Predicting cycling volumes using crowdsourced activity data

Livingston, M. , McArthur, D. , Hong, J. and English, K. (2021) Predicting cycling volumes using crowdsourced activity data. Environment and Planning B: Urban Analytics and City Science, 48(5), pp. 1228-1244. (doi: 10.1177/2399808320925822)

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Planning for cycling is often made difficult by the lack of detailed information about when and where cycling takes place. Many have seen the arrival of new forms of data such as crowdsourced data as a potential saviour. One of the key challenges posed by these data forms is understanding how representative they are of the population. To address this challenge, a limited number of studies have compared crowdsourced cycling data to ground truth counts. In general, they have found a high correlation over the long run but with limited geographic coverage, and with counters placed on routes already known to be popular with cyclists. Little is known about the relationship between cyclists present in crowdsourced data and cyclists in manual counts over shorter periods of time and on non-arterial routes. We fill this gap by comparing multi-year crowdsourced data to manual cyclist counts from a cordon count in Scotland’s largest city, Glasgow. Using regression techniques, we estimate models that can be used to adjust the crowdsourced data to predict total cycling volumes. We find that the order of magnitude can be predicted but that the predictions lack the precision that may be required for some applications.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Hong, Dr Jinhyun and Livingston, Dr Mark and Ken English, Kirstie and Mcarthur, Dr David
Authors: Livingston, M., McArthur, D., Hong, J., and English, K.
College/School:College of Social Sciences > School of Social and Political Sciences > Urban Studies
Journal Name:Environment and Planning B: Urban Analytics and City Science
Publisher:SAGE Publications
ISSN (Online):2399-8091
Published Online:18 May 2020
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in Environment and Planning B: Urban Analytics and City Science 48(5): 1228-1244
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
190698Urban Big Data Research CentreNick BaileyEconomic and Social Research Council (ESRC)ES/L011921/1S&PS - Urban Big Data