[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 …
Applications of artificial intelligence in forensic sciences: C urrent potential benefits, limitations and perspectives
N Galante, R Cotroneo, D Furci, G Lodetti… - International journal of …, 2023 - Springer
In recent years, new studies based on artificial intelligence (AI) have been conducted in the
forensic field, posing new challenges and demonstrating the advantages and disadvantages …
forensic field, posing new challenges and demonstrating the advantages and disadvantages …
A comprehensive review on deep learning algorithms: Security and privacy issues
Abstract Machine Learning (ML) algorithms are used to train the machines to perform
various complicated tasks that begin to modify and improve with experiences. It has become …
various complicated tasks that begin to modify and improve with experiences. It has become …
What makes accidents severe! explainable analytics framework with parameter optimization
Most analytics models are built on complex internal learning processes and calculations,
which might be unintuitive, opaque, and incomprehensible to humans. Analytics-based …
which might be unintuitive, opaque, and incomprehensible to humans. Analytics-based …
[HTML][HTML] An application of the hybrid AHP-PROMETHEE approach to evaluate the severity of the factors influencing road accidents
The evaluation of the severity of the factors influencing road accidents with a detailed
severity distribution is critical to plan evidence-based road safety improvements and …
severity distribution is critical to plan evidence-based road safety improvements and …
Advancing proactive crash prediction: A discretized duration approach for predicting crashes and severity
Driven by advancements in data-driven methods, recent developments in proactive crash
prediction models have primarily focused on implementing machine learning and artificial …
prediction models have primarily focused on implementing machine learning and artificial …
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] Classification of truck-involved crash severity: Dealing with missing, imbalanced, and high dimensional safety data
While the cost of road traffic fatalities in the US surpasses $240 billion a year, the availability
of high-resolution datasets allows meticulous investigation of the contributing factors to …
of high-resolution datasets allows meticulous investigation of the contributing factors to …
Analyzing the transition from two-vehicle collisions to chain reaction crashes: A hybrid approach using random parameters logit model, interpretable machine learning …
SA Samerei, K Aghabayk - Accident Analysis & Prevention, 2024 - Elsevier
Chain reaction crashes (CRC) begin with a two-vehicle collision and rapidly intensify as
more vehicles get directly involved. CRCs result in more extensive damage compared to two …
more vehicles get directly involved. CRCs result in more extensive damage compared to two …
[HTML][HTML] Severity prediction of highway crashes in Saudi Arabia using machine learning techniques
Kingdom of Among the G20 countries, Saudi Arabia (KSA) is facing alarming traffic safety
issues compared to other G-20 countries. Mitigating the burden of traffic accidents has been …
issues compared to other G-20 countries. Mitigating the burden of traffic accidents has been …