The Bus Bunching Problem: Empirical Findings from Spatial Analytics

Abstract

Service regularity is one of the most significant performance indicators for public transport routes, typically measured through headway adherence. When headway deviations become too large and corresponding headways very small, bus bunching typically occurs. In these cases, passengers experience larger waiting times and overcrowding and an overall poor level of service. This paper aims to gain insight on frequent patterns of bus bunching using spatial analytics. Local and global spatial autocorrelation tests are performed on real world Automatic Vehicle Location (AVL) data to investigate spatial structures in the data. The spatio-temporal variations of bus bunching patterns throughout the day are further modeled using the ST-DBSCAN algorithm. Results show that the last few stops of each route exhibit statistically significant spatial autocorrelation with respect to the frequency of bunching, while the duration of bunching events is longer for route segments located in the central business district. Spatio-temporal clustering indicates that bunching is observed at a higher number of stops during peak traffic periods.

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