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Road traffic forecasting — A hybrid approach combining Artificial Neural Network with Singular Spectrum Analysis

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  • Kolidakis, Stylianos
  • Botzoris, George
  • Profillidis, Vassilios
  • Lemonakis, Panagiotis

Abstract

The paper presents a comparison of a hybrid methodology which combines Singular Spectrum Analysis (SSA) with Artificial Neural Networks (ANN) against conventional ANN, applied on time series analysis and forecasting of road traffic volume. The main research objective was to develop a short-term forecast of daily traffic volume at toll stations across the Greek National Highway Network. The proposed methodology was implemented and evaluated upon a custom developed integrated forecasting software, based on the Mathworks MatLab platform. Experimental outcomes on daily data, from specific toll stations, demonstrate a superior prediction accuracy of hybrid SSA–ANN forecasting methodology against conventional ANN, when compared to performances of statistical criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R2). A comparison of results revealed that the SSA–ANN hybrid model could improve the forecasting accuracy of the conventional ANN model in the case of daily traffic volume forecasting. An Intelligent Transport System with embedded hybrid SSA–ANN forecasting algorithm could manage and analyze big data traffic volume time series in real time, providing an advanced decision support system for transportation system management and maintenance, while it would enable proactive decisions to mitigate the economic and environmental impacts of traffic congestion.

Suggested Citation

  • Kolidakis, Stylianos & Botzoris, George & Profillidis, Vassilios & Lemonakis, Panagiotis, 2019. "Road traffic forecasting — A hybrid approach combining Artificial Neural Network with Singular Spectrum Analysis," Economic Analysis and Policy, Elsevier, vol. 64(C), pages 159-171.
    • Handle: RePEc:eee:ecanpo:v:64:y:2019:i:c:p:159-171
    DOI: 10.1016/j.eap.2019.08.002
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    References listed on IDEAS

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    1. Whittaker, Joe & Garside, Simon & Lindveld, Karel, 1997. "Tracking and predicting a network traffic process," International Journal of Forecasting, Elsevier, vol. 13(1), pages 51-61, March.
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    4. Andersson, Matts & Brundell-Freij, Karin & Eliasson, Jonas, 2017. "Validation of aggregate reference forecasts for passenger transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 96(C), pages 101-118.
    5. Hassani, Hossein, 2007. "Singular Spectrum Analysis: Methodology and Comparison," MPRA Paper 4991, University Library of Munich, Germany.
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    Cited by:

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    2. Wei Zhou & Wei Wang & Xuedong Hua & Yi Zhang, 2020. "Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition," Sustainability, MDPI, vol. 12(15), pages 1-23, July.
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    More about this item

    Keywords

    Singular spectrum analysis; Artificial neural network; Traffic volume; Forecasting; Transportation;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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