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

Charalampos Bratsas, Kleanthis Koupidis, Josep-Maria Salanova, Konstantinos Giannakopoulos, Aristeidis Kaloudis and Georgia Aifadopoulou
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Charalampos Bratsas: School of Mathematics, Aristotle University of Thessaloniki, P.C. 54124 Thessaloniki, Greece
Kleanthis Koupidis: School of Mathematics, Aristotle University of Thessaloniki, P.C. 54124 Thessaloniki, Greece
Josep-Maria Salanova: Centre for Research and Technology Hellas—Hellenic Institute of Transport, P.C. 57001 Thessaloniki, Greece
Konstantinos Giannakopoulos: School of Mathematics, Aristotle University of Thessaloniki, P.C. 54124 Thessaloniki, Greece
Aristeidis Kaloudis: School of Mathematics, Aristotle University of Thessaloniki, P.C. 54124 Thessaloniki, Greece
Georgia Aifadopoulou: Centre for Research and Technology Hellas—Hellenic Institute of Transport, P.C. 57001 Thessaloniki, Greece

Sustainability, 2019, vol. 12, issue 1, 1-15

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 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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