Accurately modelling crashes, and predicting crash occurrence and associated severities
are a prerequisite for devising countermeasures and develo** effective road safety …

Identifying and assessing the likelihood and consequences of maritime accidents has been
a key focus of research within the maritime industry. However, conventional methods utilised …

Vulnerable road users (VRUs) involved crashes are a major road safety concern due to the
high likelihood of fatal and severe injury. The use of data-driven methods and heterogeneity …

Accurate estimating causal effects of crashes on highway traffic is crucial for mitigating the
negative impacts of crashes. Previous studies have built up a series of methods via …

Abstract Machine learning (ML) model interpretability has attracted much attention recently
given the promising performance of ML methods in crash frequency studies. Extracting …

The development of autonomous driving technology will lead to a transition period during
which human-driven vehicles (HVs) will share the road with autonomous vehicles (AVs) …

Millions of car crashes occur annually in the US, leaving tens of thousands of deaths and
many more severe injuries. Thus, understanding the most impactful contributors to severe …

Abstract Machine learning models such as random forests (RF) have been widely applied in
the field of road safety. RF is a prominent algorithm, overcoming the limitations of using a …

Usage-based insurance has allowed insurers to dynamically tailor insurance premiums by
understanding when and how safe policyholders drive. However, telematics information can …

Advanced driver assistance systems (ADAS) present promising benefits in mitigating road
collisions. However, these benefits are limited when risky drivers continue engaging in …