Abstract
Aiming for better mobility and more efficient utilization of transportation networks, emergent connected and autonomous vehicle (CAV) technologies, and the resulting communication capabilities can produce more coordinated and efficient routing behavior. Current routing strategies either rely on a centralized control system which can fail in scaling, or employ decentralized schemes that yield sub-optimal coordination and accordingly poor system performance. This paper presents a Decentralized Collaborative Time-dependent Shortest Path Algorithm ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dec-CTDSP</i> ) with which the CAVs optimize their routes according to the communicated mobility messages from the other CAVs within their connected cluster. These messages are assumed to carry information regarding the vehicles’ location, speed, and preferred path to their destination. We analyzed the impacts of this optimization scheme under various levels of CAV market penetration and communication radius. The results of this study reveal (1) Up to 40% improvement in mean system travel time and speed; (2) Up to 45% increase in travel time and speed prediction reliability; (3) A strong correlation between mean system travel time and network usage distribution; and (4) significant improvements in network utilization as a result of Dec-CTDSP. The performance of Dec-CTDSP, in terms of runtime, convergence, and mobility improvements, is further benchmarked against other random and Dijkstra-based algorithms. The findings of this work will help steer further research on the implementation of coordinated and decentralized multiagent routing optimization in the context of connected and autonomous vehicles.
Published Version
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