区域嵌入支持下的出租车移动链时空模式分析  

Spatio-Temporal Pattern Analysis of Taxi Mobility Chain Supported by Regional Embedding

作　　者：信睿[1,2] 杨宽 王姣娥 XIN Rui;YANG Kuan;WANG Jiaoe(College of Geodesy and Geomatics,Shandong University of Science and Technology,Qingdao 266590,China;Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]山东科技大学测绘与空间信息学院,青岛266590 [2]中国科学院地理科学与资源研究所中国科学院区域可持续发展分析与模拟重点实验室,北京100101 [3]中国科学院大学资源与环境学院,北京100049

出　　处：《地球信息科学学报》2025年第3期570-584,共15页Journal of Geo-information Science

基　　金：国家自然科学基金项目(42101452);山东省自然科学基金项目(ZR2021QD027);自然资源部数字制图与国土信息应用重点实验室开放研究基金资助项目(ZRZYBWD202202)。

摘　　要：【目的】OD数据可以反馈交通流起讫点,对交通规划与管理具有重要价值。然而,OD数据缺乏移动的过程细节,这导致现有研究多集中于分析孤立的OD数据,缺乏将OD数据组织为具有移动上下文信息的高阶单元展开探索研究。在此背景下,本文基于厦门市巡游车和网约车2种类型的出租车订单数据,以单个出租车为主体串联其多次连续的接单行为,构建出租车移动链(TMC)新型研究模型。【方法】分别从出租车的使用和TMC的特性两个维度出发设计了相应指标作为时空特征的分析工具。特别地,本文引入了自然语言处理领域的词嵌入技术并衔接聚类算法,探索车辆运营过程中形成的时空模式特征。【结果】通过对比分析运营模式不同(“车找人”和“人找车”)的2种车辆,结果表明:(1)单辆出租车日均接单量在工作日和非工作日差异不大,但巡游车司机在接单时自主性更强,连续接单量更多,工作强度更大;(2)巡游车运营呈现更强的聚集性,网约车则具有更大的空间服务范围;(3)在聚类分析中,不同的聚类数可以帮助发现不同结论。如K=2时聚类结果的空间分布大致对应两类出租车的主次运营区域,K=6时巡游车的聚类结果与高密度路网的空间分布更相关,而网约车与行政区划更相关。【结论】论文利用基于移动链的聚类分析手段,可以更好地识别出租车运营的时空特征,为精准预测交通需求、优化调度服务等提供数据支撑。[Objectives]Understanding the spatial and temporal characteristics of different types of taxis operations is crucial for transport planning and management.In this paper,a novel research model,Taxi Mobility Chain(TMC),is constructed by stringing together consecutive movements of individual taxis using order data from traditional taxis and e-hailing cars in Xiamen City.The introduction of TMC addresses the limitation of traditional research that uses isolated OD flow as the analysis unit to some extent,and provides richer background information,which can connect with more intelligent methods and has better universality.[Methods]Based on the obtained TMCs,we designed corresponding indicators as analytical tools from the dimensions of taxi usage and TMC characteristics.These tools are used for the macroscopic description of the usage of traditional taxis and e-hailing cars and the quantitative computation of the spatial morphological characteristics of TMCs.In particular,with reference to the word embedding technology in the field of Natural Language Processing(NLP),we explore the spatiotemporal pattern characteristics formed during taxi operations by using clustering algorithms after embedding the cellular areas of the study area grid.[Results]By comparing and analyzing two kinds of taxis with different operation modes("vehicles looking for people"and"people looking for vehicles"),the results of the indicator analysis module mainly indicate that the average daily number of orders received by a single taxi does not differ much between weekdays and non-weekdays,and the individual utilization rate is relatively stable.However,the drivers of traditional taxis have more autonomy in taking orders,more consecutive orders,and higher work intensity.The results of the cluster analysis module mainly indicate that traditional taxi operations show stronger aggregation and are more inclined to shuttle back and forth in some hotspots.E-hailing cars are based on individual travelling needs,with boarding and alighting points complet

关 键 词：出租车移动链 时空分析 OD数据 巡游车 网约车 区域嵌入 

分 类 号：U491.12[交通运输工程—交通运输规划与管理] TP311.13[交通运输工程—道路与铁道工程]