Identification of crash hotspots using kernel density estimation and kriging methods: a comparison

Journal of Modern Transportation

Volumn 23, Issue 2, 2015, Pages 93-106

  Thakali, Lalita ^a   Kwon, Tae J ^a   Fu, Liping ^a,b  

^a UNIVERSITY OF WATERLOO   (Canada)

^b SOUTHWEST JIAOTONG UNIVERSITY   (China)

Author keywords

Crash hotspots; Kernel density; Kriging; Performance measures

Indexed keywords

EID: 84935014670     PISSN: 2095087X     EISSN: 21960577     Source Type: Journal    
DOI: 10.1007/s40534-015-0068-0     Document Type: Article

References (37)

1. Hauer E (1992) Empirical Bayes approach to the estimation of “unsafety”: the multivariate regression method. Accid Anal Prev 24(5):457–477
2. Saccomanno FF, Grossi R, Greco D, Mehmood A (2001) Identifying black spots along highway SS107 in southern Italy using two models. J Transp Eng 127(6):515–522
3. Greibe P (2003) Accident prediction models for urban roads. Accid Anal Prev 35(2):273–285
4. Miranda-Mareno LF (2006) Statistical models and methods for identifying hazardous locations for safety improvements. PhD thesis report, University of Waterloo
5. Cheng W, Washington S (2008) New criteria for evaluating methods of identifying hot spots. Transp Res Rec 2083(1):76–85
6. Highway Safety Manual (HSM) (2010) American association of state highway and transportation officials (AASHTO). Washington
7. Tarko, A.P., Kanodia, M., 2003. Hazard elimination program––manual on improving safety of Indiana Road Intersections and Sections
8. Kuo PF, Lord D, Walden TD (2012) Using geographical information systems to effectively organize police patrol routes by grouping hot spots of crash and crime data. Zachry Department of Civil Engineering, Texas A&M University
9. Pulugurtha SS, Krishnakumar KV, Nambisan SS (2007) New methods to identify and rank high pedestrian crash zones: an illustration. Accid Anal Prev 39(4):800–811
10. Erdogan S, Yilmaz I, Baybura T, Gullu M (2008) Geographical information systems aided traffic accident analysis system case study: city of Afyonkarahisar. Accid Anal Prev 40(1):174–181
11. Anderson TK (2009) Kernel density estimation and K-means clustering to profile road accident hotspots. Accid Anal Prev 41(3):359–364
12. Blazquez CA, Celis MS (2013) A spatial and temporal analysis of child pedestrian crashes in Santiago, Chile. Accid Anal Prev 50:304–311
13. Keskin F, Yenilmez F, Çolak M, Yavuzer I, Düzgün HS (2011) Analysis of traffic incidents in METU campus. Procedia Soc Behav Sci 19:61–70
14. Kim KE, Yamashita EY (2004) Using K-means clustering algorithm to examine patterns of pedestrian involved crashes in Honolulu, Hawaii CD- Rom TRB. National Research Council, Washington DC
15. Levine N, Kim KE, Lawrence HN (1995) Spatial analysis of Honolulu motor vehicle crashes: 1. spatial patterns. Accid Anal Prev 27(5):663–674
16. Levine N (2006) Crime mapping and the CrimeStat program. Geogr Anal 38(2006):41–56
17. Levine N (2009) A motor vehicle safety planning support system: the Houston experience. In S. Geertman and J. Stillwell (eds) Planning support systems: best practice and new methods. Springer, New York, pp 93–111
18. Kundakci E, Tuydes-Yaman H (2014) Understanding the distribution of traffic accident hotspots in Ankara, Turkey. In: Proceedings of the 93rd annual TRB conference, Washington, 12–16 Jan
19. 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
20. Troung LT, Somenhalli S (2011) Using GIS to identify pedestrian-vehicle crash hot spots and unsafe bus stops. J Public Transp 14:99–114
21. Khan G, Qin X, Noyce DA (2008) Spatial analysis of weather crash patterns. J Transp Eng 134(5):191–202
22. Pulugurtha S, Vanapalli V (2008) Hazardous bus stops identification: an illustration using GIS. J Public transp 11:65–83
23. Ha HH, Thill JC (2011) Analysis of traffic hazard intensity: a spatial epidemiology case study of urban pedestrians. Comput Environ Urban Syst 35(3):230–240
24. Olea RA (1999) Geostatistics for engineers and earth scientists. Kluwer, Boston
25. Oliver MA, Webster R (1990) Kriging: a method of interpolation for geographical information systems. Int J Geogr Inf Syst 3:313–332
26. U.S. Census Bureau (2014). Metropolitan and micropolitan statistical areas. United States Census Bureau, Population Division. Retrieved 9 June 2014
27. Silverman BW (1986) Density estimation for statistics and data analysis(Vol. 26). CRC press, Boca Raton
28. Bailey TC, Gatrell AC (1995) Interactive spatial data analysis. Longman, Harlow
29. Levine N (2004). CrimeStat III: a spatial statistics program for the analysis of crime incident locations (Version 3.0), National Institute of Justice, Washington, DC
30. Brimicombe AJ (2004). On being more robust about hotpots. Paper presented at the 7th annual international crime mapping research conference, April 2004
31. Krige DG (1951) A statistical approach to some basic mine valuation problems on the Witwatersrand. J Chem Metall Min Soc S Afr 52(6):119–139
32. Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York
33. Olea RA (2006) A six-step practical approach to semivariogram modeling. Stoch Environ Res Risk Assess 20:307–318
34. Chainey S, Tompson L, Uhlig S (2008) The utility of hotspot mapping for predicting spatial patterns of crime. Secur J 21(1):4–28
35. Van Pattern J, McKeldin-Coner D Cox (2009) A microspatial analysis of robbery: prospective hot spotting in a small city. Crime Mapp 1(1):7–32
36. Xie Z, Yan J (2008) Kernel density estimation of traffic accidents in a network space. Comput Environ Urban Syst 32(5):396–406
37. Hart TC, Zandbergen PA (2012) Effects of data quality on predictive hotspot mapping. Publication 239861. National Criminal Justice Research Service, Washington, DC