A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies

Freeway networks, despite being built to handle the transportation needs of large traffic volumes, have suffered in recent years from an increase in demand that is rarely resolvable through infrastructure improvements. Therefore, the implementation of particular control methods constitutes, in many instances, the only viable solution for enhancing the performance of freeway traffic systems. The topic is fraught with ambiguity, and there is no tool for understanding the entire system mathematically; hence, a fuzzy suggested algorithm seems not just appropriate but essential. In this study, a fuzzy cognitive map-based model and a fuzzy rule-based system are proposed as tools to analyze freeway traffic data with the objective of traffic flow modeling at a macroscopic level in order to address congestion-related issues as the primary goal of the traffic control strategies. In addition to presenting a framework of fuzzy system-based controllers in freeway traffic, the results of this study demonstrated that a fuzzy inference system and fuzzy cognitive maps are capable of congestion level prediction, traffic flow simulation, and scenario analysis, thereby enhancing the performance of the traffic control strategies involving the implementation of ramp management policies, controlling vehicle movement within the freeway by mainstream control, and routing control.

Automated summary

Due to the increasing demand on freeway networks, infrastructure improvements alone cannot effectively resolve the issue of congestion. Therefore, the implementation of specific control methods is often the only viable solution. In this study, a fuzzy cognitive map-based model and a fuzzy rule-based system were proposed to analyze traffic data and model traffic flow at a macroscopic level, with the goal of addressing congestion-related issues. The results demonstrated that fuzzy inference systems and fuzzy cognitive maps can predict congestion levels, simulate traffic flow, and conduct scenario analysis, thereby improving the performance of traffic control strategies.