A literature review of machine learning algorithms for crash injury severity prediction
Introduction: Road traffic crashes represent a major public health concern, so it is of
significant importance to understand the factors associated with the increase of injury …
significant importance to understand the factors associated with the increase of injury …
Urban anomaly analytics: Description, detection, and prediction
Urban anomalies may result in loss of life or property if not handled properly. Automatically
alerting anomalies in their early stage or even predicting anomalies before happening is of …
alerting anomalies in their early stage or even predicting anomalies before happening is of …
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 …
Injury severity prediction of traffic crashes with ensemble machine learning techniques: A comparative study
A better understanding of injury severity risk factors is fundamental to improving crash
prediction and effective implementation of appropriate mitigation strategies. Traditional …
prediction and effective implementation of appropriate mitigation strategies. Traditional …
Transparent deep machine learning framework for predicting traffic crash severity
Abstract Analysis of crash injury severity is a promising research target in highway safety
studies. A better understanding of crash severity risk factors is vital for the proactive …
studies. A better understanding of crash severity risk factors is vital for the proactive …
Severity prediction of traffic accident using an artificial neural network
In this paper, an artificial neural network (ANN) was used to predict the injury severity of
traffic accidents based on 5973 traffic accident records occurred in Abu Dhabi over a 6‐year …
traffic accidents based on 5973 traffic accident records occurred in Abu Dhabi over a 6‐year …
Utilizing real-world transportation data for accurate traffic prediction
B Pan, U Demiryurek, C Shahabi - 2012 ieee 12th international …, 2012 - ieeexplore.ieee.org
For the first time, real-time high-fidelity spatiotemporal data on transportation networks of
major cities have become available. This gold mine of data can be utilized to learn about …
major cities have become available. This gold mine of data can be utilized to learn about …
GSNet: Learning spatial-temporal correlations from geographical and semantic aspects for traffic accident risk forecasting
Traffic accident forecasting is of great importance to urban public safety, emergency
treatment, and construction planning. However, it is very challenging since traffic accidents …
treatment, and construction planning. However, it is very challenging since traffic accidents …
[PDF][PDF] Traffic accident analysis using machine learning paradigms
M Chong, A Abraham, M Paprzycki - Informatica, 2005 - informatica.si
Miao Chong1, Ajith Abraham2 and Marcin Paprzycki1, 3 1Computer Science Department,
Oklahoma State University, USA, marcin@ cs. okstate. edu 2School of Computer Science …
Oklahoma State University, USA, marcin@ cs. okstate. edu 2School of Computer Science …
[HTML][HTML] A review on neural network techniques for the prediction of road traffic accident severity
The occurrence rate of death and injury due to road traffic accidents is rising increasingly
globally day by day. For several decades, the focus of research has been on getting a …
globally day by day. For several decades, the focus of research has been on getting a …