Bayesian Hierarchical Model in Driving Risk Analysis Using Naturalistic Driving Study Data

• Activity Number: 437
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Government Statistics Section
• Author(s): Youjia Fang*+ and Feng Guo
• Companies: Virginia Tech and Virginia Tech Transportation Institute
• Keywords: Bayesian ; hierarchical model ; naturalistic driving study ; risk analysis ; meta-analysis ; heterogeneity

Abstract

In this paper we develop a full Bayesian hierarchical model to evaluate the distraction-related driving risk. The data used is the driving-related distraction and the crash and near-crash data derived from Naturalistic Driving Study (NDS) data. The motivation is that drivers with different characteristics (e.g. age, gender) may have different levels of driving risk when they are exposed to driving-related distraction. The full Bayesian model developed can mitigates some difficult issues encountered by traditional meta-analysis (e.g. sensitivity to study size and extreme results, and incorporation of study-specific covariates and random components). The Bayesian hierarchical model "borrows strength" from among different groups, so one can estimate each group's driving risk and aggregated overall risk at the same time. The analysis of NDS data shows that distraction-related driving risk is related to driver's demographic characteristics (e.g. age, gender). In this sense, we may need to apply a hierarchical model, rather than a simple aggregated model, to evaluate the data with heterogeneity nature.