# Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment

arXiv: https://arxiv.org/abs/2408.00098 · HTML: https://mlsanity.com/paper/2408.00098/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 1 of its 24 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 1 of 1)

- [Deep Deterministic Policy Gradient for Urban Traffic Light Control](https://mlsanity.com/paper/1703.09035/) (2017)

## Built on by (most cited first, 2 of 2)

- [Integrating Transit Signal Priority into Multi-Agent Reinforcement Learning based Traffic Signal Control](https://mlsanity.com/paper/2411.19359/) (2024)
- [Explainable Reinforcement Learning for Adaptive Traffic Signal Control](https://mlsanity.com/paper/2607.03703/) (2026)

## Abstract (arXiv)

Model free reinforcement learning (RL) provides a potential alternative to earlier formulations of adaptive transit signal priority (TSP) algorithms based on mathematical programming that require complex and nonlinear objective functions. This study extends RL - based traffic control to include TSP. Using a microscopic simulation environment and connected vehicle data, the study develops and tests a TSP event-based RL agent that assumes control from another developed RL - based general traffic signal controller. The TSP agent assumes control when transit buses enter the dedicated short-range communication (DSRC) zone of the intersection. This agent is shown to reduce the bus travel time by about 21%, with marginal impacts to general traffic at a saturation rate of 0.95. The TSP agent also shows slightly better bus travel time compared to actuated signal control with TSP. The architecture of the agent and simulation is selected considering the need to improve simulation run time efficiency.