[HTML][HTML] Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review
Accurately modelling crashes, and predicting crash occurrence and associated severities
are a prerequisite for devising countermeasures and developing effective road safety …
are a prerequisite for devising countermeasures and developing effective road safety …
A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis
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 …
a key focus of research within the maritime industry. However, conventional methods utilised …
A hybrid approach of random forest and random parameters logit model of injury severity modeling of vulnerable road users involved crashes
Z Sun, D Wang, X Gu, M Abdel-Aty, Y Xing… - Accident Analysis & …, 2023 - Elsevier
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 …
high likelihood of fatal and severe injury. The use of data-driven methods and heterogeneity …
Inferring heterogeneous treatment effects of crashes on highway traffic: A doubly robust causal machine learning approach
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 …
negative impacts of crashes. Previous studies have built up a series of methods via …
On the interpretability of machine learning methods in crash frequency modeling and crash modification factor development
Abstract Machine learning (ML) model interpretability has attracted much attention recently
given the promising performance of ML methods in crash frequency studies. Extracting …
given the promising performance of ML methods in crash frequency studies. Extracting …
Modeling the effects of autonomous vehicles on human driver car-following behaviors using inverse reinforcement learning
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) …
which human-driven vehicles (HVs) will share the road with autonomous vehicles (AVs) …
Discovering injury severity risk factors in automobile crashes: a hybrid explainable AI framework for decision support
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 …
many more severe injuries. Thus, understanding the most impactful contributors to severe …
Geographically weighted random forests for macro-level crash frequency prediction
D Wu, Y Zhang, Q Xiang - Accident Analysis & Prevention, 2024 - Elsevier
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 …
the field of road safety. RF is a prominent algorithm, overcoming the limitations of using a …
[HTML][HTML] Using contextual data to predict risky driving events: A novel methodology from explainable artificial intelligence
Usage-based insurance has allowed insurers to dynamically tailor insurance premiums by
understanding when and how safe policyholders drive. However, telematics information can …
understanding when and how safe policyholders drive. However, telematics information can …
[HTML][HTML] On the impact of advanced driver assistance systems on driving distraction and risky behaviour: An empirical analysis of irish commercial drivers
Advanced driver assistance systems (ADAS) present promising benefits in mitigating road
collisions. However, these benefits are limited when risky drivers continue engaging in …
collisions. However, these benefits are limited when risky drivers continue engaging in …