1. Some comments on the transport system

2. Software for modelling a zero carbon system

3. Case study of the PCT

• But first some context
• Then let's talk solutions

• The transport system is not working well for anyone

• To 'fix' it, policy interventions are needed

• Policy interventions can be more effective when locally targetted

• However, there are infinite potential interventions at the local level
• Evidence is needed to prioritise among the infinity of options

• Only a systematic and objective evidence base will do

• And that means data + statistics! 🔢

• And that means computing 🖥️

• And that means human-computer interaction 👨👩💻

• And that means software is vital for sustainable transport policy

• Transport eats time. We spend on average 6% of our lives (sleeping/resting: 37%; commuting: 1%; paid work: 25%) (King and Bergh 2017).

• Transport eats space. More than half many US cities spaces are occupied by parking (~20%) and streets (~40%). In Texas, for example, 21.3% of land space was taken by surface parking (Source: oldurbanist.blogspot.co.uk)

• Transport eats energy. In 2015 it accounted for 39.9% of final energy consumption (DECC).

Final energy consumption (excluding non-energy use) was 1.9 per cent higher than in
2014 [0.3% seasonally adjusted], with rises in the domestic, transport and services sectors but with a fall in the industrial sector. The rise in consumption was due to increased
transport demand likely due to lower petroleum prices.

(DECC 2016)

• Are black boxes

• Tools are blunt

• Are sometimes too complex!

• Implications for others

See http://www.pct.bike/

• See rpubs.com/RobinLovelace/ for interactive map

• Incentivise low carbon, healthy travel

• Build cycle paths (where they are most needed, of appropriate design)
• Embed walking and cycling - urban realm improvements, facilities sign-posting
• Subsidise car sharing solutions

• Disincentivise high carbon solutions

• Creative approaches > - Reducing worktime hours: "The three best performing scenarios were those that involved employees working a four-day week as they enabled companies to reduce energy use, and employees to reduce commuting" (King and Bergh 2017).

• Setup:

library(sp)
# load data
rf_schools = readRDS("~/npct/pctSchoolsUK/pctSchoolsApp/data/west-yorkshire/rnet.Rds")
rf_commute = readRDS("~/npct/pct-data/west-yorkshire/rnet.Rds")

# create bounding box polygon
bbox_poly = stplanr::bb2poly(rf_schools)
proj4string(bbox_poly) = proj4string(rf_commute)

# spatial subset
rf_commute = rf_commute[bbox_poly,]

install.packages("stplanr")

library(stplanr)
data("flow")
nrow(flow)

## [1] 49

flow[1:3, 1:3]

##        Area.of.residence Area.of.workplace All
## 920573         E02002361         E02002361 109
## 920575         E02002361         E02002363  38
## 920578         E02002361         E02002367  10

data("cents")
cents@data[1:2,]

##       geo_code  MSOA11NM percent_fem  avslope
## 1708 E02002384 Leeds 055    0.458721 2.856563
## 1712 E02002382 Leeds 053    0.438144 2.284782

desire_lines = od2line(flow = flow, zones = cents)
plot(desire_lines)
points(cents)

library(tmap)
tmap_mode("view")

## tmap mode set to interactive viewing

m = tm_shape(rf_schools) +
  tm_lines(lwd = "dutch_slc", scale = 20, col = "darkgreen") +
  tm_shape(rf_commute) +
  tm_lines(lwd = "dutch_slc", scale = 20, col = "darkblue")