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.
Highlights
In recent decades, cities have become more crowded and jammed, which has increased the need for accurate traffic and mobility management [1] through the development of solutions based on Intelligent Transport Systems
This paper focuses on comparing four machine learning algorithms—Neural Networks, Support Vector Regression, Random Forests and Multiple Linear Regression—in terms of predicting traffic speed in urban areas based on historical statistical measures of traffic flow and speed
A comparison of neural networks, support vector machines, random forests, and multiple linear regression was implemented for predicting the traffic status of Thessaloniki
Summary
Cities have become more crowded and jammed, which has increased the need for accurate traffic and mobility management [1] through the development of solutions based on Intelligent Transport Systems. Various ways of performing short term predictions have been introduced—such as regression models [5], the nearest neighbor method (k-NN) [6], the Autoregressive Integrated Moving Average (ARIMA) [7], and the discretization modeling approach—as easier solutions to the complicated nonlinear models [8], in addition to machine learning algorithms such as Support Vector Regression (SVR) [9,10], Random Forest (RF) [11] for regression, and Neural Networks (NN) [9,11,12]. There are other studies, such as [23,24,25,26], showing that the prediction accuracy of Neural Networks and SVR is better than that of other machine learning models for short-term traffic speed prediction. This paper focuses on comparing four machine learning algorithms—Neural Networks, Support Vector Regression, Random Forests and Multiple Linear Regression—in terms of predicting traffic speed in urban areas based on historical statistical measures of traffic flow and speed.
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