PROTRIP: Probabilistic Risk-Aware Optimal Transit Planner.

Abstract

Optimal routing in urban transit networks, where variable congestion levels often lead to stochastic travel times, is usually studied with the least expected travel time (LET) as the performance criteria under the assumption of travel time independence on different road segments. However, a LET path might be subjected to high variability of travel time and therefore might not be desirable to transit users seeking a predictable arrival time. Further, there exists a spatial correlation in urban travel times due to the cascading effect of congestion across the road network. In this work, we propose a methodology and a tool that, given an origin-destination pair, a travel time budget, and a measure of the passenger's tolerance for uncertainty, provide the optimal online route choice in a transit network by balancing the objectives of maximizing on-time arrival probability and minimizing expected travel time. Our framework takes into account the correlation between travel time of different edges along a route and updates downstream distributions by taking advantage of upstream real-time information. We demonstrate the utility and performance of our algorithm with the help of realistic numerical experiments conducted on a fixed-route bus system that serves the residents of the Champaign-Urbana metropolitan area.