Computer Science ›› 2019, Vol. 46 ›› Issue (4): 14-21.doi: 10.11896/j.issn.1002-137X.2019.04.003

• Big Data & Data Science • Previous Articles     Next Articles

Visual Analysis Method of Traffic Accident Spatial-Temporal Pattern

RAO Yong-ming, ZHANG Yan-kong, XIE Wen-jun, LIU Lu, LIU Xin-yue, LUO Yue-tong   

  1. School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230000,China
  • Received:2018-10-31 Online:2019-04-15 Published:2019-04-23

Abstract: With the advancement of urbanization and the rapid growth of urban population and vehicles,urban traffic accidents are increasingly frequent,which becomes a hot social concern.This paper took the traffic accident record data of Hefei City in the past ten years as the research object,used the visual analysis method to analyze the accident time and the place information in the traffic accident record data,explored the time and space pattern of the traffic accident,and constructed the traffic accident analysis system,so as to assist relevant departments to improve frequent traffic accidents.In this paper,the concept of road accident risk degree was put forward for the first time,and a new identification method of accident-prone road section was constructed based on multi-scale time statistical zigzag chart and periodic time statistical circle map.Compared with the traditional method,this method does not need to deal with the road segmentation,thus avoiding the impact of the quality of the segmentation on the recognition results.On this basis,this paper combined traffic accident data with urban road network data,and used visual analysis technology to build a visual analysis system of traffic accidents.This system can help relevant departments to understand the time pattern of the overall urban traffic accidents and single road and the accident-prone sections,and explore the accident-prone sections under the continuous time limit or cycle time limit.In addition to time conditions,the system can also identify accident-prone sections under different weather and other limited conditions,so that traffic police departments can make decision management through road accident risk under different circumstances,deploy rescue police forces reasonably and reduce accident hazards.The proposed system has important practical significance for mitigating and curbing the growth of traffic accidents,reducing and preventing road traffic accidents.It is also conducive to the scientific and effectivemana-gement of road traffic.

Key words: Black spot, Road accident risk degree, Road segmentation, Traffic accident, Visual analysis

CLC Number: 

  • TP391.9
[1]COOK K A,THOMAS J J.Illuminating the Path:The Research and Development Agenda for Visual Analytics[M].National Visualization and Analytics Ctr,2005.
[2]ZHU Q,FU X.An overview of multimodal spatial and temporal visual analysis methods [J].Journal of Surveying and Mapping,2017,46(10):1672-1677.(in Chinese)
[3]REN L,DU Y,MA S,et al.Overview of visual analysis of big data [J].Journal of Software,2014,25(9):1909-1936.(in Chinese)
[4]ZHANG J,YANLI E,MA J,et al.Visual Analysis of Public
Utility Service Problems in a Metropolis[J].IEEE Transactions on Visualization & Computer Graphics,2014,20(12):1843-1852.
[5]CAO N,LIN Y R,SUN X,et al.Whisper:Tracing the Spatiotemporal Process of Information Diffusion in Real Time[J].IEEE Transactions on Visualization & Computer Graphics,2012,18(12):2649-2658.
[6]ZHOU Z G,HU D X,LIU Y N,et al.Visual analysis of spatial and temporal multidimensional properties of air quality monitoring data [J].Journal of computer-aided design and graphics,2017,29(8):1477-1487.(in Chinese)
[7]WANG Z,YE T,LU M,et al.Visual Exploration of Sparse Traffic Trajectory Data[J].IEEE Transactions on Visualization &Computer Graphics,2014,20(12):1813-1822.
[8]GUO H,WANG Z,YU B,et al.TripVista:Triple Perspective
Visual Trajectory Analytics and its application on microscopic traffic data at a road intersection[C]∥IEEE Pacific Visualization Symposium.IEEE Computer Society,2011:163-170.
[9]WANG Z,LU M,YUAN X,et al.Visual Traffic Jam Analysis Based on Trajectory Data[J].IEEE Transactions on Visualization & Computer Graphics,2013,19(12):2159.
[10]LU M,WANG Z,LIANG J,et al.OD-Wheel:Visual design to explore OD patterns of a central region[C]∥Visualization Symposium.IEEE,2015:87-91.
[11]LU M,WANG Z,YUAN X.TrajRank:Exploring travel behaviour on a route by trajectory ranking[C]∥Proceedings of IEEE Pacific Visualization Symposium (Pacific Vis’15).IEEE,2015:311-318.
[12]ZHANG J Q,ZHAO S X,QU R T.Spatial and temporal data visualization based on point and heat [J].Journal of Lanzhou Jiaotong University,2017,36(3):63-69.(in Chinese)
ICE--visual analytics for transportation incident datasets[C]∥IEEE International Conference on Information Reuse & Integration.IEEE,2009:200-205.
[14]PIRINGER H,BUCHETICS M,BENEDIK R.AlVis:Situation awareness in the surveillance of road tunnels[C]∥Visual Analytics Science and Technology.IEEE,2013:153-162.
[15]FAN X,HE B,PATRICK B.Context-Aware Big Data Analytics and Visualization for City-Wide Traffic Accidents[C]∥ International & Interdisciplinary Conference on Modeling & Using Context.Cham:Springer,2017.
based spatial analysis of urban traffic accidents:Case study in Mashhad,Iran[J].Journal of Traffic and Transportation Engineering (English Edition),2017(3):82-91.
[17]CHEN Q,SONG X,YAMADA H,et al.Learning deep representation from big and heterogeneous data for traffic accident inference[C]∥Thirtieth AAAI Conference on Artificial Intelligence.2016:338-344.
[18]LU H S,MAO Z J,ZHONG T Y,et al.Study on the linear intelligent screening system based on GIS-T [C]∥International Urban Transportation Academic Conference.2011.(in Chinese)
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