Policy Analysis of Adaptive Traffic Signal Control Using Reinforcement Learning-Reference-Cited by-同舟云学术

Policy Analysis of Adaptive Traffic Signal Control Using Reinforcement Learning

Published:2020-01 Issue:1 Volume:34 Page:04019046
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Genders Wade¹^ORCID,Razavi Saiedeh²

Affiliation:

1. Ph.D. Student, Dept. of Civil Engineering, McMaster Univ., 1280 Main St. West, Hamilton, ON, Canada L8S 4L8 (corresponding author). ORCID: .

2. Associate Professor, Dept. of Civil Engineering, McMaster Univ., 1280 Main St. West, Hamilton, ON, Canada L8S 4L8.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000859

Reference40 articles.

1. Abadi M. et al. 2016. “Tensorflow: Large-scale machine learning on heterogeneous distributed systems.” Preprint submitted March 14 2016. http://arxiv.org/abs/1603.04467.

2. Holonic multi-agent system for traffic signals control

3. Reinforcement Learning for True Adaptive Traffic Signal Control

4. Adaptive traffic signal control with actor-critic methods in a real-world traffic network with different traffic disruption events

5. Optimizing traffic lights in a cellular automaton model for city traffic

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