journal article·2021-09-23Open access

Severity, Spatial Pattern and Statistical Analysis of Road Traffic Crash Hot Spots in Ethiopia

Alamirew Mulugeta Tola, Tamene Adugna Demissie, Fokke Saathoff, Alemayehu Gebissa

Applied Sciences· Volume 11· Issue 19

Abstract

The reduction of traffic crashes, as well as their socio-economic consequences, has captivated the attention of safety professionals and transportation agencies. The most important activity for an effective road safety practice is to identify hazardous roadway areas based on a spatial pattern analysis of crashes and an evaluation of crash spatial relations with neighboring areas and other relevant factors. For decades, safety researchers have adopted several techniques to analyze historical road traffic crash (RTC) information using the advanced GIS-based hot spot analysis. The objective of this study is to present a GIS technique for identifying crash hot spots based on spatial autocorrelation analysis using a four-year (2014–2017) crash data across Ethiopian regions, as well as zones and towns in the Oromia region. The study considered the corresponding severity values of RTCs for the analysis and ranking of crash hot spot areas. The spatial autocorrelation tool in ArcGIS 10.5 was used to analyze the spatial patterns of RTCs and then the Getis Ord Gi* statistics tool was used to identify high and low crash severity cluster zones. The results showed that the methods used in this analysis, which incorporated Moran’s I spatial autocorrelation of crash incidents, Getis Ord Gi* and crash severity index, proved to be a fruitful strategy for identifying and ranking crash hot spots. The identified crash hot spot areas are along the entrance to and exit from Addis Ababa, Ethiopia’s capital city, so the responsible bodies and traffic management agencies should give top priority attention and conduct a thorough study to reduce the socio-economic effect of RTCs.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Traffic and Road Safety · Urban Transport and Accessibility · Injury Epidemiology and Prevention

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Faculty of Agricultural and Environmental Sciences, Geotechnics and Coastal Engineering, Rostock University, 18051 Rostock, Germany

Faculty of Civil & Environmental Engineering, Jimma University, Jimma 378, Ethiopia

Funding

Deutscher Akademischer Austauschdienst

References (73)

Show source-provided references
1. WHO (World Health Organization) (2018). Global Status Report on Road Safety, World Health Organization.
2. A GIS-based Bayesian approach for analyzing spatial-temporal patterns of intra-city motor vehicle crashes10.1016/j.jtrangeo.2006.08.005 ↗
3. A new model for determining the traffic accident black spots using GIS-aided spatial statistical methods10.1016/j.tra.2017.05.031 ↗
4. How GIS can help address the uncertain geographic context problem in social science research10.1080/19475683.2012.727867 ↗
5. Accessibility, equity and health care: Review and research directions for transport geographers10.1016/j.jtrangeo.2014.12.006 ↗
6. Space, place and health10.1080/19475683.2015.1007895 ↗
7. The potential uses of a computer animated film in the analysis of geographical patterns of traffic crashes10.1016/0001-4575(76)90007-5 ↗
8. GIS-based method for detecting high-crash-risk road segments using network kernel density estimation10.1080/10095020.2013.766396 ↗
9. Lloyd, C.D. (2010). Spatial Data Analysis: An Introduction for GIS Users, Oxford University Press.
10. Loo, B.P.Y., and Anderson, T.K. (2015). Spatial Analysis Methods of Road Traffic Collisions, CRC Press.10.1201/b18937 ↗
11. The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives10.1016/j.tra.2010.02.001 ↗
12. Mehta, G.S. (2014). Analyzing Crash Frequency and Severity Data Using Novel Techniques. [Ph.D. Thesis, University of Alabama].
13. Analytic methods in accident research: Methodological frontier and future directions
14. Lagarde, E. (2007). Road Traffic Injury Is an Escalating Burden in Africa and Deserves Proportionate Research Efforts. PLoS Med., 4.10.1371/journal.pmed.0040170 ↗
15. Elvik, R., Høye, A., Vaa, T., and Sørensen, M. (2009). The Handbook of Road Safety Measures, Emerald Group Publishing Limited. [2nd ed.].10.1108/9781848552517 ↗
16. Traffic collisions in space: Four decades of advancement in applied GIS10.1080/19475683.2015.1085440 ↗
17. Kernel density estimation and K-means clustering to profile road accident hotspots10.1016/j.aap.2008.12.014 ↗
18. GIS-based Spatial and Temporal Analysis of Aging-Involved Accidents: A Case Study of Three Counties in Florida10.1007/s12061-016-9192-4 ↗
19. Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam
20. Abegaz, T., and Gebremedhin, S. (2019). Magnitude of road traffic accident related injuries and fatalities in Ethiopia. PLoS ONE, 14.10.1371/journal.pone.0202240 ↗
21. Identifying major urban road traffic accident black-spots (RTABSs): A sub-city based analysis of evidences from the city of Addis Ababa, Ethiopia
22. Anteneh, A., and Endris, B.S. (2020). Injury related adult deaths in Addis Ababa, Ethiopia: Analysis of data from verbal autopsy. BMC Public Health, 20.10.1186/s12889-020-08944-7 ↗
23. Identifying Road Traffic Accidents Hotspots Areas Using GIS in Ethiopia: A Case Study of Hosanna Town
24. Geurts, K., and Wets, G. (2003). Black Spot Analysis Methods: Literature Review, Steunpunt Verkeersveiligheid.
25. Vistisen, D. (2002). Models and Methods for Hot Spot Safety Work. [Ph.D. Thesis, Technical University of Denmark DTU].
26. Sweroad (2001). General Directorate of Highways, A. Road Improvement and Traffic Safety Project: Black Spot Manual, Sweroad.
27. Taylor, M.A.P., Bonsall, P.W., and Young, W. (2017). Understanding Traffic Systems: Data Analysis and Presentation, Taylor and Francis Group. [2nd ed.].10.4324/9781315235370 ↗
28. Yuan, T., Zeng, X., and Shi, T. (2020). Identifying Urban Road Black Spots with a Novel Method Based on the Firefly Clustering Algorithm and a Geographic Information System. Sustainability, 12.10.3390/su12052091 ↗
29. Applying linear analysis methods to GIS-supported procedures for preventing traffic accidents: Case study of Konya10.1016/j.ssci.2010.02.016 ↗
30. Zhang, C., and Ivan, J.N. (2005). Effects of geometric characteristics on head-on crash incidence on two-lane roads in Connecticut. Transp. Res. Rec., 159–164.10.3141/1908-19 ↗
31. Elvik, R. (2008). Comparative analysis of techniques for identifying locations of hazardous roads. Transp. Res. Rec., 72–75.10.3141/2083-08 ↗
32. Sacchi, E., Sayed, T., and El-Basyouny, K. (2015). Multivariate full Bayesian hot spot identification and ranking new technique. Transp. Res. Rec., 1–9.10.3141/2515-01 ↗
33. Geographical information systems aided traffic accident analysis system case study: City of Afyonkarahisar10.1016/j.aap.2007.05.004 ↗
34. Kernel Density Estimation of traffic accidents in a network space10.1016/j.compenvurbsys.2008.05.001 ↗
35. Local Indicators of Spatial Association—LISA10.1111/j.1538-4632.1995.tb00338.x ↗
36. Local Spatial Autocorrelation Statistics: Distributional Issues and an Application10.1111/j.1538-4632.1995.tb00912.x ↗
37. The analysis of spatial association by use of distance statistics
38. Lee, M., and Khattak, A.J. (2019). Case Study of Crash Severity Spatial Pattern Identification in Hot Spot Analysis. Transp. Res. Rec.10.1177/0361198119845367 ↗
39. Levine, N. (2002). CrimeStat II: A spatial Statistics Program for the Analysis of Crime Incident Locations, Part I.
40. A review of spatial approaches in road safety10.1016/j.aap.2019.105323 ↗
41. Using Moran’s I and geostatistics to identify spatial patterns of soil nutrients in two different long-term phosphorus-application plots10.1002/jpln.201000422 ↗
42. Cheng, Z., Zu, Z., and Lu, J. (2019). Traffic Crash Evolution Characteristic Analysis and Spatiotemporal Hotspot Identification of Urban Road Intersections. Sustainability, 11.10.3390/su11010160 ↗
43. Comparison of planar and network K-functions in traffic accident analysis10.1016/j.jtrangeo.2003.10.006 ↗
44. Spatial and temporal visualisation techniques for crash analysis10.1016/j.aap.2011.05.007 ↗
45. Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation10.1016/j.aap.2013.03.003 ↗
46. The KDE+ software: A tool for effective identification and ranking of animal-vehicle collision hotspots along networks10.1007/s10980-015-0265-6 ↗
47. Wong, D.W.S., and Lee, J. (2005). Statistical Analysis of Geographic Information with ArcView GIS and ArcGIS, Wiley. Geographic Information Science.
48. Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey10.1016/j.jsr.2009.07.006 ↗
49. Spatial analysis of weather crash patterns10.1061/(ASCE)0733-947X(2008)134:5(191) ↗
50. Identifying Black Spot Accident Zones using a Geographical Information System on Kombolcha-Dessie Road in Ethiopia
51. Identification and ranking of black spots: Sensitivity analysis10.3141/1897-05 ↗
52. NSW Road Safety and Traffic Management Directorate (1999). Road Traffic Accidents in New South Wales–1997-Statistical Statement: Year Ended 31 December 1997, Roads and Traffic Authority of NSW.
53. Spatial autocorrelation and Bayesian spatial statistical method for analyzing intersections Prone to injury crashes10.3141/2136-11 ↗
54. Getis–Ord Spatial Statistics to Identify Hot Spots by Using Incident Management Data10.3141/2165-05 ↗
55. A model suggestion for the determination of the traffic accident hotspots on the Turkish highway road network: A pilot study10.1590/S1982-21702015000100011 ↗
56. O’Sullivan, D., and Unwin, D.J. (2010). Geographic Information Analysis, John Wiley & Sons. [2nd ed.].
57. Spatiotemporal patterns of elephant poaching in South-Eastern Kenya10.1071/WR11017 ↗
58. Variables Associated with Severity of Bacterial Canker and Wilt Caused by Clavibacter Michiganensis Subsp. Michiganensis in Tomato Greenhouses10.1094/PHYTO-07-15-0159-R ↗
59. ERA (Ethiopian Roads Authority) (2019). Road Sector Development Program 21 Years Performance Assessment, ERA (Ethiopian Roads Authority).
60. Federal Transport Authority (2019). Registered Vehicle Statistics, Federal Transport Authority.
61. (2021, September 15). ESRI Multi-Distance Spatial Cluster Analysis (Ripley’s k-Function). Available online: http://resources.esri.com/help/9.3/arcgisdesktop/com/gp_toolref/spatial_statistics_tools/multi_distance_spatial_cluster_analysis_ripley_s_k_function_spatial_statistics_.htm.
62. World Bank (2015). Addis Ababa, Ethiopia—Enhancing Urban Resilience (City Strength: Resilient Cities Program), World Bank.
63. Ethiopian Roads Authority (ERA) (2005). Urban Transport Study and Preparation of Pilot Project of Addis Ababa Final Report, Ethiopian Roads Authority (ERA).
64. Samson, F. (2006). Analysis of Traffic Accident in Addis Ababa: Traffic Simulation. [Master’s Thesis, Addis Ababa University].
65. Injury severity of pedestrians involved in road traffic crashes in Addis Ababa, Ethiopia
66. World Bank Group (2002). Scoping Study—Urban Mobility in Three Cities: Addis Ababa, Dar es Salaam, and Nairobi (English), World Bank Group.
67. Oromia Traffic Police Bureau (2019). Annual Crash Data Report, Oromia Traffic Police Bureau.
68. Quisumbing, A.R., and Yohannes, Y. (2005). How Fair is Workfare? Gender, Public Works, and Employment in Rural Ethiopia, The World Bank. Policy Research Working Papers.10.1596/1813-9450-3492 ↗
69. Role of gender in road accidents10.1080/15389580309857 ↗
70. Gender effects in young road users on road safety attitudes, behaviors and risk perception10.3389/fpsyg.2016.01412 ↗
71. Différences entre les hommes et les femmes face au risque routier10.1016/S0398-7620(04)99065-7 ↗
72. Liu, C., and Ye, T.J. (2011). Run-Off-Road Crashes: An On-Scene Perspective.
73. Mapping out goods flow to Addis Ababa city, Ethiopia, and it impact on environment10.1016/j.trpro.2017.05.475 ↗