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

  • Stylianos Kolidakis Section of Transportation Engineering, Department of Civil Engineering, Faculty of Engineering, Democritus University of Thrace, Kimmeria Campus - Building B, 67100 Xanthi, Greece
  • George Botzoris Section of Transportation Engineering, Department of Civil Engineering, Faculty of Engineering, Democritus University of Thrace, Kimmeria Campus - Building B, 67100 Xanthi, Greece http://orcid.org/0000-0002-8983-8058
  • Vassilios Profillidis Section of Transportation Engineering, Department of Civil Engineering, Faculty of Engineering, Democritus University of Thrace, Kimmeria Campus - Building B, 67100 Xanthi, Greece http://orcid.org/0000-0002-1309-3061
  • Alexandros Kokkalis Section of Transportation Engineering, Department of Civil Engineering, Faculty of Engineering, Democritus University of Thrace, Kimmeria Campus - Building B, 67100 Xanthi, Greece

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. and Kokkalis, A. “Real-time Intraday Traffic Volume Forecasting – A Hybrid Application Using Singular Spectrum Analysis and Artificial Neural Networks”, Periodica Polytechnica Transportation Engineering. doi: https://doi.org/10.3311/PPtr.14122.
Section
Articles