Application of Data Science Technologies in Intelligent Prediction of Traffic Congestion
Joint Authors
Yang, Xu
Luo, Shixin
Gao, Keyan
Qiao, Tingting
Chen, Xiaoya
Source
Journal of Advanced Transportation
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-04-15
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
In recent years, with the rapid development of economy, more and more urban residents, while owning their own motor vehicles, are also troubled by the traffic congestion caused by the backward traffic facilities or traffic management methods.
The loss of productivity, car accidents, high emissions, and environmental pollution caused by traffic congestion has become a huge and increasingly heavy burden on all countries in the world.
Therefore, the prediction of urban road network traffic flow and the rapid and accurate evaluation of traffic congestion are of great significance to the study of urban traffic solutions.
This paper focuses on how to apply data science technologies on vehicular networks data to present a prediction method for traffic congestion based on both real-time and predicted traffic data.
Two evaluation frameworks are established, and existing methods are used to compare and evaluate the accuracy and efficiency of the presented method.
American Psychological Association (APA)
Yang, Xu& Luo, Shixin& Gao, Keyan& Qiao, Tingting& Chen, Xiaoya. 2019. Application of Data Science Technologies in Intelligent Prediction of Traffic Congestion. Journal of Advanced Transportation،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1169728
Modern Language Association (MLA)
Yang, Xu…[et al.]. Application of Data Science Technologies in Intelligent Prediction of Traffic Congestion. Journal of Advanced Transportation No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1169728
American Medical Association (AMA)
Yang, Xu& Luo, Shixin& Gao, Keyan& Qiao, Tingting& Chen, Xiaoya. Application of Data Science Technologies in Intelligent Prediction of Traffic Congestion. Journal of Advanced Transportation. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1169728
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1169728