Chapter
Nov 12, 2013
Traffic Incident Duration Analysis Based on Cyclic Subspace Regression
Authors: Xuanqiang Wang [email protected], Shuyan Chen, Jian Gu, and Wenchang ZhenAuthor Affiliations
Publication: ICTE 2013: Safety, Speediness, Intelligence, Low-Carbon, Innovation
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.
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© 2013 American Society of Civil Engineers.
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Published online: Nov 12, 2013
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School of Transportation, Southeast University, Nanjing, Jiangsu, 210096, China. E-mail: [email protected]
Shuyan Chen
School of Transportation, Southeast University, Nanjing, Jiangsu, 210096, China
Jian Gu
School of Transportation, Southeast University, Nanjing, Jiangsu, 210096, China
Wenchang Zhen
School of Transportation, Southeast University, Nanjing, Jiangsu, 210096, China
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