per capita reductions VKT from multimodal infrastructure are typically being outpaced by urbanisation that is raising the collective VKT, and it becomes increasingly expensive to develop and expand mulitimodal infrastructure as population density increases (Loader 2017; Newman & Kenworthy 2015). Last for multimodalism, there are concerns that it provides only a short term solution since peak driving could already be passing by as OECD populations are waiting later in life to start driving (Kuhnimhof, Zumkeller, & Chlond 2013a; 2013b; McDonald 2015; Metz 2013; Goodwin & Van Dender 2013). As such, intermodalism and particularly transmodalism could be having a heyday as cities adapt from low-density and auto-dependency and will eventually lose relevance as populations concentrate within walkable range of rapid transit nodes.
knitr::include_graphics("./fig2.png")The most radical in its aims of the three planning approaches, the demand management planning approach regards parking restrictions as a potent tool for influencing whether, when, and where driving is preferred given that every private automobile journey must begin and end in a parking space (Meyer 1999; Young & Miles 2015). Rather than exclude auto-dependent visitors, the overarching aim of the demand management is reducing the appeal of driving relative to the alternatives and this can include the use of parking restrictions (Brennan, Ter Schure, & Napolitan 2013). There are a range of planning approaches used for restricting parking (Barter 2015; Litman 2006) that could be grouped as: timed through signage and parking meters to increase parking turnover; access through permits that prevent misuse; and supply through policies such as parking maximums that specify the maximum number of parking bays that developers can provide for a given land use type and unbundled parking where parking minimums—as discussed for predict and provide—are removed altogether and property buyers can purchase parking separately as required (Martens 2005, Guo & Ren 2013). Typically demand management policies apply either within the inner-city where active transport is a viable alternative for residents or within TOD zones where a rapid transit node (e.g. a train or ferry terminal, or a BRT) is within walkable range (Marsden 2006; Christiansen et al. 2017a). Notably, what constitutes a ‘walkable range’ typically varies between 250 to 800 meters in research (Singh et al. 2017), and can vary between policies that apply to same urban contexts such as Brisbane, Australia where the local authority specifies 400 meters (BCC 2014a) and the state transit authority specifies 800 meters (State of Queensland 2009).
Demand management through pricing and permits has historically been a controversial topic with media often framing this planning approach as government ‘revenue raising’ despite government claims that the intended purpose is to improve parking turnover for nearby residents and merchants (O’Sullivan & Gladstone 2019; Fuller 2018). The introduction of these planning approaches can also prompt extreme response such as homeowners threatening to leave due to the introduction of timed parking restrictions (Jacks 2019) and a merchant at a public meeting punching a councillor and attempting to strangle a second for introducing parking meters (ABC News 2015). As such, Shoup (2018) urges local governments to spend the revenue from parking meters and permits on improving nearby amenities to make demand management more agreeable for local stakeholders. Some further attempts to make parking pricing more accepted have included spatially-tapering the rate away from high-demand locations to increase nearby parking turnover and disperse long-term parking (Arnott & Rowse 1999); temporally-tapering the rate to discourage short-term parking that generates parking manoeuvres that delay traffic (Glazer & Niskanen 1992); and Shoup’s (2005) well-known demand-responsive parking approach that employs digital parking meters that dynamically set the parking rate according to how much parking is currently vacant. These demand-responsive meters are typically calibrated for maintaining a 20 percent vacancy rate to minimise cruising for parking, which entails dynamically increasing the parking rate as the vacancy rate approaches and passes 20 percent, and reducing the parking rate as the vacancy rate falls below 20 percent. Interestingly, the City of San Francisco was one of the first local governments to trial this approach and found that these digital meters found an equilibrium that reduced the average parking rate for motorists (Pierce & Shoup 2013).
While demand management through pricing and permits can be introduced anywhere there is high parking demand, parking maximums and unbundled parking in contrast should be restricted to inner cities and TOD since residents and workers will need to substitute private automobile travel with convenient rapid transit. Both parking maximums and unbundled parking are gaining popularity throughout North America, Europe, Asia, Australia, and the Middle East, and have since become binding for residential markets throughout London, New York, and most of Los Angeles (Inci 2015; Guo & Ren 2013; Gabbe & Pierce 2017). Typically, cities with urban consolidation policies are the earliest adopters of these demand management planning approaches given that parking maximums effectively limit the amount of space reserved for parking (Bajracharya et al. 2005; Griffiths & Curtis 2017; Chatman 2008), and TOD can be effective for deconcentrating large activity hubs (Pojani & Stead 2015; Willson 2005). While local variations in TOD exist, Pojani and Stead (2016) argue that TOD typically resemble one of three general types according to shape (Table 3).
Table 3. Type of TOD (Pojani & Stead 2016)
knitr::include_graphics("./tab3.png")While parking maximums and unbundled parking are typically confined to the inner-city or TOD zones, Buffalo City, NY, and Mexico City are early examples of cities adopting metropolitan-spanning blanket policies (Poon 2017; Schmitt 2017). Using a similar approach, Zurich authorities cap the current parking supply to the city’s 1990 supply to discourage further car purchases and have so far observed a per capita decline in driving (McCahill & Garrick 2014). Further associations with the introduction of parking maximums include: a 16 percent reduction in local private automobile trips (McCahill & Garrick 2014); rising public transit ridership (Chatman 2008); and a 40 percent drop in the off-street parking growth rate within London (Guo & Ren 2013).
Arguably, unbundled parking is theoretically interesting since traders and car owners become deciders of whether parking for one and/or multiple private automobiles is a necessary overhead. As such, unbundled parking exposes the market value of the land required to store private automobiles so that stakeholders can make better-informed decisions when weighing the alternatives and how many private automobiles are necessary for their household. If this raises the relative appeal of AST modes, then the knock-on effect is that their absence from the roads and parking lots can relieve traffic congestion, road capacity requirements, and the associated municipal overheads of accommodating private automobiles (Young & Miles 2015; Marsden 2006). Following a city-led reform package throughout San Francisco that requires both unbundled parking and auto-sharing parking bays within new residential buildings, these new buildings have become associated with less automobile ownership, less solo-driving, and more auto-share participation (Ter Schure, Napolitan, & Hutchinson 2012). Los Angeles includes the workplace within their planning approach by introducing an employee cash out policy where employees may request the cost of workplace parking added to their salary in lieu of receiving workplace parking, and this policy is becoming associated with rising rates of workers arriving by either car pool or public transit (Shoup 2005; Christiansen et al. 2017a). As previously noted, parking minimums typically add 10 percent to the cost of residential construction and renting thus unbundled parking not only eliminates an unnecessary expense for auto-free households but Manville (2017a) estimates that these households are between 50 and 75 percent more likely to remain auto-free. Further, Manville (2017a) argues that unbundled parking reduces socio-economic inequality by limiting the wealth transfer from households that are unable to afford automobiles to those that can and choose to drive.
While the benefits of the demand management planning approach are numerous, these are coming under increasing scrutiny and there are an expanding number of detriments associated with this disruptive reformation of the urban form. The overarching concern is that self-selection bias rather than influence explains why inner cities and TOD are associated with less driving given that auto-free households may be more inclined to choose these locations (Zahabi et al 2012, Christiansen et al. 2017b). Further, the legacy issue of already low-density and auto-dependent urban forms leave residents stranded when blanket policies are introduced, and they are unwilling or unable to relocate to location within walkable range of rapid transit. As Manville (2017b: 30) writes, ‘Low per capita VMT is good for the planet, but often miserable for the neighbourhood. And it is the neighbourhood, not the planet, that writes the zoning.’ thus residential developers remain at the mercy of market preferences. While a more patchwork approach that lets the market decide could address this legacy issue, this then introduces an urban design challenge of creating settings that are both motorist- and pedestrian-friendly so that one does not suppress the impact of the other. This challenge becomes particularly apparent when PnR and TOD are located close together or around the same transit node since PnR requires wide access roads and abundant parking while TOD requires pedestrian crossings and density (Willson 2005; Weinberger 2012). There is also a risk that abundant parking may persuade TOD residents to drive rather than walk and that PnR facilities become too expensive to expand once TOD raises local land values (Dovey et al. 2015; Ginn 2009). As Biswas and colleagues (2017) argue, this planning approach entails trading off workability for walkability or vice versa.
Demand management can also introduce socio-economic inequality. For example, the average cost of a parking space within inner Brisbane, Sydney, and Melbourne during 2016 was between 50 percent and 58 percent of the median Australian income (65.59AUD * 5days / 662AUD; 76.83AUD * 5days / 662AUD; Royal Automobile Club of Queensland 2016; Australian Bureau of Statistics 2016). Within these inner cities where parking maximums apply, everyone pays for the inner-city roads while inner-city parking could be regarded a luxury good for those that can afford it. The demand management planning approach can generate residential parking scarcity thus making private automobile ownership a luxury such as one Sydney residential parking space that sold for more than 80,000USD in 2015 (Bianchi 2015). Further, households accustomed to owning multiple automobiles will often maintain this rate of automobile ownership by using on-street parking and consequently, exhaust the local supply of on-street parking intended for visitors. With time, folk legality can emerge whereby residents believe they are entitled to nearby on-street parking thus entrenching the ownership of multiple private automobiles and jeopardising plans for reducing car dominance (Christiansen et al. 2017b; Taylor 2016). While parking permits, fines, and rates could be introduced to reclaim this on-street parking for the general public but without perfect enforcement, residents may choose to gamble on receiving the occasional fine rather than pay for parking on a daily basis thus further increasing administrative burden for local councils without reclaiming the on-street parking (Inci 2015). Demand management can also impact nearby merchants since their customers may become impatient searching for parking or dislike paying for parking thus they begin to shop elsewhere (Baker & Wood 2010) with one technical report suggesting a 25 percent reduction in customers (de Wit 2006).
Finally, the demand management planning approach can reduce urban mobility. For instance, expensive off-street parking can lengthen the period that motorists spend cruising for free or cheaper on-street parking and consequently, slow trailing traffic (Calthrop & Proost 2006). Conversely, over-priced on-street parking can provide commercial off-street parking providers with a local power monopoly that drives up the cost of parking (Froeb, Tschantz, & Crooke 2003; De Nijs 2012; Kobus et al. 2013). Notably, cruising for free or cheaper on-street parking can result in motorists to under-valuing both their time spent searching for parking and walking from these distant parking bays thus the time invested could outweigh the money saved on free or cheaper parking (Kobus et al. 2013; Glazer & Niskanen 1992). Further, spatial variability in parking rates can generate an equity conundrum since the motorists least able to afford expensive parking will also need to invest the most time cruising for parking and walking from parking (Glazer & Niskanen 1992). While the motorists will pay these costs, there are also collective costs with estimates suggesting that 30 percent of inner-city traffic is motorists cruising for parking and doubling back for cheaper parking (Shoup 2006; Weinberger 2012), which as a result slows trailing traffic (Inci 2015; Glazer & Niskanen 1992; Inci & Lindsey 2014).
As can be noted above, there is an extensive theoretical and empirical literature that explores whether parking can be used to influence modal choices, and theories and research findings remain divided across three general planning approaches that are entitled ‘predict and provide’, ‘multimodalism’, and ‘demand management’ throughout this volume. Given that so few of these studies take the plurality of planning approaches into account or explore more than a single urban area or city, this volume endeavours to be the first multi-city examination for a comprehensive and transferrable exploration of these three planning approaches. To maximise transferability and reproducibility, every effort is made to use open data and software, and the programming scripts are made available for the research community. In short, this volume aims to improve the state of knowledge by investigating how parking supply and demand explains modal choice by contrasting three major metropolitan areas that differ in size and centre structure. These research findings are intended to strengthen the empirical foundations for further research enquiry and evidence-based planning practice that aims to improve urban mobility.
This study examines two overarching research questions. The first is: (1) to what extent are existing parking-related policies and regulations for Brisbane, Sydney and Melbourne following international best practice? The second is: (2) to what extent are these parking policies and regulations having the intended impact? The data used in this examination was collected from a variety of sources given that no single agency to date has collected detailed, multi-city parking information. To improve reproducibility, these data were typically open-source and obtainable through online portals but some gaps in the data were unavoidable since the parking data collection varies substantially within and between cities. Further, the R programming script that completed all the data cleaning, spatial harmonization, calculations, and modelling is available online from the lead author’s rPubs page (https://rpubs.com/WILL_UPDATE_IF_ACCEPTED).
# states
states.sf <- readOGR('./../../Data/Australia/Census/2016_STE_shape','STE_2016_AUST') %>%
st_as_sf() %>%
st_transform(ref_used) %>%
clean_names() %>%
mutate(state = ifelse(state_name == "Australian Capital Territory", "A.C.T.", as.character(state_name))) %>%
filter(state != "Other Territories") %>%
select(state)
#collect the inner-city local government study area
lga.sf <- readOGR('./../../Data/Australia/Census/1270055003_lga_2016_aust_shape','LGA_2016_AUST') %>%
st_as_sf() %>%
clean_names() %>%
st_transform(ref_used) %>%
mutate(council = gsub( " *\\(.*?\\) *", "", lga_name16),
area.ha = as.numeric(st_area(.))/1000) %>%
select(council, area.ha)
inner.city.sf <- readOGR('./../../Data/Australia/Census/1270055003_lga_2016_aust_shape','LGA_2016_AUST') %>%
st_as_sf() %>%
clean_names() %>%
mutate(inner.city = ifelse(lga_name16 == "Sydney (C)", "Sydney", ifelse(lga_name16 == "Melbourne (C)", "Melbourne", NA))) %>%
drop_na(inner.city) %>%
st_transform(ref_used) %>%
select(inner.city)
inner.city.sf <- readOGR('./../../Data/Brisbane/SHP/SHP','City_Frame') %>%
st_as_sf() %>%
clean_names() %>%
st_transform(ref_used) %>%
mutate(inner.city = "Brisbane") %>%
select(inner.city) %>%
rbind(inner.city.sf) %>%
mutate(inner.city.ha = as.numeric(st_area(.))/10000) %>%
mutate(inner.city = factor(inner.city, levels = c("Brisbane", "Sydney", "Melbourne")))
#rapid transit nodes
rapid.transit.sf <- readOGR('.','PnRs_TODs') %>%
st_as_sf() %>%
clean_names() %>%
st_transform(ref_used) %>%
mutate(temp = as.character(name),
rtn = ifelse(temp == 'Central', 'Brisbane Central',
ifelse(temp == 'Central Station', 'Sydney Central', temp))) %>%
select(rtn) %>%
filter(!grepl("International Airport Station|Carnegie (Temporarily Closed)|Acacia Ridge Yard|
International Terminal|Acacia Ridge - Aurizon Bulk Fuel|
Acacia Ridge Intermodal Terminal|Churchill Saleyards|Domestic Terminal|
Redbank Workshops|The Workshops Rail Museum", rtn))
central.station.sf <- rapid.transit.sf %>%
filter(grepl("Brisbane Central|Sydney Central|Melbourne Central", rtn)) %>%
mutate(city = factor(gsub(" Central", "", rtn), levels = c("Brisbane", "Sydney", "Melbourne"))) %>%
select(city)
rapid.transit.sf <- cbind(rapid.transit.sf,
apply(st_distance(rapid.transit.sf, central.station.sf, by_element = FALSE),1, FUN=min)) %>%
rename("m.from.central" = names(.)[length(names(.))-1]) %>%
filter(m.from.central <= 50000) %>%
st_join(., st_buffer(central.station.sf, dist = 50000))
rapid.transit.buffer.sf <- rapid.transit.sf %>%
st_buffer(800) %>%
st_union(by_feature = FALSE) %>%
st_as_sf()
#suburbs
suburb.sf <- readOGR('./../../Data/Australia/Census/1270055001_sa1_2016_aust_shape','SA1_2016_AUST') %>%
st_as_sf() %>%
clean_names() %>%
filter(grepl("New South Wales|Victoria|Queensland", ste_name16)) %>%
st_transform(ref_used) %>%
cbind(., apply(st_distance(st_centroid(.),central.station.sf, by_element = FALSE), 1, FUN=min)) %>%
rename("m.from.central" = names(.)[length(names(.))-1]) %>%
filter(m.from.central <= 50000) %>%
cbind(., apply(st_distance(st_centroid(.),rapid.transit.sf, by_element = FALSE), 1, FUN=min)) %>%
rename("m.from.rtn" = names(.)[length(names(.))-1]) %>%
mutate(sa1_7dig16 = as.numeric(as.character(sa1_7dig16)),
tod = ifelse(m.from.rtn <= 400, "400m TOD",
ifelse(m.from.rtn > 400 & m.from.rtn <= 800, "800m TOD", "Autodependent")),
ha = as.numeric(st_area(.))/10000) %>%
select(sa1_7dig16, m.from.central, m.from.rtn, tod, ha, geometry)
df1 <- suburb.sf %>%
st_centroid() %>%
st_join(., inner.city.sf, join = st_intersects) %>%
mutate(location = ifelse(inner.city %in% NA, "Outer City", "Inner City")) %>%
st_join(., st_buffer(central.station.sf, dist = 50000)) %>%
as.tibble() %>%
select(sa1_7dig16, location, city)
suburb.sf <- suburb.sf %>%
left_join(., df1, by = "sa1_7dig16")
# 2011 Census
concordance <- read.csv("CG_SA1_2011_SA1_2016.csv") %>%
mutate(sa1_7dig11 = SA1_7DIGITCODE_2011,
sa1_7dig16 = SA1_7DIGITCODE_2016) %>%
select(sa1_7dig11, sa1_7dig16, RATIO)
cars.2011 <- read.csv("./../../Data/Australia/Census/2011_BCP_SA1_for_AUST_short-header/2011 Census BCP Statistical Areas Level 1 for AUST/AUST/2011Census_B29_AUST_SA1_short.csv") %>%
mutate(sa1_7dig11 = region_id,
car.less.dwellings.2011.n = Num_MVs_per_dweling_0_MVs,
cars.2011.n = Num_MVs_per_dweling_1_MVs + Num_MVs_per_dweling_2_MVs * 2 +
Num_MVs_per_dweling_3_MVs * 3 + Num_MVs_per_dweling_4mo_MVs * 4,
dwellings.2011.n = Num_MVs_per_dweling_Tot) %>%
select(sa1_7dig11, car.less.dwellings.2011.n, cars.2011.n, dwellings.2011.n)
mode.2011 <- read.csv("./../../Data/Australia/Census/2011_BCP_SA1_for_AUST_short-header/2011 Census BCP Statistical Areas Level 1 for AUST/AUST/2011Census_B46_AUST_SA1_short.csv") %>%
mutate(sa1_7dig11 = region_id,
mode.p.and.p.2011.n = One_method_Motorbike_scootr_P + One_method_Car_as_driver_P,
mode.intermodal.2011.n = Two_methods_Bus_Car_as_drvr_P + Two_methods_Trn_Car_as_drvr_P,
mode.ast.2011.n = One_method_Walked_only_P + One_method_Bicycle_P +
Two_mth_Bu_Trm_inc_lt_rl_P + Two_methods_Bus_Ferry_P +
Two_mt_trn_Trm_incl_lt_rl_P + Two_methods_Train_Ferry_P +
Two_methods_Train_Bus_P + One_met_Tram_incl_lt_rail_P +
One_method_Ferry_P + One_method_Bus_P + One_method_Train_P) %>%
select(sa1_7dig11, starts_with("mode."))
person.2011 <- read.csv("./../../Data/Australia/Census/2011_BCP_SA1_for_AUST_short-header/2011 Census BCP Statistical Areas Level 1 for AUST/AUST/2011Census_B01_AUST_SA1_short.csv") %>%
mutate(sa1_7dig11 = region_id,
au.born.2011.n = Birthplace_Australia_P,
population.2011.n = Birthplace_Australia_P+Birthplace_Elsewhere_P) %>%
select(sa1_7dig11, population.2011.n, au.born.2011.n)
migration.2011 <- read.csv("./../../Data/Australia/Census/2011_BCP_SA1_for_AUST_short-header/2011 Census BCP Statistical Areas Level 1 for AUST/AUST/2011Census_B38_AUST_SA1_short.csv") %>%
mutate(sa1_7dig11 = region_id,
different.address.1yr.2011.n = Tot_P - N_stated_P-Sme_Usl_ad_1_yr_ago_as_2011_P,
same.address.1yr.2011.n = Sme_Usl_ad_1_yr_ago_as_2011_P) %>%
select(sa1_7dig11, different.address.1yr.2011.n, same.address.1yr.2011.n)
collar.2011 <- read.csv("./../../Data/Australia/Census/2011_BCP_SA1_for_AUST_short-header/2011 Census BCP Statistical Areas Level 1 for AUST/AUST/2011Census_B45B_AUST_SA1_short.csv") %>%
mutate(sa1_7dig11 = region_id,
blue.collar.2011.n = P_Tot_TechnicTrades_W + P_Tot_Mach_oper_drivers + P_Tot_Labourers,
white.collar.2011.n = P_Tot_Managers + P_Tot_Professionals + P_Tot_CommunPersnlSvc_W + P_Tot_ClericalAdminis_W + P_Tot_Sales_W) %>%
select(sa1_7dig11, blue.collar.2011.n, white.collar.2011.n)
concordance <- concordance %>%
left_join(cars.2011, by = "sa1_7dig11") %>%
left_join(mode.2011, by = "sa1_7dig11") %>%
left_join(person.2011, by = "sa1_7dig11") %>%
left_join(migration.2011, by = "sa1_7dig11") %>%
left_join(collar.2011, by = "sa1_7dig11") %>%
mutate_at(c(4:15),~replace(., is.na(.), 0)) %>%
mutate_at(c(4:15), ~replace(., TRUE, round(. * RATIO, 1))) %>%
mutate_at(c(4:15),~replace(., is.na(.), 0)) %>%
select(-sa1_7dig11, -RATIO) %>%
group_by(sa1_7dig16) %>%
summarise_if(is.numeric, sum)
# 2016 Census
cars.2016 <- read.csv("./../../Data/Australia/Census/2016_GCP_SA1_for_AUS_short-header/2016 Census GCP Statistical Area 1 for AUST/2016Census_G30_AUS_SA1.csv") %>%
mutate(sa1_7dig16 = SA1_7DIGITCODE_2016,
car.less.dwellings.2016.n = Num_MVs_per_dweling_0_MVs,
cars.2016.n = Num_MVs_per_dweling_1_MVs + Num_MVs_per_dweling_2_MVs * 2 +
Num_MVs_per_dweling_3_MVs * 3 + Num_MVs_per_dweling_4mo_MVs * 4,
dwellings.2016.n = Num_MVs_per_dweling_Tot) %>%
select(sa1_7dig16, car.less.dwellings.2016.n, cars.2016.n, dwellings.2016.n)
mode.2016 <- read.csv("./../../Data/Australia/Census/2016_GCP_SA1_for_AUS_short-header/2016 Census GCP Statistical Area 1 for AUST/2016Census_G59_AUS_SA1.csv") %>%
mutate(sa1_7dig16 = SA1_7DIGITCODE_2016,
mode.p.and.p.2016.n = One_method_Motorbike_scootr_P + One_method_Car_as_driver_P,
mode.intermodal.2016.n = Two_methods_Bus_Car_as_drvr_P + Two_methods_Trn_Car_as_drvr_P,
mode.ast.2016.n = One_method_Walked_only_P + One_method_Bicycle_P +
Two_mth_Bu_Trm_inc_lt_rl_P + Two_methods_Bus_Ferry_P +
Two_mt_trn_Trm_incl_lt_rl_P + Two_methods_Train_Ferry_P + Two_methods_Train_Bus_P +
One_met_Tram_incl_lt_rail_P + One_method_Ferry_P + One_method_Bus_P +
One_method_Train_P) %>%
select(sa1_7dig16, starts_with("mode."))
person.2016 <- read.csv("./../../Data/Australia/Census/2016_GCP_SA1_for_AUS_short-header/2016 Census GCP Statistical Area 1 for AUST/2016Census_G01_AUS_SA1.csv") %>%
mutate(sa1_7dig16 = SA1_7DIGITCODE_2016,
population.2016.n = Birthplace_Australia_P+Birthplace_Elsewhere_P,
au.born.2016.n = Birthplace_Australia_P) %>%
select(sa1_7dig16, population.2016.n, au.born.2016.n)
hh.2016 <- read.csv("./../../Data/Australia/Census/2016_GCP_SA1_for_AUS_short-header/2016 Census GCP Statistical Area 1 for AUST/2016Census_G02_AUS_SA1.csv") %>% # too much hassle to concord 2011 averages
mutate(sa1_7dig16 = SA1_7DIGITCODE_2016,
median.age.2016.n = Median_age_persons,
median.hh.income.2016.n = Median_tot_hhd_inc_weekly*52,
mean.hh.size.2016.n = Average_household_size) %>%
select(sa1_7dig16, median.age.2016.n, median.hh.income.2016.n, mean.hh.size.2016.n)
migration.2016 <- read.csv("./../../Data/Australia/Census/2016_GCP_SA1_for_AUS_short-header/2016 Census GCP Statistical Area 1 for AUST/2016Census_G41_AUS_SA1.csv") %>%
mutate(sa1_7dig16 = SA1_7DIGITCODE_2016,
different.address.1yr.2016.n = Difnt_Usl_add_1_yr_ago_Tot_P,
same.address.1yr.2016.n = Sme_Usl_ad_1_yr_ago_as_2016_P) %>%
select(sa1_7dig16, different.address.1yr.2016.n, same.address.1yr.2016.n)
collar.2016 <- read.csv("./../../Data/Australia/Census/2016_GCP_SA1_for_AUS_short-header/2016 Census GCP Statistical Area 1 for AUST/2016Census_G58B_AUS_SA1.csv") %>%
mutate(sa1_7dig16 = SA1_7DIGITCODE_2016,
blue.collar.2016.n = P_TTW_Tot + P_MOD_Tot + P_Lab_Tot,
white.collar.2016.n = P_Mng_Tot + P_Pro_Tot + P_CPS_Tot + P_CA_Tot + P_Sal_Tot) %>%
select(sa1_7dig16, blue.collar.2016.n, white.collar.2016.n)
suburb.sf <- suburb.sf %>%
left_join(cars.2016, by = "sa1_7dig16") %>%
left_join(mode.2016, by = "sa1_7dig16") %>%
left_join(person.2016, by = "sa1_7dig16") %>%
left_join(hh.2016, by = "sa1_7dig16") %>%
left_join(migration.2016, by = "sa1_7dig16") %>%
left_join(collar.2016, by = "sa1_7dig16") %>%
left_join(concordance, by = "sa1_7dig16") %>%
mutate_at(c(8:34),~replace(., is.na(.), 0)) %>%
mutate(mode.total.2016.n = mode.p.and.p.2016.n + mode.intermodal.2016.n + mode.ast.2016.n,
mode.p.and.p.2016.perc = mode.p.and.p.2016.n/mode.total.2016.n*100,
mode.intermodal.2016.perc = mode.intermodal.2016.n/mode.total.2016.n*100,
mode.ast.2016.perc = mode.ast.2016.n/mode.total.2016.n*100,
leaving.by.car.2011.n = mode.p.and.p.2011.n + mode.intermodal.2011.n,
leaving.by.car.2016.n = mode.p.and.p.2016.n + mode.intermodal.2016.n,
tod.policy = factor(tod, levels = c("Autodependent", "400m TOD", "800m TOD")),
inner.policy = factor(location, levels = c("Outer City", "Inner City")),
policy.zone = factor(ifelse(inner.policy == "Inner City", "Inner City",
ifelse(tod.policy == "400m TOD", "400m TOD",
ifelse(tod.policy == "800m TOD", "800m TOD", "Outside"))),
levels = c("Outside", "Inner City", "400m TOD", "800m TOD")),
km.from.rtn = m.from.rtn/1000,
km.from.central = m.from.central/1000,
km.from.central.sq = km.from.central * km.from.central,
cars.per.dwelling.2016 = cars.2016.n/dwellings.2016.n*100,
dwellings.per.ha.2016 = dwellings.2016.n/ha,
au.born.2016 = au.born.2016.n/population.2016.n*100,
median.age.2016 = median.age.2016.n,
median.hh.income.k.2016 = median.hh.income.2016.n/1000,
mean.hh.size.2016 = mean.hh.size.2016.n,
different.address.1yr.2016 = different.address.1yr.2016.n/
(different.address.1yr.2016.n+same.address.1yr.2016.n)*100,
blue.collar.2016 = blue.collar.2016.n/
(white.collar.2016.n+blue.collar.2016.n)*100,
city = as.vector(city),
x.coord = st_coordinates(st_centroid(.))[,1],
y.coord = st_coordinates(st_centroid(.))[,2]) %>%
mutate_at(c(8:34, 36:41, 44:49),~replace(., is.na(.), 0)) %>%
mutate_at(c(8:34, 36:41, 44:49),~replace(., is.infinite(.), 0))my_sampler <- function(sampled.df, enclosing.df, sample.n) {
temp1 <- sampled.df %>%
mutate(row_id = row_number())
temp2 <- temp1 %>%
st_join(., enclosing.df, join = st_intersects) %>%
drop_na(inner.city) %>%
select(-inner.city, -inner.city.ha) %>%
mutate(portion = sample.n) %>%
st_sample(size = .$portion, type = "random", exact = TRUE) %>%
st_sf() %>%
st_intersection(enclosing.df) %>%
st_join(., temp1, join = st_intersects) %>%
as_data_frame() %>%
drop_na(row_id) %>%
group_by(row_id) %>%
summarise(inner.city.proportion = n()/sample.n,
inner.city = first(inner.city)) %>%
select(row_id, inner.city, inner.city.proportion) %>%
mutate(inner.city.proportion = ifelse(inner.city.proportion > 1.0, 1.0, inner.city.proportion))
temp1 %>%
left_join(temp2, by = "row_id") %>%
select(-row_id) %>%
mutate_if(is.numeric, funs(ifelse(is.na(.), 0, .))) %>%
mutate_if(is.numeric, funs(ifelse(is.infinite(.), 0, .)))
}
# inner-city offstreet parking
offstreet.poly.sf <- readOGR('./../../Data/Brisbane','SEQ-Park-Spaces-v2013') %>% #bris 2013
st_as_sf() %>%
clean_names() %>%
st_transform(ref_used) %>%
mutate(residential.offstreet.bays = ifelse(category_1 == "RESIDENTIAL", parks_2013, 0),
other.offstreet.bays = ifelse(category_1 != "RESIDENTIAL", parks_2013, 0),
residential.offstreet.m2 = NA,
other.offstreet.m2 = NA) %>%
select(residential.offstreet.bays, other.offstreet.bays, residential.offstreet.m2, other.offstreet.m2)
df1 <- read.csv("./../../Data/Sydney/FES2017 - Parking Data.csv") %>% #syd 2017
clean_names() %>%
as_tibble() %>%
mutate(id = block,
residential.offstreet.bays = as.numeric(as.character(sum_of_tennantparkinginternal)) + as.numeric(as.character(sum_of_tennantparkingexternal)),
other.offstreet.bays = as.numeric(as.character(sum_of_publicparkinginternal)) + as.numeric(as.character(sum_of_publicparkingexternal))) %>%
select(id, residential.offstreet.bays, other.offstreet.bays)
offstreet.poly.sf <- readOGR('./../../Data/Sydney/FES2017_Employment_Zone_Data','FES2017_Employment_Zone_Data') %>%
st_as_sf() %>%
clean_names() %>%
st_transform(ref_used) %>%
mutate(id = as.numeric(as.character(objectid)),
residential.offstreet.m2 = NA,
other.offstreet.m2 = NA) %>%
left_join(df1, by = "id") %>%
select(residential.offstreet.bays, other.offstreet.bays, residential.offstreet.m2, other.offstreet.m2) %>%
rbind(offstreet.poly.sf)
df1 <- read.csv("./../../Data/Melbourne/Off-street_car_parking_2016.csv") %>% #melb 2016
clean_names() %>%
as_tibble() %>%
mutate(id = block_id,
type = ifelse(parking_type != "Residential", "other.offstreet.bays", "residential.offstreet.bays")) %>%
group_by(id, type) %>%
summarize(parking_spaces = sum(parking_spaces)) %>%
spread(type, parking_spaces, fill = 0)
df1 <- read.csv("./../../Data/Melbourne/Floor_space_by_use_by_block2018.csv") %>%
clean_names() %>%
as_tibble() %>%
filter(census_year == "2016") %>%
mutate_all(~replace(., is.na(.), 0)) %>%
mutate(id = block_id,
residential.offstreet.m2 = parking_private_covered + parking_private_uncovered,
other.offstreet.m2 = parking_commercial_covered + parking_commercial_uncovered) %>%
select(id, residential.offstreet.m2, other.offstreet.m2) %>%
left_join(df1, by = "id")
offstreet.poly.sf <- readOGR('./../../Data/Melbourne/Blocks for Census of Land Use and Employment (CLUE) with business, employment and floor area counts','geo_export_3f3122c2-4cfc-489b-8bd1-590a8876ad0b') %>%
st_as_sf() %>%
clean_names() %>%
st_transform(ref_used) %>%
mutate(id = as.numeric(as.character(block_id))) %>%
left_join(df1, by = "id") %>%
select(residential.offstreet.bays, other.offstreet.bays, residential.offstreet.m2, other.offstreet.m2) %>%
rbind(offstreet.poly.sf)
offstreet.poly.sf <- my_sampler(offstreet.poly.sf, inner.city.sf, 100)
bris.sample <- st_buffer(offstreet.poly.sf, 0.0) %>% # for determining how much of the inner city they sampled
st_intersection(st_buffer(inner.city.sf, 0.0)) %>%
filter(inner.city == "Brisbane") %>%
mutate(area.ha = as.numeric(st_area(.))/10000) %>%
as.tibble() %>%
group_by(inner.city) %>%
summarise(sample_ha = sum(area.ha),
total_ha = mean(inner.city.ha)) %>%
mutate(sample.percent = sample_ha/total_ha*100)
# inner-city onstreet parking
onstreet.poly.sf <- readOGR("./../../Data/Melbourne/On-street Parking Bays", "geo_export_31ea2fa0-2503-4897-86c3-5067b4a40357") %>% #melb?
st_as_sf() %>%
clean_names() %>%
mutate(id = meter_id) %>%
st_transform(ref_used) %>%
select(id) %>%
mutate(bay.area.m2 = round(as.numeric(st_area(.)),0),
onstreet.bay = 1)
onstreet.poly.sf <- my_sampler(onstreet.poly.sf, inner.city.sf, 10)
# inner-city onstreet sensors
sensors.point.sf <- read.csv("./../../Data/Melbourne/On-street_Parking_Bay_Sensors.csv") %>% # bris2017
clean_names() %>%
as_tibble() %>%
mutate(id = bay_id,
sensor = 1) %>%
st_as_sf(coords = c("lon", "lat"), crs = ref_coord) %>%
st_transform(ref_used) %>%
st_join(., inner.city.sf, join = st_intersects) %>%
mutate(inner.city.proportion = ifelse(is.na(inner.city), 1, 0)) %>%
select(-inner.city.ha)
# inner-city onstreet meters
meters.point.sf <- read.csv("./../../Data/Brisbane/Parking-Meter-locations.csv") %>% # bris2017
clean_names() %>%
as_tibble() %>%
mutate(bays.per.meter = veh_bays,
bays.per.meter = ifelse(bays.per.meter == 0, 1, bays.per.meter)) %>%
st_as_sf(coords = c("longitude", "latitude"), crs = ref_coord) %>%
st_transform(ref_used) %>%
select(bays.per.meter)
meters.point.sf <- readOGR("./../../Data/Sydney/Parking_meters", "Parking_meters") %>% #syd?
st_as_sf() %>%
clean_names() %>%
mutate(bays.per.meter = as.numeric(approx_pay_s)) %>%
st_transform(ref_used) %>%
select(bays.per.meter) %>%
rbind(meters.point.sf)
df1 <- onstreet.poly.sf %>%
as.tibble() %>%
drop_na(id) %>%
select(id) %>%
group_by(id) %>%
tally() %>%
rename(bays.per.meter = n)
meters.point.sf <- read.csv("./../../Data/Melbourne/On-street_Car_Parking_Meters_with_Location.csv") %>% # melb?
clean_names() %>%
as_tibble() %>%
mutate(id = meter_id) %>%
st_as_sf(coords = c("longitude", "latitude"), crs = ref_coord) %>%
st_transform(ref_used) %>%
left_join(df1, by = "id") %>%
select(bays.per.meter) %>%
rbind(meters.point.sf) %>%
st_join(., inner.city.sf, join = st_intersects) %>%
mutate(inner.city.proportion = ifelse(is.na(inner.city), 1, 0)) %>%
select(-inner.city.ha)
# inner city residents 2011 and 2016 (SA1)
suburb.sf <- my_sampler(suburb.sf, inner.city.sf, 100)
# 2011 inner city workers (SA2)
inner.city.workers.2011.sf <- read.csv("./../../Data/Australia/Census/POW_MTWP2011.csv") %>%
clean_names() %>%
as_tibble() %>%
mutate(arriving.workers.2011 = select(., train:walked_only) %>%
rowSums(na.rm = TRUE),
id = sa2,
arriving.drivers.2011 = select(., matches("car|motorbike"), -contains("passenger")) %>%
rowSums(na.rm = TRUE)) %>%
select(id, arriving.workers.2011, arriving.drivers.2011)
inner.city.workers.2011.sf <- readOGR("./../../Data/Australia/Census/2011_SA2_shape", "SA2_2011_AUST") %>% #SA2
st_as_sf() %>%
clean_names() %>%
mutate(id = as.numeric(as.character(sa2_main))) %>%
select(id) %>%
st_transform(ref_used) %>%
left_join(inner.city.workers.2011.sf, by = "id") %>%
st_join(., inner.city.sf, join = st_intersects) %>%
drop_na(inner.city) %>%
select(arriving.workers.2011, arriving.drivers.2011)
inner.city.workers.2011.sf <- my_sampler(inner.city.workers.2011.sf, inner.city.sf, 100)
# 2016 inner city workers (SA2)
inner.city.workers.2016.sf <- read.csv("./../../Data/Australia/Census/POW_MTWP2016.csv") %>%
clean_names() %>%
as_tibble() %>%
mutate(arriving.workers.2016 = select(., train:walked_only) %>%
rowSums(na.rm = TRUE),
id = sa2,
arriving.drivers.2016 = select(., matches("car|motorbike"), -contains("passenger")) %>%
rowSums(na.rm = TRUE)) %>%
select(id, arriving.workers.2016, arriving.drivers.2016)
inner.city.workers.2016.sf <- readOGR("./../../Data/Australia/Census/2016_SA2_shape", "SA2_2016_AUST") %>% #SA2
st_as_sf() %>%
clean_names() %>%
mutate(id = as.numeric(as.character(sa2_main16))) %>%
select(id) %>%
st_transform(ref_used) %>%
left_join(inner.city.workers.2016.sf, by = "id") %>%
st_join(., inner.city.sf, join = st_intersects) %>%
drop_na(inner.city) %>%
select(arriving.workers.2016, arriving.drivers.2016)
inner.city.workers.2016.sf <- my_sampler(inner.city.workers.2016.sf, inner.city.sf, 100)Brisbane, Sydney, and Melbourne are the three largest Australian cities, and the state capitals of Queensland, New South Wales, and Victoria, respectively. Given that the major metropolitan regions for Brisbane, Sydney and Melbourne vary in size, we have we defined each major metropolitan using the Australian Bureau of Statistics’ (ABS) 2016 Statistical Area 1 (SA1) neighbourhood spatial units, and retained all SA1 that have a geographic centroid within 50 kilometres of the historical main train station for each city that was labelled ‘Central Station’ (Figure 3; data detailed below). This spatial harmonisation approach was intended to improve consistency and comparability between cities. Further, only ABS data that was publicly available from the ABS’s (2020) open data portal was used to ensure research reproducibility.
The inner-city for each major metropolitan was also captured given that parking policies such as parking maximums, unbundled parking, and market pricing can apply specifically to locations deemed ‘the inner-city’. These inner cities invariably include the Central Business District (CBD) and its immediate surroundings. As such, we employ the ABS’ 2016 Local Government Areas (LGA) labelled ‘City of Sydney’ and ‘City of Melbourne’ as the inner cities for Sydney and Melbourne, respectively. In contrast, Brisbane’s major metropolitan is consolidated under a single government thus its ‘Brisbane City Council’ (BCC) LGA exceeds its inner-city and is too large for comparability with the Sydney and Melbourne inner cities. As such, the ‘city frame’ defined within the Brisbane City Plan 2014 (BCC 2014a) that is a size comparable to the Sydney and Melbourne inner cities is available upon request from the BCC to be used within this examination (Figure 3). In addition, given that the spatial units used throughout this volume were not spatially nested within these inner-city boundaries, weightings for populations and feature located within the inner cities will be calculated to using random point sampling to provide for more accurate and consequently conservative estimates (Figure 4). Last, while these three cities could be presented in order of population size (i.e. Sydney, Melbourne, and Brisbane; Table 4), we instead present the three cities in order of data available starting with Brisbane that has the least through to Melbourne that has complete coverage so that each interpretation can build upon the previous.
tmap_mode("plot")
states.sf2 <- st_transform(states.sf, "+init=epsg:4326")
central.station.sf2 <- st_transform(central.station.sf, "+init=epsg:4326")
bbox_new <- st_bbox(subset(states.sf2, state == "New South Wales")) # current bounding box
xrange <- bbox_new$xmax - bbox_new$xmin # range of x values
yrange <- bbox_new$ymax - bbox_new$ymin # range of y values
bbox_new[1] <- bbox_new[1] - (0.0 * xrange) # xmin - left
bbox_new[3] <- bbox_new[3] + (2.1 * xrange) # xmax - right
bbox_new[2] <- bbox_new[2] - (2.2 * yrange) # ymin - bottom
bbox_new[4] <- bbox_new[4] + (0.6 * yrange) # ymax - top
main.map <- tm_shape(states.sf2, bbox = bbox_new, projection = "+init=epsg:4326") +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_text("state", col=shore, size = 0.5) +
tm_shape(central.station.sf2) +
tm_text("city", col=my.greys[9], ymod = 0.1, xmod = 1.2, size = 0.5) +
tm_shape(suburb.sf) +
tm_fill(col = "city", palette = city.trio, legend.show = FALSE) +
tm_compass(position = c(0.05, 0.55))+
tm_scale_bar(position = c(0.05, 0.49), width = 0.3) +
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1)
bris.map <- tm_shape(states.sf, bbox = st_bbox(subset(st_buffer(central.station.sf, dist = map.diameter), city == "Brisbane"))) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(suburb.sf) +
tm_polygons(col = city.trio[1], border.col = city.trio[1], alpha = 0.7, lwd = 1) +
tm_shape(rapid.transit.buffer.sf) +
tm_borders(col = my.greys[9]) +
tm_shape(inner.city.sf) +
tm_borders(col = trio[3]) +
tm_shape(central.station.sf) +
tm_symbols(col = my.palette[2], border.col = my.palette[2], shape = 21, size = 0.1) +
tm_compass(position = c("left", "bottom"))+
tm_scale_bar(position = c("left", "bottom"), width = 0.4) +
tm_layout(title = "Brisbane", title.size = 0.6, bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1)
syd.map <- tm_shape(states.sf, bbox = st_bbox(subset(st_buffer(central.station.sf, dist = map.diameter), city == "Sydney" ))) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(suburb.sf) +
tm_polygons(col = city.trio[2], border.col = city.trio[2], alpha = 0.7, lwd = 1) +
tm_shape(rapid.transit.buffer.sf) +
tm_borders(col = my.greys[9]) +
tm_shape(inner.city.sf) +
tm_borders(col = trio[3]) +
tm_shape(central.station.sf) +
tm_symbols(col = my.palette[2], border.col = my.palette[2], shape = 21, size = 0.1) +
tm_add_legend(type= "symbol",
col=c("white", "white", my.palette[2]),
border.col = c(trio[3],my.greys[9], my.palette[2]),
labels=c("Inner City", "Rapid Transit", "Central Station"),
title="Legend") +
tm_compass(position = c("left", "bottom"))+
tm_scale_bar(position = c("left", "bottom"), width = 0.4) +
tm_layout(title = "Sydney", title.size = 0.6, bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1 , legend.title.size = 0.7, legend.text.size = 0.5, legend.frame = TRUE, legend.bg.color = "white", legend.position = c("right", "center"))
melb.map <- tm_shape(states.sf, bbox = st_bbox(subset(st_buffer(central.station.sf, dist = map.diameter), city == "Melbourne" ))) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(suburb.sf) +
tm_polygons(col = city.trio[3], border.col = city.trio[3], alpha = 0.7, lwd = 1) +
tm_shape(rapid.transit.buffer.sf) +
tm_borders(col = my.greys[9]) +
tm_shape(inner.city.sf) +
tm_borders(col = trio[3]) +
tm_shape(central.station.sf) +
tm_symbols(col = my.palette[2], border.col = my.palette[2], shape = 21, size = 0.1) +
tm_compass(position = c("left", "bottom"))+
tm_scale_bar(position = c("left", "bottom"), width = 0.4) +
tm_layout(title = "Melbourne", title.size = 0.6, bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1)
main.map
print(bris.map, vp = grid::viewport(x = 0.91, y = 0.97, just=c("right", "top"), width = 0.47, height = 0.47))
print(syd.map, vp = grid::viewport(x = 0.91, y = 0.03, just=c("right", "bottom"), width = 0.47, height = 0.47))
print(melb.map, vp = grid::viewport(x = 0.09, y = 0.03, just=c("left", "bottom"), width = 0.47, height = 0.47))inner.city.sa1.sf <- suburb.sf %>%
drop_na(inner.city) %>%
filter(inner.city == "Brisbane")
temp <- inner.city.sa1.sf %>%
mutate(clipped.dwellings = inner.city.proportion * dwellings.2016.n,
unclipped.dwellings = dwellings.2016.n) %>%
group_by(inner.city) %>%
summarise(clipped.dwellings = sum(clipped.dwellings),
unclipped.dwellings = sum(unclipped.dwellings)) %>%
as.tibble()
unclipped.point.sf <- inner.city.sa1.sf %>%
mutate(portion = 100) %>%
st_sample(size = .$portion, type = "random", exact = TRUE) %>%
st_sf() %>%
st_join(., inner.city.sf, join = st_intersects)
clipped.point.sf <- unclipped.point.sf %>%
drop_na(inner.city)
unclipped.point.sf <- unclipped.point.sf %>%
mutate_at(2,~replace(., is.na(.), 0)) %>%
filter(inner.city.ha == 0)
dwe.label <- paste( "inner city dwellings (", format(round(as.numeric(temp[1,2]),0), big.mark=","), ")", sep="")
other.label <- paste("counted otherwise (", format(round(as.numeric(temp[1,3]),0), big.mark=","), ")", sep="")
inner.city.bb.sf <- inner.city.sf %>%
filter(inner.city == "Brisbane") %>%
st_buffer(., dist = 200)
tmap_mode("plot")
left.map <- tm_shape(suburb.sf, bbox = inner.city.bb.sf) +
tm_polygons(col = "white", border.col = my.greys[5], lwd = 1, showNA = TRUE,legend.show = FALSE) +
tm_shape(inner.city.sa1.sf) +
tm_polygons(col = "dwellings.2016.n", border.col = "black", lwd = 1, showNA = FALSE, palette = RColorBrewer::brewer.pal(10, 'Greens'), title = "Dwellings in 2016", style = "cont") +
tm_shape(inner.city.sf) +
tm_borders(col = "red",lwd = 3) +
tm_shape(central.station.sf) +
tm_text("city", col = "red", fontface="bold", shadow = TRUE) +
tm_compass(position = c("left", "bottom"))+
tm_scale_bar(position = c("left", "bottom"), width = 0.4) +
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
legend.bg.color = "white",legend.frame.lwd = 1,
legend.frame = "black", legend.position = c("left", "top"),
legend.text.size = 0.6, legend.title.size = 1)
right.map <- tm_shape(suburb.sf, bbox = inner.city.bb.sf) +
tm_polygons(col = "white", border.col = my.greys[5], lwd = 1, showNA = TRUE,legend.show = FALSE) +
tm_shape(unclipped.point.sf) +
tm_dots(col = my.greys[7], alpha = 0.3) +
tm_shape(clipped.point.sf) +
tm_dots(col = city.trio[1], alpha = 0.3) +
tm_shape(inner.city.sa1.sf) +
tm_polygons(border.col = "black", lwd = 1, showNA = FALSE,legend.show = FALSE, alpha = 0) +
tm_shape(inner.city.sf) +
tm_borders(col = "red",lwd = 3) +
tm_shape(central.station.sf) +
tm_text("city", col = "red", fontface="bold", shadow = TRUE) +
tm_compass(position = c("left", "bottom"))+
tm_scale_bar(position = c("left", "bottom"), width = 0.4) +
tm_add_legend(type= "symbol", col=c(city.trio[1], my.greys[5]), border.col = c(city.trio[1], my.greys[5]), alpha = c(1,0), border.alpha = c(0,1), labels=c(dwe.label, other.label), title="Dwellings in 2016") +
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
legend.bg.color = "white",legend.frame.lwd = 1,
legend.frame = "black", legend.position = c("left", "top"),
legend.text.size = 0.8, legend.title.size = 1)
tmap_arrange(left.map, right.map, ncol=2)Table 4. Inner and outer-city summaries for Brisbane, Sydney, and Melbourne
temp <- suburb.sf %>%
as.tibble() %>%
drop_na(city) %>%
mutate(inner.population.sum = population.2016.n*inner.city.proportion,
inner.dwellings.sum = dwellings.2016.n*inner.city.proportion,
inner.km2.sum = ha*inner.city.proportion/100,
outer.population.sum = population.2016.n*(1-inner.city.proportion),
outer.dwellings.sum = dwellings.2016.n*(1-inner.city.proportion),
outer.km2.sum = ha*(1-inner.city.proportion)/100) %>%
group_by(city)%>%
summarise(inner.population.sum = round(sum(inner.population.sum),0),
inner.dwellings.sum = round(sum(inner.dwellings.sum),0),
inner.km2.sum = round(sum(inner.km2.sum),0),
outer.population.sum = round(sum(outer.population.sum),0),
outer.dwellings.sum = round(sum(outer.dwellings.sum),0),
outer.km2.sum = round(sum(outer.km2.sum),0)) %>%
mutate(inner.population.per.km2 = inner.population.sum/inner.km2.sum,
inner.dwellings.per.km2 = inner.dwellings.sum/inner.km2.sum,
outer.population.per.km2 = outer.population.sum/outer.km2.sum,
outer.dwellings.per.km2 = outer.dwellings.sum/outer.km2.sum) %>%
select(city, inner.km2.sum, inner.population.sum, inner.population.per.km2,
inner.dwellings.sum, inner.dwellings.per.km2,
outer.km2.sum, outer.population.sum, outer.population.per.km2,
outer.dwellings.sum, outer.dwellings.per.km2)
temp_transpose = t(temp)
row.names(temp_transpose) <- c("City", "area (km^2)", "population (n)", "population (per km^2)",
"dwellings (n)", "dwellings (per km^2)",
"area (km^2)", "population (n)", "population (per km^2)",
"dwellings (n)", "dwellings (per km^2)")
kable(temp_transpose, format = "html", digits = 2, row.names = T, knitr.kable.NA = " ", align = c("r", "r", "r"),
caption = "Table x. Summary Statistics for Brisbane, Sydney and Melbourne") %>%
kableExtra::kable_styling("striped", full_width = F) %>%
kableExtra::pack_rows("Inner city", 2, 6) %>%
kableExtra::pack_rows("Outer city", 7, 11) The Brisbane urban form spreads linearly along the snaking Brisbane River (Figure 5) and is in the south-eastern region of the State of Queensland. With the lowest population density of the three cities, detached, single-family dwellings typify Brisbane (83 percent); however, its population is increasingly concentrating towards the inner-city on account of the recent high-rise development. In conjunction with recent population growth (18 percent from 2006 to 2016), the proportion of the population older than 65 has increased from 11 percent to 13 percent. This is due to a trend among retirees from southern Australian states moving to Brisbane for warmer weather. Consequently, by 2016 more than a quarter of Brisbane’s population was not participating in the workforce. Further, due to the high cost of inner-city housing, newcomers have typically dispersed towards the outer-city (ABS 2018). Automobile ownership is high throughout Brisbane with 94 percent of households owning an automobile (ABS 2018).
knitr::include_graphics("./fig5.jpg")Brisbane’s public transit network is ranked ‘average’ by international standards, and especially in terms of coverage, frequency, and reliability (ARCADIS 2017). Currently, the city comprises more than 380 kilometres of rail and numerous bus lines that include segregated busways (BRT) overseen and coordinated by an integrated agency (i.e. TransLink) established in 2003. Developed in the late 19th Century, Brisbane’s rail network consists of six suburban lines extending from the CBD in a radial pattern. A BRT was first introduced in 2000 and comprises 25 kilometres across three radial lines. Reflecting job centralisation, both bus and train services are much more frequent within the city core, while the remaining areas are heavily reliant on private automobiles for transport. Significant investment has been directed towards Brisbane RTN with the inner-city RTN increasingly resembling TOD (Yang & Pojani 2017). Alongside rail and BRT lines, Brisbane also has a network of river ferries (CityCats) that were introduced in 1996. These ferries have helped reorient the city back to its river and encourage inner-city densification while also promoting a greater use of public transit. Only 1 percent of workers cycle to work which could be due to Brisbane’s hilly topography and limited road infrastructure designed to protect cyclists. The most recent major transport infrastructure projects in Brisbane have included three automobile tunnels (i.e. Clem7, Airport Link, Legacy) and one automobile bridge (i.e. Go Between Bridge) with each having road tolls in place as a value capture mechanism thus suggesting that planning authorities are aiming to improve the appeal of private automobile travel while at the same time charging motorists for this privilege.
Sydney is located midway along the east coast of New South Wales and its spatial form fans out from Port Jackson that is one of the world’s largest harbours that also includes Sydney Harbour, Middle Harbour, North Harbour, Lane Cove River, and the Parramatta River (Figure 6). Once the landing site of the earliest European settlers and unlike Brisbane, Sydney is located near the coast. Today, Sydney is the economic powerhouse of the continent, and is also highly ranked globally for economic influence (Economist Intelligence Unit 2017). Further, it is the principal Australian destination for newly arriving migrants with 39 percent of the population born overseas, and the city is ranked the most populous Australian city although its population grew by only 11 percent from 2006 to 2016 thus it is beginning to lag behind Brisbane (ABS 2018). Sydney’s housing prices are among the highest in the world (Demographia 2017) while detached, single-family dwellings are uncommon relative to Brisbane (64 and 83 percent respectively), which could explain why new residential development is stretching farther and farther from the CBD.
knitr::include_graphics("./fig6.jpg")Sydney’s public transit network is ranked ‘poor’ by international standards thus worse than Brisbane and particularly in terms of accessibility (ARCADIS 2017), which could be attributable to the challenges of travelling around its harbour and reaching its Central Business District (CBD) that is located near the shore and thus far from its geographic centre. 11 percent of Sydney households are auto-free, which suggests that it is less auto-dependent than Brisbane. Its rail network (tram and train) extends 815 kilometres, and it has a metro system that is currently under development and the nation’s largest infrastructure project. The Sydney Metro will deliver 66 kilometres of track and 31 stations. In conjunction, the existing light rail network will be expanded (Transport for New South Wales 2012).
Last, Melbourne is located midway along the south coast of Victoria with its spatial form wrapping around Port Phillip Bay (Figure 7). Since Melbourne’s gold rush during the 1850s, it has remained one of the world’s wealthiest cities and was consistently ranked as the world’s most liveable city from 2007 to 2017 until it fell behind Vienna in 2018 (Economist Intelligence Unit 2018). While Melbourne’s population is only slightly behind that of Sydney, it is notable that its 2006 to 2016 population growth of 16 percent exceeds Sydney’s so it may soon be Australia’s most populous city.
knitr::include_graphics("./fig7.jpg")Melbourne’s public transit network is ranked the worst of the three cities by global standards (ARCADIS 2017) despite the city’s 250 kilometres tram network that is both iconic for the city and the largest in the world (Yarra Trams 2017). Despite its trams having priority on the road, it is notable that these trams can become trapped behind the same congestion as private automobiles, which may explain the city’s poor global ranking. Further, the Melbourne road design requires automobile motorists to frequently change lanes and take ‘hook turns’ to navigate around trams thus further contributing towards congestion. Similar to Sydney, Melbourne is currently developing a cross-city metro system that will bypass most inner-city stations (Metrotunnel 2018) thus suggesting that both Sydney and Melbourne are working towards improving the appeal of public transit, while Brisbane instead is working towards improving the appeal of driving.
As noted previously, all ABS data is available as open data from the ABS’ online portal (2020) and the ABS’ 2016 SA1 neighbourhood spatial units were used when examining residential populations. Both the 2011 and 2016 Censuses of Population and Housing (CPH) were used for exploring where residential development is concentrating, demand for off-street parking is shifting, and where modal choices are changing. Notably, the 2011 SA1 spatial units differ from the 2016 thus a conversion table available from the portal was used to link the 2011 CPH to the 2016 SA1 for accurate comparability.
In contrast to the residential populations, SA2 are the smallest geographic unit available within the Australian Statistical Geography Standard (ASGS) that captures worker populations thus SA2 are used when approximating the location of workers within the inner city (Figure 8). These SA2 units were also available from the ABS’ (2020) portal and the 2011 workplace data employs a conversion table to link with the 2016 SA2 (ABS 2018). The resident and worker measures were as follows:
Active or sustainable transport (%) to identify the proportion of the residential neighbourhood or inner-city workplace that was not driving to their workplace;
Policy Zone (nominal) to identify whether parking minimums or maximums most likely apply to the neighbourhood by distinguishing whether the neighbourhood centroid was within: the inner-city; 400 meters from a rapid transit node, 800 meters from a rapid transit node; or the remaining outer-city;
Distance from Central Station (km) to determine whether urban centrality can explain modal choices;
Distance from a Rapid Transit Node (km) to determine whether the distance beyond a walkable range can further explain modal choices;
Cars per Dwelling (cars) to estimate parking demand;
Dwellings per Hectare (dwellings) to determine whether urban density can explain modal choices:
Australian Born (%) to determine whether auto-culture can explain modal choices;
Median Age (years) to determine whether age can explain modal choices;
Mean Annual Household Income (1000AUD) to determine whether financial resources can explain modal choices;
Mean Household Size (persons) to determine whether parental responsibilities can explain modal choices; and
Relocated less than one year earlier (%) to determine whether selection effects and residing within a neighbourhood short-term can explain modal choice.
map5 <- tm_shape(inner.city.sf) +
tm_borders(col = trio[3]) +
tm_facets(by = "inner.city") +
tm_shape(states.sf) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(filter(suburb.sf, inner.city.proportion > 0)) +
tm_polygons(col = "population.2016.n", palette = viridis(7), n = 7, style = "quantile", legend.show = TRUE, title = "Inner city resident [n]", showNA = FALSE, border.col = land, lwd = 0.5) +
tm_shape(inner.city.sf) +
tm_borders(col = trio[3])+
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
panel.label.size = 0.6, panel.label.color = 'white',
panel.label.bg.color = 'black')
map6 <- tm_shape(inner.city.sf) +
tm_borders(col = trio[3]) +
tm_facets(by = "inner.city") +
tm_shape(states.sf) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(filter(inner.city.workers.2016.sf, inner.city.proportion > 0)) +
tm_polygons(col = "arriving.workers.2016", palette = viridis(7), n = 7, style = "quantile", legend.show = TRUE, title = "Inner city worker [n]", showNA = FALSE, border.col = land, lwd = 0.5) +
tm_shape(inner.city.sf) +
tm_borders(col = trio[3])+
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
panel.label.size = 0.6, panel.label.color = 'white',
panel.label.bg.color = 'black')
tmap_arrange(map5, map6, ncol = 1, asp = NULL)Both the local and state governments included in this study have policy frameworks and strategies for land use and transport planning that includes parking regulations and pricing. The timeline for this examination covers the period between the early 2000s and the present. A template analysis was conducted on these policy frameworks and strategies to discern the intentionality and implications. This is a clear, systematic, and flexible qualitative approach that structures and summarises large volumes of text to aid comparisons between texts (Brooks et al. 2014; King 2012).
While the local governments included in this study set different parking requirements according to land use, parking maximums are typical throughout these documents thus suggesting a demand management planning approach. Notably, implementation varies between these inner cities thus to examine parking implementation directly, Development Applications (DA) recently approved for new buildings were examined. The timeframes for each city vary according to availability and the specific sources for each city were detailed below.
61 DAs for inner Brisbane were collected manually from the BCC’s PD Online portal between the 16th and 24th October 2017 (BCC 2017b). These DAs include zoning, dwellings count, and parking bay counts; with the latter coded as either: resident, visitor, non-residential, motorbike, and bicycle parking bays. Notably, these 61 DAs only include DAs coded as within the ‘City Frame’ where inner-city policies should apply, and not coded as within ‘Priority Development Areas’ that were managed by Queensland State Government rather than the BCC.
49 DAs for inner Sydney were collected manually from the City of Sydney’s Development Application Search Tool between the 15th September 2017 and 6th October 2017 (City of Sydney 2017a). Specifically, it was the approved Notices of Determination issued under the New South Wales Environmental Planning and Assessment Act 1979 that were retained that cover from 2006 to 2017. These notices include dwelling counts and parking counts with the latter coded as either: resident, visitors/commercial, car share, motorcycle, or bicycle parking bays.
Last for DAs, 42 DAs for inner Melbourne were extracted from a bulk dump of the City of Melbourne’s Development Activity Model, which was last updated in May 2017 and covers from 2002 to 2013 (City of Melbourne 2017). This model monitors both commercial and residential development with information that includes project status, location, size, and parking provision. Specifically, the dwelling counts and car parking bay counts were used however it was notable that unlike the Brisbane and Sydney DAs, the Melbourne DAs do not distinguish commercial, residential, or visitor parking apart thus have the potential to be inflated within this examination.
Real estate listings pertaining to these DAs were collected where possible using real estate portals (realestate.com.au 2017; domain.com.au 2017), and in some instances for Melbourne, from further fields within the Development Activity Monitor (City of Melbourne 2017). While older listings unreliably provided details such as bedroom counts, parking bay counts per dwelling could be reliably sourced throughout the study. As such, these real estate listings when paired with DAs can provide an account of recent planning practice however a limitation was that only current listings were available thus time series analysis of changes in planning practices was not possible with this data. In sum, the average number of parking bays per dwelling listed within DAs were visualised using a violin plot to examine and contrast planning practice between cities, and real estate listings were used to deepen the interpretation of these findings.
To examine and contrast parking prices between the three inner cities, data was obtained from a variety of sources that include websites created by the three city councils, the Royal Automobile Club of Queensland reports (motoring club and mutual organisation), and www.finder.com.au (a data aggregator and comparison website). Given the range of sources, these will be provided in text within tables during the analysis.
To estimate and contrast overall parking supply for the three inner cities, the data was extracted from several disparate sources and open data was used where possible (Figure 9). For inner Brisbane, the BCC only collects and releases data detailing the locations and parking bays associated with their on-street parking meters for 2015 thus only meters and paid on-street bays can be reliably estimated (BCC 2018). In addition, the Queensland Government Department of Transport and Main Roads (TMR) have surveyed 19 percent of the inner-city surface for off-street parking thus this data was the basis of off-street parking estimates for inner Brisbane. Similarly, the City of Sydney collects and releases parking meter data that includes locations but lacks counts of the associated paid on-street parking bays. In contrast, the City of Sydney in 2017 surveyed all off-street parking within their jurisdiction, and this survey is entitled ‘the Floor space and Employment Survey (FES)’. Further, the FES distinguishes whether off-street parking was for ‘tenants’ or the ‘public’ (City of Sydney 2017b). Finally, on-street parking bay, meter, and sensor data were all available as open data through the City of Melbourne online portal for inner Melbourne (City of Melbourne 2018) thus it was uniquely possible to examine the location and area of both paid and unpaid on-street parking within inner Melbourne. Further, this portal includes ‘the Census of Land Use and Employment (CLUE)’ that surveys all off-street parking throughout inner Melbourne, and it distinguishes whether the parking was ‘commercial’ or ‘private’, and measures both the area and bay counts.
map1 <- tm_shape(inner.city.sf) +
tm_borders(col = trio[3]) +
tm_facets(by = "inner.city") +
tm_shape(states.sf) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(meters.point.sf) +
tm_dots(col = "bays.per.meter", palette = viridis(7), n = 7, style = "jenks", legend.show = TRUE, title = "On-street parking meter [n bays]", textNA = "not listed") +
tm_shape(inner.city.sf) +
tm_borders(col = trio[3]) +
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
panel.label.size = 0.6, panel.label.color = 'white',
panel.label.bg.color = 'black')
map2 <- tm_shape(inner.city.sf) +
tm_borders(col = trio[3]) +
tm_facets(by = "inner.city") +
tm_shape(states.sf) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(onstreet.poly.sf) +
tm_dots(col = "bay.area.m2", palette = viridis(7), n = 7, style = "jenks", legend.show = TRUE, title = "On-street parking bay [m-sq]") +
tm_shape(inner.city.sf) +
tm_borders(col = trio[3])+
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
panel.label.size = 0.6, panel.label.color = 'white',
panel.label.bg.color = 'black')
temp <- offstreet.poly.sf %>%
mutate(offstreet.bays = residential.offstreet.bays + other.offstreet.bays,
offstreet.m2 = ifelse(inner.city == "Melbourne", residential.offstreet.m2 + other.offstreet.m2, NA))
map3 <- tm_shape(inner.city.sf) +
tm_borders(col = trio[3]) +
tm_facets(by = "inner.city") +
tm_shape(states.sf) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(temp) +
tm_polygons(col = "offstreet.bays", palette = viridis(7), n = 7, style = "jenks", legend.show = TRUE, title = "Off-street parking bay [n]", showNA = FALSE, border.col = land, lwd = 0.5) +
tm_shape(inner.city.sf) +
tm_borders(col = trio[3])+
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
panel.label.size = 0.6, panel.label.color = 'white',
panel.label.bg.color = 'black')
map4 <- tm_shape(inner.city.sf) +
tm_borders(col = trio[3]) +
tm_facets(by = "inner.city") +
tm_shape(states.sf) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(temp) +
tm_polygons(col = "offstreet.m2", palette = viridis(7), n = 7, style = "jenks", legend.show = TRUE, title = "Off-street parking area [m-sq]", border.col = land, lwd = 0.5, colorNA = land, showNA = FALSE) +
tm_shape(inner.city.sf) +
tm_borders(col = trio[3])+
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
panel.label.size = 0.6, panel.label.color = 'white',
panel.label.bg.color = 'black')
tmap_arrange(map1, map2, map3, map4, ncol = 1, asp = NULL)To capture the location of RTNs that were potentially PnR or locations where demand management policies apply, the addresses for train, bus, and ferry terminals were scraped from a public transit website for each city (Visit Brisbane 2018; Transport for New South Wales 2018; Public Transport Victoria 2018). Following, these addresses were geocoded into centroids and only the 689 RTN located within 50 kilometres of a RTN entitled ‘Central Station’ and do not have names that indicate an airport station or workshop yard were retained given the examination should only include publicly available RTN. Further, given that BCC local government’s policies zone TOD as neighbourhoods within a 400 metre buffer that centres on RTN (BCC 2014a), while the Queensland State Government zone TOD using 800m buffers (State of Queensland 2009), both were created as donut buffers for contrasting what Kamruzzaman and colleagues (2014) describe as primary and secondary TOD catchments. Notably, these buffer ranges were distinct from the 500m provided by Pojani and Stead (2016) but given that these authors were providing a theoretically determined optimal walkable range—which Yang and Pojani (2017) explicitly define as a 10 minute walk—rather than an empirically determined range, the policy ranges were used.
Both the global and local Moran’s I were calculated to determine the extent to which observed values or model residuals are spatially autocorrelated. A Moran’s I index of 1 indicates perfect spatial clustering, a 0 that values are spatially random, and -1 indicates an antagonistic relationship between spatial units resembling a chess board. Both the Global and Local Moran’s I indices are calculated in this analysis given that a Global I reveals spatial patterning between all observations (i.e. a value representing spatial autocorrelation across an entire city), and a Local I reveals spatial patterning between an individual observation and its nearest neighbours (i.e. for each of the spatial units comprising the city). Queen contiguity was used and the Moran’s I is calculated as follows:
knitr::include_graphics("./formula1.png")Where: N is the number of spatial units indexed by i and j; x is the variable of interest or model residuals with x-hat as its mean; w_ij is a spatial weights matrix with zeros on the diagonal; and W is the sum of all weights.
If the model errors prove to be spatially autocorrelated, then a Spatial Autoregressive Combined (SAC) model will be used since this model includes a spatial lag of nearest neighbours for both the dependent variable and the model errors thus addressing spatial autocorrelation rather than assume the neighbourhood units are randomly located within each city (Kelejian & Prucha 1998). This SAC model will be a region-wide model with an indicator variable for each city and its formula is:
knitr::include_graphics("./formula2.png")Where: y_n is observations vector n×1 on dependent variable y; X_n is matrix n×k on k exogenous variables; β is vector n×1 of regression parameters; λ and ρ are scalar autoregressive parameters; W_n and M_n are spatial weighting matrices n×n on known constants; u_n is vector n×1 on model disturbances; and ε_n is vector n×1 on model innovations.
Last, a Geographically-Weighted Regression (GWR) model will used for each city to provide a place-based explanation unlike the SAC model’s region-wide explanation. As such, GWR will be used to explore any spatial heterogeneity of the key effects that are within the influence of planners e.g. where RTN are placed relative to the inner-city. Unlike the MEM and SAC models, GWR coefficients can vary spatially using a ‘moving window’ to create a continuous surface for each city. As such, the model results are presented using maps rather than tables. The utility of this approach was that it can reveal where infrastructure likely has the greatest influence. The GWR formula is:
knitr::include_graphics("./formula3.png")where rather than β_j being constant in the whole model and throughout the study area, it is instead function β_j with (u_i v_i ) denoting the centre of the spatial unit and x_ij is a spatially weighting matrix where the spatial association tapers to zero at the perimeter of the moving window (Fotheringham, Brunsdon, & Charlton 2002). The width of this moving window is also referred to as ‘kernel bandwidth’ and while this can be determined through trial and error, a cross validation of bandwidth method to empirically select the kernel bandwidth with the smallest model errors was used within this examination (Paez, Farber, & Wheeler 2011). Finally, the Moran’s I of each GWR model’s residuals was calculated using Leung and colleagues’ (2000) three moment approximation method to determine the degree to which spatial autocorrelation explains any remaining model error or ‘disturbances’ denoted by ε_i.
The implementation of parking policies for Brisbane, Sydney, and Melbourne were examined below. The analysis also investigates changes to parking and travel behaviour that could be attributed to the public policies which were in place in each city. There are four distinct units of analysis feature throughout this section: (1) the inner cities for exploring development applications, parking pricing, parking supply, and modal choices; (2) 400 metre buffers and (3) 800 metre donut buffers around RTN for exploring TOD and modal choices, and (3) the 50 kilometre major metropolitan areas that centre on each Central Station (refer to Section 2 above for clarification on how and why these units were created). The results were tabulated and plotted to provide intra-city and inter-city comparisons.
Parking policies were summarised within Table 5 for the three case study cities as each pertains to maximums, unbundling, pricing, TOD areas and PnR provision.
Table 5. Parking Policy Documents for Brisbane, Sydney, and Melbourne
knitr::include_graphics("./tab5.png")As seen in Table 5, Brisbane, Sydney, and Melbourne each have policies in place for transitioning away from conventional ‘predict and provide’ strategies such as parking minimums, and towards ‘multimodalism’ strategies such as PnR and ‘demand management’ strategies such as parking maximums, unbundling, market pricing, and TOD. This strategic realignment suggests the cities increasingly aim to improve urban mobility by: (1) reducing auto-dependence; (2) charging fair market prices for parking to reduce misuse; (3) reducing cruising for parking delays trailing traffic; (4) concentrating residents within walking distance of RTN; and (5) providing exurban, auto-dependent commuters with PnR so that they may leave their car parked outside of the inner-city rather than contribute towards inner-city traffic congestion and parking scarcity.
Among the three case study cities, Sydney was the first to introduce parking maximums in the inner-city – as early as the low 1990s, while Brisbane and Melbourne did not follow Sydney’s example until at least a decade later. Now, parking maximum policies are in place in each city (at least in the inner cities), and these maximums are set according to land use. Sydney has gone as far as proposing caps for residential parking permits, while Brisbane has recommended unbundling parking from residential development. However, in Brisbane minimum parking requirements apply nearly everywhere outside the CBD, including at major rapid transit nodes, and they were set at a level high enough to discourage unbundled parking. Moreover, maximum parking standards only work in achieving long-term change if on-street parking is appropriately priced and relatively scarce (Christiansen 2017a).
All three cities recognize in their plans and policy documents that parking oversupply is a direct consequence of earlier parking minimum standards, which have created congestion and reduced urban amenity. However, they also advise a moderate approach out of concern that zealous reduction of parking in inner cities could impact economic growth. This was in line with existing theoretical insights that parking plans need to be scaled up to the metropolitan level if businesses were to be prevented from migrating to areas with different parking rules that alter travel incentives (Marsden 2006). However, relocation issues may be less applicable to major urban centres such as Brisbane, Sydney, and Melbourne because large businesses make location choices according to agglomeration economies, amenity, and public transit accessibility rather than parking supply (Martens 2005).
Parking standards in Brisbane and Melbourne are highly prescriptive and specify requirements for many different land uses, with further differentiation according to proximity to the CBD and the size of a development. Parking requirements are far less prescriptive for specific land uses in Sydney. The latter approach was more desirable because simple one-size-fits-all standards can make it difficult to manage parking supply in large cities (Rowe, Bae, & Shen 2010). The emergence of conditions requiring car-sharing spaces in new developments in all three cities may facilitate further reductions in parking supply as it enables new projects to succeed with lower levels of parking (Ter Schure, Napolitan, & Hutchinson 2012).
Regarding pricing, Melbourne in 2002 was the first city to promote the use of fair market prices for parking as a tool to reduce car dependence. Brisbane and Sydney followed through in 2009 and 2010, respectively. Both Melbourne and Brisbane have reduced maximum stays for on-street parking in the CBD. Demand-responsive pricing may be on the near horizon in all cities but has been explicitly discussed only in Melbourne.
In the past decade, policies that promote both nodal- and corridor-based TOD have been adopted in all three cities. TOD principles have been present in planning documents for at least two decades in Sydney and Melbourne although the term ‘TOD’ was not introduced until more recently. In conjunction, parking minimums have loosened around rapid transit nodes to complement attempts to increase population and job density within TOD zones, and to reduce private transport demand. In terms of PnR, Melbourne in 2002 was the first among the case study cities to recommend the provision of PnR along rapid transport corridors. Brisbane followed in 2008-2010 but with a more nuanced approach by advising that PnR be located at least 10 kilometres from the CBD and that pre-existing PnR within 10 kilometres be converted into TOD. These findings suggest that both ‘Predict and Provide’ and ‘Multimodalism’ planning approaches are falling out of favour as ‘Demand Management’ gains traction in all three cities.
To examine whether the inner cities were following international best practice (Pojani et al. 2020), the parking and dwelling counts within approved DA were summarised (Table 6) and visualised (Figure 10) for comparing Brisbane, Sydney, and Melbourne. Notably, a y-line was included within this figure since this threshold reveals there were fewer parking bays than dwellings, and therefore where unbundled parking can be inferred. To explore parking demand in 2011 and 2016, the inner-city residential population and their automobile ownership and driving to work were summarized alongside the inner-city workers and their driving to work (Table 7). To examine parking supply, the various parking data sources detailed within section 3 were summarised and (Table 8), and key summary statistics were calculated from the parking demand table (Table 7). Notably, some of the area calculations were imputed using Melbourne average bay sizes given that Melbourne was the only city that collected both parking bay counts and area. Last, the parking rates and permit costs were summarised for each city and contrasted with cities overseas (Table 9) and since this draws from a variety of sources, the sources were included below the table. All these summaries will be examined by city and compared.
Table 6. Development Application Characteristics for Brisbane, Sydney, and Melbourne
knitr::include_graphics("./tab6.png")#development applications
dev.app <- read_csv("Connor_s_DA_data.csv") %>%
clean_names() %>%
as_tibble() %>%
mutate(application.ranked = row_number()) %>%
gather(key = "temp", value = "av.parking", brisbane:sydney) %>%
mutate(inner.city = factor(ifelse(temp == "brisbane",
"Brisbane",
ifelse(temp == "sydney",
"Sydney",
"Melbourne")),
levels = c("Brisbane", "Sydney", "Melbourne")))
# violin plots
ggplot(data = dev.app, aes(x=inner.city, y=av.parking, col = inner.city)) +
geom_violin(show.legend = FALSE) +
geom_dotplot(binaxis='y', stackdir='center', method = "histodot", dotsize=0.5, show.legend = FALSE, aes(fill = inner.city, alpha = 1.0)) +
stat_summary(fun.y=mean, geom="point", size=5, shape=18, show.legend = FALSE) +
geom_hline(yintercept=1, linetype="dashed") +
labs(y = "Parking Bays (average)") +
theme_bw() +
scale_fill_manual(values = city.trio) +
scale_color_manual(values = city.trio) +
theme_linedraw() +
theme(aspect.ratio = 0.7,
axis.title.x = element_blank(),
axis.text.x = element_text(color = "black", size = 15),
axis.title.y = element_text(color = "black", size = 18),
axis.text.y = element_text(color = "black", size = 15))Table 7. Inner-city Parking Demand for Brisbane, Sydney, and Melbourne
Table 8. Inner-city Parking Supply for Brisbane, Sydney, and Melbourne
# imputation and summaries
temp <- inner.city.sf %>%
as.tibble() %>%
group_by(inner.city) %>%
summarise(ha = sum(inner.city.ha))
temp <- suburb.sf %>%
as.tibble() %>%
mutate(population.2011 = population.2011.n * inner.city.proportion,
population.2016 = population.2016.n,
leaving.by.auto.2011 = (mode.p.and.p.2011.n + mode.intermodal.2011.n) * inner.city.proportion,
leaving.by.auto.2016 = (mode.p.and.p.2016.n + mode.intermodal.2016.n) * inner.city.proportion,
dwellings.2011 = dwellings.2011.n * inner.city.proportion,
dwellings.2016 = dwellings.2016.n * inner.city.proportion,
car.less.dwellings.2011 = car.less.dwellings.2011.n * inner.city.proportion,
car.less.dwellings.2016 = car.less.dwellings.2016.n * inner.city.proportion,
cars.2011 = cars.2011.n * inner.city.proportion,
cars.2016 = cars.2016.n * inner.city.proportion) %>%
group_by(inner.city) %>%
summarise(population.2011 = sum(population.2011),
population.2016 = sum(population.2016),
leaving.by.auto.2011 = sum(leaving.by.auto.2011),
leaving.by.auto.2016 = sum(leaving.by.auto.2016),
dwellings.2011 = sum(dwellings.2011),
dwellings.2016 = sum(dwellings.2016),
car.less.dwellings.2011 = sum(car.less.dwellings.2011),
car.less.dwellings.2016 = sum(car.less.dwellings.2016),
cars.2011 = sum(cars.2011),
cars.2016 = sum(cars.2016)) %>%
drop_na(inner.city) %>%
right_join(temp, by = "inner.city")
temp <- inner.city.workers.2011.sf %>%
as.tibble() %>%
mutate(arriving.workers.2011 = arriving.workers.2011 * inner.city.proportion,
arriving.drivers.2011 = arriving.drivers.2011 * inner.city.proportion) %>%
group_by(inner.city) %>%
summarise(arriving.workers.2011 = sum(arriving.workers.2011),
arriving.drivers.2011 = sum(arriving.drivers.2011)) %>%
drop_na(inner.city) %>%
right_join(temp, by = "inner.city")
temp <- inner.city.workers.2016.sf %>%
as.tibble() %>%
mutate(arriving.workers.2016 = arriving.workers.2016 * inner.city.proportion,
arriving.drivers.2016 = arriving.drivers.2016 * inner.city.proportion) %>%
group_by(inner.city) %>%
summarise(arriving.workers.2016 = sum(arriving.workers.2016),
arriving.drivers.2016 = sum(arriving.drivers.2016)) %>%
drop_na(inner.city) %>%
right_join(temp, by = "inner.city")
temp <- meters.point.sf %>%
as.tibble() %>%
mutate_if(is.numeric, funs(ifelse(is.na(.), 1, .))) %>%
mutate(bays.per.meter = bays.per.meter,
parking.meters = 1) %>%
group_by(inner.city) %>%
summarise(bays.at.meter = sum(bays.per.meter),
parking.meters = sum(parking.meters)) %>%
drop_na(inner.city) %>%
right_join(temp, by = "inner.city")
temp <- sensors.point.sf %>%
as.tibble() %>%
mutate(parking.sensors = 1) %>%
group_by(inner.city) %>%
summarise(parking.sensors = sum(parking.sensors)) %>%
drop_na(inner.city) %>%
right_join(temp, by = "inner.city")
temp <- onstreet.poly.sf %>%
as.tibble() %>%
mutate(bay.area.m2 = round(bay.area.m2 * inner.city.proportion),0,
metered.bay = ifelse(is.na(id), 0, 1)) %>%
group_by(inner.city) %>%
summarise(onstreet.m2 = sum(bay.area.m2),
onstreet.bay = sum(onstreet.bay),
metered.bay = sum(metered.bay)) %>%
drop_na(inner.city) %>%
right_join(temp, by = "inner.city")
temp <- offstreet.poly.sf %>%
as.tibble() %>%
mutate(residential.offstreet.bays = residential.offstreet.bays * inner.city.proportion,
other.offstreet.bays = other.offstreet.bays * inner.city.proportion,
residential.offstreet.m2 = residential.offstreet.m2 * inner.city.proportion,
other.offstreet.m2 = other.offstreet.m2 * inner.city.proportion) %>%
group_by(inner.city) %>%
summarise(residential.offstreet.bays = sum(residential.offstreet.bays),
other.offstreet.bays = sum(other.offstreet.bays),
residential.offstreet.m2 = sum(residential.offstreet.m2),
other.offstreet.m2 = sum(other.offstreet.m2)) %>%
drop_na(inner.city) %>%
right_join(temp, by = "inner.city")
temp <- temp %>%
mutate(residential.offstreet.m2 = ifelse(residential.offstreet.m2 == 0, NA, residential.offstreet.m2),
other.offstreet.m2 = ifelse(other.offstreet.m2 == 0, NA, other.offstreet.m2),
population.change = population.2016 - population.2011,
population.2011.per.ha = population.2011/ha,
population.2016.per.ha = population.2016/ha,
population.change.per.ha = population.2016.per.ha - population.2011.per.ha,
leaving.by.auto.change = leaving.by.auto.2016 - leaving.by.auto.2011,
leaving.by.auto.2011.p = leaving.by.auto.2011/population.2011 *100,
leaving.by.auto.2016.p = leaving.by.auto.2016/population.2016 *100,
leaving.by.auto.change.p = leaving.by.auto.2016 - leaving.by.auto.2011,
dwellings.change = dwellings.2016 - dwellings.2011,
dwellings.2011.per.ha = dwellings.2011/ha,
dwellings.2016.per.ha = dwellings.2016/ha,
dwellings.change.per.ha = dwellings.2016.per.ha - dwellings.2011.per.ha,
cars.change = cars.2016 - cars.2011,
cars.2011.per.dwelling = cars.2011/dwellings.2011,
cars.2016.per.dwelling = cars.2016/dwellings.2016,
cars.change.per.dwelling = cars.2016.per.dwelling- cars.2011.per.dwelling,
car.less.dwellings.change = car.less.dwellings.2016 - car.less.dwellings.2011,
car.less.dwellings.2011.p = car.less.dwellings.2011/dwellings.2011*100,
car.less.dwellings.2016.p = car.less.dwellings.2016/dwellings.2016*100,
car.less.dwellings.change.p = car.less.dwellings.2016.p - car.less.dwellings.2011.p,
arriving.workers.change = arriving.workers.2016 - arriving.workers.2011,
arriving.drivers.change = arriving.drivers.2016 - arriving.drivers.2011,
arriving.workers.2011.by.auto = arriving.drivers.2011/arriving.workers.2011*100,
arriving.workers.2016.by.auto = arriving.drivers.2016/arriving.workers.2016*100,
arriving.workers.change.by.auto = arriving.workers.2016.by.auto-arriving.workers.2011.by.auto,
av.onstreet.m2 = onstreet.m2/onstreet.bay,
known.onstreet = ifelse(inner.city == "Melbourne", onstreet.bay, bays.at.meter),
av.residential.m2 = residential.offstreet.m2 / residential.offstreet.bays,
av.other.m2 = other.offstreet.m2 / other.offstreet.bays) %>%
select(inner.city, ha,
population.2011, population.2016, population.change,
population.2011.per.ha, population.2016.per.ha, population.change.per.ha,
leaving.by.auto.2011, leaving.by.auto.2016, leaving.by.auto.change,
leaving.by.auto.2011.p, leaving.by.auto.2016.p, leaving.by.auto.change.p,
dwellings.2011, dwellings.2016, dwellings.change,
dwellings.2011.per.ha, dwellings.2016.per.ha, dwellings.change.per.ha,
cars.2011, cars.2016, cars.change,
cars.2011.per.dwelling, cars.2016.per.dwelling, cars.change.per.dwelling,
car.less.dwellings.2011, car.less.dwellings.2016, car.less.dwellings.change,
car.less.dwellings.2011.p, car.less.dwellings.2016.p, car.less.dwellings.change.p,
arriving.workers.2011, arriving.workers.2016, arriving.workers.change,
arriving.drivers.2011, arriving.drivers.2016, arriving.drivers.change,
arriving.workers.2011.by.auto, arriving.workers.2016.by.auto, arriving.workers.change.by.auto,
onstreet.bay, onstreet.m2, parking.meters, bays.at.meter, metered.bay, parking.sensors, known.onstreet,
residential.offstreet.bays, residential.offstreet.m2, other.offstreet.bays, other.offstreet.m2, av.onstreet.m2, av.residential.m2, av.other.m2) %>%
mutate(known.parking.coverage.p = ( (max(av.onstreet.m2, na.rm = TRUE) * known.onstreet) +
(max(av.residential.m2, na.rm = TRUE) * residential.offstreet.bays) +
(max(av.other.m2, na.rm = TRUE) * other.offstreet.bays)
) / 10000/ ha*100,
residential.fulfillment = cars.2016/residential.offstreet.bays*100,
worker.fulfillment = arriving.drivers.2016/other.offstreet.bays*100)
temp_transpose = t(temp)
row.names(temp_transpose) <- c("Inner City", "Area (ha)",
"2011 population (n)", "2016 population (n)", "Change (n)",
"2011 population density (n/ha)", "2016 population density (n/ha)", "Change (n/ha)",
"2011 motorists leaving (n)", "2016 motorists leaving (n)", "Change (n)",
"2011 motorists leaving (%)", "2016 motorists leaving (%)", "Change (%)",
"2011 dwellings (n)", "2016 dwellings (n)", "Change (n)",
"2011 dwelling density (n/ha)", "2016 dwelling density (n/ha)", "Change (n/ha)",
"2011 residential cars (n)", "2016 residential cars (n)", "Change (n)",
"2011 cars per dwelling (mean)", "2016 cars per dwelling (mean)", "Change (mean)",
"2011 dwellings without cars (n)", "2016 dwellings without cars (n)", "Change (n)",
"2011 dwellings without cars (%)", "2016 dwellings without cars (%)", "Change (%)",
"2011 workers (n)", "2016 workers (n)", "Change (n)",
"2011 motorists arriving (n)", "2016 motorists arriving (n)", "Change (n)",
"2011 motorists arriving (%)", "2016 motorists arriving (%)", "Change (%)",
"On-street parking (bays)", "On-street parking (m2)", "Parking meters (n)",
"Bays listed at meter (n)", "Bays with meter listed (n)",
"Parking sensors (n)", "Known on-street parking (n)",
"Residential off-street (bays)", "Residential off-street (m2)",
"Other off-street (bays)", "Other off-street (m2)",
"On-street bay size (mean m2)", "Residential bay size (mean m2)", "Other bay size (mean m2)",
"Parking coverage of inner city (%)", "Residential fulfilment (%)", "Worker fulfilment (%)")
kable(temp_transpose, format = "html", row.names = T,
knitr.kable.NA = "-",format.args = list(decimal.mark = ".", big.mark = ","),
caption = "Table x. Inner-City Parking Supply and Demand for Brisbane, Sydney, and Melbourne") %>%
kableExtra::kable_styling("striped", full_width = F, ) %>%
kableExtra::pack_rows("Parking Demand", 3, 41) %>%
kableExtra::pack_rows("Parking Supply", 42, 52) %>%
kableExtra::pack_rows("Derived Statistics", 53, 58)knitr::include_graphics("./tab9.png")In terms of planning practice, the average number of parking spaces per approved development application was 1.1 for inner Brisbane (Table 6), and the majority (45 out of all 61) featured at least 1 parking space per dwelling (Figure 10). These findings suggest that either: the parking maximums were poorly enforced; the developers anticipate greater demand for unbundled parking; or that these dwellings typically have three or more bedrooms. Notably, Toowong and Milton are two inner Brisbane suburbs in this sample where parking minimums rather than maximums apply—under the Brisbane City Plan 2014—which could explain Brisbane’s relatively high parking supply. Further, it could in part be skewed by the development type since some of the ‘oversupplied’ buildings are luxury apartment towers in the CBD with many three- and four-bedroom units that were allowed up to 1.5 and two parking spaces respectively. Interestingly, a 550-room student dormitory with no residential parking spaces was approved in 2017 in the Toowong suburb. Its approval angered nearby residents despite it being located next to a train station and featuring 20 auto-sharing spaces, 38 visitor parking spaces, and 238 bicycle parking spaces (Rayment 2017). Another interesting case was the Brisbane Skytower that was a 938 dwelling tower located within the CBD. Its original developer excavated an eight-storey basement before going bankrupt. The new developer was required to retain the basement to prevent structural damage to the site and adjoining buildings but decided to unbundle the resultant 697 parking spaces. Consequently, the developer was left with approximately 250 surplus spaces, and has since sought planning approval to convert these to commercial parking. Apart from these two examples, little unbundled parking has been provided in Brisbane.
Examining inner Brisbane parking demand revealed that the dwelling stock has grown by 27 percent and car ownership by 26 percent from 2011 to 2016 thus residential development does not appear to be reducing car ownership (Table 7). Notably, inner Brisbane was the only study area where cars always exceeded dwellings, and where the dwelling stock growth outpaced the car-free dwelling growth (23 percent). The inner Brisbane resident population growth (47 percent) also outpaced the dwelling stock growth thus revealing that inner-city household sizes have grown. Like inner Sydney and Melbourne, the percentage of inner Brisbane residents driving to work declined (3 percent) although notably it initially exceeded the remaining inner cities by at least six percent thus it had the largest margin for improvement. Further, while the inner Brisbane workforce has grown by 9 percent and thus grown the range of potential workplaces located within walkable range, this growth was less than half the workforce growth observed for inner Sydney and Melbourne, and inner Brisbane alone had a growing percentage of workers arriving by car. As such, these findings suggest that inner Brisbane parking policy and practice is accommodating rather than constraining residential parking demand thus explaining why driving to work remains relatively common. Further, these findings suggest that these parking policies are heightening worker parking demand and thus explaining why driving to the inner-city is on the rise.
Last for inner Brisbane, parking supply and restrictions were examined and while it was not possible to estimate the overall on-street parking supply from the limited data available, the city’s 883 parking meters manage 6,924 paid on-street parking bays (Table 8). Again, there were limitations for estimating the inner Brisbane off-street parking given that the local land use authority (i.e. BCC) has confirmed that they do not collect this information thus the only data source was an 18 percent sample collected by the state transport authority (i.e. the Department of Transport and Main Roads). Based upon this sample, it was possible to determine that inner Brisbane has at least 6,119 residential off-street bays and at least 60,014 further bays that can include commercial and workplace bays. When compared to parking demand, this suggests that only 1 in 4.6 residential cars has a known off-street parking bay, and 1 in 1.6 cars driven by an inner-city worker has a known other bay, which could explain why parking maximums were poorly enforced. Further based upon the average bays sizes within the City of Melbourne data and the total inner Brisbane surface of 1,928 hectares, it was possible to estimate that all known parking for inner Brisbane would cover 5 percent of the surface if laid flat. Notably, this coverage was exceptionally high by international standards considering that this figure excludes both (1) the free on-street parking and (2) the 82 percent of the inner-city blocks that may contain off-street parking. Finally examining parking rates, paid on-street and commercial off-street parking was priced midway between inner Sydney and Melbourne however it was notable that parking permits were exceptionally cheap (10AUD per annum) by international standards thus suggesting this is a small administrative fee rather than taking the value of parking space into account.
In sum, the limited data collected and made available for inner Brisbane suggests that while a demand management planning approach was apparent, it is relatively soft in terms of restricting parking supply. This may be attributable to the challenges of properly calibrating parking maximums—or minimums for that matter—when the current parking supply is mostly unknown. In contrast, the city’s data collection efforts draw focus on paid on-street and off-street parking, which could suggest that the city’s demand management planning approach focuses on constraining through pricing rather than supply and appears to have limited success given that workers arriving by car is on the rise.
In contrast to the planning practice observed for inner Brisbane, approved development applications for inner Sydney provide a stronger indication of unbundled parking with an average of 0.67 parking spaces per dwelling (Table 6) thus revealing that the majority of dwellings were not being sold with a parking bay (Figure 10). The application with the lowest parking provision rate offered just one parking space per nine dwellings, and notably it was the oldest application (in 2006, in the Roseberry suburb) that had the highest parking provision rate of 1.1 spaces per dwelling, which suggests a declining trend. Furthermore, only three out of the 49 inner Sydney development applications offered more than one parking space per dwelling. As for auto-sharing, 22 applications included between 1 and 6 spaces. These findings suggest that relative to inner Brisbane, inner Sydney was a stronger enforcer of parking maximums.
Perhaps unsurprising given this stronger enforcement of parking maximums, car free-dwelling growth (18 percent; Table 7) is outpacing dwelling stock growth (15 percent) thus suggesting that demand management is causing inner Sydney to reconsider their requirements for private automobiles (18 percent). Further, growth in residential car ownership is falling behind (12 percent). Further, the growth in inner-city residents driving to work is falling behind (6 percent) and the overall proportion that is driving is falling (2 percent fewer). While the number of inner-city workers has risen by 26 percent, the number of cars arriving has only grown by half this amount (13 percent), and the overall proportion of workers that drive is again falling (3 percent fewer).
Similar to inner Brisbane, the only on-street parking data available was paid on-street parking given that the city’s 1,365 parking meters were linked with 20,897 on-street parking bays thus more than twice the bays observed for inner Brisbane (Table 8). Unlike inner Brisbane, inner Sydney collects the number of off-street parking bays for every block thus was possible to determine that inner Sydney has 11,959 known residential bays and 5,919 known other bays. Notably, neither parking supply has the capacity to contain the 2016 parking demand since only one in 5.13 residents’ cars has known residential parking bay and one in 20.99 workers cars has known workplace or commercial off-street parking. As such, it was clear that there was a considerable volume of off-street parking that was not being captured. Lastly, inner Sydney has the cheapest hourly rate for on-street parking and off-street parking out of the three study areas (Table 9) although it also has the most expensive rates for longer periods, which is problematic in theory given that this pricing would encourage higher parking turnover that can slow traffic. Inner Sydney also has the highest parking permit fees of the three study areas but again this figure is priced well below international standards thus suggesting that this fee does not take the value of parking into account.
In sum, these findings suggest that the inner Sydney demand management planning approach entails strong enforcement of parking maximums, and that this is having intended influence given that car ownership and driving are declining. Notably, this was most likely occurring at sites of new residential and workplace development given that parking supply remains mostly unknown and therefore difficult to manage.
Inner Melbourne approved development applications had 0.76 parking spaces per dwelling on average (Table 6). While this exceeds the inner Sydney average, it still suggests that unbundled parking was commonplace with only 12 of 42 applications having one or more parking bays per dwelling (Figure 10). The highest provision rate was 1.61 spaces per dwelling for a development located in the Docklands (a recently redeveloped waterfront area) but given that this complex includes commercial space, some parking may be intended for customers. As in Sydney, the lowest parking provision rate in the Melbourne inner-city was 1 parking space per 9 dwellings.
Inner Melbourne had the highest dwelling stock growth (41 percent) of the three study areas (Table 7), which was further outpaced by its car-free dwelling growth (65 percent) thus this was a strong indication that inner Melbourne is relieving auto-dependency. Further, the growth in cars owned by residents is slowing (19 percent), the average number of cars per dwelling is declining (0.12 fewer cars), and consequently the growth in residents leaving by car is slowing (11 percent). Despite the number of inner-city workers growing by 22 percent, growth in number arriving by car is slowing (9 percent) and the overall number has dropped by 5 percent.
Although discussed last, inner Melbourne has the most comprehensive parking data available of the three study areas. For instance, inner Melbourne can count 23,310 on-street parking bays with 2,928 linked to one of 1,962 parking meters, and 3,111 with parking sensors in place so that motorists can pay for parking by the minute (Table 8). Further, inner Melbourne measures the physical dimensions of parking thus it was possible to determine that there was 33.95 hectares of on-street parking. For off-street parking, there were 49,371 known residential parking bays thus exceeding the 36,698 cars owned by inner-city residents in 2016, and 143,740 other bays thus relatively close to the 150,913 cars arriving with inner-city workers in 2016. As such, inner Melbourne is the only study area that has a clear indication of parking supply and therefore in a position to begin adaptive reuse of residential parking. It was also possible to estimate that there was 207.83 hectares of residential parking and 157.28 hectares of other off-street parking, which combined with the on-street parking suggests that parking could cover 11 percent of the inner-city if laid flat thus close to estimates for multiple American cities (Scharnhorst 2018). Lastly for inner Melbourne, parking pricing was examined and notably the on-street parking rate was the highest of the three study areas although some of this parking will be managed by parking sensors thus motorists can pay by the minute rather than need to pay for longer than their stay up front which is the case for more conventional parking meters. Further, commercial off-street parking was relatively cheap for inner Melbourne, which in theory encourages motorists to seek off-street parking and thus spend less time cruising for vacant on-street while delaying trailing traffic. Like inner Brisbane and Sydney, inner Melbourne parking permits were cheap by international standards thus again suggesting that these permits poorly account for the value of parking.
In sum, these findings suggest that inner Melbourne’s demand management planning approach includes strong enforcement of parking maximums, and this enforcement is having the intended influence by reducing parking demand. Further, inner Melbourne’s data collections efforts make it possible to identify parking oversupply and begin repurposing surplus parking as more productive land use types.
These three summaries of inner-city planning practice, parking demand, parking supply, and parking pricing suggest that the demand management planning approach is reducing parking demand within inner Sydney and Melbourne. Further, inner Melbourne’s relatively exceptional data collection efforts mean that the inner-city is also exceptionally well placed to commence large scale adaptive reuse of underutilized parking space. In contrast, inner Brisbane’s relatively soft enforcement of parking maximums could explain why parking demand continues to rise within this inner-city, and why driving to work continues to rise. Potentially worsening this problem, the BCC adopted Amendment v17 (BCC 2019) on the 29th November 2019 that lifts current parking maximums to appease motorists (Stone 2019). This amendment suggests that the land use planning authority is departing from international standards of best practice (Pojani et al. 2020) that is clearly delivering within inner Sydney and Melbourne, and is potentially returning back to the predict and provide planning approach at least as it relates to parking.
The initial examination of the proportion of suburb (SA1) residents that utilise AST during their morning commute revealed that this modal choice was most common near rapid transit nodes located closer to the inner-city within all three study areas (Figure 11). Given that the dependent variable has high spatial autocorrelation (Global Moran’s I = 0.8; p < 0.001), a SAC model will be used rather than a standard linear regression to ensure that the spatial autocorrelation is taken into account. Further, Local Moran’s I between each suburb and its contiguous suburbs was plotted (Figure 11) to determine where this positive spatial autocorrelation was generally located, and whether any negative spatial autocorrelations were occurring. In this case, a negative spatial correlation would identify where the modal choices of one suburb were suppressing the same modal choice within neighbouring suburbs such as motorists from an upstream suburb driving through a downstream suburb and in doing so, make the downstream suburb less pedestrian- friendly. Local positive spatial autocorrelation was typically located between rapid transit corridors for all three cities thus suggesting that it was the absence of rapid transit nodes rather than the presence that was influencing this modal choice. Further, local positive spatial autocorrelation was observed towards the periphery for Brisbane thus suggesting that urban centrality is a key determinant of modal choice in Brisbane. Interestingly, while local negative spatial autocorrelation was relatively uncommon, it did occur near some rapid transit nodes including rapid transit nodes located just within the inner-city frame for Brisbane and Melbourne.
Given that AST modal choice was highly spatially autocorrelated, the SAC model was used to account for both spatial autocorrelation between the observed modal choice and the model residuals. Notably, the SAC model residuals were not spatially autocorrelated (Global Moran’s I = - 0.11; p > 0.05) thus this SAC model has addressed spatial autocorrelation and it was appropriate to explain the AST modal choice by the model findings that follow (Table 10). Relative to Brisbane, the suburb proportion choosing the AST modal choice increases by 17.95 percent when the suburb was located within Sydney (p < 0.001) and by 8.83 percent when it was located within Melbourne (p < 0.001). Next examining policy zones relative to the outer-city where parking minimums were typically in place, the proportion of each suburb choosing the AST modal choice increases by 6.51 percent (p < 0.001) when it was located within 400 meters of a rapid transit node and by 3.08 percent (p < 0.001) when it was located within 800 meters of a rapid transit node. It also decreased by 0.22 percent (p < 0.05) for every kilometre away from the rapid transit node thus suggesting that living within a convenient walk of a rapid transit node is a key determinant of the AST modal choice. Notably, being located within the inner-city frame had no discernible association with the AST modal choice although this could be masked by the findings that AST modal choice decreased by 0.47 percent (p < 0.001) for each kilometre further from central station thus suggesting urban centrality is a key determinant. The AST modal choice declines by 0.05 percent (p < 0.001) as the average number of cars per dwelling rises, but increases by 0.13 percent (p < 0.001) as the suburb median age rises and 0.04 percent as the suburb median household income rises by 1,000AUD. Surprisingly, AST rises by 4.88 percent (p < 0.001) as the average household size grows by one person and by 0.24 percent (p < 0.001) for each additional percent of the suburb population that has lived within the suburb for less than one year. In sum, it was proximity to rapid transit nodes that has the greatest predictive power for explaining the AST modal choice followed by urban centrality, which is a positive finding given that the placement of rapid transit nodes is within the control of planning authorities and practitioners.
dv <- "ast.dv"
lab.frag <- "Active or Sustainable Transit [%]"
sl.iv <- "~ policy.zone + km.from.rtn + km.from.central + cars.per.dwelling.2016 + dwellings.per.ha.2016 + au.born.2016 + median.age.2016 + median.hh.income.k.2016 + mean.hh.size.2016 + different.address.1yr.2016 + as.vector(city)"
sl.data <- suburb.sf %>%
select(city, mode.p.and.p.2016.perc, mode.intermodal.2016.perc, mode.ast.2016.perc,
km.from.rtn, km.from.central, km.from.central.sq, cars.per.dwelling.2016,
dwellings.per.ha.2016, km.from.central.sq, cars.per.dwelling.2016,
dwellings.per.ha.2016, au.born.2016, median.age.2016, median.hh.income.k.2016,
mean.hh.size.2016, different.address.1yr.2016, blue.collar.2016,
policy.zone, x.coord, y.coord) %>%
mutate(ast.dv = mode.intermodal.2016.perc + mode.ast.2016.perc)%>%
mutate_at(c(5:15),~replace(., is.na(.), 0)) %>%
mutate_at(c(5:15),~replace(., is.infinite(.), 0)) %>%
na.omit() %>%
slice(-c(2085, 2086, 2418, 4298, 5065)) %>% #removes zero neighbours idenitfied below
slice(-c(5818, 20114, 20123, 20134, 20137)) #for some reason slice re-indexes after 5 so positions shift
# to speed up while testing
#sl.data <- sl.data[sample(nrow(sl.data), 2000), ]
sacb <- poly2nb(sl.data, queen=T)
sacw <- nb2listw(sacb, style="W", zero.policy=TRUE)
moran.global.i.1 <- moran.test(sl.data$ast.dv, listw=sacw, zero.policy=TRUE)
moran.plot(sl.data$ast.dv,
listw=sacw,
zero.policy=TRUE,
xlab= paste("Values for ", lab.frag),
ylab= paste("Lagged Values for", lab.frag),
main= paste("Global Moran's I =", round(moran.global.i.1$estimate[1], 2),
gtools::stars.pval(getElement(moran.global.i.1, "p.value"))),
labels=FALSE)
moran.local.i <- localmoran(sl.data$ast.dv, sacw, zero.policy=TRUE)
sl.data <- sl.data %>%
mutate(dv.localmi = moran.local.i[,1],
dv.localz = moran.local.i[,4]) %>%
na.omit(dv.localz) %>%
mutate(dv.mcluster = cut(dv.localz, breaks = c(min(dv.localz), -1.96, 1.96, max(dv.localz)),
include.lowest = TRUE, labels = c("Negative Correlation",
"Not Significant",
"Positive Correlation")))
sac.model <- spatialreg::gstsls(as.formula(paste(dv, sl.iv)), data=sl.data, sacw)
summary(sac.model, correlation=TRUE)
sem.model <- spatialreg::GMerrorsar(as.formula(paste(dv, sl.iv)), data=sl.data, sacw)
summary(sem.model, correlation=TRUE)
sl.data <- sl.data %>%
mutate(SAC.residuals = residuals(sac.model))
moran.global.i.2 <- moran.test(sl.data$SAC.residuals, listw=sacw, zero.policy=TRUE)
moran.plot(sl.data$SAC.residuals,
listw=sacw,
zero.policy=TRUE,
xlab= "Residuals for SAC model",
ylab= "Lagged Residuals for SAC model",
main= paste("Global Moran's I =", round(moran.global.i.2$estimate[1], 2),
gtools::stars.pval(getElement(moran.global.i.2, "p.value"))),
labels=FALSE)
moran.local.i <- localmoran(sl.data$SAC.residuals, sacw, zero.policy=TRUE)
sl.data <- sl.data %>%
mutate(sac.e.localmi = moran.local.i[,1],
sac.e.localz = moran.local.i[,4]) %>%
na.omit(dv.localz) %>%
mutate(sac.e.mcluster = cut(sac.e.localz, breaks = c(min(sac.e.localz), -1.96, 1.96, max(sac.e.localz)),
include.lowest = TRUE, labels = c("Negative Correlation",
"Not Significant",
"Positive Correlation")))dv <- "ast.dv"
gwr.iv <- "~ km.from.rtn + km.from.central + cars.per.dwelling.2016 + dwellings.per.ha.2016 + median.age.2016 + median.hh.income.k.2016 + mean.hh.size.2016 + different.address.1yr.2016"
gwr.data <- suburb.sf %>%
select(city, mode.p.and.p.2016.perc, mode.intermodal.2016.perc, mode.ast.2016.perc,
km.from.rtn, km.from.central, km.from.central.sq, cars.per.dwelling.2016,
dwellings.per.ha.2016, km.from.central.sq, cars.per.dwelling.2016,
dwellings.per.ha.2016, au.born.2016, median.age.2016, median.hh.income.k.2016,
mean.hh.size.2016, different.address.1yr.2016, blue.collar.2016,
policy.zone, x.coord, y.coord) %>%
mutate(ast.dv = mode.intermodal.2016.perc + mode.ast.2016.perc,
auto.req = mode.intermodal.2016.perc)%>%
mutate_at(c(5:15),~replace(., is.na(.), 0)) %>%
mutate_at(c(5:15),~replace(., is.infinite(.), 0)) %>%
na.omit()
#gwr.data <- gwr.data[sample(nrow(gwr.data), 3000), ] #random subset for rapid testinggwr.data.bris <- filter(gwr.data, city == "Brisbane")
GWRbandwidth <- gwr.sel(as.formula(paste(dv, gwr.iv)), data= gwr.data.bris, coords=cbind(gwr.data.bris$x.coord, gwr.data.bris$y.coord), adapt=T)
gwr.model.bris <- gwr(as.formula(paste(dv, gwr.iv)), data= gwr.data.bris,
coords=cbind(gwr.data.bris$x.coord, gwr.data.bris$y.coord),
adapt=GWRbandwidth, hatmatrix=TRUE, se.fit=TRUE)
gwr.model.bris
gwr.model.results.bris <- gwr.data.bris %>%
select(geometry) %>%
st_bind_cols(., as.data.frame(gwr.model.bris$SDF))gwr.morantest(gwr.model.bris, nb2listw(poly2nb(gwr.data.bris), zero.policy = TRUE), zero.policy = TRUE)gwr.data.syd <- filter(gwr.data, city == "Sydney")
GWRbandwidth <- gwr.sel(as.formula(paste(dv, gwr.iv)), data= gwr.data.syd, coords=cbind(gwr.data.syd$x.coord, gwr.data.syd$y.coord), adapt=T)
gwr.model.syd <- gwr(as.formula(paste(dv, gwr.iv)), data= gwr.data.syd,
coords=cbind(gwr.data.syd$x.coord, gwr.data.syd$y.coord),
adapt=GWRbandwidth, hatmatrix=TRUE, se.fit=TRUE)
gwr.model.syd
gwr.model.results.syd <- gwr.data.syd %>%
select(geometry) %>%
st_bind_cols(., as.data.frame(gwr.model.syd$SDF))gwr.morantest(gwr.model.syd, nb2listw(poly2nb(gwr.data.syd), zero.policy = TRUE), zero.policy = TRUE)gwr.data.melb <- filter(gwr.data, city == "Melbourne")
GWRbandwidth <- gwr.sel(as.formula(paste(dv, gwr.iv)), data= gwr.data.melb, coords=cbind(gwr.data.melb$x.coord, gwr.data.melb$y.coord), adapt=T)
gwr.model.melb <- gwr(as.formula(paste(dv, gwr.iv)), data= gwr.data.melb,
coords=cbind(gwr.data.melb$x.coord, gwr.data.melb$y.coord),
adapt=GWRbandwidth, hatmatrix=TRUE, se.fit=TRUE)
gwr.model.melb
gwr.model.results.melb <- gwr.data.melb %>%
select(geometry) %>%
st_bind_cols(., as.data.frame(gwr.model.melb$SDF))
gwr.model.results <- rbind(gwr.model.results.bris, gwr.model.results.syd) %>%
rbind(gwr.model.results.melb)gwr.morantest(gwr.model.melb, nb2listw(poly2nb(gwr.data.melb), zero.policy = TRUE), zero.policy = TRUE)knitr::include_graphics("./tab10.png")map1 <- tm_shape(st_buffer(central.station.sf, dist = map.diameter)) +
tm_dots() +
tm_facets(by = "city") +
tm_shape(states.sf) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(sl.data) +
tm_fill(col="ast.dv", palette = viridis(7), n = 7, style = "quantile",
title = "Active or Sustainable Transit [%]") +
tm_shape(rapid.transit.buffer.sf) +
tm_borders(col = my.greys[9]) +
tm_shape(inner.city.sf) +
tm_borders(col = trio[3]) +
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
legend.title.size=0.6, legend.text.size=0.4, legend.height = 0.4,
panel.label.size = 0.6, panel.label.color = 'white',
panel.label.bg.color = 'black')
map2 <- tm_shape(st_buffer(central.station.sf, dist = map.diameter)) +
tm_dots() +
tm_facets(by = "city") +
tm_shape(states.sf) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(sl.data) +
tm_fill(col = "dv.mcluster",
title = paste("Active or Sustainable Transit [%]","\nGlobal Moran's I =",
round(moran.global.i.1$estimate[1], 2),
gtools::stars.pval(getElement(moran.global.i.1, "p.value")),
"\nLocal Moran's I"), sep = "",
palette = viridis(3)) +
tm_shape(rapid.transit.buffer.sf) +
tm_borders(col = my.greys[9]) +
tm_shape(inner.city.sf) +
tm_borders(col = trio[3]) +
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
legend.title.size=0.6, legend.text.size=0.4, legend.height = 0.4,
panel.label.size = 0.6, panel.label.color = 'white',
panel.label.bg.color = 'black')
map3 <- tm_shape(st_buffer(central.station.sf, dist = map.diameter)) +
tm_dots() +
tm_facets(by = "city") +
tm_shape(states.sf) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(sl.data) +
tm_fill(col = "sac.e.mcluster",
title = paste("SAC Model residuals",
"\nGlobal Moran's I = ",
round(moran.global.i.2$estimate[1], 2),
gtools::stars.pval(getElement(moran.global.i.2, "p.value")),
"\nLocal Moran's I", sep = ""),
palette = viridis(3)) +
tm_shape(rapid.transit.buffer.sf) +
tm_borders(col = my.greys[9]) +
tm_shape(inner.city.sf) +
tm_borders(col = trio[3]) +
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
legend.title.size=0.6, legend.text.size=0.4, legend.height = 0.4,
panel.label.size = 0.6, panel.label.color = 'white',
panel.label.bg.color = 'black')
map4 <- tm_shape(st_buffer(central.station.sf, dist = map.diameter)) +
tm_dots() +
tm_facets(by = "city") +
tm_shape(states.sf) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(gwr.model.results) +
tm_fill(col="localR2", palette = viridis(7), n = 7, midpoint = 0,
style = "quantile", title = "GWR Model\nLocal R-sq") +
tm_shape(rapid.transit.buffer.sf) +
tm_borders(col = my.greys[9]) +
tm_shape(inner.city.sf) +
tm_borders(col = trio[3]) +
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
legend.title.size=0.6, legend.text.size=0.4, legend.height = 0.4,
panel.label.size = 0.6, panel.label.color = 'white',
panel.label.bg.color = 'black')
tmap_arrange(map1, map2, map3, map4, ncol = 1, asp = NULL)Last, to explore whether there was place-based variation in SAC model findings, a GWR model was created for each city. Notably, policy zones were omitted from this model since GWR creates continuous parameter surfaces and thus is poorly suited for modelling discrete spatial boundaries. The GWR models explained 91 percent of the variance for the AST modal choice throughout Sydney (Quasi-global R2 = 0.91), 89 percent throughout Melbourne (Quasi-global R2 = 0.89), and 84 percent throughout Brisbane (Quasi-global R2 = 0.84). As such, the independent variables retained from the SAC model best explain the AST modal choice for Sydney thus there were relatively more exogenous factors that explain the AST modal choice within Brisbane. Plotting the local R2 for these models revealed that the weakest model fit was along Brisbane’s rapid transit corridors and throughout Melbourne’s outer suburbs (Figure 11). Both the model parameters for distance from rapid transit nodes and distance from central were also plotted given that these are model parameters within the influence of practitioners (Figure 12, 13, & 14).
For Brisbane, residing further from rapid transit nodes was particularly detrimental to the AST modal choice along the westbound rapid transit corridor until Darra was reached, which notably is where the Rosewood, Ipswich, and Springfield Line services diverge (Figure 12). This suggests that the frequency of services determines the appeal of walking. Again, this was particularly detrimental up the northern rapid transit corridor until reaching Northgate, which is the point by which the Sunshine Coast and Shorncliffe Line services separate. Notably, this influence was less discernible along the southbound corridor. Interestingly, residing further from Central Station was particularly detrimental to the AST modal choice between the rapid transit corridors, which suggest that AST loses appeal when it entails riding direct rather than rapid public transit, and thus encountering the same traffic congestion that they would within a private automobile. Last, residing further from Central Station was less detrimental towards the end of the rapid transit corridors. It is possible that this was because the rapid transit route would resemble the driving route.
map5 <- tm_shape(states.sf, bbox = st_bbox(subset(st_buffer(central.station.sf, dist = map.diameter), city == "Brisbane"))) +
tm_dots() +
tm_shape(states.sf) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(gwr.model.results) +
tm_fill(col="km.from.rtn", palette = viridis(7), n = 7,
midpoint = 0, title = "Distance from\nRTN [km]", style = "quantile") +
tm_shape(lga.sf) +
tm_borders(col = trio[3], lty = "dotted") +
tm_shape(rapid.transit.buffer.sf) +
tm_borders(col = my.greys[9]) +
tm_shape(subset(lga.sf, area.ha > 200000)) +
tm_text("council", col = trio[3], fontface="bold", shadow = TRUE, size = 0.4) +
tm_compass(position = c("left", "bottom")) +
tm_scale_bar(position = c("left", "bottom"), width = 0.4) +
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
legend.title.size=0.5, legend.text.size=0.4,
legend.bg.color = "white", legend.bg.alpha = 0.5)
map6 <- tm_shape(states.sf, bbox = st_bbox(subset(st_buffer(central.station.sf, dist = map.diameter), city == "Brisbane"))) +
tm_dots() +
tm_shape(states.sf) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(gwr.model.results) +
tm_fill(col="km.from.central", palette = viridis(7), n = 7,
midpoint = 0, title = "Distance from\nCentral [km]", style = "quantile") +
tm_shape(lga.sf) +
tm_borders(col = trio[3], lty = "dotted") +
tm_shape(rapid.transit.buffer.sf) +
tm_borders(col = my.greys[9]) +
tm_shape(subset(lga.sf, area.ha > 200000)) +
tm_text("council", col = trio[3], fontface="bold", shadow = TRUE, size = 0.4) +
tm_compass(position = c("left", "bottom")) +
tm_scale_bar(position = c("left", "bottom"), width = 0.4) +
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
legend.title.size=0.5, legend.text.size=0.4,
legend.bg.color = "white", legend.bg.alpha = 0.3)
tmap_arrange(map5, map6, ncol = 1, asp = NULL)For Sydney, residing further from rapid transit nodes was also detrimental to the AST modal choice and was more apparent than was the case for Brisbane. Notably, this influence was least apparent for the westbound Richmond line from Blacktown, which is also where it separates from the Western line, and along the southbound Illawarra and Cronulla lines, and northbound North Shore line. Like Brisbane, it was clear that the influence of walkability was most apparent where lines overlap thus frequent services could have a crucial role. Also resembling Brisbane, residing further from Central Station had less impact at locations along rapid transit corridors.
map7 <- tm_shape(states.sf, bbox = st_bbox(subset(st_buffer(central.station.sf, dist = map.diameter), city == "Sydney"))) +
tm_dots() +
tm_shape(states.sf) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(gwr.model.results) +
tm_fill(col="km.from.rtn", palette = viridis(7), n = 7,
midpoint = 0, title = "Distance from\nRTN [km]", style = "quantile") +
tm_shape(lga.sf) +
tm_borders(col = trio[3], lty = "dotted") +
tm_shape(rapid.transit.buffer.sf) +
tm_borders(col = my.greys[9]) +
tm_shape(subset(lga.sf, area.ha > 200000)) +
tm_text("council", col = trio[3], fontface="bold", shadow = TRUE, size = 0.4) +
tm_compass(position = c("left", "bottom")) +
tm_scale_bar(position = c("left", "bottom"), width = 0.4) +
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
legend.title.size=0.5, legend.text.size=0.4,
legend.bg.color = "white", legend.bg.alpha = 0.5)
map8 <- tm_shape(states.sf, bbox = st_bbox(subset(st_buffer(central.station.sf, dist = map.diameter), city == "Sydney"))) +
tm_dots() +
tm_shape(states.sf) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(gwr.model.results) +
tm_fill(col="km.from.central", palette = viridis(7), n = 7,
midpoint = 0, title = "Distance from\nCentral [km]", style = "quantile") +
tm_shape(lga.sf) +
tm_borders(col = trio[3], lty = "dotted") +
tm_shape(rapid.transit.buffer.sf) +
tm_borders(col = my.greys[9]) +
tm_shape(subset(lga.sf, area.ha > 200000)) +
tm_text("council", col = trio[3], fontface="bold", shadow = TRUE, size = 0.4) +
tm_compass(position = c("left", "bottom")) +
tm_scale_bar(position = c("left", "bottom"), width = 0.4) +
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
legend.title.size=0.5, legend.text.size=0.4,
legend.bg.color = "white", legend.bg.alpha = 0.3)
tmap_arrange(map7, map8, ncol = 1, asp = NULL)Last with Melbourne, residing further from a rapid transit node appears less detrimental to the AST modal choice when compared to Sydney. Further, this influence was mostly apparent close to the inner-city in contrast to both Brisbane and Sydney where it extends along rapid transit corridors. As such, it was generally constrained to the area covered by Melbourne’s extensive tram network. Finally, and like Brisbane and Sydney, living further from Central Station was particularly detrimental between the rapid transit corridors where direct public transit services must share the roads with general traffic.
map9 <- tm_shape(states.sf, bbox = st_bbox(subset(st_buffer(central.station.sf, dist = map.diameter), city == "Melbourne"))) +
tm_dots() +
tm_shape(states.sf) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(gwr.model.results) +
tm_fill(col="km.from.rtn", palette = viridis(7), n = 7,
midpoint = 0, title = "Distance from\nRTN [km]", style = "quantile") +
tm_shape(lga.sf) +
tm_borders(col = trio[3], lty = "dotted") +
tm_shape(rapid.transit.buffer.sf) +
tm_borders(col = my.greys[9]) +
tm_shape(subset(lga.sf, area.ha > 200000)) +
tm_text("council", col = trio[3], fontface="bold", shadow = TRUE, size = 0.4) +
tm_compass(position = c("left", "bottom")) +
tm_scale_bar(position = c("left", "bottom"), width = 0.4) +
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
legend.title.size=0.5, legend.text.size=0.4,
legend.bg.color = "white", legend.bg.alpha = 0.5,
legend.position = c("right", "top"))
map10 <- tm_shape(states.sf, bbox = st_bbox(subset(st_buffer(central.station.sf, dist = map.diameter), city == "Melbourne"))) +
tm_dots() +
tm_shape(states.sf) +
tm_polygons(col = land, border.col = shore, lwd = 1) +
tm_shape(gwr.model.results) +
tm_fill(col="km.from.central", palette = viridis(7), n = 7,
midpoint = 0, title = "Distance from\nCentral [km]", style = "quantile") +
tm_shape(lga.sf) +
tm_borders(col = trio[3], lty = "dotted") +
tm_shape(rapid.transit.buffer.sf) +
tm_borders(col = my.greys[9]) +
tm_shape(subset(lga.sf, area.ha > 200000)) +
tm_text("council", col = trio[3], fontface="bold", shadow = TRUE, size = 0.4) +
tm_compass(position = c("left", "bottom")) +
tm_scale_bar(position = c("left", "bottom"), width = 0.4) +
tm_layout(bg.color = ocean, asp = 1, frame = "black", frame.lwd = 1,
legend.title.size=0.5, legend.text.size=0.4,
legend.bg.color = "white", legend.bg.alpha = 0.3,
legend.position = c("right", "top"))
tmap_arrange(map9, map10, ncol = 1, asp = NULL)Throughout the literature on land use and transport planning, parking policy has repeatedly been regarded a potent tool for influencing our modal choices given that land use determines when, where, and whether our modal choices are convenient and cost-effective (Manville 2017a). While possibly true in theory, it was generally less simplistic in practice given that metropolitan land use planning authority is typically fragmented between various local governments while transport planning authority is typically the domain of state and federal governments thus making coordinate planning a challenge and policy inequality a possibility (Young & Miles 2015). Further, empirical research has done little to clear a path forwards given that it tends to focus on the less important yet readily available and typically provide weak and often contradictory results (Manville 2017b). As such, the focus needs to be broadened to urban mobility at metropolitan-scale rather than continually narrowing the focus to local-scale urban mobility that impacts sites upstream and downstream.
It is also time to reflect on what urban mobility could be moving forward. Estimates suggest that private automobiles are in motion just 5 percent of the time (Button 2006) and thus are typically unproductive and a public burden given that storing these private automobiles requires finite urban space. Further, private automobiles appear to be losing appeal throughout OECD countries where young adults are waiting longer to purchase and start driving (Goodwin & Van Dender 2013). Abreast of this social trend, emerging technologies such as ride-hailing, ride sharing, and micromobility are solving the last mile problem of public transport, and services such as MaaS are reducing the transaction costs of routing, scheduling, reserving, and paying for public transport (Sipe & Pojani 2018). Finally, the decision regarding which old patterns of urban mobility should be carried forward is becoming urgent with autonomous vehicles starting to emerge. For instance, should cities be designed to accommodate high volumes of private autonomous vehicles that will continue to require road and parking space—or possibly cruising space while waiting for the owner—or should cities be designed around smaller volumes of public and commercial autonomous vehicles shared between many to reduce road and parking demand, and free up vast tracts of finite urban land for more productive uses (González-González et al. 2020). If not already, then peak-car is about to pass (Kuhnimhof et al. 2013a; 2013b; McDonald 2015; Metz 2013; Goodwin & Van Dender 2013) thus urban planning officials and practitioners need to begin ceasing or at least slowing the supply of road and parking capacity to avoid burdening future generations with sprawling cities that are blanketed by artificial surfaces (Steele 2018).
To clarify discussion within this complex space, we began this volume of Progress in Planning by developing a comparative framework of urban mobility for exploring planning approaches that utilize parking policy to influence the modal choices of our increasingly urban civilisation. This framework arranges general planning approaches along a continuum of pragmatist and reformist leanings, and groups these together as the: (1) predict and provide; (2) multimodal; and demand management planning approaches.
The predict and provide planning approach has dominated land use and transport planning for at least fifty years (Goulden, Ryley, & Dingwall 2014) and most pragmatic since it accommodates motorists already residing within auto-dependent locations. It aims to predict peak-demand for parking at a given land use type (McCahill & Garrick 2014), and provide this volume of parking to ensure all parking is self-contained and parking overspill does not occur given that this can generate nearby parking disputes (Shoup 1999), and to reduce time spent cruising for scarce parking that slows trailing traffic (Seibert 2008). By providing parking, this planning approach also aims to expand customer catchments for merchants, provide spot accessibility for shoppers (Box 2000), and revitalise downtown areas (Meyer & Mc Shane 1983). In practice, this predict and provide planning approach typically entails compliance with parking minimums that specify how much parking a given land use type requires.
In contrast, the multimodalism planning approach is a relatively recent invention. It shares the predict and provide planning approach’s pragmatism by accommodating those already residing within auto-dependent locations but is radical by attempting to eliminate motorists’ route redundancy, road capacity and inner-city parking demands by shifting parking to outer-city park ‘n’ ride facilities (Kimpton et al. 2020), and providing feeder public transit services and kiss `n’ ride drop off bays for easier access to rapid transit services (Ison & Mulley 2014; Parkhurst & Meek. 2014; Meek et al. 2015). Arguably, it is a critical period for understanding the multimodalism planning approach since it requires coordination between state transit authorities, and land use authorities at either end of the journey and particularly if shared Autonomous Vehicles and MaaS are to succeed in transferring travellers from low-capacity to high-capacity vehicles.
Last within our comparative framework of urban mobility, the demand management planning approach is the most radical since its reformist aims are to concentrate urban population within inner cities and near rapid transit nodes (i.e. Transit Orientated Development; Marsden 2006; Christiansen et al. 2017a), and reduce the appeal of driving relative to the alternatives by restricting parking supply and pricing parking (Brennan, Ter Schure, & Napolitan 2013). Given that private automobile journeys must begin and end in a parking bay (Meyer 1999; Young & Miles 2015), parking maximums, unbundled parking, employee cash out policies, and innovations such as demand responsive parking meters are proving potent tools for discouraging driving (Martens 2005; Guo & Ren 2013; Shoup 2005; Christiansen et al. 2017a). Notably, this could be poised to change given that smartphone enabled services are emerging that enable motorists to reserve and hire parking directly from residents that have surplus or even momentarily available parking, and private autonomous vehicles could potentially be left cruising nearby streets or sent elsewhere until the owner is ready thus increasing the vehicle kilometres travelled by domestic vehicles.
This comparative framework of urban mobility provided the theoretical foundations for our template analysis that identified and contrasted planning approaches within and between the three largest Australian major metropolitan areas: Brisbane, Sydney, and Melbourne. Specifically, land use and transport policy strategies and frameworks were examined to identify the theoretical foundations and aims for each policy. This analysis revealed that all three cities have expressed concerns that earlier predict and provide planning approaches have left behind a legacy of parking over supply, inner-city traffic congestion, and entrenched expectations for abundant and convenient parking. These concerns are well-founded given that the expectation for parking can manifest as a sense of entitlement and folk legality personal claims over public parking space (Christiansen et al. 2017b; Taylor 2016). A further commonality between the three cities was having policies advocating for a moderate transition away from the predict and provide planning approach out of concern that a zealous reduction of inner-city and TOD parking would impact local economic development. This concern may also be well-founded given that generous parking allocation has been used to spur downtown economic revitalisation and its removal can send shoppers elsewhere (Meyer & McShane 1983; Box 2000; Baker & Wood 2010). Concepts such as PnR, TOD, and parking maximums became more apparent over time for all three cities thus suggesting that all three cities are transitioning towards multimodalism and demand management planning approaches. Specifically regarding multimodalism, Melbourne was the earliest to recommended placing PnR along rapid transport corridors as early as 2002, and Brisbane later in 2009 recommended that PnR be located further than 10 kilometres of the inner-city be converted into TOD to discourage commuters from rail heading (i.e. driving most of their commute and switching towards the end to avoid inner-city congestion and parking rates). In contrast, Sydney was the demand management leader by discussing parking maximums during the early 1990s while Brisbane and Melbourne both lagged almost a decade behind. All three cities also calibrated their parking maximums—and parking minimums for that matter—according to land use type, although notably Sydney was the least exhaustive in terms of land use types thus suggesting a more bespoke, one-size-fits-all planning approach that is frequently criticised within the planning literature (Rowe, Bae, & Shen 2010). Sydney went further by proposing caps for residential parking permits and requirements for car sharing bays, which is associated with improving the public acceptance and outcomes within projects that have lower levels of parking (Ter Schure, Napolitan, & Hutchinson 2012). Brisbane also explicitly recommends unbundling parking from residential development. The TOD principles have been in place throughout Sydney and Melbourne policies for at least two decades but it is notable that the term ‘TOD’ only emerged in relatively recent times thus suggesting a gradual clarification of aims and recognition of how urban mobility operates within cities. Finally, regarding pricing, Melbourne is the earliest to mention fair market pricing which may explain why this city charges the most for on-street parking. It is also the first to mention demand responsive pricing and already has on-street sensors in place thus dynamically priced parking meters may be on the horizon. Brisbane and Sydney later mention this concept in 2009 and 2010, respectively. Cumulatively, our template analysis reveals that all three cities aim to relieve private auto-dependence and traffic congestion by reducing the appeal of driving relative to AST alternatives thus keeping to international standards of best practice (Pojani et al. 2020). Notably, Brisbane appears to be losing confidence given their recently adopted Amendment v17 (BCC 2019) on the 29th November 2019 that raises the cap on prior parking maximums to appease motorists (Stone 2019).
Development applications, parking supply, parking demand, and parking pricing were examined for all three inner cities to identify the inner-city planning practice that has emerged from the policies. Contrasting approved development applications for all three cities revealed that while Sydney and Melbourne’s applications typically had fewer parking spaces than dwellings, Brisbane’s applications typically had more parking than dwellings thus suggesting that either Brisbane is a relatively weak enforcer of parking maximums or that developers typically anticipate a high demand for unbundled parking. Examining parking demand throughout three inner cities revealed that Sydney and Melbourne’s inner-city dwelling stock growth is outpacing their car ownership growth unlike Brisbane thus demand management appears to be maintaining parking demand at a steady rate rather than reducing parking demand. Brisbane also had the slowest growing inner-city workforce and is notably the only city where driving into the inner-city is on the rise. For parking supply, Melbourne had the most comprehensive data collection thus it was possible to determine that parking would cover 11 percent of the inner-city if laid flat, which resembles estimates for American cities (Scharnhorst 2018). Further, it was possible to determine that Melbourne’s inner-city parking supply currently exceeds parking demand thus Melbourne is well-positioned for broad-scale adaptive re-use of parking. In contrast, Brisbane and Sydney’s on-street parking data only included paid parking, while Brisbane’s off-street parking data covered just 18 percent of the inner-city and Sydney’s covered the entire inner-city but would not contain cars belonging to residents and workers thus suggesting there were limitations to the use of this data. In the case of Brisbane, it was possible to estimate that all known parking would cover 5 percent of the inner-city if laid flat, which arguably is high by international standards given the limited data collection. Parking permits within all three cities were exceptionally cheap by international standards thus suggesting that Australian parking permits poorly account for the true market value of on-street parking (Shoup 2005). Finally, inner Sydney had the cheapest short term rates for on-street and off-street parking yet the most expensive longer term rates, which is problematic in theory given that this incentivises cruising for vacant on-street parking and increases parking turnover thus slowing traffic (Glazer & Niskanen 1992; Calthrop & Proost 2006).
Having examined planning policy and inner-city planning practice, our final aim was to determine whether these policies were associated with AST modal choices at the metropolitan-scale and if so, where in particular given that metropolitan transport planning was the domain of state departments while metropolitan land use planning was split between multiple local governments thus requiring greater coordination. Our multi-regional model revealed residing within the inner-city or walking distance of a rapid transit node primarily explained the AST modal choice. Given that these locations were both zoned for parking maximums and unbundled parking, it suggests that the demand management planning approach is successfully achieving its intended aims, and supports the arguments made by a list of demand management advocates (Meyer 1999; Young & Miles 2015; Shoup 2005; Manville 2017b). Distance beyond these locations was associated with a further decline in the AST modal choice, while neighbourhood characteristics such as household wealth, older residents, residential density, fewer cars per dwelling, larger households, and recently arriving residents were to a lesser extent associated with increasing the AST modal choice. Our place-based models revealed further nuisances, which included that the model had a relatively weak fit for explaining the AST modal choice for Brisbane and outer Melbourne thus suggesting exogenous factors within these urban contexts. Further, distance from rapid transit nodes was particularly influential where train services overlapped and were therefore more frequent thus supporting the perceptions of local experts from earlier interviews (Searle et al. 2014). Notably, distance from the inner-city was most influential between rapid transit corridors where direct and feeder transit services typically operate and compete with general traffic thus confirming the critical importance of rapid transit for persuading motorists to use public transit (Marsden 2006; Christiansen et al. 2017a). Lastly, strong association between distance from rapid transit nodes and the AST modal choice generally radiated past the end of train lines. As such, this finding reveals the upstream PnR catchment areas (i.e. park `n’ rider sheds) and suggests that motorists are more inclined to utilize PnR located in the direction of their general route (Kimpton et al. 2020). # 6. Concluding remarks
This volume of Progress in Planning drew focus on the persistent problem of urban mobility. To this aim, we developed a comparative framework of urban mobility to compare planning approaches throughout the three largest Australian metropolitan regions: Brisbane, Sydney, and Melbourne. Specifically, this entailed a qualitative template analysis of policies, a quantitative examination and estimations of planning practice, and spatial modelling of modal choice. Overall, we found these three cities were transitioning away from the once dominant predict and provide planning approach in favour of more progressive multimodalism and demand management planning approaches. While the timeline, implementation, and outcomes vary between cities and none have adopted a comprehensive metropolitan-wide parking policy to reduce policy inequality, the state and local councils were generally following or at least aware of international best practice (Pojani et al. 2020). Considering our findings, we have a set of recommendations for researchers, and a further set of recommendations for practitioners.
Our most significant challenges for this study and recommendations for research can be listed as follows. First, given that researchers are typically narrowing their focus for improving elements of urban mobility, there is relatively poor clarity regarding how these elements interact at the metropolitan-scale and are inhibit urban mobility. As such, we have developed a comparative framework of urban mobility with the aim of clarifying and broadening discussion, and we urge researchers to build upon our foundations by introducing other distinct planning approaches that can or are already operating within this research space. Second, given that land use and transport planning is the subject matter of broad array of research disciplines (e.g. urban planning, geography, engineering, computer science, and economics), research from outside disciplines is frequently overlooked and familiar concepts are frequently described using inconsistent terms. As such, we urge researchers to adopt a more transdisciplinary approach by developing and extending intellectual frameworks that can exist outside of a single disciplinary perspective. Third, a considerable challenge for empirical studies and particularly multi-city empirical studies that aim to provide generalisable research findings is that so few cities keep a comprehensive record of pre-existing parking supply. Aside from raising broader concerns regarding how so many cities have calibrated their parking minimums or determined that further parking was required without baseline measures, the answers to most urgent research questions will require parking data that captures key elements such as: unpaid parking in addition to paid parking; bay sizes and typical car dimensions for slimming down parking bays and encouraging motorists to choose smaller vehicles; driveways and manoeuvring spaces to gain a clear sense of the room required to store private automobiles; off-street parking throughout suburban areas to identify where personal claims for public on-street parking signify the repurposing and therefore misuse of off-street parking; parking purpose to clarify the relationship between parking and nearby merchants and services; and why a particular PnR was chosen.
Our recommendations for practice are the more salient points drawn from earlier empirical studies and our research findings and are necessary for the success of a broader package of transport and mobility interventions. First, cities require comprehensive parking strategies designed and implemented at the metropolitan scale rather than left fragmented between local councils and can be responsible for policy inequalities. Second, demand management planning approaches need to be consistently implemented along rapid transit lines, and within inner cities to reduce parking misuses such treating general parking as informal PnR, excessive cruising and doubling back for cheaper parking, and false claims for public parking space. Third, both multimodalism and demand management planning approaches need to be complemented by substantial investments in infrastructure that ensures: that active transport is convenient, pleasant, and safe; and that public transit has road priority, is rapid, affordable, frequent, and reliable. Fourth, parking should be unbundled wherever viable alternatives to driving exist since this improves housing affordability and awareness regarding how many cars a household requires. Fifth, requiring new residential development to include car-sharing and drop-off bays can ensure that residents still have access to flexible transport without requiring a private car. Further, it prepares new residential development for a future where ride-hailing and autonomous vehicles may become commonplace. Sixth, the true cost of parking bays should be reflected in parking permits and demand responsive parking rates should be in place so that taxpayers can stop subsidising the true cost of parking. Seventh, parking fines must be set at a sufficiently high to discourage risk-taking illegal parking. Eighth and last, greater attention needs to be directed towards what could be termed as ‘urban mobility’s missing middle’ since these commuters reside between rapid transit corridors, and therefore their public transit services must wade through the same traffic congestion as they would while driving. Priority lanes for their feeder public transit services could increase the appeal of AST relative to driving since it bypasses traffic congestion.
While we consider our research findings as particularly relevant for understanding and improving urban mobility within an Australian context, we have maximised the generalisability and reproducibility by: developing a comparative framework of urban mobility that is well-suited for interdisciplinary research; contrasting three major metropolitans with distinct urban forms; using primarily open data; and making our programming script available online. Given that mobility trends suggest peak-car will soon if not already come to pass and emerging technologies and services are poised to further disrupt and complicate urban mobility, it is now a critical time to decide how much space we should continue to reserve for private automobiles and how much should be repurposed for more productive uses. Further, it is a critical time for understanding how different transport modes can operate more harmoniously within a single urban context rather than repeating history by giving travel priority to just a single mode at the expense of AST. As such, we can contend that this volume of Progress in Planning will interest a broad range of research disciplines and professions that are striving towards improving urban mobility and ensuring that major metropolitans can thrive.
This research is conducted through a project funded by the Australian Research Council Linkage Project grant LP160100031 with additional support from the industry partner, the Queensland Department of Transport and Main Roads. We wish to acknowledge the Department of Transport and Main Roads for their cooperation and the supply of some data on which this research is based. However, the interpretations of the analysis are solely those of the authors and do not necessarily reflect the views and opinions of the Department or any of its employees.
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