Real-Time Cross-Fleet Pareto-Improving Truck Platoon Coordination

TL;DR

This paper presents a real-time, distributed cross-fleet truck platoon coordination strategy that guarantees Pareto improvements for participating fleets by leveraging multiple hubs, reducing information sharing, and enhancing profits in transport networks.

Contribution

It introduces a novel Pareto-improving coordination method for multi-fleet truck platooning across hubs, ensuring higher fleet profits and enabling real-time, distributed implementation.

Findings

Significant profit increase for smaller fleets with cross-fleet platooning.

Strategy achieves near-optimal system-wide profit compared to centralized optimization.

Effective in reducing information sharing while maintaining coordination quality.

Abstract

This paper studies a multi-fleet platoon coordination system in transport networks that deploy hubs to form trucks into platoons. The trucks belong to different fleets that are interested in increasing their profits by platooning across fleets. The profit of each fleet incorporates platooning rewards and costs for waiting at hubs. Each truck has a fixed route and a waiting time budget to spend at the hubs along its route. To ensure that all fleets are willing to participate in the system, we develop a cross-fleet Pareto-improving coordination strategy that guarantees higher fleet profits than a coordination strategy without cross-fleet platoons. By leveraging multiple hubs for platoon formation, the coordination strategy can be implemented in a real-time and distributed fashion while largely reducing the amount of travel information to be shared for system-wide coordination. We evaluate the proposed strategy in a simulation study over the Swedish transportation network. The cross-fleet platooning strategy significantly improves fleets' profits compared with single-fleet platooning, especially the profits from smaller fleets. The cross-fleet platooning strategy also shows strong competitiveness in terms of the system-wide profit compared to the case when a system planner optimizes all fleets' total profit.