Abstract:
Roadway congestion leads to wasted time and money and environmental damage. One possible solution is adding more roadway capacity, but this can be impractical especially in urban environments and still may not make up for a poorly-calibrated traffic signal schedule. As such, it is becoming increasingly important to use existing road networks more efficiently. Recent research has focused on developing more efficient traffic signal control algorithms.

To further reduce delays, in this proposal, we consider the synergistic idea of dynamic routing, or changing vehicles’ routes through the network to minimize the current delay. We generate new routes by simulating the current traffic state forward at each vehicle decision point, based on knowledge of other vehicles’ current routes and the traffic signals’ control algorithms, and returning the fastest routes according to this forward simulation.

Work to date has produced several promising results. We evaluated our algorithm using the SUMO microscopic simulator on different road networks (both synthetic and real-world examples) using different traffic signal control algorithms (fixed-timing plans and schedule-driven intersection control). Experiments carried out on combinations of networks and traffic signal control algorithms show that our rerouting protocol reduces delay for both vehicles participating in route guidance (adopters) and those that do not (non-adopters) and that the reduction in delay generally increases as the proportion of adopters does. In addition, we are able to achieve real-time performance on the (relatively small) networks we’ve tried by learning a neural net approximation of expensive signal control algorithms. As future work, we propose adding a detector model instead of relying on perfect location information directly from the simulator, improving our ability to predict vehicles entering the network during routing simulations, and extending our approach to work on larger networks and more complicated traffic signal strategies.

Thesis Committee Members:
Stephen Smith, Chair
Zachary Rubinstein
Jeff Schneider
Hsu-Chieh Hu, Miovision

More Information