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Title:

Hot-Spot Identification: Categorical Binary Model Approach

Accession Number:

01476140

Record Type:

Component

Availability:

Transportation Research Board Business Office

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Washington, DC 20001 United States
Order URL: http://www.trb.org/main/blurbs/170273.aspx

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Order URL: http://worldcat.org/isbn/9780309287036

Abstract:

An alternative methodology is presented for hot-spot identification based on a probabilistic model. In this method, the ranking criterion for hot-spot identification conveys the probability of a site’s being a hot spot or not being a hot spot. A binary choice model is used to link the outcome to a set of factors that characterize the risk of the sites under analysis on the basis of two categories (0/1) for the dependent variable. The proposed methodology consists of two main steps. After a threshold value for the number of accidents is set to distinguish hot spots from safe sites (Category 1 or 0, respectively), a binary model based on this classification is applied. This model allows the construction of a site list ordered by using the probability of a site’s being a hot spot. In the second step, the selection strategy can target a fixed number of sites with the greatest probability or all sites exceeding a specific probability, such as .5. To demonstrate the proposed methodology, simulated urban intersection data from Porto, Portugal, covering 5 years are used. The results of the binary model show a good fit. To evaluate and compare the probabilistic method with other commonly used methods, the performance of each method is tested by its power to detect true hot spots. The test results indicate the superiority of the proposed method. This method is simple to apply, and critical issues such as assumptions of a prior distribution effect and the regression-to-the-mean phenomenon are overcome. Further, the model provides a realistic and intuitive perspective.

Monograph Accession #:

01514599

Report/Paper Numbers:

13-0095

Language:

English

Authors:

Ferreira, Sara
Couto, António

Pagination:

pp 1–6

Publication Date:

2013

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Issue Number: 2386
Publisher: Transportation Research Board
ISSN: 0361-1981

ISBN:

9780309287036

Media Type:

Print

Features:

Figures (1) ; References (23) ; Tables (4)

Geographic Terms:

Subject Areas:

Data and Information Technology; Highways; Safety and Human Factors; I80: Accident Studies

Files:

TRIS, TRB, ATRI

Created Date:

Feb 5 2013 12:11PM

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