Learning a robust classifier for short-term traffic state prediction

Abstract

Accurate and real-time prediction of short-term traffic states is a crucial research topic in modern intelligent transportation systems. However, effectively modeling traffic state predictions is difficult due to the complicated characteristics of stochastic and dynamic traffic processes. In addition, collected traffic data are typically influenced by external factors (e.g., weather, traffic jams and accidents), leading to errors and missing data. This increases the difficulty in selecting an effective method for predicting traffic conditions over time. To improve the traffic state prediction performance and alleviate the negative effect of traffic data with outliers, a novel multiclass classification least squares twin support vector machine model based on the robust L2,p-norm (0<p≤2) distance, known as PLSTSVM, was proposed. We adjusted the PLSTSVM parameters to balance the prediction accuracy and training time. To solve the optimization problem of the PLSTSVM, an iterative algorithm was developed, which has great potential for solving other optimization problems. In addition, an integrated classification indicator system based on speed, traffic volume, occupancy rate and ample degree was used, increasing the feasibility of the traffic state analysis. To improve the learning and generalization abilities of the nonlinear PLSTSVM, we combined the polynomial kernel function and the radial basis function to construct a hybrid kernel function. The results on two real traffic datasets demonstrate that our model yields better prediction performance and robustness than other competitors, which make unsatisfactory predictions.