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Traffic Speed Prediction with Neural Networks

In: Operations Research Proceedings 2017

Author

Listed:
  • Umut Can Çakmak

    (Sabanci University
    Sabanci University)

  • Mehmet Serkan Apaydın

    (Istanbul Şehir University)

  • Bülent Çatay

    (Sabanci University
    Sabanci University)

Abstract

With the increasing interest in creating Smart Cities, traffic speed prediction has attracted more attention in contemporary transportation research. Neural networks have been utilized in many studies to address this problem; yet, they have mainly focused on the short-term prediction while longer forecast horizons are needed for more reliable mobility and route planning. In this work we tackle the medium-term prediction as well as the short-term. We employ feedforward neural networks that combine time series forecasting techniques where the predicted speed values are fed into the network. We train our networks and select the hyper-parameters to minimize the mean absolute error. To test the performance of our method, we consider two multi-segment routes in Istanbul. The speed data are collected from floating cars for every minute over a 5-month horizon. Our computational results showed that accurate predictions can be achieved in medium-term horizon.

Suggested Citation

  • Umut Can Çakmak & Mehmet Serkan Apaydın & Bülent Çatay, 2018. "Traffic Speed Prediction with Neural Networks," Operations Research Proceedings, in: Natalia Kliewer & Jan Fabian Ehmke & Ralf Borndörfer (ed.), Operations Research Proceedings 2017, pages 737-743, Springer.
    • Handle: RePEc:spr:oprchp:978-3-319-89920-6_98
    DOI: 10.1007/978-3-319-89920-6_98
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