Understanding Fixed Object Crashes with SHRP2 Naturalistic Driving Study Data.
Degree: MS, Civil and Environmental Engineering, 2018, Virginia Tech
Fixed-object crashes have long time been considered as major roadway safety concerns. While previous relevant studies tended to address such crashes in the context of roadway departures, and heavily relied on police-reported accidents data, this study integrated the SHRP2 NDS and RID data for analyses, which fully depicted the prior to, during, and after crash scenarios. A total of 1,639 crash, near-crash events, and 1,050 baseline events were acquired. Three analysis methods: logistic regression, support vector machine (SVM) and artificial neural network (ANN) were employed for two responses: crash occurrence and severity level. Logistic regression analyses identified 16 and 10 significant variables with significance levels of 0.1, relevant to driver, roadway, environment, etc. for two responses respectively. The logistic regression analyses led to a series of findings regarding the effects of explanatory variables on fixed-object event occurrence and associated severity level. SVM classifiers and ANN models were also constructed to predict these two responses. Sensitivity analyses were performed for SVM classifiers to infer the contributing effects of input variables. All three methods obtained satisfactory prediction performance, that was around 88% for fixed-object event occurrence and 75% for event severity level, which indicated the effectiveness of NDS event data on depicting crash scenarios and roadway safety analyses.
Advisors/Committee Members: Wang, Linbing (committeechair), Abbas, Montasir Mahgoub (committee member), Li, Yingfeng (committee member).
Subjects/Keywords: Fixed-object crash; Naturalistic driving study; Logistic regression analysis; Support vector machine; Artificial neural network
to Zotero / EndNote / Reference
APA (6th Edition):
Hao, H. (2018). Understanding Fixed Object Crashes with SHRP2 Naturalistic Driving Study Data. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/84942
Chicago Manual of Style (16th Edition):
Hao, Haiyan. “Understanding Fixed Object Crashes with SHRP2 Naturalistic Driving Study Data.” 2018. Masters Thesis, Virginia Tech. Accessed July 17, 2019.
MLA Handbook (7th Edition):
Hao, Haiyan. “Understanding Fixed Object Crashes with SHRP2 Naturalistic Driving Study Data.” 2018. Web. 17 Jul 2019.
Hao H. Understanding Fixed Object Crashes with SHRP2 Naturalistic Driving Study Data. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2019 Jul 17].
Available from: http://hdl.handle.net/10919/84942.
Council of Science Editors:
Hao H. Understanding Fixed Object Crashes with SHRP2 Naturalistic Driving Study Data. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/84942