  Traffic light control is important for reducing congestion in urban mobility
systems. This paper proposes a real-time traffic light control method using
deep Q learning. Our approach incorporates a reward function considering queue
lengths, delays, travel time, and throughput. The model dynamically decides
phase changes based on current traffic conditions. The training of the deep Q
network involves an offline stage from pre-generated data with fixed schedules
and an online stage using real-time traffic data. A deep Q network structure
with a "phase gate" component is used to simplify the model's learning task
under different phases. A "memory palace" mechanism is used to address sample
imbalance during the training process. We validate our approach using both
synthetic and real-world traffic flow data on a road intersecting in Hangzhou,
China. Results demonstrate significant performance improvements of the proposed
method in reducing vehicle waiting time (57.1% to 100%), queue lengths (40.9%
to 100%), and total travel time (16.8% to 68.0%) compared to traditional fixed
signal plans.
