Combining Support Vector Regression with Scaling Methods for Highway Tollgates Travel Time and Volume Predictions
Paper in proceedings, 2017

Toll roads or controlled-access roads are very commonly used, e.g. in Asia. Drivers expect to drive smoother and faster on the toll roads compared to on regular roads. However, long queues on toll roads, particularly at the tollgates, often happen and create many problems. Being able to accurately predict travel time and volume of the tollgates would allow appropriate measures to improve traffic flow and safety to be taken. This paper describes a novel investigation on the use of scaling methods with Support Vector Regression (SVR) for highway tollgates travel time and volume prediction tasks as well as an investigation of the most important features for these tasks. Experiments were done as part of the Knowledge Discovery and Data Mining (KDD) Cup 2017. Suitability of certain scaling methods for dfferent types of time series and reasoning why certain features are important for these tasks are also discussed.

time series analysis

SVR with scaling

highway tollgates

traffic volume prediction

Traffic flow prediction

robust scaling

machine learning

support vector regression



Amanda Yan Lin

Mechanics and Maritime Sciences

Chalmers, Computer Science and Engineering (Chalmers)

Mengcheng Zhang

Mechanics and Maritime Sciences

Chalmers, Computer Science and Engineering (Chalmers)

Selpi Selpi

Mechanics and Maritime Sciences

Proceedings of International Work-Conference on Time Series Analysis (ITISE 2017), Granada, 18-20 September 2017

Vol. 1 411-421

Driving Forces

Sustainable development

Areas of Advance


Subject Categories

Computer and Information Science

Probability Theory and Statistics

Computer Science



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