基于大数据的上海市共享汽车出行模式研究

doi:10.18306/dlkxjz.2021.12.005

Abstract

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

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.

Figures/Tables 13

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References 33

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订单编号	用户编号	取车网点编号	取车网点名称	还车网点编号	还车网点名称	取车时间	还车时间
2504	4	1530	五角场创智天地	5221	汤连得宝山店	2018-04-06T17:13	2018-04-06T17:53
2505	4	5155	虹口百联	7491	仙乐斯广场	2018-01-04T15:25	2018-01-04T18:45
2511	4	175	青浦工商学院	147	大宁国际	2018-01-06T16:57	2018-01-06T18:49

功能划分	POI类型
居住功能	住宅区,商务住宅
工作功能	写字楼,公司企业,工厂
休闲功能	餐饮服务,购物服务,公园广场,风景名胜
教育医疗功能	教育机构,医院

类型		指标	指标描述
空间特征	取车地功能区特征	混合功能区	取车地中混合功能区所占比例
		居住功能区	取车地中居住功能区所占比例
		工作功能区	取车地中工作功能区所占比例
		休闲功能区	取车地中休闲功能区所占比例
		教育医疗功能区	取车地中教育医疗功能区所占比例
	还车地功能区特征	混合功能区	还车地中混合功能区所占比例
		居住功能区	还车地中居住功能区所占比例
		工作功能区	还车地中工作功能区所占比例
		休闲功能区	还车地中休闲功能区所占比例
		教育医疗功能区	还车地中教育医疗功能区所占比例
时间特征	时点	凌晨	3:00~7:00间出行次数所占比例
		早高峰	7:00~11:00间出行次数所占比例
		午间	11:00~17:00间出行次数所占比例
		晚高峰	17:00~20:00间出行次数所占比例
		夜晚	20:00~24:00间出行次数所占比例
		半夜	0:00~3:00间出行次数所占比例
	时长	平均出行时长	平均出行时长
	出行频次		研究期内日均出行次数

类型		指标	类型1 通勤高频模式	类型2 夜间高频模式	类型3 夜间低频模式	类型4 全时段低频模式	类型5 晚高峰低频模式	类型6 通勤中频模式
空间特征	取车地功能区特征	混合	0.22	0.57	0.50	0.95	0.29	0.29
		居住	0.11	0.16	0.12	0.02	0.15	0.11
		工作	0.23	0.06	0.12	0.01	0.20	0.22
		休闲	0.19	0.12	0.14	0.01	0.18	0.19
		教育医疗	0.18	0.07	0.09	0.01	0.14	0.15
	还车地功能区特征	混合	0.20	0.62	0.51	0.95	0.30	0.28
		居住	0.10	0.11	0.11	0.01	0.16	0.09
		工作	0.21	0.07	0.10	0.01	0.16	0.22
		休闲	0.25	0.11	0.14	0.01	0.19	0.20
		教育医疗	0.18	0.06	0.03	0.01	0.15	0.15
时间特征	时点	凌晨	0.06	0.13	0.01	0.08	0.01	0.13
		早高峰	0.22	0.03	0.01	0.16	0.01	0.59
		中午	0.24	0.04	0.01	0.27	0.01	0.05
		晚高峰	0.29	0.05	0.03	0.29	0.93	0.05
		夜晚	0.15	0.40	0.90	0.07	0.03	0.04
		半夜	0.03	0.35	0.03	0.11	0.01	0.13
	时长	平均时长/min	78.12	43.52	73.20	97.48	108.88	102.48
出行频次		人均出行/(次/d)	0.698	0.602	0.136	0.128	0.126	0.356
各类型人数/人			2394	2200	7744	9622	6781	6979

类型		指标	类型1：日间高频模式	类型2：晚间低频模式	类型3：日间加班低频模式	类型4：晚间休闲低频模式
空间特征	取车地功能区特征	混合	0.47	0.93	0.08	0.08
		居住	0.17	0.02	0.03	0.04
		工作	0.11	0.01	0.46	0.02
		休闲	0.13	0.01	0.02	0.81
		教育医疗	0.09	0.02	0.36	0.03
	还车地功能区特征	混合	0.46	0.09	0.34	0.30
		居住	0.17	0.30	0.11	0.12
		工作	0.11	0.19	0.21	0.07
		休闲	0.14	0.22	0.09	0.39
		教育医疗	0.09	0.17	0.20	0.08
时间特征	时点	凌晨	0.07	0.08	0.04	0.05
		早高峰	0.15	0.15	0.18	0.17
		中午	0.25	0.14	0.31	0.10
		晚高峰	0.23	0.32	0.25	0.24
		夜晚	0.19	0.20	0.15	0.36
		半夜	0.10	0.11	0.06	0.07
	时长	平均时长/min	49.95	63.56	82.67	80.32
出行频次		人均出行/(次/d)	1.059	0.298	0.234	0.247
各类型人数/人			5144	9378	6897	5466

Car-sharing travel patterns in Shanghai based on big data

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