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Modeling and forecasting vehicular t...

Williams, Billy M.

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Modeling and forecasting vehicular traffic flow as a seasonal stochastic time series process.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling and forecasting vehicular traffic flow as a seasonal stochastic time series process./
作者:	Williams, Billy M.
面頁冊數:	222 p.
附註:	Source: Dissertation Abstracts International, Volume: 60-01, Section: B, page: 0292.
Contained By:	Dissertation Abstracts International60-01B.
標題:	Engineering, Civil. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9916358
ISBN:	0599151471

Modeling and forecasting vehicular traffic flow as a seasonal stochastic time series process.
Williams, Billy M.

Modeling and forecasting vehicular traffic flow as a seasonal stochastic time series process. - 222 p.

Source: Dissertation Abstracts International, Volume: 60-01, Section: B, page: 0292.

Thesis (Ph.D.)--University of Virginia, 1999.

Extensive data collection is now commonplace for urban freeway and street network systems. Research efforts are underway to unleash the system management potential inherent in this unprecedented access to traffic condition data. A key element in this research area is traffic condition forecasting. Reliable and accurate condition forecasts will enable transportation management systems to dynamically anticipate the future state of the system rather than merely respond to the current situation.

ISBN: 0599151471Subjects--Topical Terms:

783781
Engineering, Civil.

Modeling and forecasting vehicular traffic flow as a seasonal stochastic time series process.
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245 1 0 $a Modeling and forecasting vehicular traffic flow as a seasonal stochastic time series process.
300 $a 222 p.
500 $a Source: Dissertation Abstracts International, Volume: 60-01, Section: B, page: 0292.
500 $a Adviser: Lester A. Hoel.
502 $a Thesis (Ph.D.)--University of Virginia, 1999.
520 $a Extensive data collection is now commonplace for urban freeway and street network systems. Research efforts are underway to unleash the system management potential inherent in this unprecedented access to traffic condition data. A key element in this research area is traffic condition forecasting. Reliable and accurate condition forecasts will enable transportation management systems to dynamically anticipate the future state of the system rather than merely respond to the current situation.
520 $a Recent traffic flow prediction efforts have focused on application of neural networks, non-parametric regression using nearest neighbor algorithms, multiple class linear regression based on automatic clustering, time series analysis techniques, and hybrid models combining two or more of these approaches.
520 $a Seasonal time series methods, i.e., seasonal ARIMA models and Holt-Winters smoothing, have not been among the investigated forecasting techniques. The need to explore these techniques is motivated by a strong theoretical expectation that they will provide accurate and parsimonious traffic condition models.
520 $a The findings presented in this dissertation establish the traffic flow prediction superiority of seasonal time series methods, especially seasonal ARIMA modeling, over the recently developed methods mentioned above. The research also contributes a specific application of time series outlier modeling theory to vehicular traffic flow data. This outlier detection and modeling procedure uncovered a common ARIMA model form among the seasonally stationary series used in this research. This common model form is ARIMA (1,0,1) (0,1,1)S where S is the length of the series seasonal cycle.
590 $a School code: 0246.
650 4 $a Engineering, Civil. $3 783781
650 4 $a Operations Research. $3 626629
650 4 $a Engineering, System Science. $3 1018128
650 4 $a Transportation. $3 555912
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710 2 0 $a University of Virginia. $3 645578
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