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Adaptive traffic signal control, which adjusts traffic signal timingaccording to real-time traffic, has been shown to be an effective method toreduce traffic congestion. Available works on adaptive traffic signal controlmake responsive traffic signal control decisions based on human-craftedfeatures (e.g. vehicle queue length). However, human-crafted features areabstractions of raw traffic data (e.g., position and speed of vehicles), whichignore some useful traffic information and lead to suboptimal traffic signalcontrols. In this paper, we propose a deep reinforcement learning algorithmthat automatically extracts all useful features (machine-crafted features) fromraw real-time traffic data and learns the optimal policy for adaptive trafficsignal control. To improve algorithm stability, we adopt experience replay andtarget network mechanisms. Simulation results show that our algorithm reducesvehicle delay by up to 47% and 86% when compared to another two popular trafficsignal control algorithms, longest queue first algorithm and fixed time controlalgorithm, respectively.

Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network