Traffic Signal Control Using Genetic Decomposed Fuzzy Systems
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Traffic Signal Control Using Genetic Decomposed Fuzzy Systems Runmei Li1 • Shujing Xu1
Received: 15 January 2020 / Revised: 6 March 2020 / Accepted: 8 March 2020 Ó Taiwan Fuzzy Systems Association 2020
Abstract In this paper, Decomposed Fuzzy Systems (DFS) structure is applied to design single intersection signal fuzzy controller. The DFS structure is to decompose each fuzzy variable into layers of fuzzy systems and each layer is to characterize one traditional fuzzy set. DFS adjusts the fuzzy membership function, the leading part, and enriches the fuzzy rule base through structural changes, thus provides the system with more adjustable parameters to facilitate possible adaptation in fuzzy rules, but without introducing a learning burden. It also can be found that the function approximation capability of the DFS is much better than that of the traditional fuzzy systems. At the same time, in order to solve possible defects brought by expert experience, Genetic Algorithm (GA) is applied to the optimization of DFS rule base in this paper. Taking the four-phase single intersection as a case study, an intersection signal control algorithm is obtained using the proposed DFS based on Genetic Algorithm (G-DFS). Simulation results show that the G-DFS controller reduces average vehicle delay, queuing length, average parking rate, and average vehicle travel time effectively, and the controller can smoothly adapt to different traffic flow changes. Keywords Intersection signal Fuzzy control Decomposed Fuzzy Systems Genetic Algorithm
& Runmei Li [email protected] Shujing Xu [email protected] 1
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
1 Introduction With the rapid development of social economy, the number of vehicles has increased sharply, and traffic congestion on urban roads has become more and more serious, causing inconvenience for people to travel. The optimal adjustment of traffic signal is of great significance to alleviate traffic congestion and improve transportation efficiency. With the continuous development of computer technology and artificial intelligence methods, the application of intelligent control algorithms in signal timing optimization has become more and more widespread. Scholars have used intelligent algorithms to obtain better signal timing schemes [1–3]. Vehicles arrival distribution at an intersection goes with uncertainty and randomness, which should be considered in intersection signal timing optimization to obtain higher passage efficiency. Therefore, intersection signal timing optimization based on fuzzy systems has become one of hotspots for researchers in the field of traffic control and management. In 1965, Zadeh presented the definition of fuzzy sets (Type-1 fuzzy sets) [4]. In 1977, Pappis put forward the application of fuzzy systems to traffic control firstly [5]. By establishing rule base or expert system, they can control various traffic conditions with good results. After that, researchers have proposed a large number of fuzzy
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