Deep Reinforcement Learning-Based Traffic Signal Control Using High-Resolution Event-Based Data

Abstract

Reinforcement learning (RL)-based traffic signal control has been proven to have great potential in alleviating traffic congestion. The state definition, which is a key element in RL-based traffic signal control, plays a vital role. However, the data used for state definition in the literature are either coarse or difficult to measure directly using the prevailing detection systems for signal control. This paper proposes a deep reinforcement learning-based traffic signal control method which uses high-resolution event-based data, aiming to achieve cost-effective and efficient adaptive traffic signal control. High-resolution event-based data, which records the time when each vehicle-detector actuation/de-actuation event occurs, is informative and can be collected directly from vehicle-actuated detectors (e.g., inductive loops) with current technologies. Given the event-based data, deep learning techniques are employed to automatically extract useful features for traffic signal control. The proposed method is benchmarked with two commonly used traffic signal control strategies, i.e., the fixed-time control strategy and the actuated control strategy, and experimental results reveal that the proposed method significantly outperforms the commonly used control strategies.

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The different versions of the original document can be found in:

• http://dx.doi.org/10.3390/e21080744 under the license https://creativecommons.org/licenses/by

• https://www.mdpi.com/1099-4300/21/8/744/pdf under the license http://creativecommons.org/licenses/by/3.0/

• https://dblp.uni-trier.de/db/journals/entropy/entropy21.html#WangXHZC19,

https://www.mdpi.com/1099-4300/21/8/744,

https://www.mdpi.com/1099-4300/21/8/744/pdf,

https://doi.org/10.3390/e21080744,

https://academic.microsoft.com/#/detail/2965341638 under the license cc-by

• https://www.mdpi.com/1099-4300/21/8/744,

https://doaj.org/toc/1099-4300

• https://www.mdpi.com/1099-4300/21/8/744/pdf,

http://dx.doi.org/10.3390/e21080744

under the license https://creativecommons.org/licenses/by/4.0/