Optimizing Signalized Intersections Performance under Conventional and Automated Vehicles Traffic

TL;DR

This paper introduces an intelligent intersection control system that optimizes signal timing and AV trajectories in real-time, significantly reducing travel times and preventing queues in undersaturated traffic conditions.

Contribution

It develops a novel algorithm for simultaneous optimization of signal control and AV trajectories, enhancing intersection efficiency under mixed traffic.

Findings

38-52% reduction in average travel time compared to conventional control

Successful prevention of queue formation in simulations

Real-time data collection enables adaptive traffic management

Abstract

Automated vehicles, or AVs (i.e. those that have the ability to operate without a driver and can communicate with the infrastructure) may transform the transportation system. This study develops and simulates an algorithm that can optimize signal control simultaneously with the AV trajectories under undersaturated traffic flow of AV and conventional vehicles. This proposed Intelligent Intersection Control System (IICS) operates based on real-time collected arrival data at detection ranges around the center of intersection. Parallel to detecting arrivals, the optimized trajectories and signal control parameters will be transmitted to AVs and the signal controller to be implemented. Simulation experiments using the proposed IICS algorithm successfully prevented queue formation up to undersaturated condition. Comparison of the algorithm to operations with conventional actuated control shows 38-52% reduction in average travel time compared to conventional signal control.