CONTACT US: Contact info
|
Citation |
Gutierrez-Osorio C, Pedraza C. J. Traffic Transp. Eng. Engl. Ed. 2020; 7(4): 432-446. |
|
Copyright |
(Copyright © 2020, Periodical Offices of Chang'an University, Publisher Elsevier Publishing) |
|
DOI |
|
PMID |
unavailable |
|
Abstract |
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 and techniques to analyze and predict traffic accidents and determine the most relevant elements that contribute to road accidents. The research of road accident prediction aims to respond to the challenge of offer tools to generate a more secure mobility environment, and ultimately, save lives. This paper aims to provide an overview of the state of the art in the prediction of road accidents through machine learning algorithms and advanced techniques for analyzing information, such as convolutional neural networks and long short-term memory networks, among other deep learning architectures. Furthermore, in this article, a compendium and study of the most used data sources for the road accident forecast is made. And a classification is proposed according to its origin and characteristics, such as open data, measurement technologies, onboard equipment and social media data. For the analysis of the information, the different algorithms employed to make predictions about road accidents are listed and compared, as well as their applicability depending on the types of data being analyzed, along with the results obtained and their ease of interpretation and analysis. The best results reported by the authors are obtained when two or more analytic techniques are combined, in such a way that analysis of the obtained results is strengthened. Among the future challenges in road traffic forecasting lies the enhancement of the scope of the proposed models and predictions by the incorporation of heterogeneous data sources, that include geo spatial data, information from traffic volume, traffic statistics, video, sound, text and sentiment from social media, that many authors concur that can improve the precision and accuracy of the analysis and predictions. Language: en |
|
Keywords |
Data analysis; Machine learning; Road accident forecasting; Traffic accident prediction; Traffic engineering |