Linear Regression Crash Prediction Models: Issues and Proposed Solutions

Rakha, H.; Arafeh, M.; Abdel-Salam, A.G.; Guo, F.; Flintsch, A.M.

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Linear Regression Crash Prediction Models: Issues and Proposed Solutions

2010-05-01
By Rakha, H. ; Arafeh, M. ; Abdel-Salam, A.G.

File Language:

English

Details

Creators:

Rakha, H. ; Arafeh, M. ; Abdel-Salam, A.G. ; Guo, F. ; Flintsch, A.M.
Corporate Creators:

Virginia Tech Transportation Institute
Subject/TRT Terms:
Publication/ Report Number:

VT-2008-02
Resource Type:

Tech Report
Geographical Coverage:

United States ; Virginia
TRIS Online Accession Number:

01164268
Edition:

Final report.
Corporate Publisher:

Virginia. Dept. of Transportation ; United States. Dept. of Transportation. Research and Innovative Technology Administration
NTL Classification:

NTL-SAFETY AND SECURITY-Accidents ; NTL-SAFETY AND SECURITY-Highway Safety
Abstract:

The paper develops a linear regression model approach that can be applied to crash data to predict vehicle crashes. The proposed approach involves novice data aggregation to satisfy linear regression assumptions; namely error structure normality and homoscedasticity. The proposed approach is tested and validated using data from 186 access road sections in the state of Virginia. The approach is demonstrated to produce crash predictions consistent with traditional negative binomial and zero inflated negative binomial general linear models. It should be noted however that further testing of the approach on other crash datasets is required to further validate the approach.
Format:

PDF
Funding:

DTRS99-G-003
Download URL:

https://rosap.ntl.bts.gov/view/dot/25646/dot_25646_DS1.pdf
File Type:

[PDF - 472.13 KB]
Collection(s):

University Transportation Centers ; US Transportation Collection
Main Document Checksum:

urn:sha-512:0b0e45582e4ba41c784ee9ba6c1e2947d5a3dc9f64e64c538b9aaf33d1a818e1f9a1dbdff4ded10abee58509656ce6171ff5070a4044cdbb0a8850f844a41c42

File Language:

English

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