Real-Time Travel Time Prediction Using Multi-Level k-Nearest Neighbor Algorithm and Data Fusion Method

Estimating and predicting the travel time on freeways with reasonable accuracy is essential for successful implementation of an intelligent transportation system. However, the related previous studies have some problems. The statistics-based methods have problems in accuracy, and some others are limited to predicting the travel time only during short time intervals. Another challenging matter is that the existing road sensors have some limitations in being directly utilized because data from road sensors have a lot of errors. In this study, we propose a new algorithm called multi-level k-Nearest Neighbor (k-NN), which is designed for predicting travel time with higher computational efficiency and prediction accuracy. The algorithm consists of three parts: classification, global matching, and local matching. As a part of the proposed algorithm, in order to overcome the problems of data errors, we provide a data fusion method that combines the traffic data from ILDs and DSRC. The results show that the proposed multi-level k-NN with the data fusion can effectively predict the future travel time within less than 5% error range, even in congested traffic situations.