Traffic Speed Prediction with Neural Networks

Umut Can Çakmak (), Mehmet Serkan Apaydın and Bülent Çatay
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Umut Can Çakmak: Sabanci University
Mehmet Serkan Apaydın: Istanbul Şehir University
Bülent Çatay: Sabanci University

A chapter in Operations Research Proceedings 2017, 2018, pp 737-743 from Springer

Abstract: 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.

Keywords: Neural networks; Forecasting; Machine learning; Time series analysis; Exponential smoothing; Moving average; Traffic speed prediction (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-319-89920-6_98

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DOI: 10.1007/978-3-319-89920-6_98

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