Vehicle Travel Time Predication based on Multiple Kernel Regression

With the rapid development of transportation and logistics economy, the vehicle travel time prediction and planning become an important topic in logistics. Travel time prediction, which is indispensible for traffic guidance, has become a key issue for researchers in this field. At present, the prediction of travel time is mainly short term prediction, and the predication methods include artificial neural network, Kaman filter and support vector regression (SVR) method etc. However, these algorithms still have some shortcomings, such as highcomputationcomplexity, slow convergence rate etc. This paper exploits the learning ability of multiple kernel learning regression (MKLR) in nonlinear prediction processing characteristics, logistics planning based on MKLR for vehicle travel time prediction. The method for Vehicle travel time prediction includes the following steps: (1) preprocessing historical data; (2) selecting appropriate kernel function, training the historical data and performing analysis ;(3) predicting the vehicle travel time based on the trained model. The experimental results show that, through the analysis of using different methods for prediction, the vehicle travel time prediction method proposed in this paper, archives higher accuracy than other methods. It also illustrates the feasibility and effectiveness of the proposed prediction method.