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).

Highlights

  • Travel time is defined as the time to traverse a route between a specified origin and destination

  • We propose a new travel time estimation model based on an evolving fuzzy neural network using traffic flow data collected from existing loop detectors

  • We designed a new travel time estimator based on an evolving fuzzy neural network by using traffic flow data collected from existing loop detectors

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Summary

Introduction

Travel time is defined as the time to traverse a route between a specified origin and destination. It is an important performance measure for road users and transportation managers alike as it is identified and understood by both groups. Travel Time Estimation in Freeway traffic management systems (ATMS), travel time reflects the traffic conditions of road network, and affects the drivers’ route planning. Delays in travel time lead to increases in trip costs, vehicle emissions, and energy consumption. It is beneficial and challenging for using the travel time as an effective index to take measures on traffic congestion

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