A Survey on Traffic Signal Control Methods

By Hua Wei, Guanjie Zheng, Vikash V. Gayah, Zhenhui Jessie Li. arXiv.org, 2019

297 citations recorded by the source corpus.

Traffic signal control is an important and challenging real-world problem, which aims to minimize the travel time of vehicles by coordinating their movements at the road intersections. Current traffic signal control systems in use still rely heavily on oversimplified information and rule-based methods, although we now have richer data, more computing power and advanced methods to drive the development of intelligent transportation. With the growing interest in intelligent transportation using machine learning methods like reinforcement learning, this survey covers the widely acknowledged transportation approaches and a comprehensive list of recent literature on reinforcement for traffic signal control. We hope this survey can foster interdisciplinary research on this important topic.

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

View Paper: https://www.semanticscholar.org/paper/7bf26f0d5c1514df20a04e1f7b34102d4311e968

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Chacha Chen
2. Kai Xu
3. Hua Wei
4. Guanjie Zheng

In Building and Construction

1. Chacha Chen
2. Kai Xu
3. Nan Xu
4. Hua Wei
5. Guanjie Zheng
6. Yuanhao Xiong
7. Ming Yang
8. Zhenhui

In Control and Systems Engineering

1. Hua Wei
2. Guanjie Zheng
3. Chacha Chen
4. Kai Xu
5. Nan Xu
6. Yuanhao Xiong
7. Ming Yang
8. Zhenhui

Citations in the corpus

• Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control (2020, methodology): “• PressLight (Wei et al. 2019a): a recently developed learning-based method that incorporates pressure in the state and reward design fo the RL model.”