2016-05-04. Slides: rpubs.com/RobinLovelace

Presentation structure

  • Demo of the tool and software for sustainable transport
  • Propensity to Cycle Tool (PCT): design and development
  • Uses of the PCT
  • Future work

Demo of the tool and software for sustainable transport

Live demo of the PCT

Software as tools

The wider context

for recumbents

I: Software

Transport planning tools: expensive…

And potentially dangerous!

The solution

  • New breed of software products

Why open source?

  • The global transition away from fossil fuels is big business
  • Many planners are scrambling to make their cities healthier and cleaner
  • But if solutions cannot be controled and shared, how can they scale?

Which companies are ready for a transition away from fossil fuels:

Tools of the trade: general to specific

General tools

  • QGIS
  • GRASS
  • R
  • Python

R's spatial ecosystem

QGIS

New software: aequilibrae

MATSim

SUMO

Why R?

The packages used

Can be installed and loaded in 6 lines of code:

pkgs <- c("devtools", "shiny", "rgdal", "rgeos", "ggmap") # official packages
install.packages(pkgs) 
library(devtools) # enables installation of leaflet
gh_pkgs <- c("rstudio/leaflet", "robinlovelace/stplanr") 
install_github(gh_pkgs) # install packages on github
lapply(c(pkgs, "leaflet", "stplanr"), library, character.only = T) # load all

RStudio

RStudio Desktop is highly recommended for Shiny development.

Shiny

shiny is a framework for creating online interactive data visualisation 'apps'.

  • A framework for making R output interactive
  • An 'app' development framework
  • A (small) growing community of developers centred around RStudio
  • A way to structure online tools: separation of GUI and server via server.R and ui.R files.

II: The Propensity to Cycle Tool (PCT): context, design and features

Policy context

  • House of Commons Criticising the DfT for using closed models
  • 'Impact' and 'engagement' increasingly important for research funding
  • Complex data cannot be adequately summarised in a single static graphic
  • Phase I: proof of concept (February - August 2015)
  • Phase II: nationwide deployment (November 2015 - March 2017)
  • National launch: July 2016

See: cedar.iph.cam.ac.uk/research/modelling/npct-tool

Modelling context

Interactive online tools

Design criteria

  • Interactive basemap
  • Points, lines and polygons
  • Cascading effects of different scenarios > - Tidy!

Early user testing

The underlying theory

  • Model works at the 'desire line' (or orgin-destination) level
  • Uses 'distance decay' + hilliness to estimate which existing trips could be cycled
  • Other factors (e.g. demographics, route network characteristics) could be added
  • Local impacts of meeting national targets
  • Any model could be used to create scenarios of the future - e.g. for local targets
  • Check out our paper: http://arxiv.org/abs/1509.04425

Zoom-dependent selection and freeze Scope

Phase I (Feb - July 2016)

  • Build and test a prototype model
  • Identify 'desire lines' of greatest potential
  • Make the tool scalable nationally
  • Create a website that will make the Propensity to Cycle Tool a publicly accessible resource

Phase II (January 2016 - March 2017)

Version 1 - nationwide (V1 launch: June 2016)

  • Route-allocated hilliness, network layer (complete)
  • Include health outcomes (HEAT)
  • National-level results (Anna Goodman)
  • V1.5 - smaller (LSOA) zones (Jan 2017)
  • Training

Version 2 - local deployment

  • Include non-commute trips
  • Compatibility with Local Authority trip demand models
  • Micro-level analysis (V 2.2)

Additional work/spin outs

  • Case studies of use (e.g. Manchester, Kent, Yorkshire)
  • Method for identifying severance
  • Case study along HS2 route
  • 'Hackathons' to stimulate the tool's development

Interactive map of current flows

Route allocation

Route network analysis

Zoom to a specific area

III: Uses of the PCT

What kind of questions can it help answer?

  • Where should we build for existing cyclists?
  • New cyclists in the medium term?
  • Long-term strategy?
  • The question depends on the city

4 scenarios of change

  • Government Target
  • Gender Equality
  • Go Dutch
  • Ebikes

Scenario shift in desire lines

Scenario shift in network load I

Scenario shift in network load II

Example: Trinity Way, Manchester

Example: Where to build cycle paths in Leeds?

IV Future research ideas

Future development (near term)

Potential futures: e.g. community contributed

  • Custom scenarios (e.g. 20% cycling by 2020 in Bristol)
  • Combining with route-network data, e.g. via link to sDNA
  • Scenarios estimating impacts from specific interventions
  • International comparisons of propensity to cycle
  • A global propensity to cycle map?

How could transport infrastructure change if people worked from home?

How best to reallocate space away from cars?

How transport engineers can encourage car sharing?

Fork my code!

  • The work is open source so can used as a foundation for further work by others
  • E.g. councils with the technical know-how
  • E.g. cycle campaigner 'hackers'

Links and references