Maximizing truck platooning participation with preferences

Truck platooning refers to trucks traveling in convoy with longitudinal proximity. By reducing aerodynamic drag, platooning cuts truck energy use and operating cost along with other benefits to the freight transportation system. This research proposes a platform-based platooning system to maximize the participation of truck platooning considering stability of the formed platoons that arises from truck preferences of platooning partners. Preference-based platooning is of both scientific interest and practical importance to the trucking sector, which is highly fragmented especially in the US. The preferences depend on the benefits of fuel saving and schedule adjustment to coordinate the time for platoon formation. The formed platoons are stable in the sense that no two trucks in two platoons want to break away from their current platoons and platoon with each other. Tackling this operation planning problem, the proposed system involves a platform interacting with individual trucks in a way that reduces truck communication and computation burdens, mitigates truck privacy concerns, and overcomes trucks misreporting private information. The central methodological investigation of the interactive process is on how to solve the Maximum Stable Truck Platooning Participation (MS-TPP) problem, for which a twophase algorithmic approach is proposed. The underlying idea of this approach is to progressively reduce the lengths of truck preference lists, by eliminating truck pairs that do not affect the MS-TPP solution presence and separating trucks in odd rotations - which are key constructs in this approach - from the rest of the truck population. Theoretical properties and computational complexity of this two-phase algorithmic approach are investigated, along with a comparison with an integer programming approach and extensive numerical experiments in the context of a northern Illinois road network in the US. We find that the proposed two-phase algorithmic approach is very efficient compared to the integer programming approach in forming a maximum number of truck platoons while ensuring stability. In addition, the percentage of trucks that end up platooning by solving MS-TPP is much greater than using a greedy approach. The algorithmic approach is computationally scalable for solving large problem instances. The advantages of MSTPP, in terms of the percentage of trucks in platooning and the average utility gain, become even more prominent as we deal with a larger system with more trucks.