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 de

SUBMITTER: Tang J 

PROVIDER: S-EPMC4735499 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature