Performance comparison of machine learning methods in the bus arrival time prediction problem

The problem of predicting the movement of public transport is one of the most popular problems in the field of transport planning due to its practical significance. Various parametric and non-parametric models are used to solve this problem. In this paper, heterogeneous information affecting the prediction value is used to predict the arrival time of public transport, and a comparison of the main machine learning algorithms for the public transport arrival time forecasting is given: neural networks, support vector regression. An experimental analysis of the algorithms was carried out on real traffic information about bus routes in Samara, Russia.