PROGRESS IN GEOGRAPHY ›› 2021, Vol. 40 ›› Issue (12): 2035-2047.doi: 10.18306/dlkxjz.2021.12.005

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Car-sharing travel patterns in Shanghai based on big data

TONG De1(), ZHOU Xincan1, GONG Yongxi2,*()   

  1. 1. School of Urban Planning and Design, Peking University Shenzhen Graduate School, Laboratory for Urban Future, Peking University (Shenzhen), Shenzhen 518055, China
    2. School of Architecture, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
  • Received:2021-01-06 Revised:2021-04-09 Online:2021-12-28 Published:2022-02-28
  • Contact: GONG Yongxi E-mail:tongde@pkusz.edu.cn;gongyx@hit.edu.cn
  • Supported by:
    The Future City Laboratory of Peking University (Shenzhen), Tiehan Research Open Project Fund(201804)

Abstract:

Based on the big data of car-sharing operation and urban point of interest (POI) data of Shanghai in 2018, the spatial, temporal, and frequency characteristics of car-sharing users' travel were studied, and the K-mean clustering of users' travel patterns was carried out to explore typical travel patterns. The research shows that: 1) There is a significant difference in car-sharing behavior between working days and weekends. Car-sharing trips on working days are more concentrated in the mixed functional areas in the central urban area, and the use volume is large in the morning and evening rush hours. The distribution of car-sharing travel space on weekends is scattered, with higher usage, shorter average single use time, and only a peak in the evening. 2) In Shanghai, car-sharing travel behavior can be divided into 10 modes: working day commuting in medium-high frequency mode, working day nocturnal high frequency mode, working day occassional dinner and home trip, occassional long-distance commuting and home trip over long distance at night low frequency mode, weekend daytime recreational activity high frequency mode, weekend away from home in the evening recreational activity and medium-long-haul low frequency mode, weekend evening recreational long-distance travel low frequency mode, weekend overtime work-related low frequency mode, and so on. 3) Medium and high frequency users mainly used shared cars to realize long-distance commuting and long-distance recreational activities on weekends, and the spatial area is mainly concentrated in the central city and sub-central areas; Low-frequency users used shared cars mostly in situations where public transportation cannot meet the demand and taxi costs are too high, such as night, long-distance, and weekend overtime work-related trips, and the spatial distribution is relatively scattered. It can promote the development of car sharing market in megacities by providing users with differentiated vehicle plans and optimizing vehicle spatial scheduling.

Key words: car-sharing, travel patterns, K-mean clustering, Shanghai