[HTML][HTML] Modern data sources and techniques for analysis and forecast of road accidents: A review

C Gutierrez-Osorio, C Pedraza - Journal of traffic and transportation …, 2020 - Elsevier
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 …

A systematic review of prediction methods for emergency management

D Huang, S Wang, Z Liu - International Journal of Disaster Risk Reduction, 2021 - Elsevier
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 …

Real-time crash risk prediction on arterials based on LSTM-CNN

P Li, M Abdel-Aty, J Yuan - Accident Analysis & Prevention, 2020 - Elsevier
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 …

Hetero-convlstm: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data

Z Yuan, X Zhou, T Yang - Proceedings of the 24th ACM SIGKDD …, 2018 - dl.acm.org
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 …

Deep spatio-temporal graph convolutional network for traffic accident prediction

L Yu, B Du, X Hu, L Sun, L Han, W Lv - Neurocomputing, 2021 - Elsevier
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 …

Crash data augmentation using variational autoencoder

Z Islam, M Abdel-Aty, Q Cai, J Yuan - Accident Analysis & Prevention, 2021 - Elsevier
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 …

Real-time crash prediction on expressways using deep generative models

Q Cai, M Abdel-Aty, J Yuan, J Lee, Y Wu - Transportation research part C …, 2020 - Elsevier
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 …

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 …

Machine learning approaches to traffic accident analysis and hotspot prediction

D Santos, J Saias, P Quaresma, VB Nogueira - Computers, 2021 - mdpi.com
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 …

Accident risk prediction based on heterogeneous sparse data: New dataset and insights

S Moosavi, MH Samavatian, S Parthasarathy… - Proceedings of the 27th …, 2019 - dl.acm.org
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 …