Traffic Flow Data Forecasting Based on Interval Type-2 Fuzzy Sets Theory

作     者：Runmei Li Chaoyang Jiang Fenghua Zhu Xiaolong Chen 

作者机构：Beijing Jiaotong University the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2016年第3卷第2期

页      面：141-148页

核心收录：

学科分类：08[工学] 082303[工学-交通运输规划与管理] 082302[工学-交通信息工程及控制] 0823[工学-交通运输工程] 

基　　金：supported by the Fundamental Research Funds for the Central Universities(2014JBM007) 

主　　题：Data handling Forecasting Fuzzy sets Membership functions Uncertainty analysis 

摘      要：This paper proposes a long-term forecasting scheme and implementation method based on the interval type-2 fuzzy sets theory for traffic flow data. The type-2 fuzzy sets have advantages in modeling uncertainties because their membership functions are fuzzy. The scheme includes traffic flow data preprocessing module, type-2 fuzzification operation module and long-term traffic flow data forecasting output module, in which the Interval Approach acts as the core algorithm. The central limit theorem is adopted to convert point data of mass traffic flow in some time range into interval data of the same time range (also called confidence interval data) which is being used as the input of interval approach. The confidence interval data retain the uncertainty and randomness of traffic flow, meanwhile reduce the influence of noise from the detection data. The proposed scheme gets not only the traffic flow forecasting result but also can show the possible range of traffic flow variation with high precision using upper and lower limit forecasting result. The effectiveness of the proposed scheme is verified using the actual sample application. © 2014 Chinese Association of Automation.