Fuzzy logic based multi-objective optimization of a multi-agent transit control system

被引:5
作者
Morri, Nabil [1 ,3 ]
Hadouaj, Sameh [2 ,3 ]
Ben Said, Lamjed [1 ,2 ]
机构
[1] Liwa Coll Technol, BIT Dept, Abu Dhabi 41009, U Arab Emirates
[2] Higher Coll Technol, CIS Dept, Ibn Battuta Gate Bldg Sheikh Zayed Rd, Abu Dhabi 53735, U Arab Emirates
[3] Univ Tunis, SMART Lab, Tunis 2000, Tunisia
关键词
Multi-objective optimization; Fuzzy logic; Control transit system; Multi-agent system; Simulation; Transit key performance indicator; MODEL;
D O I
10.1007/s12293-022-00384-7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper models a transit control system for the management of traffic perturbations of public transport. The transit system data is voluminous and highly dynamic. Moreover, the transit domain has a remarkable lack of intelligent systems to monitor and maintain better performance. Consequently, realizing an intelligent transit control system has become a consistent need. The modeling of the system addresses a problem of optimizing performance measures based on key performance indicators. Its objective is to find the optimal control action in disturbance cases. The solution consists in combining all performance measures in a single measure by using fuzzification without neglecting the space and time requirements of the traffic. To model and implement our system we used a multi-agent approach. The experiments performed were based on real network traffic data. The obtained results demonstrate the relevance of the proposed fuzzy approach in our optimization problem and show the advantage of the multi-agent system in the modeling of our control system. We prove that the proposed control system achieves better results than certain existing fuzzy approaches and is able to manage disturbances with a better performance than the existing solutions.
引用
收藏
页码:71 / 87
页数:17
相关论文
共 39 条
[1]  
Aho A., 1992, Foundations of Computer Science"
[2]  
Banihashemi M, 2000, TRANSPORT RES REC, P23
[3]  
Ceder A, 2015, PUBLIC TRANSIT PLANNING AND OPERATION: MODELING, PRACTICE AND BEHAVIOR, 2ND EDITION, P1
[4]   Multi-choice goal programming [J].
Chang, Ching-Ter .
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, 2007, 35 (04) :389-396
[5]   EFFECTIVE CONTROL THROUGH COHERENT DECENTRALIZATION WITH PREEMPTIVE GOALS [J].
CHARNES, A ;
CLOWER, RW ;
KORTANEK, KO .
ECONOMETRICA, 1967, 35 (02) :294-&
[6]   Asynchronous Fault Detection Observer for 2-D Markov Jump Systems [J].
Cheng, Peng ;
Wang, Hai ;
Stojanovic, Vladimir ;
He, Shuping ;
Shi, Kaibo ;
Luan, Xiaoli ;
Liu, Fei ;
Sun, Changyin .
IEEE TRANSACTIONS ON CYBERNETICS, 2022, 52 (12) :13623-13634
[7]  
Chivers I, 2015, Intro program fortran, P453, DOI [DOI 10.1007/978-3-319-17701-4_23, 10.1007/978-3-319-17701-4, DOI 10.1007/978-3-319-17701-4]
[8]  
Cohon Jared L., 2004, Multiobjective programming and planning, V140
[9]  
Commission E, 2011, ROADMAP SINGLE EUROP
[10]  
dArcier BF, 2012, THESIS LET