A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed

Lelitha Vanajakshi, Laurence R. Rilett

Research output: Contribution to conferencePaper

89 Scopus citations

Abstract

The ability to predict traffic variables such as speed, travel time or flow, based on real time data and historic data, collected by various systems In transportation networks, Is vital to the Intelligent transportation systems (ITS) components such as in-vehicle route guidance systems (RGS), advanced traveler information systems (ATIS), and advanced traffic management systems (ATMS). In the context of prediction methodologies, different time series, and artificial neural networks (ANN) models have been developed in addition to the historic and real time approach. The present paper proposes the application of a recently developed pattern classification and regression technique called support vector machines (SVM) for the short-term prediction of traffic speed. An ANN model is also developed and a comparison of the performance of both these techniques is carried out, along with real time and historic approach results. Data from the freeways of San Antonio, Texas were used for the analysis.

Original languageEnglish (US)
Pages194-199
Number of pages6
StatePublished - 2004
Event2004 IEEE Intelligent Vehicles Symposium - Parma, Italy
Duration: Jun 14 2004Jun 17 2004

Other

Other2004 IEEE Intelligent Vehicles Symposium
CountryItaly
CityParma
Period6/14/046/17/04

ASJC Scopus subject areas

  • Modeling and Simulation
  • Automotive Engineering
  • Computer Science Applications

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    Vanajakshi, L., & Rilett, L. R. (2004). A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed. 194-199. Paper presented at 2004 IEEE Intelligent Vehicles Symposium, Parma, Italy.