Abstract
Long queues often happen on toll roads, especially at the tollgates. These create many problems including having an impact on the regular roads nearby. If travel time and traffic volume at the tollgates can be predicted accurately in advance, this would allow traffic authorities to take appropriate measures to improve traffic flow and the safety of road users. This paper describes a novel combination of scaling methods with Support Vector Machines for Regression (SVR) for travel time and tollgate volume prediction tasks, as part of the Knowledge Discovery and Data Mining (KDD) Cup 2017. A new method is introduced to handle missing data by utilising the structure of the road network. Moreover, experiments with reduced data were conducted to evaluate whether the conclusions from combining scaling methods with SVR could be generalised.
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Acknowledgements
S. acknowledges strategic funding support from Chalmers Area of Advance Transport while writing this paper.
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Lin, A.Y., Zhang, M., Selpi (2018). Using Scaling Methods to Improve Support Vector Regression’s Performance for Travel Time and Traffic Volume Predictions. In: Rojas, I., Pomares, H., Valenzuela, O. (eds) Time Series Analysis and Forecasting. ITISE 2017. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-96944-2_8
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