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[HTML][HTML] Modern data sources and techniques for analysis and forecast of road accidents: A review
Road accidents are one of the most relevant causes of injuries and death worldwide, and
therefore, they constitute a significant field of research on the use of advanced algorithms …
therefore, they constitute a significant field of research on the use of advanced algorithms …
A systematic review of prediction methods for emergency management
With the trend of global warming and destructive human activities, the frequent occurrences
of catastrophes have posed devastating threats to human life and social stability worldwide …
of catastrophes have posed devastating threats to human life and social stability worldwide …
Real-time crash risk prediction on arterials based on LSTM-CNN
Real-time crash risk prediction is expected to play a crucial role in preventing traffic
accidents. However, most existing studies only focus on freeways rather than urban arterials …
accidents. However, most existing studies only focus on freeways rather than urban arterials …
Hetero-convlstm: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data
Predicting traffic accidents is a crucial problem to improving transportation and public safety
as well as safe routing. The problem is also challenging due to the rareness of accidents in …
as well as safe routing. The problem is also challenging due to the rareness of accidents in …
Deep spatio-temporal graph convolutional network for traffic accident prediction
Traffic accidents usually lead to severe human casualties and huge economic losses in real-
world scenarios. Timely accurate prediction of traffic accidents has great potential to protect …
world scenarios. Timely accurate prediction of traffic accidents has great potential to protect …
Crash data augmentation using variational autoencoder
In this paper, we present a data augmentation technique to reproduce crash data. The
dataset comprising crash and non-crash events are extremely imbalanced. For instance, the …
dataset comprising crash and non-crash events are extremely imbalanced. For instance, the …
Real-time crash prediction on expressways using deep generative models
Real-time crash prediction is essential for proactive traffic safety management. However,
develo** an accurate prediction model is challenging as the traffic data of crash and non …
develo** an accurate prediction model is challenging as the traffic data of crash and non …
A study of freeway crash risk prediction and interpretation based on risky driving behavior and traffic flow data
M Guo, X Zhao, Y Yao, P Yan, Y Su, C Bi… - Accident Analysis & …, 2021 - Elsevier
The prediction of traffic crashes is an essential topic in traffic safety research. Most of the
previous studies conducted experiments on real-time crash prediction of expressways or …
previous studies conducted experiments on real-time crash prediction of expressways or …
Machine learning approaches to traffic accident analysis and hotspot prediction
Traffic accidents are one of the most important concerns of the world, since they result in
numerous casualties, injuries, and fatalities each year, as well as significant economic …
numerous casualties, injuries, and fatalities each year, as well as significant economic …
Accident risk prediction based on heterogeneous sparse data: New dataset and insights
Reducing traffic accidents is an important public safety challenge, therefore, accident
analysis and prediction has been a topic of much research over the past few decades. Using …
analysis and prediction has been a topic of much research over the past few decades. Using …