DocumentCode :
2537221
Title :
Analysis of the logistic model for accident severity on urban road environment
Author :
Zhuanglin Ma ; Shao, Chunfu ; Yue, Hao ; Sheqiang Ma
Author_Institution :
MOE Key Lab. for Transp. Complex Syst., Beijing Jiaotong Univ., Beijing, China
fYear :
2009
fDate :
3-5 June 2009
Firstpage :
983
Lastpage :
987
Abstract :
In order to identify the possible contributory factors on urban road environment to accident severity, the logistic regression model was applied to traffic accident data collected from police-reports. A total of 2117 accidents occurred in Shijiazhuang in 2002 were considered for the purpose of this paper. Accident severity (dependent variable) in this paper is a dichotomous variable with two categories: extra serious or major accident and ordinary or minor accident. Because of the binary nature of this dependent variable, the logistic regression model was found suitable. Of nine independent variables obtained from police-reports, five were found most significant associated with accident severity, namely, road cross-section, accident location, road alignment, road type and lighting condition. A statistical interpretation is given of the model-developed estimates in terms of odds ratio concept. The findings show that the logistic regression model as used in this paper provides a better understanding of risk factors related to urban road environment.
Keywords :
accidents; logistics; regression analysis; road safety; traffic; Shijiazhuang; accident location; accident severity; dichotomous variable; lighting condition; logistic regression model; major accident; minor accident; odds ratio concept; police-reports; road alignment; road cross-section; road type; statistical interpretation; traffic accident data; urban road environment; Electronic mail; Engineering management; Environmental management; Injuries; Logistics; Road accidents; Road safety; Road transportation; Traffic control; Vehicle crash testing; accident severity; logistic model; traffic safety; urban road environment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Vehicles Symposium, 2009 IEEE
Conference_Location :
Xi´an
ISSN :
1931-0587
Print_ISBN :
978-1-4244-3503-6
Electronic_ISBN :
1931-0587
Type :
conf
DOI :
10.1109/IVS.2009.5164414
Filename :
5164414
Link To Document :
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