Data Augmented Deep Behavioral Cloning for Urban Traffic Control Operations Under a Parallel Learning Framework

It is indispensable for professional traffic signal engineers to perform manual operations of traffic signal control (TSC) to mitigate traffic congestion, especially with complicated scenarios. However, such a task is time-consuming, and the level of congestion mitigation heavily relies on individua...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2022-06, Vol.23 (6), p.5128-5137
Main Authors: Li, Xiaoshuang, Ye, Peijun, Jin, Junchen, Zhu, Fenghua, Wang, Fei-Yue
Format: Article
Language:English
Subjects:
Cloning
Complex systems
Data models
Decision making
deep behavioral cloning
Engineers
Gallium nitride
Generative adversarial networks
Intelligent traffic signal operations
Knowledge engineering
Machine learning
parallel learning
Problem solving
Skills
Task analysis
Traffic congestion
Traffic control
Traffic engineering
Traffic flow
Traffic models
Traffic signals
Urban areas
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Summary:It is indispensable for professional traffic signal engineers to perform manual operations of traffic signal control (TSC) to mitigate traffic congestion, especially with complicated scenarios. However, such a task is time-consuming, and the level of congestion mitigation heavily relies on individual expertise in engineering practice. Therefore, it is cost-effective to learn traffic engineers' knowledge to enhance the problem-solving skills for a large-scale urban traffic network. In this paper, a data augmented deep behavioral cloning (DADBC) method is proposed to imitate the problem-solving skills of traffic engineers. The method is under a conceptual framework, parallel learning (PL) framework, that incorporates machine learning techniques for solving decision-making problems in complex systems. The DADBC method enhances a hybrid demonstration by exploiting a generative adversarial network (GAN) and then uses the deep behavioral cloning (DBC) model to learn traffic engineers' control schemes. According to the validation results using the real manipulation data from Hangzhou, China, our method can imitate complex human behaviors in intervening traffic signal control operations to improve traffic efficiency in urban areas.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2020.3048151