Factors affecting bus bunching at the stop level: A geographically weighted regression approach

Abstract

Efficient operation of bus networks is vital for urban centers. Unfortunately, factors such as uneven passenger loads and congestion hinder the adherence to posted schedules, leading to reliability issues. Most notably, bus bunching has been identified as a significant reliability problem, impacting both users and operators. Bus bunching is treated as a route-level problem in the relevant literature, while spatial patterns in explanatory factors are overlooked. Diverging from the typically performed route-level analysis, this study exploits Automatic Vehicle Location data to investigate factors affecting bus bunching at the network level, while taking into account their spatial variability. For this purpose, a Geographically Weighted Regression Model is applied to model bus bunching, using bus stop and network attributes as explanatory variables. Results for approximately 360 bus stops in Athens, Greece underline the superiority of the proposed model to Ordinary Least Squares Regression and corroborate the presence of spatial variability in the factors affecting bus bunching. Indeed, the number of traffic lanes at the stop level is positively associated with bunching in heavy traffic segments, whereas a higher number of lanes is negatively linked to bunching in less congested regions. Further, the number of bunching occurrences generally increases with the number of routes serving each stop, as well as with the distance from subway stops in the outer parts of the city. Such findings highlight the need to consider spatial structures in relevant models and can help improve their reliability and accuracy.

Keywords

• Bus Bunching
• Spatial Autocorrelation
• Geographically Weighted Regression
• AVL Data
• Intelligent Transportation Systems