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