  This paper develops a reinforcement learning (RL) scheme for adaptive traffic
signal control (ATSC), called "CVLight", that leverages data collected only
from connected vehicles (CV). Seven types of RL models are proposed within this
scheme that contain various state and reward representations, including
incorporation of CV delay and green light duration into state and the usage of
CV delay as reward. To further incorporate information of both CV and non-CV
into CVLight, an algorithm based on actor-critic, A2C-Full, is proposed where
both CV and non-CV information is used to train the critic network, while only
CV information is used to update the policy network and execute optimal signal
timing. These models are compared at an isolated intersection under various CV
market penetration rates. A full model with the best performance (i.e., minimum
average travel delay per vehicle) is then selected and applied to compare with
state-of-the-art benchmarks under different levels of traffic demands, turning
proportions, and dynamic traffic demands, respectively. Two case studies are
performed on an isolated intersection and a corridor with three consecutive
intersections located in Manhattan, New York, to further demonstrate the
effectiveness of the proposed algorithm under real-world scenarios. Compared to
other baseline models that use all vehicle information, the trained CVLight
agent can efficiently control multiple intersections solely based on CV data
and can achieve a similar or even greater performance when the CV penetration
rate is no less than 20%.
