[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 …
Safety in Traffic Management Systems: A Comprehensive Survey
Traffic management systems play a vital role in ensuring safe and efficient transportation on
roads. However, the use of advanced technologies in traffic management systems has …
roads. However, the use of advanced technologies in traffic management systems has …
[HTML][HTML] Real-time crash risk forecasting using Artificial-Intelligence based video analytics: A unified framework of generalised extreme value theory and …
With the recent advancements in computer vision and artificial intelligence, traffic conflicts
occurring at an intersection and associated traffic characteristics can be obtained at the …
occurring at an intersection and associated traffic characteristics can be obtained at the …
Prediction and analysis of likelihood of freeway crash occurrence considering risky driving behavior
Y Ma, J Zhang, J Lu, S Chen, G Xing, R Feng - Accident Analysis & …, 2023 - Elsevier
The prediction of the likelihood of vehicle crashes constitutes an indispensable component
of freeway safety management. Due to data collection limitations, studies have used mainly …
of freeway safety management. Due to data collection limitations, studies have used mainly …
Transfer learning for spatio-temporal transferability of real-time crash prediction models
CK Man, M Quddus, A Theofilatos - Accident Analysis & Prevention, 2022 - Elsevier
Real-time crash prediction is a heavily studied area given their potential applications in
proactive traffic safety management in which a plethora of statistical and machine learning …
proactive traffic safety management in which a plethora of statistical and machine learning …
Real-time crash risk prediction in freeway tunnels considering features interaction and unobserved heterogeneity: a two-stage deep learning modeling framework
Real-time prediction of crash risk is an effective method for enhancing traffic safety, but it is
not fully explored in freeway tunnels. A two-stage deep learning modeling framework …
not fully explored in freeway tunnels. A two-stage deep learning modeling framework …
The impact of electromobility in public transport: An estimation of energy consumption using disaggregated data in Santiago, Chile
Electromobility in public transport has become a promising way to reduce environmental
pollution. Several contributions have sought to estimate the energy consumption of buses in …
pollution. Several contributions have sought to estimate the energy consumption of buses in …
Wasserstein generative adversarial network to address the imbalanced data problem in real-time crash risk prediction
Real-time crash risk prediction models aim to identify pre-crash conditions as part of active
traffic safety management. However, traditional models which were mainly developed …
traffic safety management. However, traditional models which were mainly developed …
inTformer: A Time-Embedded Attention-Based Transformer for Crash Likelihood Prediction at Intersections Using Connected Vehicle Data
The real-time crash likelihood prediction model is an essential component of the proactive
traffic safety management system. Over the years, numerous studies have attempted to …
traffic safety management system. Over the years, numerous studies have attempted to …
Real-time accident anticipation for autonomous driving through monocular depth-enhanced 3D modeling
The primary goal of traffic accident anticipation is to foresee potential accidents in real time
using dashcam videos, a task that is pivotal for enhancing the safety and reliability of …
using dashcam videos, a task that is pivotal for enhancing the safety and reliability of …