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Combining Support Vector Regression with Scaling Methods for Highway Tollgates Travel Time and Volume Predictions

Amanda Yan Lin (Institutionen för mekanik och maritima vetenskaper ; Institutionen för data- och informationsteknik (Chalmers)) ; Mengcheng Zhang (Institutionen för mekanik och maritima vetenskaper ; Institutionen för data- och informationsteknik (Chalmers)) ; . Selpi (Institutionen för mekanik och maritima vetenskaper)
Proceedings of International Work-Conference on Time Series Analysis (ITISE 2017), Granada, 18-20 September 2017 Vol. 1 (2017), p. 411-421.
[Konferensbidrag, refereegranskat]

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.

Nyckelord: Traffic flow prediction; traffic volume prediction; highway tollgates; time series analysis; SVR with scaling; robust scaling; SVR; support vector regression; machine learning



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Denna post skapades 2017-08-21. Senast ändrad 2017-11-09.
CPL Pubid: 251312