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Modelling Cycling Flow for the estimation of cycling risk at a meso urban spatial level

Meade, S; Stewart, K


S Meade

K Stewart


One of the prevailing challenges in cycling research, or indeed any vulnerable road user research, is the availability of data to ascertain a representative level of 'exposure' or simply how much cycling there is – " when and where ". Therefore, it is difficult for researchers and ultimately local authorities to determine if changes in observed accident trends over time are due to increased accident risk, (users or environment becomes more unsafe) or if they are a function of the higher numbers of cyclists using the existing roads and routes resulting in more incidents, i.e. increased exposure. This paper describes the use of traditional transport modelling in the form of the gravity model, to develop a base year flow matrix, with recently developed open source transport modelling software and an open source bike routing application to assign realistic cycling flows to the network and finally validation against observed network link flows. The cyclist flows provide the 'exposure' variable to examine cyclist safety performance at macro and meso levels. The results highlight the need for a local level mobility-based exposure metric to describe cyclist safety performance and the superior ability of local accident prediction models to describe safety performance of cyclists in urban contexts, where population based, and global models mask urban spatial patterns of safety performance.


Meade, S., & Stewart, K. (2018). Modelling Cycling Flow for the estimation of cycling risk at a meso urban spatial level. Transportation Research Procedia, 34, 59-66.

Journal Article Type Article
Conference Name International Symposium of Transport Simulation (ISTS’18) and the International Workshop on Traffic Data Collection and its Standardization (IWTDCS’18)
Acceptance Date May 23, 2018
Online Publication Date Dec 4, 2018
Publication Date 2018-12
Deposit Date Oct 9, 2019
Publicly Available Date Oct 9, 2019
Print ISSN 2352-1465
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 34
Pages 59-66
Keywords Cyclists Flow; Exposure; Macro Model; Meso Model; Geographically Weighted Regression; Safety Performance
Public URL


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