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• W2032131451 abstract "Short-term traffic volume data are characterized by rapid and intense fluctuations with frequent shifts to congestion. Currently, research in short-term traffic forecasting deals with these phenomena either by smoothing them or by accounting for them by nonlinear models. But, these approaches lead to inefficient predictions particularly when the data exhibit intense oscillations or frequent shifts to boundary conditions (congestion). This paper offers a set of tools and methods to assess on underlying statistical properties of short-term traffic volume data, a topic that has largely been overlooked in traffic forecasting literature. Results indicate that the statistical characteristics of traffic volume can be identified from prevailing traffic conditions; for example, volume data exhibit frequent shifts from deterministic to stochastic structures as well as transitions between cyclic and strongly nonlinear behaviors. These findings could be valuable in the implementation of a variable prediction strategy according to the statistical characteristics of the prevailing traffic volume states." @default.
• W2032131451 created "2016-06-24" @default.
• W2032131451 creator A5018938672 @default.
• W2032131451 creator A5022276464 @default.
• W2032131451 creator A5090479134 @default.
• W2032131451 date "2006-10-01" @default.
• W2032131451 modified "2023-10-18" @default.
• W2032131451 title "Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume" @default.
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• W2032131451 doi "https://doi.org/10.1016/j.trc.2006.09.002" @default.
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