An artificial immune network to control interrupted flow at a signalized intersection

Abstract

To monitor and control interrupted flow at signalized intersections, several Traffic Signal Control Systems (TSCSs) were developed based on optimization and artificial intelligence techniques. Although learning can provide intelligent ways to deal with disturbances, existing approaches still lack concepts and mechanisms that enable direct representation of knowledge and explicit learning, particularly to capture and reuse previous experiences with disturbances. This article addresses this gap by designing a new TSCS based on innovative concepts and mechanisms borrowed from biological immunity. Immune memory enables the design of a Case-Based Reasoning (CBR) System in which cases provide a direct representation of knowledge about disturbances. Immune network theory enables the design of a Reinforcement Learning (RL) mechanism to interconnect cases, capture explicit knowledge about the outcomes (success and failure) of control decisions and enable decision-making by taking advantage of previous outcomes in reaction to new occurrences of disturbances. We provide a detailed description of new learning algorithms, both to create the case-base and to interconnect cases using RL. The performance of the suggested TSCS is assessed by benchmarking it against two standard control strategies from the literature, namely fixed-time and adaptive control using the Longest Queue First – Maximal Weight Matching (LQF-MWM) algorithm. The suggested TSCS is applied on an intersection simulated using VISSIM, a state-of-the-art traffic simulation software. The results show that the suggested TSCS is able to handle different traffic scenarios with competitive performance, and that it is recommended for extreme situations involving blocked approaches and high traffic flow.