Strategic Hub-Based Platoon Coordination under Uncertain Travel Times
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Abstract (arXiv)
We study the strategic interaction among vehicles in a non-cooperative platoon coordination game. Vehicles have predefined routes in a transportation network with a set of hubs where vehicles can wait for other vehicles to form platoons. Vehicles decide on their waiting times at hubs and the utility function of each vehicle includes both the benefit from platooning and the cost of waiting. We show that the platoon coordination game is a potential game when the travel times are either deterministic or stochastic, and the vehicles decide on their waiting times at the beginning of their journeys. We also propose two feedback solutions for the coordination problem when the travel times are stochastic and vehicles are allowed to update their strategies along their routes. The solutions are evaluated in a simulation study over the Swedish road network. It is shown that uncertainty in travel times affects the total benefit of platooning drastically and the benefit from platooning in the system increases significantly when utilizing feedback solutions.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Apr 2021.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2024Cooperation and Fairness in Multi-Agent Reinforcement Learningcited by 5
- 2025An Automated Reinforcement Learning Reward Design Framework with Large Language Model for Cooperative Platoon Coordinationcited by 1
- 2026Decentralized Autonomous Traffic Management through Corridor Networkscited by 0
- 2026Decentralized Coordination of Autonomous Traffic Through Advanced Air Mobility Corridorscited by 0
- 2026Decentralized Coordination of Autonomous Traffic Through Advanced Air Mobility Corridors22 Jun 2026
- 2026Decentralized Autonomous Traffic Management through Corridor Networks22 Jun 2026
- 2025An Automated Reinforcement Learning Reward Design Framework with Large Language Model for Cooperative Platoon Coordination28 Apr 2025
- 2024Cooperation and Fairness in Multi-Agent Reinforcement Learning19 Oct 2024