JOURNAL BROWSE
Search
Advanced SearchSearch Tips
A Selection of High Pedestrian Accident Zones Using Traffic Accident Data and GIS: A Case Study of Seoul
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Selection of High Pedestrian Accident Zones Using Traffic Accident Data and GIS: A Case Study of Seoul
Yang, Jong Hyeon; Kim, Jung Ok; Yu, Kiyun;
  PDF(new window)
 Abstract
To establish objective criteria for high pedestrian accident zones, we combined Getis-ord Gi* and Kernel Density Estimation to select high pedestrian accident zones for 54,208 pedestrian accidents in Seoul from 2009 to 2013. By applying Getis-ord Gi* and considering spatial patterns where pedestrian accident hot spots were clustered, this study identified high pedestrian accident zones. The research examined the microscopic distribution of accidents in high pedestrian accident zones, identified the critical hot spots through Kernel Density Estimation, and analyzed the inner distribution of hot spots by identifying the areas with high density levels.
 Keywords
Pedestrian Accident;Hotspot;Getis-ord Gi*;Kernel Density Estimation;
 Language
Korean
 Cited by
 References
1.
Anderson, T.K. (2009), Kernel density estimation and k-means clustering to profile road accident hotspots, Accident Analysis and Prevention, Vol. 41, No. 3, pp. 359-364. crossref(new window)

2.
Bailey, T.C. and Gatrell, A.C. (1995), Interactive Spatial Data Analysis, Longman, Essex, UK.

3.
Blazquez, C.A. and Celis, M.S. (2013), A spatial and temporal analysis of child pedestrian crashes in Santiago, Chile, Accident Analysis and Prevention, Vol. 50, pp. 304-311. crossref(new window)

4.
Flahaut, B., Mouchart, M., Martin, E.S., and Thomas, I. (2003), The local spatial autocorrelation and the kernel method for identifying black zones, Accident Analysis and Prevention, Vol. 35, No. 6, pp. 991-1004. crossref(new window)

5.
Jang, M., Jang, J., Joo, S., and So, K. (2012), Evaluations of pedestrian environment improvement plan and its performance, Transportation Technology and Policy, Vol. 9, No. 2, pp. 77-85. (in Korean)

6.
Jang, K., Park. S.H., Kang, S., Song, K.H., Kang, S., and Chung, S. (2013), Evaluation of pedestrian safety: pedestrian crash hot spots and risk factors for injury severity, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2393, pp. 104-116.

7.
Jo, J., Park, W., and Kim, M. (2014), Development and application of traffic safety forecast index based on weather informations, Transportation Technology and Policy, Vol 11, No. 2, pp. 62-71. (in Korean)

8.
Khan, G., Qin, X., and Noyce, D. (2008), Spatial analysis of weather crash patterns, Journal of Transportation Engineering, Vol. 134, No. 5, pp. 191-202. crossref(new window)

9.
Kingham, S., Sabel, C.E., and Bartie, P. (2011), The impact of the school run on road traffic accidents: a spatio-temporal analysis, Journal of Transport Geography, Vol. 19, No. 4, pp. 705-711. crossref(new window)

10.
Kuo, P., Zeng, X., and Lord, D. (2011), Guidelines for choosing hot-spot analysis tools based on data characteristics, network restrictions, and time distributions, Proceedings of the 91st Annual Meeting of the Transportation Research Board, Transportation Research Board, 22-26 January, Washington, D.C., USA. pp. 1-21.

11.
Loo, B.P.Y., Yao, S., and Wu, J. (2011), Spatial point analysis of road crashes in shanghai: a GIS-based network kernel density method, Proceedings of 19th International Conference on Geoinformatics, IEEE, 24-26 June, Shanghai, China, pp. 1-6.

12.
Manepalli, U.R.R., Bham, H.G., and Kandada, S. (2011), Evaluation of hotspots identification using kernel density estimation (K) and Getis-ord (Gi*) on I-630, Proceedings of the 3rd International Conference on Road Safety and Simulation, Transportation Research Board, 14-16 September, Indianapolis, USA, pp.1-17.

13.
O'Sullivan, D. and Unwin, D.J. (2002), Geographic Information Analysis, John Wiley & Sons Inc., Hoboken, N.J.

14.
Park, J. and Han, M. (2014), A study on the implementation of walking environment projects by analyzing characteristics of pedestrian accidents by local government types, Journal of Korean Society of Transportation, Vol. 32, No. 6, pp. 615-627. (in Korean with English abstract) crossref(new window)

15.
Plug, C., Xia, J., and Caulfield, C. (2011), Spatial and temporal visualisation techniques for crash analysis, Accident Analysis and Prevention, Vol. 43, No. 6, pp. 1937-1946. crossref(new window)

16.
Prasannakumar, V., Vijith, H., Charutha, R., and Geetha, N. (2011), Spatio-temporal clustering of road accidents: GIS based analysis and assessment, Procedia Social and Behavioral Sciences, Vol. 21, pp. 317-325. crossref(new window)

17.
Pulugurtha, S.S., Krishnakumar, V.K., and Nambisan, S.S. (2007), New methods to identify and rank high pedestrian crash zones, Accident Analysis and Prevention, Vol. 39, No. 4, pp. 800-811. crossref(new window)

18.
Rankavat, S. and Tiwari, G. (2013), Pedestrian accident analysis in Delhi using GIS, Journal of the Eastern Asia Society for Transportation Studies, Vol. 10, pp. 1446-1457.

19.
Schabenberger, O. and Gotway, C.A. (2005), Statistical Methods for Spatial Data Analysis, Chapman & Hall/CRC, Boca Raton, Florida.

20.
Silverman, B.W. (1986), Probability Density Estimation for Statistics and Data Analysis, Chapman and Hall, New York, N.Y.

21.
Tobler, W.R. (1970), A computer movie simulating urban growth in the Detroit region, Economic Geography, Vol. 46, pp. 234-240. crossref(new window)

22.
Truong, L.T. and Somenahalli, S.V.C. (2011), Using GIS to identify pedestrian-vehicle crash hot spots and unsafe bus stops, Journal of Public Transportation, Vol. 14, No. 1, pp. 99-114. crossref(new window)

23.
Vemulapalli, S.S. (2015), GIS-Based Spatial and Temporal Analysis of Aging-Involved Crashes in Florida, Master's thesis, Florida State University, Florida, USA, 126p.

24.
Xie, Z. and Yan, J. (2008), Kernel density estimation of traffic accidents in a network space, Computers, Environment, and Urban Systems, Vol. 32, No. 5, pp. 396-406. crossref(new window)