A Hybrid Soft Computing Approach to Link Travel Speed Estimation

Abstract

The field of dynamic vehicle routing and scheduling is growing at a strong pace nowadays due to the many potential applications in urban traffic management. In recent times there have been many attempts to estimate the vehicle travel times over congested links. As opposed to the previous decade where traffic information was collected mainly by fixed devices with high maintenance costs, the advent of GPS has resulted in data being progressively collected using probe cars equipped with GPS-based communication modules. Typically traditional methods used for analyzing the data collected using fixed devices need to be extended. The aim of this research is to propose a hybrid method for estimating the optimal link speed using the data acquired from probe cars using combination of the fuzzy c-means (FCM) algorithm with multiple regression analysis. The paper describes how the probe data are analyzed and automatically classified into three groups of speed patterns and the link speed is predicted from these clusters using multiple regression. In performance tests, the proposed method is robust and is able to provide accurate travel time estimates.

This research was supported by the MIC (Ministry of Information and Communication), Korea, under the ITRC (Information Technology Research Center) support program super-vised by the IITA (Institute of Information Technology Assessment), and the BK21 project.