An adaptive-margin support vector regression for short-term traffic flow forecast

Dali Wei, Hongchao Liu

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

The day-to-day volatility of traffic series provides valuable information for accurately tracking the complex characteristics of short-term traffic such as stochastic noise and nonlinearity. Recently, support vector regression (SVR) has been applied for short-term traffic forecasting. However, standard SVR adopts a global and fixed ε-margin, which not only fails to tolerate the day-to-day traffic variation, but also requires a blind and time-consuming searching procedure to obtain a suitable value for ε. In this work, on the ground of stochastic modeling of day-to-day traffic variation, we propose an adaptive SVR short-term traffic forecasting model. The time-varying deviation of the day-to-day traffic variation, described in a bilevel formula, is integrated into SVR as heuristic information to construct an adaptive ε-margin, in which both local and normalized factors are considered. Comparative experiments using field traffic data indicate that the proposed model consistently outperforms the standard SVR with an improved computational efficiency.

Original languageEnglish
Pages (from-to)317-327
Number of pages11
JournalJournal of Intelligent Transportation Systems: Technology, Planning, and Operations
Volume17
Issue number4
DOIs
StatePublished - Oct 2 2013

Keywords

  • Day-to-Day Traffic Variation
  • Short-Term Traffic Forecasting
  • Support Vector Regression
  • ε-margin

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