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- W2038096780 abstract "Predicting traffic incident duration is very important for advanced traffic incident management. An accurate prediction of incident duration contributes a lot to making appropriate decisions to deal with incidents for traffic managers and getting traffic information to travelers in a timely manner. Cyclic subspace regression (CSR) yields the least squares regression (LSR), principal component regression (PCR), partial least squares regression (PLSR), and a lot of middle regression methods during its solving process. According to a certain criterion, it can select optimal model parameters within a very broad solution space. In this paper, the cyclic subspace regression was applied to analyze the relationship between incident duration and its influence factors. The experiments' results indicated that the models based on cyclic subspace regression have a promising application to predict incident duration." @default.
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- W2038096780 date "2013-10-09" @default.
- W2038096780 modified "2023-09-28" @default.
- W2038096780 title "Traffic Incident Duration Analysis Based on Cyclic Subspace Regression" @default.
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- W2038096780 doi "https://doi.org/10.1061/9780784413159.414" @default.
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