Intelligent Fuzzy Traffic Signal Control System for Complex Intersections Using Fuzzy Rule Base Reduction

Symmetry Pub Date : 2024-09-09 DOI:10.3390/sym16091177
Tamrat D. Chala, László T. Kóczy
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Abstract

In this study, the concept of symmetry is employed to implement an intelligent fuzzy traffic signal control system for complex intersections. This approach suggests that the implementation of reduced fuzzy rules through the reduction method, without compromising the performance of the original fuzzy rule base, constitutes a symmetrical approach. In recent decades, urban and city traffic congestion has become a significant issue because of the time lost as a result of heavy traffic, which negatively affects economic productivity and efficiency and leads to energy loss, and also because of the heavy environmental pollution effect. In addition, traffic congestion prevents an immediate response by the ambulance, police, and fire brigades to urgent events. To mitigate these problems, a three-stage intelligent and flexible fuzzy traffic control system for complex intersections, using a novel hybrid reduction approach was proposed. The three-stage fuzzy traffic control system performs four primary functions. The first stage prioritizes emergency car(s) and identifies the degree of urgency of the traffic conditions in the red-light phase. The second stage guarantees a fair distribution of green-light durations even for periods of extremely unbalanced traffic with long vehicle queues in certain directions and, especially, when heavy traffic is loaded for an extended period in one direction and the short vehicle queues in the conflicting directions require passing in a reasonable time. The third stage adjusts the green-light time to the traffic conditions, to the appearance of one or more emergency car(s), and to the overall waiting times of the other vehicles by using a fuzzy inference engine. The original complete fuzzy rule base set up by listing all possible input combinations was reduced using a novel hybrid reduction algorithm for fuzzy rule bases, which resulted in a significant reduction of the original base, namely, by 72.1%. The proposed novel approach, including the model and the hybrid reduction algorithm, were implemented and simulated using Python 3.9 and SUMO (version 1.14.1). Subsequently, the obtained fuzzy rule system was compared in terms of running time and efficiency with a traffic control system using the original fuzzy rules. The results showed that the reduced fuzzy rule base had better results in terms of the average waiting time, calculated fuel consumption, and CO2 emission. Furthermore, the fuzzy traffic control system with reduced fuzzy rules performed better as it required less execution time and thus lower computational costs. Summarizing the above results, it may be stated that this new approach to intersection traffic light control is a practical solution for managing complex traffic conditions at lower computational costs.
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利用模糊规则库还原复杂交叉口的智能模糊交通信号控制系统
本研究采用对称概念来实现复杂交叉口的智能模糊交通信号控制系统。这种方法表明,在不影响原始模糊规则库性能的前提下,通过还原法实施还原的模糊规则,构成了一种对称方法。近几十年来,城市交通拥堵已成为一个重要问题,因为交通拥堵会造成时间损失,对经济生产率和效率产生负面影响,并导致能源损失,还因为交通拥堵会造成严重的环境污染。此外,交通拥堵还妨碍了救护车、警察和消防队对紧急事件做出及时反应。为了缓解这些问题,提出了一种针对复杂交叉口的三阶段智能灵活模糊交通控制系统,采用了一种新颖的混合还原方法。三阶段模糊交通控制系统主要有四个功能。第一阶段确定紧急车辆的优先次序,并识别红灯阶段交通状况的紧急程度。第二阶段保证绿灯持续时间的公平分配,即使在某些方向车龙较长、交通极不平衡的情况下,尤其是在一个方向长时间车流密集,而冲突方向车龙较短,需要在合理时间内通过的情况下。第三阶段通过使用模糊推理引擎,根据交通状况、一辆或多辆紧急车辆的出现以及其他车辆的总体等待时间来调整绿灯时间。利用一种新颖的模糊规则库混合缩减算法,缩减了通过列出所有可能的输入组合而建立的原始完整模糊规则库,从而显著减少了原始规则库,即减少了 72.1%。使用 Python 3.9 和 SUMO(1.14.1 版)实现并模拟了所提出的新方法,包括模型和混合缩减算法。随后,将获得的模糊规则系统与使用原始模糊规则的交通控制系统在运行时间和效率方面进行了比较。结果表明,缩小后的模糊规则库在平均等待时间、计算油耗和二氧化碳排放量方面都有更好的效果。此外,减少了模糊规则的模糊交通控制系统的性能更好,因为它所需的执行时间更短,因此计算成本更低。综合上述结果,可以说这种新的交叉路口交通灯控制方法是一种以较低计算成本管理复杂交通状况的实用解决方案。
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