Hierarchical Bayesian Framework for Bus Dwell Time Prediction

In many applications, uncertainty regarding the duration of activities complicates the generation of accurate plans and schedules. Such is the case for the problem considered in this paper - predicting the arrival times of buses at signalized intersections. Direct vehicle-to-infrastructure communication of location, speed and heading information offers unprecedented opportunities for real-time optimization of traffic signal timing plans, but to be useful bus arrival time prediction must reliably account for bus dwell time at near-side bus stops. To address this problem, we propose a novel, Bayesian hierarchical approach for constructing bus dwell time duration distributions from historical data. Unlike traditional statistical learning techniques, the proposed approach relies on minimal data, is inherently adaptive to time varying task duration distribution, and provides a rich description of confidence for decision making, all of which are important in the bus dwell time prediction context. The effectiveness of this approach is demonstrated using historical data provided by a local transit authority on bus dwell times at urban bus stops. Our results show that the dwell time distributions generated by our approach yield significantly more accurate predictions than those generated by both standard regression techniques and a more data intensive deep learning approach.

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