A Simple Traffic Signal Control Using Queue Length Information

TL;DR

This paper introduces a real-time queue length-based traffic signal control method that adjusts green times at intersections, demonstrating improvements in delay, stops, and queue length through simulation under various traffic demands.

Contribution

The paper presents a novel queue length estimation and control algorithm for signalized intersections that enhances performance using real-time QL data, tested via microscopic simulation.

Findings

Improves average delay by up to 6% under random arrivals.

Reduces number of stops by up to 9%.

Decreases queue length by up to 11%.

Abstract

Developments in sensor technologies, especially emerging connected and autonomous vehicles, facilitate better queue length (QL) measurements on signalized intersection approaches in real time. Currently there are very limited methods that utilize QL information in real-time to enhance the performance of signalized intersections. In this paper we present methods for QL estimation and a control algorithm that adjusts maximum green times in actuated signals at each cycle based on QLs. The proposed method is implemented at a single intersection with random and platoon arrivals, and evaluated in VISSIM (a microscopic traffic simulation environment) assuming 100 % accurate cycle-by-cycle queue length information is available. To test the robustness of the method, numerical experiments are performed where traffic demand is increased and by 20\% relative to the demand levels for which signal timing parameters are optimized. Compared to the typical fully-actuated signal control, the proposed QL-based method improves average delay, number of stops, and QL for random arrivals, by 6 %, 9 %, and 10 % respectively. In addition, the method improves average delay, number of stops, and QL by 3 %, 3 %, and 11 % respectively for platoon vehicle arrivals.