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Title:

Creating a Predictive Model for Pavement Deterioration using Geographic Weighted Regression

Accession Number:

01659637

Record Type:

Component

Availability:

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Order URL: http://worldcat.org/issn/03611981

Abstract:

This paper represents an effort by the New York City Department of Transportation (NYCDOT) to develop, by means of geographically weighted regression (GWR), an empirical pavement deterioration model for the five boroughs of New York City. Evaluation of probabilistic and deterministic models from existing literature, as well as statistical modeling techniques and applications, were examined to inform model selection. A model using GWR was selected as the preferred methodology. Validation tests demonstrated an adjusted R2 of .808 and a standard error (s) representing 4.8% of the mean. Emphasis is placed on the novelty and practicality of GWR as a means for pavement deterioration prediction. Future applications of this model will aid decision makers in strategically allocating limited funds to priority projects.

Report/Paper Numbers:

18-03144

Language:

English

Authors:

Romano, Maddalena
Siegel, Maxwell S
Chan, Hui Yan (Terri)

Pagination:

pp 166-175

Publication Date:

2018-12

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Volume: 2672
Issue Number: 40
Publisher: Sage Publications, Incorporated
ISSN: 0361-1981
EISSN: 2169-4052
Serial URL: http://journals.sagepub.com/home/trr

Media Type:

Print

Features:

Figures (3) ; References (27) ; Tables (1)

Geographic Terms:

Subject Areas:

Highways; Pavements

Files:

TRIS, TRB, ATRI

Created Date:

Jan 8 2018 10:46AM

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