Abstract
Problem definition: We propose and analyze a novel bidding mechanism that, based on new 5G-based inter-vehicle communication technologies, brings economic efficiency to ad-hoc and unexpected traffic congestion points, while avoiding negative externalities to anyone involved. Academic/Practical Relevance: Road traffic delays and congestion is one of the most vexing problems of modern times. Many price-based approaches, such as tolls and zone-congestion pricing, have been implemented in practice and studied in the literature. However, such approaches are not applicable to ad-hoc and unexpected congestion points that frequently occur; concretely in this study, forced merging. We show how one can achieve economic efficiency in these situations, by combining the potential of new technologies with dynamic pricing. Methodology: We analyze the performance of a mechanism that bids sequentially from tail to head of a dense platoon of vehicles (T2H), with the condition that the vehicle can advance only if it wins the bid, imposing, thus, no negative externalities. For benchmarking, we also analyze a mechanism that bids sequentially from head to tail (H2T), clearly favoring the merging driver. In both cases, we show that optimal bids and value functions can be computed via simple Dynamic Programming recursions. Results: For uniformly distributed time valuations, we prove that the expected social welfare of T2H is close to a partial-information social optimum, and that the expected social welfare of H2T is lower than that of T2H, as long as the platoon is not too short. We also propose fast Approximate Dynamic Programming algorithms that perform very well in our numerical experiments, making them suitable for implementation. Managerial Implications: This is a novel proposal for the future management of algorithm-assisted, connected road traffic, which has the potential to achieve economic efficiency without negative externalities to anyone involved; the latter being critical for mass adoption.
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