Real-time Intraday Traffic Volume Forecasting – A Hybrid Application Using Singular Spectrum Analysis and Artificial Neural Networks

Authors

  • Stylianos Kolidakis
    Affiliation

    Section of Transportation Engineering, Department of Civil Engineering, Faculty of Engineering, Democritus University of Thrace, Kimmeria Campus - Building B, 67100 Xanthi, Greece

  • George Botzoris ORCID
    Affiliation

    Section of Transportation Engineering, Department of Civil Engineering, Faculty of Engineering, Democritus University of Thrace, Kimmeria Campus - Building B, 67100 Xanthi, Greece

  • Vassilios Profillidis ORCID
    Affiliation

    Section of Transportation Engineering, Department of Civil Engineering, Faculty of Engineering, Democritus University of Thrace, Kimmeria Campus - Building B, 67100 Xanthi, Greece

  • Alexandros Kokkalis
    Affiliation

    Section of Transportation Engineering, Department of Civil Engineering, Faculty of Engineering, Democritus University of Thrace, Kimmeria Campus - Building B, 67100 Xanthi, Greece

https://doi.org/10.3311/PPtr.14122

Abstract

The present paper provides a comparative evaluation of hybrid Singular Spectrum Analysis (SSA) and Artificial Neural Networks (ANN) against conventional ANN, applied on real time intraday traffic volume forecasting. The main research objective was to assess the applicability and functionality of intraday traffic volume forecasting, based on toll station measurements. The proposed methodology was implemented and evaluated upon a custom developed forecasting software toolbox, based on the software Mathworks MatLab, by using real data from Iasmos-Greece toll station. Experimental results demonstrated a superior ex post forecasting accuracy of the proposed hybrid forecasting methodology against conventional ANN, when compared to performance of usual statistical criteria (Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, Coefficient of Determination R2, Theil's inequality coefficient). The obtained results revealed that the hybrid model could advance forecasting accuracy of a conventional ANN model in intraday traffic volume forecasting, while embedding hybrid forecasting algorithm in an Intelligent Transport System could provide an advanced decision support module for transportation system maintenance, operation and management.

Keywords:

singular spectrum analysis, artificial neural network, traffic analysis, ex post forecast, transportation

Published Online

2019-09-11

How to Cite

Kolidakis, S., Botzoris, G., Profillidis, V., Kokkalis, A. (2020) “Real-time Intraday Traffic Volume Forecasting – A Hybrid Application Using Singular Spectrum Analysis and Artificial Neural Networks”, Periodica Polytechnica Transportation Engineering, 48(3), pp. 226–235. https://doi.org/10.3311/PPtr.14122

Issue

Section

Articles