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Volume 12
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Article

A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places

by
Charalampos Bratsas
1,2,*,
Kleanthis Koupidis
1,2,
Josep-Maria Salanova
3,
Konstantinos Giannakopoulos
1,
Aristeidis Kaloudis
1 and
Georgia Aifadopoulou
3
1
School of Mathematics, Aristotle University of Thessaloniki, P.C. 54124 Thessaloniki, Greece
2
Open Knowledge Foundation Greece, P.C. 54352 Thessaloniki, Greece
3
Centre for Research and Technology Hellas—Hellenic Institute of Transport, P.C. 57001 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(1), 142; https://doi.org/10.3390/su12010142
Submission received: 17 November 2019 / Revised: 12 December 2019 / Accepted: 18 December 2019 / Published: 23 December 2019
(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)
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Abstract

Rising interest in the field of Intelligent Transportation Systems combined with the increased availability of collected data allows the study of different methods for prevention of traffic congestion in cities. A common need in all of these methods is the use of traffic predictions for supporting planning and operation of the traffic lights and traffic management schemes. This paper focuses on comparing the forecasting effectiveness of three machine learning models, namely Random Forests, Support Vector Regression, and Multilayer Perceptron—in addition to Multiple Linear Regression—using probe data collected from the road network of Thessaloniki, Greece. The comparison was conducted with multiple tests clustered in three types of scenarios. The first scenario tests the algorithms on specific randomly selected dates on different randomly selected roads. The second scenario tests the algorithms on randomly selected roads over eight consecutive 15 min intervals; the third scenario tests the algorithms on random roads for the duration of a whole day. The experimental results show that while the Support Vector Regression model performs best at stable conditions with minor variations, the Multilayer Perceptron model adapts better to circumstances with greater variations, in addition to having the most near-zero errors.
Keywords: traffic prediction; machine learning; neural networks; SVR; random forest; multiple linear regression traffic prediction; machine learning; neural networks; SVR; random forest; multiple linear regression

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MDPI and ACS Style

Bratsas, C.; Koupidis, K.; Salanova, J.-M.; Giannakopoulos, K.; Kaloudis, A.; Aifadopoulou, G. A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places. Sustainability 2020, 12, 142. https://doi.org/10.3390/su12010142

AMA Style

Bratsas C, Koupidis K, Salanova J-M, Giannakopoulos K, Kaloudis A, Aifadopoulou G. A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places. Sustainability. 2020; 12(1):142. https://doi.org/10.3390/su12010142

Chicago/Turabian Style

Bratsas, Charalampos, Kleanthis Koupidis, Josep-Maria Salanova, Konstantinos Giannakopoulos, Aristeidis Kaloudis, and Georgia Aifadopoulou. 2020. "A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places" Sustainability 12, no. 1: 142. https://doi.org/10.3390/su12010142

APA Style

Bratsas, C., Koupidis, K., Salanova, J.-M., Giannakopoulos, K., Kaloudis, A., & Aifadopoulou, G. (2020). A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places. Sustainability, 12(1), 142. https://doi.org/10.3390/su12010142

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