PhD Defence Ms Ying Zhang

Department of Urban and Regional Planning and Geo-information Management


Title of defence

Bike-sharing usage: mining on the trip data of bike-sharing users


Bike-sharing systems aim to increase bike use and improve accessibility of urban transit. These systems have grown in popularity and are expanding rapidly across cities worldwide. Studies have shown that cycling for both utilitarian and recreational purposes has increased in some cities after running bike-sharing programs. The latest generation of bike-sharing programs has employed information technology that produces station-based data or trip-level data. It facilitates to analyse the studies of the actual use of these systems. Researchers have shown a huge interest in studying transport supply and travel demand aspects of bike-sharing systems. Despite the interesting findings and important implications of these studies, yet a number of research gaps can be identified, like travel demand characteristics and patterns of bike-sharing systems, travel demand dynamics of bike-sharing systems, and travel behaviour of bike-sharing users.

Moreover, the majority of studies dealt with American and European systems, and its findings cannot be transferred directly to Asian cites that exhibit quite different urban features and cycle demand from American and European cities. The largest amount of bike-sharing programs is running in China but only a few researches can be found in scientific literature. Within this context, this research aims to explore methods and approaches to extract information on the actual use of a bike-sharing system and the performance of the system by employing datasets selected from the operational database from the bike-sharing system in the city of Zhongshan, China.

Firstly, this research unravels travel demand characteristics and patterns of system usage, in terms of system efficiency, trip characteristics, and station activity patterns. As a result, this research indicates and discusses the efficiency of the system usage based on a set of system metrics, the spatial and temporal characteristics of single trips, and the weekday and weekend patterns for bike use at the level of stations.

Secondly, this research examines the effect of surrounding built environment factors on the use of public bikes at station level. This study employs a spatial multiple linear regression model to examine and elaborate the statistical relations between surrounding built environment variables and the actual use of public bikes at stations, incorporating the spatial correlation between nearby stations. Trip demand, and the demand to supply ratio, both at bike station level, are the two dependent variables that have been estimated. The model results uncover how each built environment factors and nearby stations affect the demand as well as the D/S at station level. The findings contribute to an understanding how spatial factors drive BSS demand in the context of a Chinese city, to be able to plan successfully new bike-sharing systems or stations in those cities.

Thirdly, this research explores the travel demand dynamics of bike-sharing systems both from a temporal and spatial perspective, employing spatial and statistical analyses. The findings contribute to an understanding of how users and system usage change over the years, and how the expansion of the system affects the usage of the system.

Finally, this research provides a new insight into exploring the travel behaviour of bike-sharing users. Such work examines the spatial and temporal patterns of bike-sharing trips, trip chains, and transition activities. The analysis incorporates information of land use types around bike stations, time information of pickup and return actions, and transition activities in between bike trips.

This thesis contributes to a better understanding of the actual use and performance of bike-sharing systems. This research aims to develop methods and approaches to extract information from an operational database with respect to trip characteristics of bike-sharing users. It offers new research perspectives and approaches to unravel the characteristics and patterns of bike-sharing usage, like travel demand characteristics and dynamics and travel behaviour, at the level of trips, stations and users. The findings of this research can be beneficial to researchers, urban planners and policy makers to extend and improve their knowledge about such systems. This knowledge is useful for the improvement and expansion of existing systems, as well as the adoption of new systems.


Ying Zhang was born in Xinyang, China, on 13 July 1987. She received her MSc degrees in urban planning and Geo-information management in 2011 under a joint master’s program between Wuhan University and Faculty of ITC of the University of Twente. In March 2012, she began her PhD project at PGM department of the Faculty of ITC of the University of Twente, which was supported by a four-year doctoral scholarship from the China Scholarship Council and the research fund of ITC. Her PhD research aims at investigating the bike-sharing usage by mining on the trip data of bike-sharing users. During her PhD study, she has published journal papers, and attended international conferences and various courses.

Zhang, Y., van Maarseveen, M.F.A.M. (promoter) and Thomas, T. (co-promoter)  (2017) Bike-sharing usage : mining on the trip data of bike-sharing users. Enschede, University of Twente Faculty of Geo-Information and Earth Observation (ITC), 2017. ITC Dissertation 306, ISBN: 978-90-365-4394-1.

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Event starts: Wednesday 20 September 2017 at 14:30
Venue: UT, Waaier 4
City where event takes place: Enschede

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