Strategic Decision-Making in Platoon Coordination
Time: Thu 2020-09-03 15.00
Location: Harry Nyquist, Malvinas väg 10, Q-huset, våningsplan 7, KTH Campus, Stockholm (English)
Subject area: Electrical Engineering
Doctoral student: Alexander Johansson , Reglerteknik
Opponent: Professor Yafeng Yin, University of Michigan
Supervisor: Associate professor Jonas Mårtensson, Reglerteknik
The need for sustainable transportation solutions is urgent as the demand for mobility of goods and people is expected to multiply in the upcoming decades. One promising solution is truck platooning, which shows great potential in reducing the fuel consumption and operational costs of trucks. In order to utilize the benefits of truck platooning to the fullest, trucks with different routes in a transportation network need coordination to efficiently meet and form platoons. This thesis addresses platoon coordination when trucks form platoons at hubs, where some trucks need to wait for others in order to meet, and there is a reward for platooning and a cost for waiting. Three contributions on the topic platoon coordination are presented in this thesis.
In the first contribution, we consider platoon coordination among trucks that have pre-defined routes in a network of hubs, and the travel times are either deterministic or stochastic. The trucks are owned by competing transportation companies, and each truck decides on its waiting times at hubs in order to optimize its own operational cost. We consider a group of trucks to form a platoon if it departs from a hub and enters the road at the same time. The strategic interaction among trucks when they coordinate for platooning is modeled by non-cooperative game theory, and the Nash equilibrium is considered as the solution concept when the trucks make their decisions at the beginning of their journeys. In case of stochastic travel times, we also develop feedback-based solutions wherein trucks repeatedly update their decisions. We show in a simulation study of the Swedish transportation network that the feedback-based solutions achieve platooning rates up to 60 %.
In the second contribution, we propose models for sharing the platooning profit among platoon members. The platooning benefit is not equal for all trucks in a platoon; typically, the lead truck benefits less than its followers. The incentive for transportation companies to cooperate in platooning may be low unless the profit is shared. We formulate platoon coordination games based on profit-sharing models, and in a simulation of a single hub, the outcomes of the platoon coordination games are evaluated. The evaluation shows that the total profit achieved when the trucks aim to maximize their own profits, but the platooning benefit is evened out among platoon members, is nearly as high as when each truck aims to maximize the total profit in the platooning system.
In the last contribution, we study a problem where trucks arrive to a hub according to a stochastic arrival process. The trucks do not share a priori information about their arrivals; this may be sensitive information to share with others. A coordinator decides, based on the statistical distribution of arrivals, when to release the trucks at the hub in the form of a platoon. Under the assumption that the arrivals are independent and identically distributed, we show that it is optimal to release the trucks at the hub when the number of trucks exceeds a certain threshold. This contribution shows that simple and dynamic coordination approaches can obtain a high profit from platooning, even under high uncertainty and limited a priori information.