Game Theoretic Models for Profit-Sharing in Multi-fleet Platoons

Johansson, Alexander; Mårtensson, Jonas

doi:10.1109/itsc.2019.8917349

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…The mechanisms of forming a platoon impact the profitability of each truck. A comparative study on the interaction mechanisms between leading and tailing trucks is carried out in Johansson and Mårtensson (2019), which suggests that the overall fuel savings are subject to the leading truck selection mechanism.…”

Section: Noncooperative Centralized Coordinationmentioning

confidence: 99%

Section: Noncooperative Centralized Coordinationmentioning

confidence: 99%

“…The models in the aforementioned studies assume all trucks start at the same node and the only adjustable decision is the departure time (Johansson & Mårtensson, 2019; Johansson et al., 2018), which may not be practically feasible. Moreover, trucks belonging to the same company may compete with each other under these noncooperative assumptions, resulting in a suboptimal result for the company.…”

Section: Literature Review: Truck Platooning Coordinationmentioning

confidence: 99%

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Decentralized coordination for truck platooning

Zeng

Wang

Rajan

2022

Computer aided Civil Eng

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Coordination for truck platooning refers to the active formation of a group of heavy-duty vehicles traveling at close spacing to reduce the overall truck operations costs. Conventionally, this coordination is achieved by optimizing various truck-related parameters, such as schedules, velocities, and routes, based on an objective function that minimizes a certain cost, for example, fuel usage. However, prevalent algorithms for the coordination problem are typically integer-constrained, which are not only hard to solve but are not readily scalable to increasing fleet sizes and networks. In this paper, to overcome these limitations, we propose a centralized formulation to optimize the truck parameters and solve a multidimensional objective cost function including fuel, operation time costs and preferential penalty. Furthermore, to improve the scalability of our proposed approach, we propose a decentralized algorithm for the platoon coordination problem involving multiple fleets and objectives. We perform both theoretical and numerical studies to evaluate the performance of our decentralized algorithm against the centralized solution. Our analysis indicates that the computation time of the proposed decentralized algorithms is invariant to the increasing fleet size, at the cost of a small relative gap to the optimum cost given by the centralized method. We discuss these results and present future directions for research.

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Section: Noncooperative Centralized Coordinationmentioning

confidence: 99%

Section: Noncooperative Centralized Coordinationmentioning

confidence: 99%

Section: Literature Review: Truck Platooning Coordinationmentioning

confidence: 99%

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Decentralized coordination for truck platooning

Zeng

Wang

Rajan

2022

Computer aided Civil Eng

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“…To increase the probability of platoons forming, we require technology that allows vehicles from different logistic service providers to cooperate. Encouraging such competitive formations will be done by monetarily incentivising vehicles, along the lines of the fuel savings sharing presented in [4]. The authors found that optimal individual utility is reached with an even distribution of profits between competing vehicles.…”

Section: Introductionmentioning

confidence: 99%

Automated Negotiation Mechanism and Strategy for Compensational Vehicular Platooning

Sebe

Baarslag

Müller

2021

PRIMA 2020: Principles and Practice of Multi-Agent Systems

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Our research is developing flexible strategies for forming and routing future platoons of automated urban logistics vehicles. We propose the notion of compensational platooning using automated negotiation between agents representing vehicles. After the vehicles reach the end of a common route, an agent can propose part of its route along with a monetary value to platoon partners for further together-travel. If negotiation is successful, a new platoon is formed and follows the proposed route. If the compensation is too small or the route proposed oversteps the agent's limitations, the offer is rejected and the vehicles continue their travel separately. A contribution of this paper is a negotiation strategy that proposes compensation based on beliefs of what the opponent's payment threshold would be. In doing so, the bid with the highest acceptance likelihood is calculated, keeping negotiations short and effective. Our model is tested on a synthetic network and a real urban example. We show that by using negotiation, vehicles can identify mutually beneficial new routes that a centralised/distributed approach would not find, with utility improvements of up to 8%.

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“…Luo et al [14] used an integer programming to design coordination algorithms with multiple speeds. Johansson et al [15] considered a game-theoretic formulation to study the share of economical benefits due to platooning. Sun and Yin [16] quantifies the share of benefits due to platooning and studied the allocation of benefits.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Coordinated Vehicle Platooning: An Analytical Approach Based on Stochastic Dynamic Programming

Xiong

Sha

Jin

2020

Preprint

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Platooning connected and autonomous vehicles (CAVs) can improve traffic and fuel efficiency. However, scalable platooning operations requires junction-level coordination, which has not been well studied. In this paper, we study the coordination of vehicle platooning at highway junctions. We consider a setting where CAVs randomly arrive at a highway junction according to a general renewal process. When a CAV approaches the junction, a system operator determines whether the CAV will merge into the platoon ahead according to the positions and speeds of the CAV and the platoon. We formulate a Markov decision process to minimize the discounted cumulative travel cost, i.e. fuel consumption plus travel delay, over an infinite time horizon. We show that the optimal policy is threshold-based: the CAV will merge with the platoon if and only if the difference between the CAV's and the platoon's predicted times of arrival at the junction is less than a constant threshold. We also propose two ready-to-implement algorithms to derive the optimal policy. Comparison with the classical value iteration algorithm implies that our approach explicitly incorporating the characteristics of the optimal policy is significantly more efficient in terms of computation. Importantly, we show that the optimal policy under Poisson arrivals can be obtained by solving a system of integral equations. We also validate our results in simulation with Real-time Strategy (RTS) using real traffic data. The simulation results indicate that the proposed method yields better performance compared with the conventional method.

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Game Theoretic Models for Profit-Sharing in Multi-fleet Platoons

Cited by 11 publications

References 14 publications

Decentralized coordination for truck platooning

Decentralized coordination for truck platooning

Automated Negotiation Mechanism and Strategy for Compensational Vehicular Platooning

Optimizing Coordinated Vehicle Platooning: An Analytical Approach Based on Stochastic Dynamic Programming

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