| 基于大数据的上海市共享汽车出行模式研究 |
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| 引用本文： | 仝德,周心灿,龚咏喜.基于大数据的上海市共享汽车出行模式研究[J].地理科学进展,2021,40(12):2035-2047. |
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| 作者姓名： | 仝德 周心灿 龚咏喜 |
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| 作者单位： | 北京大学深圳研究生院城市规划与设计学院,北京大学未来城市实验室(深圳),广东深圳518055;哈尔滨工业大学(深圳)建筑学院,广东深圳518055 |
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| 基金项目： | 北京大学(深圳)未来城市实验室铁汉科研开放课题基金(201804) |
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| 摘 要： | 共享汽车是城市交通发展的新潮流,论文基于2018年上海市共享汽车运营大数据和城市POI数据,研究共享汽车用户出行的空间、时间和频次特征,并对用户开展出行模式的K均值聚类分析,以期挖掘典型出行规律,为共享汽车运营优化提供依据。研究表明：① 工作日与休息日共享汽车使用行为差异显著,工作日共享汽车出行较集中于中心城区的混合功能区,早晚通勤高峰使用量大;休息日共享汽车出行空间布局较分散,使用量更高、平均单次使用时长更短且仅存在傍晚一个使用高峰。② 上海市共享汽车出行行为可分为工作日通勤的中高频模式,工作日夜间活动的高频模式,工作日偶尔用车晚餐及返家、偶尔用车远距离通勤和夜间长距离返家的低频模式,休息日日间休闲的高频模式、休息日傍晚离家休闲的中长途低频模式、休息日傍晚在休闲地之间穿梭的远距离低频模式、休息日日间加班的低频模式等10种模式。③ 中高频用户主要使用共享汽车实现远距离通勤和周末中长距离休闲活动,空间区域主要集中在中心城区和副中心区域;低频用户使用共享汽车多为夜间、长距离、休息日加班等公共交通难以满足需求、出租车成本过高的情景,空间分布也比较分散。可通过对用户提供差别化用车方案、优化车辆空间调度等方式推进超大城市共享汽车市场发展。
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| 关 键 词： | 共享汽车 出行模式 K均值聚类 上海 |
| 收稿时间： | 2021-01-06 |
| 修稿时间： | 2021-04-09 |
Car-sharing travel patterns in Shanghai based on big data |
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| TONG De,ZHOU Xincan,GONG Yongxi.Car-sharing travel patterns in Shanghai based on big data[J].Progress in Geography,2021,40(12):2035-2047. |
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| Authors: | TONG De ZHOU Xincan GONG Yongxi |
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| Institution: | 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 |
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| 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. |
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| Keywords: | car-sharing travel patterns K-mean clustering Shanghai |
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