Abstract:

This paper presents an experimental comparison of several statistical machine learning methods for short-term prediction of travel times on road segments. The comparison incluses linear regression, neural networks, regression trees, k-nearest neighbors, and locally-weighted regression, tested on the same historical data. In spite of the expected superiority of non-linear methods over linear regression, the only non-linear method that could consistently outperform linear regression was locally-weighted regression. This suggests that novel iterative linear regression algroithms should be a preferred prediction method for large-scale travel time prediction.