scholarly journals Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control

2020 ◽  
Vol 34 (04) ◽  
pp. 3414-3421 ◽  
Author(s):  
Chacha Chen ◽  
Hua Wei ◽  
Nan Xu ◽  
Guanjie Zheng ◽  
Ming Yang ◽  
...  
Keyword(s):  
New York ◽  
Reinforcement Learning ◽  
Traffic Congestion ◽  
Large Scale ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Urban Networks ◽  
Traffic Lights ◽  
Signal Coordination

Traffic congestion plagues cities around the world. Recent years have witnessed an unprecedented trend in applying reinforcement learning for traffic signal control. However, the primary challenge is to control and coordinate traffic lights in large-scale urban networks. No one has ever tested RL models on a network of more than a thousand traffic lights. In this paper, we tackle the problem of multi-intersection traffic signal control, especially for large-scale networks, based on RL techniques and transportation theories. This problem is quite difficult because there are challenges such as scalability, signal coordination, data feasibility, etc. To address these challenges, we (1) design our RL agents utilizing ‘pressure’ concept to achieve signal coordination in region-level; (2) show that implicit coordination could be achieved by individual control agents with well-crafted reward design thus reducing the dimensionality; and (3) conduct extensive experiments on multiple scenarios, including a real-world scenario with 2510 traffic lights in Manhattan, New York City 1 2.

scholarly journals Application of Multi-Agent Deep Reinforcement Learning to Optimize Real-World Traffic Signal Controls

2021 ◽  
Author(s):  
Maxim Friesen ◽  
Tian Tan ◽  
Jürgen Jasperneite ◽  
Jie Wang
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Traffic Congestion ◽  
Traffic Signal ◽  
Traffic Model ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Logic ◽  
Rule Based ◽  
Multi Agent

Increasing traffic congestion leads to significant costs associated by additional travel delays, whereby poorly configured signaled intersections are a common bottleneck and root cause. Traditional traffic signal control (TSC) systems employ rule-based or heuristic methods to decide signal timings, while adaptive TSC solutions utilize a traffic-actuated control logic to increase their adaptability to real-time traffic changes. However, such systems are expensive to deploy and are often not flexible enough to adequately adapt to the volatility of today's traffic dynamics. More recently, this problem became a frontier topic in the domain of deep reinforcement learning (DRL) and enabled the development of multi-agent DRL approaches that could operate in environments with several agents present, such as traffic systems with multiple signaled intersections. However, most of these proposed approaches were validated using artificial traffic grids. This paper therefore presents a case study, where real-world traffic data from the town of Lemgo in Germany is used to create a realistic road model within VISSIM. A multi-agent DRL setup, comprising multiple independent deep Q-networks, is applied to the simulated traffic network. Traditional rule-based signal controls, currently employed in the real world at the studied intersections, are integrated in the traffic model with LISA+ and serve as a performance baseline. Our performance evaluation indicates a significant reduction of traffic congestion when using the RL-based signal control policy over the conventional TSC approach in LISA+. Consequently, this paper reinforces the applicability of RL concepts in the domain of TSC engineering by employing a highly realistic traffic model.

scholarly journals Traffic Signal Control Model on Isolated Intersection Using Reinforcement Learning: A Case Study on Algiers City, Algeria

2021 ◽  
Vol 35 (5) ◽  
pp. 417-424
Author(s):  
Fares Bouriachi ◽  
Hicham Zatla ◽  
Bilal Tolbi ◽  
Koceila Becha ◽  
Allaeddine Ghermoul
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Light ◽  
Traffic Lights ◽  
On The Road ◽  
Isolated Intersection

Traffic jams and congestion in our cities are a major problem because of the huge increase in the number of cars on the road. To remedy this problem, several control methods are proposed to prevent or reduce traffic congestion based on traffic lights. There are few works using reinforcement learning technique for traffic light control and recent studies have shown promising results. However, existing works have not yet tested the methods on the real-world traffic data and they only focus on studying the rewards without interpreting the policies. In this paper, we proposed a reinforcement learning algorithm to address the traffic signal control problem in real multi-phases isolated intersection. A case study based on Algiers city is conducted the simulation results from the different scenarios show that our proposed scheme reduces the total travel time of the vehicles compared to those obtained with traffic-adaptive control.

scholarly journals Application of Multi-Agent Deep Reinforcement Learning to Optimize Real-World Traffic Signal Controls

2021 ◽  
Author(s):  
Maxim Friesen ◽  
Tian Tan ◽  
Jürgen Jasperneite ◽  
Jie Wang
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Traffic Congestion ◽  
Traffic Signal ◽  
Traffic Model ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Logic ◽  
Rule Based ◽  
Multi Agent

Increasing traffic congestion leads to significant costs associated by additional travel delays, whereby poorly configured signaled intersections are a common bottleneck and root cause. Traditional traffic signal control (TSC) systems employ rule-based or heuristic methods to decide signal timings, while adaptive TSC solutions utilize a traffic-actuated control logic to increase their adaptability to real-time traffic changes. However, such systems are expensive to deploy and are often not flexible enough to adequately adapt to the volatility of today's traffic dynamics. More recently, this problem became a frontier topic in the domain of deep reinforcement learning (DRL) and enabled the development of multi-agent DRL approaches that could operate in environments with several agents present, such as traffic systems with multiple signaled intersections. However, most of these proposed approaches were validated using artificial traffic grids. This paper therefore presents a case study, where real-world traffic data from the town of Lemgo in Germany is used to create a realistic road model within VISSIM. A multi-agent DRL setup, comprising multiple independent deep Q-networks, is applied to the simulated traffic network. Traditional rule-based signal controls, currently employed in the real world at the studied intersections, are integrated in the traffic model with LISA+ and serve as a performance baseline. Our performance evaluation indicates a significant reduction of traffic congestion when using the RL-based signal control policy over the conventional TSC approach in LISA+. Consequently, this paper reinforces the applicability of RL concepts in the domain of TSC engineering by employing a highly realistic traffic model.

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