title: Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.
abstract: Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).
authors.affiliation: School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.|Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, United States of America.
authors.forename: Jinjun
authors.initials: J
authors.lastname: Tang
authors.affiliation: Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China.
authors.forename: Yajie
authors.initials: Y
authors.lastname: Zou
authors.affiliation: Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, United States of America.
authors.forename: John
authors.initials: J
authors.lastname: Ash
authors.affiliation: School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
authors.forename: Shen
authors.initials: S
authors.lastname: Zhang
authors.affiliation: School of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
authors.forename: Fang
authors.initials: F
authors.lastname: Liu
authors.affiliation: Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, United States of America.
authors.forename: Yinhai
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D017143:Fuzzy Logic; D016571:Neural Networks, Computer; D012044:Regression Analysis; D013997:Time Factors; D014186:Transportation
references.citation: PLoS One. 2015;10(3):e0119044
references.pmid: 25780910
references.citation: IEEE Trans Syst Man Cybern B Cybern. 2001;31(6):902-18
references.pmid: 18244856