The uncertainty of travel times on urban roadways is a major source of frustration for commuters. While recurring congestion can be anticipated and planned for, by both individual commuters and planning agencies, dealing with unexpected traffic events (e.g. incidents) requires more careful analysis. In this talk, two strategies for improving the commuter experience in stochastic road networks will be discussed. The first is a proactive strategy for route planning that incorporates the underlying travel time uncertainty in the network. This routing strategy provides a solution that maximizes the probability of on-time arrival given a commuter's travel-time budget. Several theoretical and practical advancements are developed to obtain computationally efficient solutions that enable commercial applications. The second approach is a reactive strategy that dynamically re-routes traffic flow in response to unexpected events in the network. Once again, a computationally efficient framework is developed to solve the optimal re-route strategy in real-time. In both cases, experimental results on models of real-world road networks validate the effectiveness of the strategies.
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