Using Scaling Methods to Improve Support Vector Regression’s Performance for Travel Time and Volume Predictions
Book chapter, 2018

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 conclusions from combining scaling methods with SVR could be generalised.

support vector regression


SVR with scaling

Travel time prediction

time series analysis

traffic volume prediction



Amanda Yan Lin

Chalmers, Mechanics and Maritime Sciences

Chalmers, Computer Science and Engineering (Chalmers)

Mengcheng Zhang

Chalmers, Computer Science and Engineering (Chalmers)

Chalmers, Mechanics and Maritime Sciences

Selpi Selpi

Chalmers, Mechanics and Maritime Sciences, Vehicle Safety

Time Series Analysis and Forecasting, Contributions to Statistics


Areas of Advance

Information and Communication Technology


Subject Categories

Information Science

Computer Science



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