Improving the Estimation of Travel Demand for Traffic Simulation: Part II: Final Report

Wu, Yao; Davis, Gary A.; Levinson, David

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Improving the Estimation of Travel Demand for Traffic Simulation: Part II: Final Report

2004-12-01
By Wu, Yao ; Davis, Gary A. ; Levinson, David

File Language:

English

Details

Creators:

Wu, Yao ; Davis, Gary A. ; Levinson, David
Corporate Creators:

University of Minnesota. Dept. of Civil Engineering
Corporate Contributors:

University of Minnesota. Center for Transportation Studies ; University of Minnesota. Intelligent Transportation Systems Institute
Subject/TRT Terms:
Publication/ Report Number:

CTS 04-11
Resource Type:

Tech Report
Geographical Coverage:

United States ; Minnesota
TRIS Online Accession Number:

987633
OCLC Number:

57424466
Edition:

Final report
Corporate Publisher:

University of Minnesota. Center for Transportation Studies
Abstract:

This report examined several methods for estimating Origin-Destination (OD) matrices for freeways using loop detector data. Least squares based methods were compared in terms of both off-line and on-line estimation. Simulated data and observed data were used for evaluating the static and recursive estimators. For off-line estimation, four fully constrained least squares methods were compared. The results showed that the variations of a constrained least squares approach produced more efficient estimates. For on-line estimation, two recursive least squares algorithms were examined. The first method extends Kalman Filtering to satisfy the natural constraints of the OD split parameters. The second was developed from sequential quadratic programming. These algorithms showed different capabilities to capture an abrupt change in the split parameters. Practical recommendations of the choice of different algorithms are given.
Format:

PDF
Download URL:

https://rosap.ntl.bts.gov/view/dot/39368/dot_39368_DS1.pdf
File Type:

[PDF - 3.45 MB]
Collection(s):

University Transportation Centers ; US Transportation Collection
Main Document Checksum:

urn:sha256:d39e8ac5ce71f608049fa37a6e68775870b810d74b8971bce8739d4feb77b830

File Language:

English

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