TY - JOUR
T1 - A metro smart card data-based analysis of group travel behaviour in Shanghai, China
AU - Zhang, Yongping
AU - Manley, Ed
AU - Martens, Karel
AU - Batty, Michael
N1 - Publisher Copyright:
© 2023
PY - 2024/1
Y1 - 2024/1
N2 - Group travel behaviour widely exists in cities, but has not been well investigated by researchers. To fill this gap, this paper develops a co-existence-based methodological framework to systematically explore the spatiotemporal characteristics of group travel behaviour. We apply our framework to a case study of Shanghai, China, using a one-month tranche of metro smart card data. Results show that most travellers perform a small number of group trips, together with a small number of co-travellers. They usually travel in a dyad or triad group and form far more small social communities than large ones. Group travel behaviour is distinctly different from individual travel in terms of both time and space: group travellers are more likely to travel during weekends, on holidays, and in the afternoons and evenings. They also prefer to perform group behaviour near stations located in the city centre or the centres of new towns in suburban areas, and close to attractions and public facilities. The analysis we present has various potential applications such as improving the management of public events and supporting the design of group ticket policy.
AB - Group travel behaviour widely exists in cities, but has not been well investigated by researchers. To fill this gap, this paper develops a co-existence-based methodological framework to systematically explore the spatiotemporal characteristics of group travel behaviour. We apply our framework to a case study of Shanghai, China, using a one-month tranche of metro smart card data. Results show that most travellers perform a small number of group trips, together with a small number of co-travellers. They usually travel in a dyad or triad group and form far more small social communities than large ones. Group travel behaviour is distinctly different from individual travel in terms of both time and space: group travellers are more likely to travel during weekends, on holidays, and in the afternoons and evenings. They also prefer to perform group behaviour near stations located in the city centre or the centres of new towns in suburban areas, and close to attractions and public facilities. The analysis we present has various potential applications such as improving the management of public events and supporting the design of group ticket policy.
KW - Big data
KW - China
KW - Co-existence
KW - Group travel behaviour
KW - Smart card data
KW - Space and time
UR - http://www.scopus.com/inward/record.url?scp=85179112619&partnerID=8YFLogxK
U2 - 10.1016/j.jtrangeo.2023.103764
DO - 10.1016/j.jtrangeo.2023.103764
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AN - SCOPUS:85179112619
SN - 0966-6923
VL - 114
JO - Journal of Transport Geography
JF - Journal of Transport Geography
M1 - 103764
ER -