A Hybrid Soft Computing Approach to Link Travel Speed Estimation

  • Seung-Heon Lee
  • M. Viswanathan
  • Young-Kyu Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


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.


Travel Time Traffic Information Transportation Research Part Link Travel Time Dynamic Traffic Assignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Seung-Heon Lee
    • 1
  • M. Viswanathan
    • 1
  • Young-Kyu Yang
    • 1
  1. 1.College of SoftwareKyungwon UniversitySouth Korea

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