A Case-Based Reasoning System to Control Traffic at Signalized Intersections

Traffic control systems (TCS) contribute to relieve congestion in cities. Although many optimization and intelligent approaches exist to develop TCS, only a few works have investigated Case Based Reasoning (CBR) to control traffic at signalized intersections. Existing works usually state that the case-base is created using experts' knowledge but do not specify how this knowledge is acquired and how the case-base is built. In this article, we design a CBR system to control traffic at a single signalized intersection. We develop a hybrid methodology to create the case-base using simulation-optimisation, Condensed Nearest Neighbour algorithm (CNN) and a rule-based system. The algorithm is implemented in Python and applied on an intersection simulated using VISSIM, a state-of-the-art traffic simulation software. The performance of the system is assessed and compared to the Longest Queue First with Maximal Weight Matching (LQF-MWM) algorithm. Results show that the implemented system is able to handle different traffic scenarios with competitive performance.