k Nearest Neighbor Model for Multiple Time Step Prediction of Short Term Traffic Condition
AbstractOne of the most critical functions of an intelligent transportation system (ITS) is to provide accurate and real time prediction of traffic condition. This paper develops a short term traffic condition prediction model based on the k nearest neighbor algorithm. In the prediction model, the time varying and continuous characteristic of traffic flow is considered, and the multi time step prediction model is proposed based on the single time step model. To test the accuracy of the proposed multi time step prediction model, GPS data of taxis in Foshan city, China, are used. The results show that the multi time step prediction model with spatial temporal parameters provides a good performance compared with the support vector machine (SVM) model, artificial neural network (ANN) model, real time data model, and history data model. The results also appear to indicate that the proposed k nearest neighbor model is an effective approach in predicting the short term traffic condition.