Optimal decentralized signal control for platooning in connected vehicle networks

In the last decade, pressure-based schemes such as Back Pressure and Max Weight algorithms have been widely researched and applied for traffic signal control due to their simplicity and proven throughput maximization. In such algorithms, the next chosen signal phase at an intersection in a road network is the one with the highest measured weight, representing the pressure of traffic movements at the intersection, determined based on a single characteristic of the traffic flow or vehicles’ state at that intersection. This paper develops a new optimal Max Weight control mechanism to enhance the network throughput and reduce vehicle delays in a network using a concept of platooning enabled by Connected Vehicles (CVs). To this end, we propose a new proven optimal Max Weight control scheme where the weight consists of several features including the platoon delay, as well as the speed and position of vehicles within the platoon. To the best of our knowledge, this work is the first to propose a platoon pressure-based concept considering multiple configurable attributes in formulating the pressure. Furthermore, we provide a rigorous stability proof that ensures the throughput optimality of the proposed control scheme. In addition, we also develop a machine learning procedure in this paper to optimize the weighting parameter of each attribute contributing to the total pressure enabling its seamless deployment in practice. A number of simulation results demonstrate the feasibility of the learning procedure and show that our Max Weight platoon pressure-based scheme outperforms the state-of-the-art and well-known existing pressure-based algorithms. • Develop a novel MaxWeight decentralized signal control scheme considering vehicle platooning. • Propose the weight (or pressure) based on the platoons’ features or characteristics. • Develop a cross entropy method as the learning algorithm to optimize the parameters of the control algorithm. • Provide theoretical proof for the stability property of the proposed MaxWeight scheme. • Numerically show significant improvements over other well-known existing pressure-based schemes.

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Institutions

Publication Details

Journal
Transportation Research Part C Emerging Technologies
Published
2024-09-02
DOI
https://doi.org/10.1016/j.trc.2024.104832
Citations
3
Primary Topic
Network Time Synchronization Technologies
Type
article
Field-Weighted Citation Impact
1.05

Funders

Controls
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article

Optimal decentralized signal control for platooning in connected vehicle networks

T.A. Hoang, Neil Walton, Hai L. Vu
3 citations
Transportation Research Part C Emerging Technologies
Network Time Synchronization Technologies
1.05
article

Optimal decentralized signal control for platooning in connected vehicle networks

T.A. Hoang, Neil Walton, Hai L. Vu
article en
3 citations

Abstract

In the last decade, pressure-based schemes such as Back Pressure and Max Weight algorithms have been widely researched and applied for traffic signal control due to their simplicity and proven throughput maximization. In such algorithms, the next chosen signal phase at an intersection in a road network is the one with the highest measured weight, representing the pressure of traffic movements at the intersection, determined based on a single characteristic of the traffic flow or vehicles’ state at that intersection. This paper develops a new optimal Max Weight control mechanism to enhance the network throughput and reduce vehicle delays in a network using a concept of platooning enabled by Connected Vehicles (CVs). To this end, we propose a new proven optimal Max Weight control scheme where the weight consists of several features including the platoon delay, as well as the speed and position of vehicles within the platoon. To the best of our knowledge, this work is the first to propose a platoon pressure-based concept considering multiple configurable attributes in formulating the pressure. Furthermore, we provide a rigorous stability proof that ensures the throughput optimality of the proposed control scheme. In addition, we also develop a machine learning procedure in this paper to optimize the weighting parameter of each attribute contributing to the total pressure enabling its seamless deployment in practice. A number of simulation results demonstrate the feasibility of the learning procedure and show that our Max Weight platoon pressure-based scheme outperforms the state-of-the-art and well-known existing pressure-based algorithms. • Develop a novel MaxWeight decentralized signal control scheme considering vehicle platooning. • Propose the weight (or pressure) based on the platoons’ features or characteristics. • Develop a cross entropy method as the learning algorithm to optimize the parameters of the control algorithm. • Provide theoretical proof for the stability property of the proposed MaxWeight scheme. • Numerically show significant improvements over other well-known existing pressure-based schemes.

Transportation Research Part C Emerging TechnologiesVol. 167
Durham University (GB), Monash University (AU)
Monash University, Scheme for Promotion of Academic and Research Collaboration, Australian Research Council
Affordable and clean energy
Openalex Percentile: Top 22%
Network Time Synchronization Technologies
1.05
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