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Travel time estimation and short-ter...

Singh, Ajay Kumar.

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Travel time estimation and short-term prediction in urban arterial networks using conditional independence graphs and state-space neural networks.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Travel time estimation and short-term prediction in urban arterial networks using conditional independence graphs and state-space neural networks./
Author:	Singh, Ajay Kumar.
Description:	188 p.
Notes:	Adviser: Ghassan Abu-Lebdeh.
Contained By:	Masters Abstracts International45-01.
Subject:	Engineering, Civil. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1438159
ISBN:	9780542899386

Travel time estimation and short-term prediction in urban arterial networks using conditional independence graphs and state-space neural networks.
Singh, Ajay Kumar.

Travel time estimation and short-term prediction in urban arterial networks using conditional independence graphs and state-space neural networks. - 188 p.

Adviser: Ghassan Abu-Lebdeh.

Thesis (M.S.)--Michigan State University, 2006.

An important component of Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) is the travel time estimation and short-term prediction on urban arterial networks. This thesis develops robust and efficient average travel time estimation and short-term prediction model for both congested and non-congested conditions appearing throughout a day on a network. A State-Space Neural Network model is proposed. An innovative implementation of Conditional Independence graph is used to identify the independence and interaction between observable traffic parameters that are used to estimate and predict the travel time. This led to the selection of relevant variables from a set of independent variables for travel time prediction. The predictive and computational performance of the Conditional Independence graph coupled with State-Space Neural Network outperformed the traditional State-Space Neural Network model in this study. The travel time estimation and prediction models are developed for links and routes in an arterial network.

ISBN: 9780542899386Subjects--Topical Terms:

783781
Engineering, Civil.

Travel time estimation and short-term prediction in urban arterial networks using conditional independence graphs and state-space neural networks.
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008 110915s2006 eng d
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035 $a (UMI)AAI1438159
035 $a AAI1438159
040 $a UMI $c UMI
100 1 $a Singh, Ajay Kumar. $3 1291485
245 1 0 $a Travel time estimation and short-term prediction in urban arterial networks using conditional independence graphs and state-space neural networks.
300 $a 188 p.
500 $a Adviser: Ghassan Abu-Lebdeh.
500 $a Source: Masters Abstracts International, Volume: 45-01, page: 0396.
502 $a Thesis (M.S.)--Michigan State University, 2006.
520 $a An important component of Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) is the travel time estimation and short-term prediction on urban arterial networks. This thesis develops robust and efficient average travel time estimation and short-term prediction model for both congested and non-congested conditions appearing throughout a day on a network. A State-Space Neural Network model is proposed. An innovative implementation of Conditional Independence graph is used to identify the independence and interaction between observable traffic parameters that are used to estimate and predict the travel time. This led to the selection of relevant variables from a set of independent variables for travel time prediction. The predictive and computational performance of the Conditional Independence graph coupled with State-Space Neural Network outperformed the traditional State-Space Neural Network model in this study. The travel time estimation and prediction models are developed for links and routes in an arterial network.
590 $a School code: 0128.
650 4 $a Engineering, Civil. $3 783781
650 4 $a Transportation. $3 555912
690 $a 0543
690 $a 0709
710 2 0 $a Michigan State University. $3 676168
773 0 $t Masters Abstracts International $g 45-01.
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790 1 0 $a Abu-Lebdeh, Ghassan, $e advisor
791 $a M.S.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1438159