In cyipt/cyipt: Cycling Infrastructure Prioritisation Toolkit

Outline

• Context: harnessing data for transport planning
• The Propensity to Cycle Tool (PCT)
• The Cycling Infrastructure Prioritisation Toolkit (CyIPT)

Introduction

How to transition to active cities? From this...

knitr::include_graphics("https://pbs.twimg.com/media/DOH94nXUIAAgcll.jpg")

To this?

knitr::include_graphics("https://pbs.twimg.com/media/DJaWCo0U8AAzQGW.jpg:large")

With available resources

knitr::include_graphics("https://pbs.twimg.com/media/DDhLUr7VwAIp2_a.jpg:large")

Context 'evidence overload'?

knitr::include_graphics("https://larrycuban.files.wordpress.com/2015/02/data-overload-2.jpg")

• Challenge: operationalise data
• Challenge: make locally specific

Data for walking and cycling investment

• Travel behaviour data
• Route network data
• Existing infrastructure (road widths, traffic, future possibilities)
• Road safety data
• Air pollution data
• Crowdsourced data

The international dimension

knitr::include_graphics("https://raw.githubusercontent.com/ATFutures/who/master/fig/sevnet2.png")

• Not a UK-specific issue, but benefits of country-specific tools

The Propensity to Cycle Tool (PCT)

What can the PCT do? - see www.pct.bike

knitr::include_graphics("~/npct/pct-team/figures/pct-frontpage.png")

Context: from concept to implementation

• 3 years in the making
• Origins go back further
• "An algorithm to decide where to build next"!
• Internationalisation of methods (World Health Organisation funded project)

knitr::include_graphics("https://github.com/npct/pct-team/blob/master/figures/pct-metalogo.png?raw=true")

The research landscape [see @lovelace_propensity_2017]

knitr::include_graphics("../figures/jtlu-paper-front-page.png")

The PCT in context (source: Lovelace et al. 2017)

dft = readr::read_csv("~/npct/pct-team/data-sources/cycle-tools-wide.csv")
dft$Tool = gsub("Permeability Assessment Tool", "PAT", dft$Tool)
knitr::kable(dft[-5, ])

Policy feedback

"The PCT is a brilliant example of using Big Data to better plan infrastructure investment. It will allow us to have more confidence that new schemes are built in places and along travel corridors where there is high latent demand."

• Shane Snow: Head of Seamless Travel Team, Sustainable and Acessible Travel Division

"The PCT shows the country’s great potential to get on their bikes, highlights the areas of highest possible growth and will be a useful innovation for local authorities to get the greatest bang for their buck from cycling investments  and realise cycling potential."

• Andrew Jones, Parliamentary Under Secretary of State for Transport

The PCT in CWIS

Included in Cycling and Walking Infrastructure Strategy (CWIS)

knitr::include_graphics("~/npct/pct-team/figures/front-page-leeds-pct-demo.png")

The Cycling Infrastructure Prioritisation Toolkit (CyIPT)

Overview of the project

• 12 month project funded by DfT's Innovation Challenge Fund (ICF)

• Aim: tackle the challenge that cycling uptake is often limited by infrastructural barriers which could be remediated cost-effectively, yet investment is often spent on less cost-effective interventions, based on assessment of only a few options.

• Project team:

  • Robin Lovelace (University of Leeds)
  • Malcolm Morgan (University of Leeds)
  • John Parkin (University of West of England)
  • Martin Lucas-Smith (Cyclestreets.net)
  • Adrian Lord (Phil Jones Associates)

## Tookit design
# knitr::include_graphics("../figures/schematic-flow-diagram.png")

Modelling cycling uptake

• We can use 'backcasting' to estimate long-term potential under ideal questions (PCT)
• But transport authorities need forecasts of future uptake
• From specific interventions in order to do this
• There is much existing work on this
• But none that is 'operationalisable'

• How to operationalise available data?

Data on infrastructure-uptake at a regional level

• Clear link between infrastructure and uptake

knitr::include_graphics("../../cyipt-website/images/ttwa-uptake.png")

New datasets:

• DfT's Transport Direct data
• 2001 OD data (manipulated and joined with 2011 data)

# model <- readRDS("../cyipt-securedata/uptakemodel/ml1.Rds")
# summary(model)
# jtools::interact_plot(model = model, pred = routes_infra_length, modx = routes_pspeed20)
## Detecting a signal from the noise
# > - Very simple model of uptake (Bristol):
## lm(formula = p_uptake ~ dist + exposure, data = l, weights = all11)
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.372e-02  4.207e-03   5.639 2.28e-08 ***
## dist        -1.671e-07  8.424e-07  -0.198  0.84283    
## exposure     4.147e-02  1.523e-02   2.724  0.00658 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7972 on 906 degrees of freedom
## Multiple R-squared:  0.008318,   Adjusted R-squared:  0.006128 
## F-statistic: 3.799 on 2 and 906 DF,  p-value: 0.02274

Operationalising the data

See: https://www.cyipt.bike (password protected)

knitr::include_graphics(c("../../cyipt-website/images/infrastructure/large/lightsegregation.jpg", "../../cyipt-website/images/recommended.png"))

Wider context: Open source tools

• Online interfaces reduce barriers
• But there are benefits of running analysis locally
• Various software options, including:

• QGIS mapping software
• sDNA QGIS plugin
• R (see upcoming course 26th - 27th April)
• Key feature of CyIPT and PCT:
• Open source and provides open data downloads

Modelling cycling uptake

knitr::include_graphics("~/npct/pct-team/flow-model/dd-anna.jpg")

• Hilliness and distance are (relatively) unchanging over time
• Model based on polynomial logit model of both:

$$ logit(pcycle) = \alpha + \beta_1 d + \beta_2 d^{0.5} + \beta_3 d^2 + \gamma h + \delta_1 d h + \delta_2 d^{0.5} h $$

logit_pcycle = -3.9 + (-0.59 * distance) + (1.8 * sqrt(distance) ) + (0.008 * distance^2)

Links:

• The PCT in action: http://www.pct.bike/
• The CyIPT: http://cyipt.bike/
• A paper on the PCT (Lovelace et al 2017): https://www.jtlu.org/index.php/jtlu/article/view/862
• An article explaining R for transport planning with stplanr: cran.r-project.org

cyipt/cyipt documentation built on Aug. 16, 2020, 10:24 p.m.