Spatial and Temporal Analysis of Road Traffic Accidents Using Kernel Density Estimation: Case Study: Isfahan Province

Subject Areas :

hossein aghamohammadi ^{1 *} , Mahdis rahmati ² , Saeid Behzadi ³ , Ali Asghar Alesheikh ⁴

1 -
2 - Department of Remote Sensing and GIS, Faculty of Agriculture, Water, Food and Functional Foods, Islamic Azad University, Science and Research Branch, Tehran, Iran
3 - Department of Water Resources Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
4 - عضو هیات علمی دانشگاه خواجه نصیرالدین طوسی

Received: 2025-05-17 Accepted : 2025-06-12 Published : 2025-07-24

Keywords: Kernel Density Estimation, Spatiotemporal analysis, Traffic accidents, Clustered pattern, Average Nearest Neighbor,

Abstract :

Road traffic accidents and their socio-economic impacts are a global issue. This is why detecting the dangerous areas and taking better precautionary steps is a priority in almost every nation. Iran is one of the countries that suffers the most from road accidents and fatalities, along with Isfahan Province, which faces a high rate of road accidents that result in injuries and fatalities. However, there has not been much effort made regarding traffic safety problems, particularly in identifying accident hotspots and analyzing their spatiotemporal pattern on a monthly scale. In this study, a novel approach was employed: the Average Nearest Neighbor(ANN) method was used to determine the spatial distribution pattern, and the Kernel Density Estimation(KDE) method was used to analyze the monthly density of accident events within a GIS environment. This enabled the investigation of the spatiotemporal patterns of road accidents in Isfahan province. This resulted in a better understanding of Isfahan Province's road accident spatiotemporal patterns. The findings indicated that the accident events' spatial distribution also showed a clustered distribution pattern for each month. In addition, after classifying accident densities, all months were evaluated for the highly hazardous and hazardous categories. These categories, which changed spatially each month, were mainly concentrated along the roads leading into the metropolitan region of Isfahan. Furthermore, the highly hazardous category reached its peak in the month of Farvardin, which coincides with the Nowruz holidays and is likely exacerbated by increased traffic. The findings of this study can assist urban planners in gaining a clearer and more intuitive understanding of road traffic safety issues. This improved insight can support the development of targeted strategies for addressing high-risk accident areas, improving road safety measures, enhancing emergency response facilities, increasing police presence in accident-prone zones, and implementing other effective interventions.

References:


خبرگزاری ایسنا. (1398، 10 بهمن). اصفهان در بین ۵ استان نخست تصادفات منجر به فوت است. اخذ شده از https://www.isna.ir/news/98111007051
رحمتی، مهدیس ، آقامحمدی زنجیر آباد، حسین، بهزادی، سعید و آل شیخ، علی اصغر . (1402). بررسی الگوهای مکانی-زمانی تصادفات ترافیکی برون شهری استان اصفهان در محیط GIS. پژوهش های سنجش از دور و اطلاعات مکانی, 2(1), 79-96. doi: 10.22061/jrsgr.2024.10770.1058
سميعا، ج؛ رنجبر شوبی م؛ نیک پور، ع. (١٤٠٣). تحلیل الگوی مکانی - زمانی تصادفات در جاده کندوان با استفاده از روش¬های آمار فضایی. فصلنامه علمی پژوهشی اطلاعات جغرافیایی (سپهر) ۳۳ (۱۳۲)، ۱۰۱-۸۵
صیدایی، اسکندر، جهانگیر، ابراهیم، دارابخانی، رسول و پناهی، علی . (1399). شناخت نقاط حادثه‌خیز محورهای استان البرز با استفاده از ‌روش تخمین تراکم کرنل. پژوهش-های جغرافیای انسانی, 52(3), 939-951. doi: 10.22059/jhgr.2019.232146.1007447
مرادی، علی، رحمانی، خالد، هوشمندی شجاع، مصطفی، رحیمی سپهر، حسن، و خورشیدی، علی. (1395). مروری بر وضعیت حوادث رانندگی در ایران در مقایسه با سایر کشورها. مجله علمی پزشکی قانونی، 22(1 (مسلسل 77)، 45-53. SID. https://sid.ir/paper/53553/fa

Afolabi, J. O., & Gbadamosi, K. T. (2017). Road traffic crashes in Nigeria: Causes and consequences. Transport & Logistics: The International Journal, 17(42). https://www.isna.ir/news/98111007051
Afolayan, A., Easa, S. M., Abiola, O. S., Alayaki, F. M., & Folorunso, O. (2022). GIS-Based Spatial Analysis of Accident Hotspots: A Nigerian Case Study. Infrastructures, 7(8), 103. https://doi.org/10.3390/infrastructures7080103
Aghasi, N. H. M. (2019). Application of GIS for Urban Traffic Accidents: A Critical Review. Journal of Geographic Information System, 11, 82-96. https://doi.org/10.4236/jgis.2019.111007
Alsahfi, T. (2024). Spatial and Temporal Analysis of Road Traffic Accidents in Major Californian Cities Using a Geographic Information System. ISPRS International Journal of Geo-Information, 13(5), 157. https://doi.org/10.3390/ijgi13050157
Anderson, T. K. (2009). Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention, 41(3), 359–364. https://doi.org/10.1016/j.aap.2008.12.014
Baranyai, D., & Sipos, T. (2022). Black-Spot Analysis in Hungary Based on Kernel Density Estimation. Sustainability, 14(14), 8335. https://doi.org/10.3390/su14148335
Bhalla, K., Naghavi, M., Shahraz, S., Bartels, D., & Murray, C. J. L. (2009). Building national estimates of the burden of road traffic injuries in developing countries from all available data sources: Iran. Injury Prevention, 15, 150–158. https://doi.org/10.1136/ip.2008.020826
Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27. https://doi.org/10.1109/TIT.1967.1053964
Dereli, M. A., & Erdogan, S. (2017). A new model for determining the traffic accident black spots using GIS-aided spatial statistical methods. Transportation Research Part A: Policy and Practice, 103, 106–117. https://doi.org/10.1016/j.tra.2017.05.031
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:10.1016/j.aap.2007.05.004
Esri. (n.d.). Average Nearest Neighbor (Spatial Statistics)-ArcGIS Pro. In ArcGIS Pro documentation. Retrieved 29 December, 2024, from https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/average-nearest-neighbor.htm
Gupta, S., Garg, P. K., & Ghosh, S. K. (2022). Analysis of road accidents using GIS. SSRG International Journal of Civil Engineering, 9(9), 1–7. https://doi.org/10.14445/23488352/IJCE-V9I9P101
Han, J. W., & Kamber, M. (2001). Data mining: Concepts and techniques. Morgan Kaufmann Publishers
Hazaymeh, K., Almagbile, A., & Alomari, A. H. (2022). Spatiotemporal analysis of traffic accidents hotspots based on geospatial techniques. ISPRS International Journal of Geo-Information, 11(4), 260.
Kalantari, M., Zanganeh Shahraki, S., Yaghmaei, B., Ghezelbash, S., Ladaga, G., & Salvati, L. (2021). Unraveling urban form and collision risk: The spatial distribution of traffic accidents in Zanjan, Iran. International Journal of Environmental Research and Public Health, 18, 4498. https://doi.org/10.3390/ijerph18094498
Le, K. G., Liu, P., & Lin, L. T. (2019). Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam. Geo-Spatial Information Science, 23(2), 153–164. https://doi.org/10.1080/10095020.2019.1683437
Mohaymany, A. S., Shahri, M., & Mirbagheri, B. (2013). GIS-based method for detecting high-crash-risk road segments using network kernel density estimation. Geo-Spatial Information Science, 16(2), 113–119. https://doi.org/10.1080/10095020.2013.766396
Satria, R., & Castro, M. (2016). GIS tools for analyzing accidents and road design: A review. Transportation Research Procedia, 18, 242–247. https://doi.org/10.1016/j.trpro.2016.12.033
Shaaban, H., Siam, A., & Badran, A. (2021). Analysis of traffic crashes and violations in a developing country. Transportation Research Procedia, 55, 1689-1695. https://doi.org/10.1016/j.trpro.2021.07.160
Shahzad, M. (2020). Review of road accident analysis using GIS technique. International Journal of Injury Control and Safety Promotion, 27(4), 472–481. https://doi.org/10.1080/17457300.2020.1811732
Silverman, B. W. (1986). Density estimation for statistics and data analysis. London: Chapman & Hall.
Smith, M.-J., Goodchild, M.-F., & Longley, P.-A. (2015). Geospatial analysis: A comprehensive guide to principles, techniques and software tools. The Winchelsea Press.
Stephen, L., Kelakom, G. G., Sojan, J. M., Sreelakshmi, K. S., & Vishnu, N. B. (2018). Identification and analysis of accident blackspots using GIS. International Research Journal of Engineering and Technology, 5(03), 3455–3459.
Tola, A. M., Demissie, T. A., Saathoff, F., & Gebissa, A. (2021). Severity, spatial pattern and statistical analysis of road traffic crash hot spots in Ethiopia. Applied Sciences, 11(19), 8828. https://doi.org/10.3390/app11198828
Wang, M., Yi, J., Chen, X., Zhang, W., & Qiang, T. (2021). Spatial and Temporal Distribution Analysis of Traffic Accidents Using GIS-Based Data in Harbin. Journal of Advanced Transportation, 2021, Article ID 9207500, 10 pages. https://doi.org/10.1155/2021/9207500
World Health Organization. (2018). Global status report on road safety 2018. WHO Press
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:10.1016/j.compenvurbsys.2008.05.001