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- W838265720 abstract "Short-term traffic exhibits complex characteristics—stochastic noise, non-stationary behavior and non-linearity. The day-to-day volatility of traffic series provides valuable information for accurately tracking these characteristics. Recently, Support Vector Regression (SVR) has been applied for short-term traffic forecasting. However, standard SVR adopts a global and fixed e-margin, which cannot adequately tolerate and utilize the time-varying day-to-day traffic variation. In this work, on the ground of stochastic modeling of day-to-day traffic variation, an adaptive SVR short-term traffic forecasting model is proposed. A bi-level model is established to describe the traffic variation, which combines the time-varying characteristic to the day-to-day traffic variation. The obtained time-varying deviation is then integrated into SVR to construct an adaptive e-margin, in which both local and normalized factors are considered. Comparative experiments using filed traffic data indicate that the new adaptive SVR consistently outperforms the standard SVR." @default.
- W838265720 created "2016-06-24" @default.
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- W838265720 date "2012-01-01" @default.
- W838265720 modified "2023-09-23" @default.
- W838265720 title "Adaptive Support Vector Regression for Short-Term Traffic Flow Forecast" @default.
- W838265720 hasPublicationYear "2012" @default.
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