A genetic algorithm-based support vector machine for bus travel time prediction

Abstract

Information about public transport travel time is a key indicator of service performance, and is valued by passengers and operators. Among many different approaches, Support Vector Machines (SVM) has recently gained attention in predicting bus travel times. The training process of SVMs involves solving a quadratic programming problem which is slow when dealing with large training data. This paper proposes a Least Squares SVM (LS-SVM) method that expedites the training process by simplifying the quadratic programming problem using a linear regression technique. Also, to ensure the accuracy of the prediction results, a Genetic Algorithm (GA) is used to determine the optimal set of model parameters. The GA based LS-SVM approach is tested using real-world travel time data from a bus route in Melbourne, Australia. The comparison of the results in this paper to those obtained in a previous study using artificial neural networks shows that the proposed method produces more accurate results.