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Applying spatial analysis to detect ...

Schwab, David Dwight.

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Applying spatial analysis to detect traffic crash patterns in a rural county and statistical analysis to associate contributing factors.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applying spatial analysis to detect traffic crash patterns in a rural county and statistical analysis to associate contributing factors./
Author:	Schwab, David Dwight.
Description:	72 p.
Notes:	Source: Masters Abstracts International, Volume: 54-04.
Contained By:	Masters Abstracts International54-04(E).
Subject:	Geography. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1587826
ISBN:	9781321721928

Applying spatial analysis to detect traffic crash patterns in a rural county and statistical analysis to associate contributing factors.
Schwab, David Dwight.

Applying spatial analysis to detect traffic crash patterns in a rural county and statistical analysis to associate contributing factors. - 72 p.

Source: Masters Abstracts International, Volume: 54-04.

Thesis (M.S.)--Indiana University of Pennsylvania, 2015.

The capacity to identify hazardous roadway segments quantitatively can guide remedial engineering efforts and make smarter use of limited government resources. This research evaluates spatial analytic tools, employing the local network Moran's I indicator, to statistically identify hazardous roadway segments. Hazardous segments are defined as those with crashes occurring in greater proportion to traffic volume and in relation to adjacent roadway segments proportions of crashes.

ISBN: 9781321721928Subjects--Topical Terms:

524010
Geography.

Applying spatial analysis to detect traffic crash patterns in a rural county and statistical analysis to associate contributing factors.
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020 $a 9781321721928
035 $a (MiAaPQ)AAI1587826
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100 1 $a Schwab, David Dwight. $3 3195109
245 1 0 $a Applying spatial analysis to detect traffic crash patterns in a rural county and statistical analysis to associate contributing factors.
300 $a 72 p.
500 $a Source: Masters Abstracts International, Volume: 54-04.
500 $a Adviser: Robert P. Sechrist.
502 $a Thesis (M.S.)--Indiana University of Pennsylvania, 2015.
520 $a The capacity to identify hazardous roadway segments quantitatively can guide remedial engineering efforts and make smarter use of limited government resources. This research evaluates spatial analytic tools, employing the local network Moran's I indicator, to statistically identify hazardous roadway segments. Hazardous segments are defined as those with crashes occurring in greater proportion to traffic volume and in relation to adjacent roadway segments proportions of crashes.
520 $a In a traditional non-uniform analysis high incident segments correlate with traffic volume masking. Transformation of the roadway network into a uniform network reveals more high incident segments. The veracity of the findings were tested using the Chi-square test, Spearman's Rho and binary logistic regression.
520 $a The analysis identified 27 hazardous segments, four were statistically analyzed. Hazardous segments displayed significant explanatory variables and statistical dependencies. Scene visitations revealed evidence of contributing factors. This methodology can be employed to identify crash clustering and understand causal factors.
590 $a School code: 0318.
650 4 $a Geography. $3 524010
650 4 $a Geographic information science and geodesy. $3 2122917
650 4 $a Statistics. $3 517247
690 $a 0366
690 $a 0370
690 $a 0463
710 2 $a Indiana University of Pennsylvania. $b Geography and Regional Planning. $3 3195110
773 0 $t Masters Abstracts International $g 54-04(E).
790 $a 0318
791 $a M.S.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1587826