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Predicting Short-Term Traffic Speed and Speed Drops in the Urban Area of a Medium-Sized European City—A Traffic Control and Decision Support Perspective

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Intelligent Systems and Applications in Business and Finance

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 415))

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Abstract

Traffic speed and traffic jam prediction are necessary for a successful regulation of traffic flow and also for the prevention of accidents. This chapter contributes to the body of knowledge on traffic characteristics prediction by focusing on the possibilities of traffic speed prediction in an urban area of a medium-sized European city—the Finnish capital of Helsinki. The predictive ability of simple models such as ARIMA-family models, Linear Regression, K-Nearest Neighbor and Extreme Gradient Boosted Tree (XGBoost) is investigated with the prediction horizons of 5, 10 and 15 min. The main goal is to find out if the results provided by these models can be sufficient for traffic control in medium-sized city areas. Open data is obtained from the Finnish Transport Agency and the city of Helsinki is chosen for the purpose of the analysis. Particular attention is paid to the possibilities of predicting sudden speed drops and traffic jams in the highly regulated metropolitan area of Helsinki. Traffic and weather data are considered as inputs and traffic jams are identified from the predicted speed, i.e. using a timeseries approach, and using a classification approach. The results indicate that XGBoost outperforms all the other considered models for all prediction horizons, but the speed drops are clearly underestimated by the timeseries models. On the other hand classification-oriented models such as decision trees seem to be better suited for the prediction of traffic jams (speed drops below 40 km/h) from the same data and provide promising results.

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Acknowledgements

This chapter is based on and extends the results and analysis available in the MSc thesis by Teemu Mankinen [17].

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Authors and Affiliations

  1. School of Business and Management, LUT University, Yliopistonkatu 34, 53850, Lappeenranta, Finland

    Teemu Mankinen, Jan Stoklasa & Pasi Luukka

  2. Faculty of Arts, Department of Economic and Managerial Studies, Palacký University Olomouc, Křížkovského 8, 771 47, Olomouc, Czech Republic

    Jan Stoklasa

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  1. Teemu Mankinen
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  2. Jan Stoklasa
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Correspondence to Teemu Mankinen .

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Editors and Affiliations

  1. Lappeenranta, Finland

    Pasi Luukka

  2. Lappeenranta, Finland

    Jan Stoklasa

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Mankinen, T., Stoklasa, J., Luukka, P. (2022). Predicting Short-Term Traffic Speed and Speed Drops in the Urban Area of a Medium-Sized European City—A Traffic Control and Decision Support Perspective. In: Luukka, P., Stoklasa, J. (eds) Intelligent Systems and Applications in Business and Finance. Studies in Fuzziness and Soft Computing, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-030-93699-0_7

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