Identifying Spatial Patterns of Road Accidents in Madaba City by Applying Getis-Ord Gi* Spatial Statistic
Article Sidebar
Main Article Content
Abstract
Road safety has become a subject of great interest among policymakers worldwide as they seek effective strategies to mitigate traffic accidents. There exist various approaches to examine the occurrences of road traffic accidents in terms of their spatial information and consequences. Geographical information systems (GIS) have been widely employed to analyse the spatial patterns of road traffic accidents; they offer various statistical analysis tools to reveal the hotspot locations of road accidents. Reducing the number of traffic accidents and overcoming their negative impact by defining the hotspot locations gain serious attention from the Public Security Directorate (PSD) in Jordan. This study analyses road traffic accidents in Madaba City using spatial statistics to determine the hotspot locations. The Gtis-Ord (Gi*) spatial statistics method was applied to 5730 reported traffic accidents between 2017 and 2019. The results accurately located the groups of chosen accidents and identified 37 hotspots, accounting for 1.89% of the reported cases. The Maximum Z score was 30.99, and 691 reported cases led to the identification of 13 high-priority hotspots. These hotspots occur at significant thoroughfares, busy roundabouts, and uncontrolled intersections. Driving errors and excessive speeding were the most common causes of fatal and non-fatal accidents in Madaba City. Efficient countermeasures to mitigate the number of accidents in Madaba City include adding more police inspectors to the city center, installing speed cameras, and putting up traffic signs at uncontrolled intersections. The outcomes of this work may encourage the PSD to adopt the GIS statistical tools in analyzing the spatial patterns of road traffic accidents to achieve more accurate results.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
DOS 2021. Report on Jordan in Numbers for the year 2021, Retrieved from: https://dosweb.dos.gov.jo/. (access date: April, 2023).
PSD 2021. Annual report of traffic accidents in Jordan for the year 2021. Retrieved from: https://psd.gov.jo/en-us. (access date: April, 2023)
S. Al Jazzazi, W. Al Mhairat, and A.Al Zyod, A Study of Factors Affecting Highway Accident Rates in Jordan, International Journal of Transportation Systems. 3(2018) 30-40.
N. Manap, M.N. Borhan, M. R. Yazidm, K. A. Hambali, and A. Rohan, Determining Spatial Patterns of Road Accidents at Expressway by Applying Getis-Ord Gi* Spatial Statistic, International Journal of Recent Technology and Engineering. 8(3S3) (2019) 345–350. https://doi.org/10.35940/ijrte.C1004.1183S319.
R. Satria, and R. Castro, GIS Tools for Analysing Accidents and Road Design: A Review, Proceedings of the Transportation Research. 18 (2016) 242 – 247. https://doi.org/10.1016/j.trpro.2016.12.033
Z. Xie, and J. Yan, Kernel Density Estimation of Traffic Accidents in a Network Space, Computers, Environment, and Urban Systems. 35(5)(2008)396-406. https://doi.org/10.1016/j.compenvurbsys.2008.05.001.
J.K. Krisp, and O.Špatenková, Kernel Density Estimations for Visual Analysis of Emergency Response Data, Geographic Information and Cartography for Risk and Crisis Management, Lecture Notes in Geoinformation and Cartography. (2010) 395-408. Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-03442-8_27.
A. Soltani, and S. Askari, Analysis of intra-urban traffic accidents using spatiotemporal visualization techniques, Transport and Telecommunication Journal. 15(3) (2014) 227–232. https://doi.org/10.2478/ttj-2014-0020.
L. Thakali, T.J. Kwon, and L. Fu, Identification of Crash Hotspots Using Kernel Density Estimation and Kriging Methods: A Comparison, J. Mod. Transport. 23(2) (2015), 93–106. https://doi.org/10.1007/s40534-015-0068-0.
S. Hashimoto, S. Yoshiki, R. Saeki, Y. Mimura, R.O. Ando, and S.Nanba, Development and application of traffic accident density estimation models using kernel density estimation, Journal of Traffic and Transportation Engineering (English Edition). 3(3) (2016) 262–270. https://doi.org/10.1016/j.jtte.2016.01.005.
A. Abdulhafedh, Identifying vehicular crash high risk locations along highways via spatial autocorrelation indices and kernel density estimation, World Journal of Engineering and Technology, 05(02), (2017) 198–215. https://doi.org/10.4236/wjet.2017.52016
S. Kumar, J. S. Kumar, S. V. Kumar, and R. A. Raja, Identification of Accident Hotspots in Madurai Using GIS, International Journal of Engineering Research & Technology, Proceedings of the RTICCT - 2017 Conference. 5(17) (2017) 1-5. https://doi.org/10.17577/IJERTCONV5IS17008.
B. Romano, and Z. Jiang, Visualizing Traffic Accident Hotspots Based on Spatial-Temporal Network Kernel Density Estimation, Proceedings of the 25th ACM SIGSPATIAL International Conference. 98(2017)1–4. https://doi.org/10.1145/3139958.3139981
M. A. Aghajani, R. S. Dezfoulian, and A.R. Arjroody, Applying GIS to identify the spatial and temporal patterns of road accidents using spatial statistics (case study: Ilam Province, Iran), Transportation Research Procedia.25(2017) 2126–2138. https://doi.org/10.1016/j.trpro.2017.05.409
U. Ahmad, K. T. Hossain, and M. A. Hossain, Identification of Urban Traffic Accident Hotspot Zones Using GIS: A Case Study of Dhaka Metropolitan Area, Journal of Geographic Studies. 3(1) (2019) 36-42. https://doi.org/10.21523/gcj5.19030104.
G. LE, P. LIU, and L. T. LIN, Road Traffic Accident Black Spot Determination by using Kernel Density Estimation Algorithm and Cluster Statistical Significant Evaluation a Case Study in Hanoi, Vietnam, International Federation of Surveyors. (2019) 1-15.
S. Lakshmi, I. Srikanth, and M. Srikanth, Identification of Traffic Accident Hotspots using Geographical Information System (GIS), International Journal of Engineering and Advanced Technology. 9 (2) (2019) 4429- 4438. https://doi.org/10.35940/ijeat.B3848.129219
M. S. Yahya, E. E. Safian, and B. Burhan, Spatial Pattern and Hotspots of Urban Rail Public Transport to Public Access Using Geospatial Techniques in Selangor, Malaysia, Malaysian Journal of Social Sciences and Humanities. 6(1) (2021)234-244. https://doi.org/10.47405/mjssh.v6i1.634.
Q. Ma, G. Huang, and X. Tang, GIS-Based Analysis of Spatial-Temporal Correlations of Urban Traffic Accidents, European Transport Research Review. 13(50) (2021) 1-11. https://doi.org/10.1186/s12544-021-00509-y.
K. Hazaymeh, A. Almagbile, and A.H. Alomari, Spatiotemporal Analysis of Traffic Accidents Hotspots Based on Geospatial Techniques, International Journal of Geo-Information. 11(260) (2022) 1-18. https://doi.org/10.3390/ijgi11040260.
A. folayan, S. M. Easa, O.S. Abiola, F.M. Alayaki, and O. Folorunso, GIS-Based Spatial Analysis of Accident Hotspots: A Nigerian Case Study, Infrastructures. 7(103) (2022) 1-23. https://doi.org/10.3390/infrastructures7080103
B.M., Engr. A., O.C., Engr. I., & L.S., Engr. Dr. G. (2023). Modelling Reaeration Coefficient of Stream using Regression Analytical Method - A case of Mmubete Stream, Rivers State Nigeria. In Indian Journal of Environment Engineering (Vol. 3, Issue 1, pp. 22–29). https://doi.org/10.54105/ijee.a1840.053123
Jegede, F. O., Ibem, E. O., & Oluwatayo, A. A. (2019). Influence of Building Maintenance Practices on the Security of Lives and Property in Public Housing in Lagos State, Nigeria. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 2, pp. 2745–2751). https://doi.org/10.35940/ijitee.b6610.129219
Rashmi, G. D., & Narayani, Dr. V. (2019). Reverse Path Nearby Cluster (Rpnc) Query Optimization u sing Trajectory Clustering with Ensemble Learning (Tcel) In Spatial Networks. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1, pp. 7562–7572). https://doi.org/10.35940/ijeat.a1930.109119
Diang, T. K., Ngong, Dr. T. H., Lum, N. P., Ebob, A. L., & Akoh, Dr. N. R. (2023). Dynamics, Implications and Management Strategies of Transport Constraints in Fako Division, South West Region-Cameroon. In Indian Journal of Transport Engineering (Vol. 3, Issue 2, pp. 10–19). https://doi.org/10.54105/ijte.b1908.113223
Vishnoi, Dr. A., & Dwivedi, A. (2022). Challenges of Plastic Waste and It’s Recycling, A Threat to Environment: A Case Study of Kanpur City. In Indian Journal of Social Science and Literature (Vol. 1, Issue 4, pp. 1–5). https://doi.org/10.54105/ijssl.c1011.061422