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Analysing Seasonality of Londoner’s Cycling Patterns and Behaviour

Kamargianni, Maria; (2016) Analysing Seasonality of Londoner’s Cycling Patterns and Behaviour. In: 14th World Conference on Transport Research. Conference on Transport Research: Shanghai, China. Green open access

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Abstract

The aim of this paper is to analyse the characteristics of Londoners’ cycle trips and develop models to identify the factors affecting the number of cycling trips in the summer and in the winter. Data from the London Travel Demand Survey (2010-2013) is used to evaluate the differences between summer and winter cyclists and what may influence higher frequency cycling. The sample consists of 12,900 individuals. Cycling frequencies vary greatly between the two seasons with an average of 7.3 days per month dropping off significantly to just 1.7 days in the winter. Model estimation results showed that young cyclists are the most impacted by seasonal effects, cycling significantly more in the summer than the winter. The results further demonstrated that White British people cycle less than those from other ethnic groups in the summer, but are less impacted by the different conditions in the winter. This outlines a lot of potential for increasing cycling through better targeting promotional policies. It further shows promising results if the younger generation can retain their cycling habits and become less sensitive to seasonal variations.

Type: Proceedings paper
Title: Analysing Seasonality of Londoner’s Cycling Patterns and Behaviour
Event: 14th World Conference on Transport Research - WCTR 2016
Location: Shanghai, China
Dates: 10 July 2016 - 15 July 2016
Open access status: An open access version is available from UCL Discovery
Publisher version: https://www.wctrs-conference.com/wctr-2016.html
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Bicycles; Cycling; Number of trips; Seasonality; London; Poison regression; Sustainable mobility
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10040019
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