基于地理流空间的巡游车与网约车人群出行模式研究

doi:10.12082/dqxxkx.2023.210769

地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (4): 726-740.doi: 10.12082/dqxxkx.2023.210769

基于地理流空间的巡游车与网约车人群出行模式研究

王鹏洲¹^,²(), 赵志远¹^,²^,³^,^*(), 姚伟¹^,², 吴升¹^,²^,³, 汪艳霞⁴, 方莉娜¹^,²^,³, 邬群勇¹^,²^,³

1.福州大学数字中国研究院（福建），福州 350003
2.空间数据挖掘与信息共享教育部重点实验室，福州 350003
3.海西政务大数据应用协同创新中心，福州 350002
4.福州市勘测院有限公司，福州 350108

收稿日期:2021-12-01 修回日期:2022-03-18 出版日期:2023-04-25 发布日期:2023-04-19
通讯作者: ^*赵志远（1989—），男，安徽亳州人，博士，从事时空大数据分析与挖掘。E-mail: zyzhao@fzu.edu.cn
作者简介:王鹏洲（1997—），男，山西晋中人，硕士，从事时空轨迹数据挖掘。E-mail: pzwang_gis@163.com
基金资助:
国家重点研发计划项目(2017YFB0503500);中国博士后科学基金项目(2019M652244);福建省中央引导地方科技发展专项(2020L3005)

Human Travel Patterns by E-hailing Cars and Traditional Taxis based on Geographic Flow Space

WANG Pengzhou¹^,²(), ZHAO Zhiyuan¹^,²^,³^,^*(), YAO Wei¹^,², WU Sheng¹^,²^,³, WANG Yanxia⁴, FANG Lina¹^,²^,³, WU Qunyong¹^,²^,³

1. Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350003, China
2. Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou 350003, China
3. Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350002, China
4. Fuzhou Investigation & Surveying Institute Co., Ltd., Fuzhou 350108, China

Received:2021-12-01 Revised:2022-03-18 Online:2023-04-25 Published:2023-04-19
Contact: ZHAO Zhiyuan
Supported by:
National Key Research and Development Program of China(2017YFB0503500);China Postdoctoral Science Foundation(2019M652244);The Central Guided Local Development of Science and Technology Project of Fujian(2020L3005)

摘要/Abstract

摘要：

城市出租汽车是居民出行的重要方式之一，地理流空间理论为发掘人群出行特征，优化车辆运营效率提供了新视角。本文利用厦门市出租汽车轨迹数据，采用地理流空间分析理论，对人群出行的整体随机性质进行了分析，基于流相似性度量识别并分析了丛集、汇聚、发散和社区4种典型模式及混合模式的空间分布特征，对比了基于巡游车和网约车2种车辆的人群出行模式。结果表明流空间理论能够系统性发现人群出行典型模式及混合模式，主要体现在：① 基于2类车辆的人群出行流在空间中呈现出显著的非随机特征；② 巡游车和网约车的典型模式在空间分布上有明显差别，网约车的有关模式分布范围更广，在厦门岛外各区中心及岛内东部软件园等区域附近较为突出，且网约车由于其订单由用户需求驱动，更容易发现潜在的高出行需求区域，同时出行结构更容易形成社区模式，而巡游车主要分布在传统岛内知名城市地标附近；③ 同一区域内巡游车和网约车出行混合模式普遍存在，约占典型模式的四分之一左右，而且不同类型车辆的主要混合模式存在差异，综合考虑混合模式能够提高城市公共设施规划的精确性和科学性。本文结果可以为车辆调度优化和城市交通规划提供支持，也表明地理流空间理论能够更有效揭示地理流对象的空间模式特征。

关键词: 地理流空间, 流聚类, 出行流模式, 出租车, 网约车, 人类移动性, 轨迹数据, 混合流模式

Abstract:

Traditional taxis and the e-hailing cars are two main transport vehicles for the public in current taxi market, which aim to satisfy the customized travel demand in daily lives of citizens in urban public transportation system. Due to the differences in service modes and commercial patterns, the two vehicles are appropriate for different target groups. Investigating the spatial and temporal characteristics of these two types of vehicle based on human travel flows can support the applications such as optimization of the urban public transportation and land use planning. The geographical flow space theory proposed recently provides a new theoretical perspective as well as a systematical analysis framework in studying the flow patterns of the travels by different types of vehicles. In this paper, we adopt this formulated theory framework to describe the travel flow. We select five typical flow patterns, namely random, clustering, aggregation, divergence, and community patterns, to reveal their spatial distribution characteristics and compare the differences in their travel patterns. The trajectory dataset of traditional taxis and the e-hailing cars in Xiamen City is employed to validate the effectiveness of the geographical flow space theory. We find that: (1) the travel flows of the two types of vehicles present significant non-random characteristics in flow space; (2) people tend to choose e-hailing cars for long distance travel, while prefer the traditional taxis for short and medium distance travels; (3) the two types of cars show different spatial distribution characteristics of the four typical flow patterns. The travels by e-hailing cars are more widely distributed and exhibit clustering patterns around the sub-centers at the suburban areas outside the core Xiamen Island and the east-southern software park area inside the Xiamen Island. Due to the travel demand driven model, the e-hailing cars satisfy the emerging high travel demand areas and tend to form community patterns. While the traditional cars are mainly distributed around the well-known city landmarks (e.g., Zengcuoan, Zhongshan road) on the Island; (4) approximately a quarter of the local areas have more than one typical flow patterns. Different types of cars exhibit different co-location flow patterns and spatial distribution characteristics. The mixed flow patterns derived from the geographical flow theory provide a more comprehensive perspective to better understand the travel flows, which can mitigate the misleading information from each isolated flow pattern. The above findings imply that the geographical flow theory can help to better understand the characteristics of the geographical flows and can be used to improve the applications based on related results.

Key words: geographical flow space, flow clustering, travel flow patterns, traditional taxis, e-hailing taxis, human mobility, trajectory data, mixed flow patterns

王鹏洲, 赵志远, 姚伟, 吴升, 汪艳霞, 方莉娜, 邬群勇. 基于地理流空间的巡游车与网约车人群出行模式研究[J]. 地球信息科学学报, 2023, 25(4): 726-740.DOI:10.12082/dqxxkx.2023.210769

WANG Pengzhou, ZHAO Zhiyuan, YAO Wei, WU Sheng, WANG Yanxia, FANG Lina, WU Qunyong. Human Travel Patterns by E-hailing Cars and Traditional Taxis based on Geographic Flow Space[J]. Journal of Geo-information Science, 2023, 25(4): 726-740.DOI:10.12082/dqxxkx.2023.210769

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参考文献 40

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车辆ID	上车时间	上车经度/°E	上车纬度/°N	下车时间	下车经度/°E	下车纬度/°N
87a1*****a	2019-05-31 08:49:00	118.1****2	24.5****1	2019-05-31 09:17:00	118.1****3	24.4****6
f8bb*****2	2019-05-31 07:04:00	118.1****1	24.4****5	2019-05-31 07:11:00	118.1****8	24.4****3
ff50*****7	2019-05-31 07:48:00	118.0****5	24.4****3	2019-05-31 08:27:00	118.1****8	24.4****0
9b28*****f	2019-05-31 07:51:00	118.1****8	24.5****6	2019-05-31 08:34:00	118.0****8	24.5****3

	巡游车		网约车
	早高峰	晚高峰	早高峰	晚高峰
节点数	174	180	291	333
边数	365	406	552	693
平均度	4.20	4.51	3.75	4.16
图密度	0.02	0.03	0.01	0.01
模块度	0.48	0.46	0.63	0.63
社区数	16	12	23	28

	厦门站	中山路	厦门大学	曾厝垵
汇聚模式	672	347	260	285
丛集到达占比/%	19	53	43	47
发散模式	811	455	318	293
丛集出发占比/%	19	40	68	54

基于地理流空间的巡游车与网约车人群出行模式研究

Human Travel Patterns by E-hailing Cars and Traditional Taxis based on Geographic Flow Space

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参考文献 40

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模式	巡游车早高峰	巡游车晚高峰	网约车早高峰	网约车晚高峰
发散-社区	8	7	15	13
发散-汇聚	4	2	1	5
发散-丛集	1	2	4	1
社区-汇聚	11	4	30	19
社区-丛集	22	10	17	21
汇聚-丛集	1	0	11	6
汇聚-发散-社区	1	10	1	3
汇聚-发散-丛集	0	0	1	4
汇聚-社区-丛集	8	1	20	19
丛集-发散-社区	1	3	2	7
丛集-汇聚-发散-社区	3	8	0	4
混合模式总体占比/%	28	24	27	25

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	厦门站	中山路	厦门大学	曾厝垵	丛集出发汇总
厦门站	0	69	51	31	151
中山路	31	0	81	71	183
厦门大学	36	118	0	63	217
曾厝垵	64	65	30	0	159
丛集到达汇总	131	183	111	134	-