Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks

TL;DR

This paper introduces a deep reinforcement learning approach for adaptive traffic light control using real-time data, significantly improving traffic flow efficiency in vehicular networks through a complex state-action model and advanced neural network techniques.

Contribution

The paper presents a novel deep reinforcement learning model that dynamically adjusts traffic signals based on sensor data, incorporating advanced neural network components for improved performance.

Findings

Model outperforms traditional methods in simulations

Reduces average waiting time at intersections

Demonstrates effective real-time traffic management

Abstract

Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. In terms of how to dynamically adjust traffic signals' duration, existing works either split the traffic signal into equal duration or extract limited traffic information from the real data. In this paper, we study how to decide the traffic signals' duration based on the collected data from different sensors and vehicular networks. We propose a deep reinforcement learning model to control the traffic light. In the model, we quantify the complex traffic scenario as states by collecting data and dividing the whole intersection into small grids. The timing changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map the states to rewards. The proposed model is composed of several components to improve the performance, such as dueling network, target network, double Q-learning network, and prioritized experience replay. We evaluate our model via simulation in the Simulation of Urban MObility (SUMO) in a vehicular network, and the simulation results show the efficiency of our model in controlling traffic lights.