Project R-8532


Prediction of Motorcycle Crash Severity in Mixed Traffic Environment: Logit Models and Bayesian Networks Approach (Research)


Mixed traffic with the majority share of motorcycle is the common traffic configuration in Vietnam. With the rapid rising of manufactured motorcycle and the substantial increase in the number of motorists in Vietnam, it is expected that the chance of road crash occurrence with fatalities will increase. The objective of the study is to explore factors contributing to the severity of motorcycle crashes under mixed traffic environment with a focus on road design and infrastructural element. The analysis uses the crash severity data from Ho Chi Minh given that it has the nation's highest traffic crash fatality rate. To ensure the robustness of the research findings with respect to different methodologies employed, this study will estimate three different severity models (the logistic regression model, the standard ordered logit model, and the partially constrained generalized logit model) and Bayesian Networks (BNs). the results will provide insights to develope effective countermeasures to reduce the severity of motorcycle crashes and improve traffic system safety performance. This study will deliver significant policy relevant information that will be used by different stakeholders to improve traffic safety and diminish the negative societal and economical impacts of severe traffic crashes.

Period of project

01 January 2018 - 23 January 2018