Details:

Creators:
Jeihani, Mansoureh ; Mazloomdoust, Gholamhossein ; Ghoseiri, Keivan ;
Jeihani, Mansoureh ; Mazloomdoust, Gholamhossein ; Ghoseiri, Keivan ; Less -
Corporate Creators:
Morgan State University ;
Corporate Contributors:
Maryland. State Highway Administration. Office of Policy & Research ;
Subject/TRT Terms:
[+]
Crash Analysis
Traffic Safety, Motorcycle Crashes,
Motorcycle Accidents
Traffic Safety

Publication/ Report Number:
MD-10-SP909B4J ;
Resource Type:
Tech Report ;
Geographical Coverage:
Maryland ;
Corporate Publisher:
Maryland. State Highway Administration. Office of Policy & Research ;
Abstract:
The goal of this study was to identify recurring or common road characteristics of motorcycle crashes in Maryland from 1998 to 2007. Motorcycle crash data was obtained from the National Highway Traffic Safety Administration’s Crash Outcome Data Evaluation System (CODES), and road inventory data was obtained from the Maryland State Highway Administration. Both sets of data were integrated, and fault tree analysis and variable selection methods were utilized to find the highest frequency crash cases. A categorical principal component analysis was used to specify the minimum number of variables that explain most of the observed variance. In addition, ordinal logistic models were developed to estimate the number of motorcycle crashes for road segments within each road class. The results of the logistic ordinal regression analysis show that area type, median type, speed limit, average annual daily traffic (AADT), international roughness index (IRI), and the number of through lanes affect the number of motorcycle crashes on Maryland road segments. Interestingly, government control and shoulder type were shown to have no significant impact on the number of motorcycle crashes. The developed ordinal logistic model can also calculate the number of motorcycle crashes for each road characteristic. Engineers and safety officials can use this study’s results to develop solutions for identified safety deficiencies.
Format:
PDF ;
Funding:
SP909B4J ;
Collection(s):
US Transportation Collection
Main Document Checksum:
[+]
urn:sha256:f86c6c04eabff43304b41844f45fd9966b0bdb3ebdebf565804c6cc733e0c170
File Type:

Supporting Files

• No Additional Files

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