交通运输工程与信息学报2026，Vol.24Issue(1)：25-37,13.DOI:10.19961/j.cnki.1672-4747.2025.07.011
网约车订单数据驱动的地铁接驳出行识别与服务范围分析
Metro feeder-trip identification and service-area analysis using ridesourcing data
摘要
Abstract
[Background]Despite the continuous expansion of urban rail networks,service coverage gaps persist in urban transportation systems.Because of its flexibility and efficiency,ridesourcing has become a key mode for bridging the first-and last-mile segments of metro travel.[Objective]To accurately identify ridesourcing-metro integration trips and delineate their service areas,thereby pro-viding a foundation for improving transfer efficiency and optimizing platform resources.[Method]Metro-integrated ridesourcing trips were accurately identified by matching detailed origin and desti-nation addresses.On this basis,service areas were constructed using point aggregation and hierarchi-cal clustering algorithms to examine their effectiveness in delineating the spatial service coverage of metro systems.[Data]The ridesourcing trip dataset from Tianjin,which contains detailed origin and destination address information,provides a reliable basis for accurately identifying metro-integrated trips and conducting empirical analysis.[Result]Hierarchical clustering demonstrates superior accu-racy in delineating the spatial service areas of ridesourcing-metro integration,particularly for com-plex spatial patterns.Its adaptability to heterogeneous regional demands enables the construction of buffer zones that more closely reflect actual travel distributions.[Conclusion]Metro stations in cen-tral areas primarily serve short-distance feeder trips and are characterized by larger buffer areas but lower trip densities.In contrast,suburban stations tend to support longer-distance trips,with smaller buffer zones yet higher trip densities.The results suggest that differentiated transfer management strategies are required for urban centers and suburban areas.Optimizing the layout and dispatching of transfer points based on service-area characteristics can enhance the transfer efficiency and service quality of the public transportation systems.关键词
城市交通/网约车接驳地铁/出行识别/服务范围构建/层次聚类算法/空间特性Key words
urban traffic/ridesourcing-metro integration/trip identification/service area construc-tion/hierarchical clustering algorithm/spatial characteristics分类
交通工程引用本文复制引用
孟裕,卢浩,解瑞源,于维杰,马新卫..网约车订单数据驱动的地铁接驳出行识别与服务范围分析[J].交通运输工程与信息学报,2026,24(1):25-37,13.基金项目
国家自然科学基金项目(52202387) （52202387）
