Abstract The objective of this paper is to identify hot spots of bus bunching events at the network level, both in time and space, using Automatic Vehicle Location (AVL) data from the Athens (Greece) Public Transportation System. A two-step spatio-temporal clustering analysis is employed for identifying localized hot spots in space and time and for refining detected hot spots, based on the nature of bus bunching events. First, the Spatio-Temporal Density Based Scanning Algorithm with Noise (ST-DBSCAN) is applied to distinguish bunching patterns at the network level and subsequently a k++means algorithm is employed to distinguish different types of bunching clusters. Results offer insights on specific time periods and route segments, where bus bunching events are more likely to occur and, also, on how bus bunching clusters change over time. Further, headway deviation analysis reveals the differences in the characteristics of the various bunching event types per line, showing that routes running on shared corridors experience more issues while underlying causes may vary per line. Collectively, results can help guide practice toward more flexible solutions and control strategies. Indeed, depending on the type of spatio-temporal patterns detected, appropriate improvements in service planning and real-time control strategies may be identified in order to mitigate their negative effects and improve quality of service. In light of emerging electric public transport systems, the proposed framework can be also used to determine preventive strategies and improve reliability in affected stops prior to the deployment of charging infrastructure.

中文翻译：

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识别城市网络中公交车聚集的时空模式

摘要 本文的目的是使用来自雅典（希腊）公共交通系统的自动车辆定位 (AVL) 数据，在网络层面识别公交车聚集事件的热点，包括时间和空间。两步时空聚类分析用于识别空间和时间上的局部热点，并根据总线聚集事件的性质细化检测到的热点。首先，应用基于时空密度的带噪声扫描算法（ST-DBSCAN）在网络层面区分聚束模式，随后采用k++means算法区分不同类型的聚束簇。结果提供了关于特定时间段和路​​线段的见解，其中更可能发生公交车聚集事件，以及公交车聚集集群如何随时间变化。更多，车头时距偏差分析揭示了每条线路各种聚集事件类型特征的差异，表明在共享走廊上运行的路线会遇到更多问题，而每条线路的根本原因可能有所不同。总的来说，结果可以帮助指导实践朝着更灵活的解决方案和控制策略发展。事实上，根据检测到的时空模式的类型，可以确定服务规划和实时控制策略的适当改进，以减轻它们的负面影响并提高服务质量。鉴于新兴的电动公共交通系统，拟议的框架还可用于在部署充电基础设施之前确定预防策略并提高受影响站点的可靠性。