Travel Time Prediction Using k Nearest Neighbor Method with Combined Data from Vehicle Detector System and Automatic Toll Collection System

Because of the development of scientific technology, drivers now have access to a variety of information to assist their decision making. In particular, an accurate prediction of travel time is valuable to drivers, who can use it to choose a route or decide on departure time. Although many researchers have sought to enhance their accuracy, such predictions are often limited by errors that result from the lagged pattern of predicted travel time, the use of nonrepresentative samples for making predictions, and the use of inefficient and nontransferable models. The proposed model predicts travel times on the basis of the k nearest neighbor method and uses data provided by the vehicle detector system and the automatic toll collection system. By combining these two sets of data, the model minimizes the limitations of each set and enhances the prediction's accuracy. Criteria for traffic conditions allow the direct use of data acquired from the automatic toll collection system as predicted travel time. The proposed model's predictions are compared with the predictions of other models by using actual data to show that the proposed model predicts travel times much more accurately. The proposed model's predictions of travel time are expected to be free from the problems associated with an insufficient number of samples. Further, unlike the widely used artificial neural network and Kalman filter methods, the proposed model does not require long training programs, so the model is easily transferable.