The Stack Archive

Big data seeks to increase number of cyclists in cities

Wed 16 Sep 2015

A team of British academics is developing an open access web-based tool, using big data and analytics models to support local authorities plan new cycling infrastructure.

The Propensity to Cycle Tool (PCT), backed by the Department for Transport (DfT) and Cambridge University’s Centre for Diet and Activity Research, builds on various transport and geographical datasets including Census flow data and National Travel Survey Data and provides predictions into where cycling is most likely to thrive, as well as insights into its consequent societal benefits.

Origin-destination (OD) data is used to visualise ‘desire lines’ and identify infrastructure that needs to be improved, while geographic data provides information on hilliness and terrain. The study suggests that using this data model will support the DfT select “parts of the country with the greatest propensity to cycle” and help prioritise strategic investment. The researchers argue that providing higher-quality infrastructure advised by the tool will be an effective way of promoting cycling as a ‘safe, accessible and convenient transport option.’

 Overview of the PCT map interface. The lines represent trips between origin and destination (OD) pairs for Coventry. Width represents the total number of trips.

Overview of the PCT map interface. The lines represent trips between origin and destination (OD) pairs
for Coventry. Width represents the total number of trips.

‘The Propensity to Cycle Tool (PCT) seeks to inform […] decisions by providing an evidence-based support tool that models current and potential future distributions and volumes of cycling across cities and regions,’ reads the academic paper.

The method takes a pool of English cities to generate estimations at a local level in a range of potential scenarios. The govtarget scenario translates the UK’s national target of doubling cycling from 3% of commutes to 6%. It predicts an above-average increase in areas with many short cyclable trips with a low current rate of cycling. The scenario indicates where investment in increased cycle use will achieve the greatest impact in the short term.

A further scenario looks at gender equality in cycling, matching the current proportion of female cyclists to the current proportion of male cyclists in each OD pair. gendereq illustrates a greater impact in areas where cycling is highly ‘gender-unequal’. It demonstrates that a greater rate of cycling would be achieved in this case where cycling is already a popular form of transport.

The researchers also consider godutch and ebikes as scenario models, mapping what could happen if English commuters cycled as much as the Dutch or if there was a widespread uptake of electric bicycles.

These potential cases are mapped on an interactive display, freely available online as a prototype. The team hopes that the open source approach will inspire the use of the PCT by authorities in other cities around the world, to assist a ‘global transition’ away from reliance on fossil fuels.


analytics Big Data maps news research transport UK
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