Browse > Article
http://dx.doi.org/10.22640/lxsiri.2018.48.1.201

A Visualization of Traffic Accidents Hotspot along the Road Network  

Cho, Nahye (Department of Geoinformatics, University of Seoul)
Jun, Chulmin (Department of Geoinformatics, University of Seoul)
Kang, Youngok (Department of Social Studies, Ewha Womans University)
Publication Information
Journal of Cadastre & Land InformatiX / v.48, no.1, 2018 , pp. 201-213 More about this Journal
Abstract
In recent years, the number of traffic accidents caused by car accidents has been decreasing steadily due to traffic accident prevention activities in Korea. However, the number of accidents in Seoul is higher than that of other regions. Various studies have been conducted to prevent traffic accidents, which are human disasters. In particular, previous studies have performed the spatial analysis of traffic accidents by counting the number of traffic accidents by administrative districts or by estimating the density through kernel density method in order to identify the traffic accident cluster areas. However, since traffic accidents take place along the road, it would be more meaningful to investigate them concentrated on the road network. In this study, traffic accidents were assigned to the nearest road network in two ways and analyzed by hotspot analysis using Getis-Ord Gi* statistics. One of them was investigated with a fixed road link of 10m unit, and the other by computing the average traffic accidents per unit length per road section. As a result by the first method, it was possible to identify the specific road sections where traffic accidents are concentrated. On the other hand, the results by the second method showed that the traffic accident concentrated areas are extensible depending on the characteristic of the road links. The methods proposed here provide different approaches for visualizing the traffic accidents and thus, make it possible to identify those sections clearly that need improvement as for the traffic environment.
Keywords
Traffic Accidents; Road Network; Hotspot Analysis; Visualization; Hotspot zones of traffic accidents;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Prasannakumar V, Vijith H, Charutha R, Geetha N. 2011. Spatio-temporal clustering of road accidents: GIS based analysis and assessment. Procedia Soc Behav Sci. 21: 317-325.   DOI
2 Kang YO, Son SR, Cho NH. 2017. Analysis of Traffic Accidents Injury Severity in Seoul using Decision Trees and Spatiotemporal Data Visualization. Journal of Cadastre & Land InformatiX 47(2): 233-254.   DOI
3 Sung BJ, Bae GH, Yoo HH. 2015. Analysis of Temporal and Spatial Distribution of Traffic Accidents in Jinju. The Journal of Korean Society for Geospatial Information Science. 23(2): 3-9.
4 Son SR, Kang YO. 2017. Spatio-temporal Pattern of Traffic Accident of Female Drivers in Seoul, Journal of the Korean Cartographic Association, 17(2): 89-98.
5 Lee SJ, Cho HS, Song WH, Sohn HG. 2015. A Study on Spatial Characteristic and Influence Factor of Traffic Accident in Seoul. Korean Society for Geospatial Information Science.. 132-133.
6 Hong SK. 1998. Developing a Visualization System for Spatio - Temporal Linear Point Data. The Journal of Korean Urban Geographical Society. 1(1): 85-100.
7 Anderson TK. 2009. Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention. 41(3): 359-364.   DOI
8 Ceder A, Livneh M. 1978. Further evaluation of the relationships between road accidents and average daily traffic. Accident Analysis & Prevention, 10(2): 95-109.   DOI
9 Chen C, Zhang G, Liu XC, Ci Y, Huang H, Ma J, Chen Y, Guan H. 2016. Driver injury severity outcome analysis in rural interstate highway crashes: a two-level Bayesian logistic regression interpretation. Accid Anal Prev. 97: 69-78.   DOI
10 Elvik R. 2013. Risk of road accident associated with the use of drugs: a systematic review and meta-analysis of evidence from epidemiological studies. Accid Anal Prev. 60: 254-267.   DOI
11 Okabe A, Okunuki KI, Shiode S. 2006. SANET: a toolbox for spatial analysis on a network. Geographical analysis, 38(1): 57-66.   DOI
12 Erdogan S, Yilmaz I, Baybura T, Gullu M. 2008. Geographical information systems aided traffic accident analysis system case study: city of Afyonkarahisar. Accident Analysis & Prevention, 40(1): 174-181.   DOI
13 Kang YO, Cho NH, Son SR. 2018. Spatiotemporal characteristics of elderly population's traffic accidents in Seoul using space-time cube and space-time kernel density estimation. PLoS one, 13(5): e0196845.   DOI
14 Levine N, Kim KE, Nitz LH. 1995. Spatial analysis of Honolulu motor vehicle crashes: I. Spatial patterns. Accident Analysis & Prevention, 27(5): 663-674.   DOI
15 McSwiggan G, Baddeley A, Nair G. 2017. Kernel density estimation on a linear network. Scandinavian Journal of Statistics, 44(2): 324-345.   DOI
16 Ng JC, Hauer E. 1989. Accidents on rural two-lane roads: differences between seven states (with discussion and closure) (No. 1238).
17 Okabe A, Sugihara K. 2012. Spatial analysis along networks: statistical and computational methods. John Wiley & Sons.
18 Openshaw S. 1984. Ecological fallacies and the analysis of areal census data. Environment and planning A, 16(1): 17-31.   DOI
19 Xie Z, Yan J. 2008. Kernel density estimation of traffic accidents in a network space. Computers, Environment and Urban Systems, 32(5): 396-406.   DOI
20 Romano B, Jiang Z. 2017. Visualizing Traffic Accident Hotspots Based on Spatial-Temporal Network Kernel Density Estimation. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (p. 98). 2017, November. ACM.
21 Yamada I, Thill JC. 2004. Comparison of planar and network K-functions in traffic accident analysis. Journal of Transport Geography, 12(2): 149-158.   DOI
22 도로교통공단, http://www.index.go.kr