  Traffic signal control is safety-critical for our daily life. Roughly
one-quarter of road accidents in the U.S. happen at intersections due to
problematic signal timing, urging the development of safety-oriented
intersection control. However, existing studies on adaptive traffic signal
control using reinforcement learning technologies have focused mainly on
minimizing traffic delay but neglecting the potential exposure to unsafe
conditions. We, for the first time, incorporate road safety standards as
enforcement to ensure the safety of existing reinforcement learning methods,
aiming toward operating intersections with zero collisions. We have proposed a
safety-enhanced residual reinforcement learning method (SafeLight) and employed
multiple optimization techniques, such as multi-objective loss function and
reward shaping for better knowledge integration. Extensive experiments are
conducted using both synthetic and real-world benchmark datasets. Results show
that our method can significantly reduce collisions while increasing traffic
mobility.
