Adaptive strategies for route selection en-route in transportation networks

Tai, Tak Shing; Yeung, Chi Ho

doi:10.1016/j.cjph.2021.07.024

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…Lin et al [25] proposed the Social Vehicle Route Selection (SVRS) algorithm, which combines historical and current drive information and uses game evolution method to calculate the optimal routes. Tai et al [26] modeled vehicles by studying two-dimensional metacellular automata to better coordinate vehicles in dense road networks with route greedy updates of appropriate frequency. Tanimoto et al [27] explored the route selection problem using a metacellular automata simulation that coincides with evolutionary game theory, modeling the interaction between vehicles as an n-chicken game and solving this game by providing appropriate information to the driver agents, which in turn alleviates urban traffic congestion.…”

Section: Related Workmentioning

confidence: 99%

Population Game-Assisted Multi-Agent Reinforcement Learning Method for Dynamic Multi-Vehicle Route Selection

Yan,

Cai

2024

Electronics

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To address urban traffic congestion, researchers have made various efforts to mitigate issues such as prolonged travel time, fuel wastage, and pollutant emissions. These efforts primarily involve microscopic route selection from the vehicle perspective, multi-vehicle route optimization based on traffic flow information and historical data, and coordinated route optimization that models vehicle interaction as a game behavior. However, existing route selection algorithms suffer from limitations such as a lack of heuristic, low dynamicity, lengthy learning cycles, and vulnerability to multi-vehicle route conflicts. To further alleviate traffic congestion, this paper presents a Period-Stage-Round Route Selection Model (PSRRSM), which utilizes a population game between vehicles at each intersection to solve the Nash equilibrium. Additionally, a Period Learning Algorithm for Route Selection (PLA-RS) is proposed, which is based on a multi-agent deep deterministic policy gradient. The algorithm allows the agents to learn from the population game and eventually transition into autonomous learning, adapting to different decision-making roles in different periods. The PSRRSM is experimentally validated using the traffic simulation platform SUMO (Simulation of Urban Mobility) in both artificial and real road networks. The experimental results demonstrate that PSRRSM outperforms several comparative algorithms in terms of network throughput and average travel cost. This is achieved through the coordination of multi vehicle route optimization, facilitated by inter-vehicle population games and communication among road agents during training, enabling the vehicle strategies to reach a Nash equilibrium.

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Section: Related Workmentioning

confidence: 99%

Population Game-Assisted Multi-Agent Reinforcement Learning Method for Dynamic Multi-Vehicle Route Selection

Yan,

Cai

2024

Electronics

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“…Most traditional traffic signal optimization control systems use traditional fixed timing schemes, which cannot be adjusted according to the real-time status of intersections and are prone to traffic congestion if the traffic flow is too high. Therefore, many scholars have started to study Adaptive Traffic Signal Control (ATSC), such as developing new adaptive signal optimization control systems that set different signals according to different geographical areas [3]; using pressurized routing algorithms in the field of communication to effectively reduce traffic congestion and decrease the average travel time of vehicles on the road [4]; introducing greedy ideas and modeling two-dimensional automata matrices for vehicles so that they can explore rerouting paths in congested areas [5]; and setting up new traffic light schedules that take holiday factors into account to model the behavior of traffic lights [6]. However, all these ATSC systems rely on manually designed traffic signal schemes, which have many limitations.…”

Section: Introductionmentioning

confidence: 99%

Optimization Control of Adaptive Traffic Signal with Deep Reinforcement Learning

Cao,

Wang,

Zhang

et al. 2024

Electronics

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The optimization and control of traffic signals is very important for logistics transportation. It not only improves the operational efficiency and safety of road traffic, but also conforms to the direction of the intelligent, green, and sustainable development of modern cities. In order to improve the optimization effect of traffic signal control, this paper proposes a traffic signal optimization method based on deep reinforcement learning and Simulation of Urban Mobility (SUMO) software for urban traffic scenarios. The intersection training scenario was established using SUMO micro traffic simulation software, and the maximum vehicle queue length and vehicle queue time were selected as performance evaluation indicators. In order to be more relevant to the real environment, the experiment uses Weibull distribution to simulate vehicle generation. Since deep reinforcement learning takes into account both perceptual and decision-making capabilities, this study proposes a traffic signal optimization control model based on the deep reinforcement learning Deep Q Network (DQN) algorithm by considering the realism and complexity of traffic intersections, and first uses the DQN algorithm to train the model in a training scenario. After that, the G-DQN (Grouping-DQN) algorithm is proposed to address the problems that the definition of states in existing studies cannot accurately represent the traffic states and the slow convergence of neural networks. Finally, the performance of the G-DQN algorithm model was compared with the original DQN algorithm model and Advantage Actor-Critic (A2C) algorithm model. The experimental results show that the improved algorithm increased the main indicators in all aspects.

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“…Researchers have proposed several solutions, such as fuzzy reflector-based localization and color reflector-based self-localization. The main drawbacks of these methods are that the amount of computation power they need is too large and the antiinterference ability is poor (Suwoyo, Hidayat, et al, 2022;Tai & Yeung, 2022).…”

Section: Introductionmentioning

confidence: 99%

A method of tracking and positioning of industrial robots

Tengfeng

2023

IJEC

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In this paper, an improved tracking and localization algorithm of an omni-directional mobile industrial robot is proposed to meet the high positional accuracy requirement, improve the robot’s repeatability positioning precision in the traditional trilateral algorithm, and solve the problem of pose lost in the moving process. Laser sensors are used to identify the reflectors, and by associating the reflectors identified at a particular time with the reflectors at a previous time, an optimal triangular positioning method is applied to realize the positioning and tracking of the robot. The experimental results show that positioning accuracy can be satisfied, and the repeatability and anti-jamming ability of the omni-directional mobile industrial robot will be greatly improved via this algorithm.

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Adaptive strategies for route selection en-route in transportation networks

Cited by 5 publications

References 21 publications

Population Game-Assisted Multi-Agent Reinforcement Learning Method for Dynamic Multi-Vehicle Route Selection

Population Game-Assisted Multi-Agent Reinforcement Learning Method for Dynamic Multi-Vehicle Route Selection

Optimization Control of Adaptive Traffic Signal with Deep Reinforcement Learning

A method of tracking and positioning of industrial robots

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