You are not logged in.

Big data analytics and visualization with spatio-temporal correlations for traffic accidents

Fan, Xialoiang, He, Baoqin, Wang, Cheng, Li, Jonathan, Cheng, Ming, Huang, Huaqiang and Liu, Xiao 2015, Big data analytics and visualization with spatio-temporal correlations for traffic accidents, in ICA3PP 2015 : Proceedings of the 2015 International Conference on Algorithms and Architectures for Parallel Processing, Springer, Berlin, Germany, pp. 255-268, doi: 10.1007/978-3-319-27122-4_18.

Attached Files
Name Description MIMEType Size Downloads

Title Big data analytics and visualization with spatio-temporal correlations for traffic accidents
Author(s) Fan, Xialoiang
He, Baoqin
Wang, Cheng
Li, Jonathan
Cheng, Ming
Huang, Huaqiang
Liu, XiaoORCID iD for Liu, Xiao orcid.org/0000-0002-4151-8522
Conference name Algorithms and Architectures for Parallel Processing. Conference (2015 : 3rd: Zhangjiahjie, China)
Conference location Zhangjiahjie, China
Conference dates 18-20 Nov. 2015
Title of proceedings ICA3PP 2015 : Proceedings of the 2015 International Conference on Algorithms and Architectures for Parallel Processing
Editor(s) Wang, Guojun
Zomaya, Albert
Perez, Gregorio Martinez
Li, Kenli
Publication date 2015
Series Lecture notes in computer science; v.9529
Start page 255
End page 268
Total pages 14
Publisher Springer
Place of publication Berlin, Germany
Keyword(s) big data analytics
accident occurrence analysis
crash type analysis
spatio-temporal correlation
visualization
Summary Big data analytics for traffic accidents is a hot topic and has significant values for a smart and safe traffic in the city. Based on the massive traffic accident data from October 2014 to March 2015 in Xiamen, China, we propose a novel accident occurrences analytics method in both spatial and temporal dimensions to predict when and where an accident with a specific crash type will occur consequentially by whom. Firstly, we analyze and visualize accident occurrences in both temporal and spatial view. Second, we illustrate spatio-temporal visualization results through two case studies in multiple road segments, and the impact of weather on crash types. These findings of accident occurrences analysis and visualization would not only help traffic police department implement instant personnel assignments among simultaneous accidents, but also inform individual drivers about accident-prone sections and the time span which requires their most attention.
ISBN 9783319271224
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-319-27122-4_18
Field of Research 080110 Simulation and Modelling
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082675

Document type: Conference Paper
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 90 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Fri, 08 Apr 2016, 15:17:18 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.