IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control

By Hua Wei, Guanjie Zheng, Huaxiu Yao, Zhenhui Jessie Li. 2018

771 citations recorded by the source corpus.

The intelligent traffic light control is critical for an efficient transportation system. While existing traffic lights are mostly operated by hand-crafted rules, an intelligent traffic light control system should be dynamically adjusted to real-time traffic. There is an emerging trend of using deep reinforcement learning technique for traffic light control and recent studies have shown promising results. However, existing studies 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 propose a more effective deep reinforcement learning model for traffic light control. We test our method on a large-scale real traffic dataset obtained from surveillance cameras. We also show some interesting case studies of policies learned from the real data.

Subfields: Automotive Engineering, Building and Construction, Control and Systems Engineering

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Citations in the corpus

A survey of data fusion in smart city applications (2019, methodology): “data sources, data fusion techniques, and decision making (optimization and control) algorithms [109], [200].”
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CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario (2019, background): “Traffic signal control problem, one of the biggest urban problems, is drawing increasing attention in recent years [5, 9, 10].”
Integrating Independent and Centralized Multi-agent Reinforcement Learning for Traffic Signal Network Optimization (2019, methodology): “While early works proposed to optimize traffic light controllers based on expert knowledge and traditional model-based planning [4, 9, 18], there are promising recent results on applying flexible model-free methods in reinforcement learning (RL) [21] and deep RL, such as DQN in particular [16], to f”
CoLight: Learning Network-level Cooperation for Traffic Signal Control (2019, methodology): “Following existing studies [28, 30], we use the average travel time to evaluate the performance of different models for traffic signal control.”
Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control (2020, methodology): “Following the previous work on trafﬁc signal control study (Wei et al. 2018), we conduct experiments on Cityﬂow (Zhang et al. 2019) 6 .”
Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, methodology): “Finally, they compared the framework with baselines (fixed time [158], Max Pressure [159], CGRL [160], Individual RL [161], OneModel [162], Neighbor RL [163], GCN [155], and Co-Light [157]) on two real datasets, and concluded that their model outperforms the state-of-the-art.”
Flow: A Modular Learning Framework for Mixed Autonomy Traffic (2017, background): “Wei, et al. [90], [89] employs RL and graph attention networks for control of trafﬁc signals.”
Transfer Learning in Deep Reinforcement Learning: A Survey (2020, methodology): “It also thrives in domains such as health informatics [6], electricity networks [7], intelligent transportation systems [8], [9], to name just a few.”