Estimation of transfer walking time distribution in multimodal public transport systems based on smart card data

Abstract

Transfers are a major contributor to travel time unreliability for journeys in public transport. Thus, connections between services in the public transport network must be reliable. To plan such reliable transfers from e.g. busses to trains, it is crucial to know the necessary walking times from stops to platforms. This paper presents an innovative approach for estimation of walking time distributions from bus stops to train platforms based on a matching of smart card data and automatic vehicle location data. The observed times from bus stop to rail platform turns out to have a large variance, due to two reasons: differences in passenger walking speeds, and passengers who are doing activities during the transfer. To account for these variations a hierarchical Bayesian mixture model is applied, where the time for passengers walking directly and passengers doing activities during the transfer follows separate distributions. The proposed methodology is applied to 129 stations in the Eastern part of Denmark, where the tap-in devices are located at the train platform. Results from two stations with different characteristics are presented in details along with justifications and analyses of model accuracy. The outcome of the model with distributions of the necessary walking times from bus stops to train platforms is important input for timetabling connections, and the data-driven methodology can easily be applied at scale.