Economic-Driven Adaptive Traffic Signal Control

TL;DR

This paper introduces a novel economic-inspired adaptive traffic signal control model using reinforcement learning, which optimizes signal timing and interest rates to reduce congestion at intersections.

Contribution

It proposes a new economic-driven model with a hyper control variable and employs a double dueling deep Q network to optimize traffic signals based on traffic patterns.

Findings

The model effectively reduces traffic congestion in simulation.

It outperforms traditional traffic signal control methods.

The approach maintains smoother traffic flow at intersections.

Abstract

With the emerging connected-vehicle technologies and smart roads, the need for intelligent adaptive traffic signal controls is more than ever before. This paper proposes a novel Economic-driven Adaptive Traffic Signal Control (eATSC) model with a hyper control variable - interest rate defined in economics for traffic signal control at signalized intersections. The eATSC uses a continuous compounding function that captures both the total number of vehicles and the accumulated waiting time of each vehicle to compute penalties for different directions. The computed penalties grow with waiting time and is used for signal control decisions. Each intersection is assigned two intelligent agents adjusting interest rate and signal length for different directions according to the traffic patterns, respectively. The problem is formulated as a Markov Decision Process (MDP) problem to reduce congestions, and a two-agent Double Dueling Deep Q Network (DDDQN) is utilized to solve the problem. Under the optimal policy, the agents can select the optimal interest rates and signal time to minimize the likelihood of traffic congestion. To evaluate the superiority of our method, a VISSIM simulation model with classic four-leg signalized intersections is developed. The results indicate that the proposed model is adequately able to maintain healthy traffic flow at the intersection.