Travel time prediction with immune genetic algorithm and support vector regression
Travel time prediction, one of the most fundamental problems in almost all subsystems of Intelligent Transportation Systems (ITS) has attracted great attention in recent years. Lots of methods have been researched for this problem, including historical averages method, multiple linear regression and BP neural network. In this paper, an algorithm which combines support vector regression (SVR) and immune genetic algorithm (IGA) is designed for travel time prediction. SVR is used for establishing prediction model and IGA is used for optimizing the parameters of the model. To avoid over fitting problem, a new set which is called the validation set is introduced for the selection of fitness in IGA. Based on the actual traffic data from Langfang City, it is proved that the integration of SVR and IGA is applicable and performs better than historical averages method, multiple linear regression and BP neural network in travel time prediction.
2016 12th World Congress on Intelligent Control and Automation (WCICA)