Using Scaling Methods to Improve Support Vector Regression’s Performance for Travel Time and Volume Predictions
Paper i proceeding, 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

Travel time prediction

SVR with scaling

SVR

traffic volume prediction

tollgate

time series analysis

Författare

Amanda Yan Lin

Student vid Chalmers

Mengcheng Zhang

Student vid Chalmers

Selpi Selpi

Chalmers, Mekanik och maritima vetenskaper, Fordonssäkerhet

Time Series Analysis and Forecasting, Contributions to Statistics

115-127
978-3-319-96943-5 (ISBN)

Time Series Analysis and Forecasting : ITISE 2017
Granada, Spain,

Styrkeområden

Informations- och kommunikationsteknik

Transport

Ämneskategorier

Systemvetenskap

Datavetenskap (datalogi)

DOI

10.1007/978-3-319-96944-2_8

Mer information

Senast uppdaterat

2022-03-24