A hybrid modelling framework of machine learning and extreme value theory for crash risk estimation using traffic conflicts
A multivariate method for evaluating safety from conflict extremes in real time
Addressing endogeneity in modeling speed enforcement, crash risk and crash severity simultaneously
Addressing unobserved heterogeneity at road user level for the analysis of conflict risk at tunnel toll plaza: a correlated grouped random parameters logit approach with heterogeneity in means
Evaluating gender differences in injury severities of non-helmet wearing motorcyclists: accommodating temporal shifts and unobserved heterogeneity
Integrating macro and micro level crash frequency models considering spatial heterogeneity and random effects
Investigating the effects of sleepiness in truck drivers on their headway: an instrumental variable model with grouped random parameters and heterogeneity in their means
Modeling endogeneity between motorcyclist injury severity and at-fault status by applying a Bayesian simultaneous random-parameters model with a recursive structure
Modelling animal-vehicle collision counts across large networks using a Bayesian hierarchical model with time-varying parameters
Real-time crash potential prediction on freeways using connected vehicle data
The impact of weekday, weekend, and holiday crashes on motorcyclist injury severities: accounting for temporal influence with unobserved effect and insights from out-of-sample prediction