A Deep Reinforcement Learning Network for Traffic Light Cycle Control
New Jersey Institute of Technology · University of Houston · +1 more institution
Abstract
Existing inefficient traffic light cycle control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. Existing works either split the traffic signal into equal duration or only leverage limited traffic information. In this paper, we study how to decide the traffic signal duration based on the collected data from different sensors. We propose a deep reinforcement learning model to control the traffic light cycle. In the model, we quantify the complex traffic scenario as states by collecting traffic data and dividing the whole intersection into small…
Citation impact
- FWCI
- 37.26
- Percentile
- 100%
- References
- 31
Authors
4- XLXiaoyuan LiangCorresponding
New Jersey Institute of Technology
- XDXunsheng Du
University of Houston
- GWGuiling Wang
New Jersey Institute of Technology
- ZHZhu Han
Kyung Hee University
Topics & keywords
- Reinforcement learning
- Leverage (statistics)
- Duration (music)
- Traffic generation model
- Intersection (aeronautics)
- Network traffic simulation
- Traffic signal
- Traffic simulation