| Univariate Short-Term Prediction of Road Travel Times |
| Citation: |
Nikovski, D.; Nishiuma, N.; Goto, Y.; Kumazawa, H., "Univariate Short-Term Prediction of Road Travel Times", International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1074-1079, September 2005 (IEEE Xplore) |
| Date: | September 2005 |
| MERL Contact: | Daniel Nikovski |
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. |
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