Geographical Engineering of Territory

Geographical Engineering of Territory

Spatial analysis of urban accidents using traffic police data (Case study: Urban accidents in Ardabil in 2016)

Authors
yaghob abdali 1
Maryam Beyranvandzadeh 2
Amir Hoseiniyanrad 3
Hedayat Darvishi 4
1 phd student Geography and urban planning graduate student at Tehran University
2 Director of Geographic Information System Center, Research Assistant
3 Assistant Professor of Lorestan University Geography Department
4 PhD in Geography and Land Management, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
10.22034/jget.2023.272252.1244
Abstract
Abstract

Traffic accidents are one of the leading causes of death and disability, causing an annual death of 1.23 million and tens of millions injured and injured worldwide. Meanwhile, a significant proportion of fatalities and injuries resulting from traffic accidents occur among urban accidents. According to the 2014 report of the Statistics Center of Iran, about 9% of accidents in the city lead to death and 60% lead to injuries. Therefore, due to the importance of this issue, patterns of urban accidents in the city of Ardabil were investigated using the GIS. The present study is a quantitative-analytical study. The data required for the study were extracted from the Ardabil Traffic Police as well as the municipal database and the Statistics Center of Iran. The map of Ardabil city areas was used to show the distribution of events. Nearest Neighborhood, Local Moran and Global Moran indices were used to determine the pattern of incident distribution, Medium Center Test was used to focus events, Ellipse Deviation test was used for distribution, and Kernel Density Test was used to identify the focus of the incident. Statistical analysis was performed using SPSS and ARC-GIS software. The results showed that the central part, northern and eastern suburbs of Ardabil are the most vulnerable areas of Ardabil in terms of intra-city traffic accidents. Intersections, squares and boulevards are the site of most traffic accidents that lead to the formation of accident hotspots
Keywords
Spatial analysis
urban accidents
traffic and traffic
Ardabil
Subjects
ابدالی، یعقوب (1396). تحلیل فضایی کیفیت زندگی و بزهکاری در بافت­های ناکارآمد شهری (مطالعه موردی: بخش مرکزی تهران). پایان­نامه کارشناسی ارشد، رشته جغرافیا و برنامه­ریزی شهری، دانشکده جغرافیا، دانشگاه تهران.
ابدالی، یعقوب و رمضانی حاجی محله، عارفه (1398). تحلیل فضایی تصادفات درون شهری سال­های 1390-1395 (مطالعه موردی: تصادفات منجر به فوت موتورسیکلت سواران در منطقه 6 شهرداری تهران). کنفرانس بین­المللی مطالعات بین رشته­ای در مدیریت و مهندسی، دانشگاه تهران، موسسه پژوهشی مدیریت مدبر.
اسماعیل­زاده، حسن؛ فنی، زهره و عبدلی، سیده فاطمه (1398). هوشمندسازی، رویکردی در تحقیق توسعۀ پایداری شهری (مطالعۀ موردی: منطقۀ 6 تهران). فصلنامه پژوهش­های جغرافیای انسانی، دورۀ 51، شمار 1: 157- 145.
خاکسار, حسن, الماسی, و گهرپور. (2022). کاربرد مدل‌های فضایی-مکانی در پیش‌بینی فراوانی تصادفات جاده‌ای (مطالعه موردی: شبکه راه‌های اصلی استان همدان). پژوهشنامه حمل و نقل, 19(1), 45-58.
رسولی، حمید (1396). تحلیل زمانی- مکانی تصادفات منجر به فوت درون شهری شهر تبریز محدوده زمانی سال­های 1389 الی 1395. پایان­نامه کارشناسی ارشد، رشته سنجش از دور و سیستم اطلاعات جغرافیایی، استاد راهنما: نیکجو، محمدرضا و ولی­زاده کامران، خلیل. دانشکده جغرافیا، دانشگاه تبریز.
زنگی­آبادی، علی؛ شیران، غلامرضا و گشتیل، خیری (1391). بررسی علل تصادفات در بزرگراه­ها (مورد مطالعه: بزرگراه­های درون شهری اصفهان). فصلنامه راهور، دوره 1391، شماره 17: 57- 37.
عسگری، علی (1390). تحلیل­های آمار فضایی با Arc GIS. انتشارات سازمان فناوری اطلاعات و ارتباطات شهرداری تهران، چاپ اول، تهران.
کلانتری، محسن؛ پوراحمد، احمد؛ ابدالی، یعقوب و الله­قلی­پور، سارا (1397). تحلیل فضایی کیفیت زندگی در کانون­های جرم­خیز (مطالعه موردی: بخش مرکزی شهر تهران). پژوهشنامه جغرافیای انتظامی، سال ششم، شماره 21: 30- 1.
گلی، علی (1391). بررسی پراکنش فضایی تصادفات درون شهری و برون شهری کشور (مطالعه موردی: تصادفات 1386). فصلنامه مخاطرات محیطی، سال اول، شماره 1: 68- 53.
مرکز آمار ایران، سالنامه آماری کشور (1394). فصل پانزدهم امور قضایی.
ملکی، سعید و مدانلوجویباری، مسعود (1395). نقش شهرداری الکترونیک در توسعۀ فضای شهری (مطالعه موردی: محلات منطقه 6 شهرداری تهران). فصلنامه آمایش جغرافیایی فضا، سال ششم، شماره 22: 208- 193.
Aghajani, M. A., Dezfoulian, R. S., Arjroody, A. R., & Rezaei, M. (2017). Applying GIS to identify the spatial and temporal patterns of road accidents using spatial statistics (case study: Ilam Province, Iran). Transportation research procedia, 25, 2126-2138.‏
Anderson, T. K. (2009). Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention, 41(3), 359-364.‏
Aziz, S., & Ram, S. (2022). A Review of the Spatial Analysis Techniques for the Identification of Road Accident Black Spots and It’s Application in Context to India. In International Road Federation World Meeting & Exhibition (pp. 511-524). Springer, Cham.‏
Cheng, W., & Washington, S. (2008). New criteria for evaluating methods of identifying hot spots. Transportation Research Record, 2083(1), 76-85.‏
Corben, B. F., Ambrose, C., & Foong, C. W. (1990). Evaluation of accident black spot treatments (No. Report 11).‏
Elvik, R. (2008). A survey of operational definitions of hazardous road locations in some European countries. Accident Analysis & Prevention, 40(6), 1830-1835.‏
Erdogan, S. (2009). Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey. Journal of safety research, 40(5), 341-351.
ESRI, 2016. An Overview of the Spatial Statistics Toolbox. ArcGIS 10.5 Online Help System (ArcGIS 10.5 Desktop, Release 10.5, 2016). Environmental Systems Research Institute, Redlands, CA.
ESRI, 2018. An Overview of the Spatial Statistics Toolbox. ArcGIS 10.5 Online Help System (ArcGIS 10.5.1 Desktop, Release 10.5.1, 2018). Environmental Systems Research Institute, Redlands, CA.
Flahaut, B., Mouchart, M., San Martin, E., & Thomas, I. (2003). The local spatial autocorrelation and the kernel method for identifying black zones: A comparative approach. Accident Analysis & Prevention, 35(6), 991-1004.‏
Fountas, G., Fonzone, A., Gharavi, N., Rye, T., 2020. The joint effect of weather and lighting conditions on injury severities of single-vehicle accidents. Analytic Methods in Accident Research, 27, 100124.
Ghandour, A. J., Lovallo, M., & Telesca, L. (2019). Time-clustering behavior and cycles in the time dynamics of car accident sequences in Lebanon. Physica A: Statistical Mechanics and its Applications, 516, 178-184.‏
Golze, J., Feuerhake, U., & Sester, M. (2022). SPATIAL ANALYSIS OF EXTERNAL INFLUENCES ON TRAFFIC ACCIDENTS USING OPEN DATA. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 125-132.‏
Guillen, M., Nielsen, J. P., Ayuso, M., P´erez-Mar´ın, A. M., 2019. The Use of Telematics Devices to Improve Automobile Insurance Rates. Risk Analysis, 39(3), 662-672
Haghighat, F. (2011). Application of a multi-criteria approach to road safety evaluation in the Bushehr Province, Iran. Promet-Traffic&Transportation, 23(5), 341-352.‏
Hyder, A. A., Paichadze, N., Toroyan, T., & Peden, M. M. (2017). Monitoring the decade of action for global road safety 2011–2020: an update. Global public health, 12(12), 1492-1505.‏
IMPERSIMI, E. (2022). Spatial-temporal analysis of traffic accidents in Athens: The case of pedestrians.‏ Master Thesis, National technical university of Athens.
Jayan, D. K., & Ganeshkumar, B. (2010). Identification of accident hot spots: a GIS based implementation for Kannur district, Kerala. International journal of Geomatics and Geosciences, 1(1), 51.‏
Jones, A. P., Langford, I. H., & Bentham, G. (1996). The application of K-function analysis to the geographical distribution of road traffic accident outcomes in Norfolk, England. Social Science & Medicine, 42(6), 879-885.‏
Karasahin, M., & Terzi, S. (2002, September). Determination of hazardous locations on highways through GIS: A case study-rural road of Isparta-Antalya. In International Symposium on GIS (pp. 23-26).
Kim, K., & Yamashita, E. Y. (2007). Using ak‐means clustering algorithm to examine patterns of pedestrian involved crashes in Honolulu, Hawaii. Journal of advanced transportation, 41(1), 69-89.‏
Lee, J., Chae, J., Yoon, T., & Yang, H. (2018). Traffic accident severity analysis with rain-related factors using structural equation modeling–A case study of Seoul City. Accident Analysis & Prevention, 112, 1-10.‏
Levine, N., Kim, K. E., & Nitz, L. H. (1995). Spatial analysis of Honolulu motor vehicle crashes: II. Zonal generators. Accident Analysis & Prevention, 27(5), 675-685.‏
Mahata, D., Narzary, P. K., & Govil, D. (2019). Spatio-temporal analysis of road traffic accidents in Indian large cities. Clinical Epidemiology and Global Health.‏
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.‏
Montella, A. (2010). A comparative analysis of hotspot identification methods. Accident Analysis & Prevention, 42(2), 571-581.‏
Okabe, A., Satoh, T., & Sugihara, K. (2009). A kernel density estimation method for networks, its computational method and a GIS‐based tool. International Journal of Geographical Information Science, 23(1), 7-32.‏
Prasannakumar, V., Vijith, H., Charutha, R., & Geetha, N. (2011). Spatio-temporal clustering of road accidents: GIS based analysis and assessment. Procedia-social and behavioral sciences, 21, 317-325.‏
Ryder, B., Gahr, B., Egolf, P., Dahlinger, A., & Wortmann, F. (2017). Preventing traffic accidents with in-vehicle decision support systems-The impact of accident hotspot warnings on driver behaviour. Decision support systems, 99, 64-74.‏
Saadat, S., Rahmani, K., Moradi, A., & Darabi, F. (2019). Spatial analysis of driving accidents leading to deaths related to motorcyclists in Tehran. Chinese journal of traumatology, 22(3), 148-154.‏
Schuurman, N., Cinnamon, J., Crooks, V. A., & Hameed, S. M. (2009). Pedestrian injury and the built environment: an environmental scan of hotspots. BMC public health, 9(1), 1-10.‏
Seidel, D., Hähn, N., Annighöfer, P., Benten, A., Vor, T., & Ammer, C. (2018). Assessment of roe deer (Capreolus capreolus L.)–vehicle accident hotspots with respect to the location of ‘trees outside forest'along roadsides. Applied geography, 93, 76-80.‏
Shafabakhsh, G. A., Famili, A., & Bahadori, M. S. (2017). GIS-based spatial analysis of urban traffic accidents: Case study in Mashhad, Iran. Journal of traffic and transportation engineering (English edition), 4(3), 290-299.
Shafabakhsh, G. A., Famili, A., & Bahadori, M. S. (2017). GIS-based spatial analysis of urban traffic accidents: Case study in Mashhad, Iran. Journal of traffic and transportation engineering (English edition), 4(3), 290-299.‏
Statistische ¨Amter, 2021. Verkehrsunf¨alle 2020. Fachserie. 8, Verkehr. 7.
Whitelegg, J. (1987). A geography of road traffic accidents. Transactions of the Institute of British Geographers, 161-176.‏
World Health Organization, 2019. Global Status Report on Road Safety 2018. World Health Organization, Geneva.
Yassin, S. S., Pooja, 2020. Road accident prediction and model interpretation using a hybrid K-means and random forest algorithm approach. SN Applied Sciences, 2(9), 1576
Yunus, S., & Abdulkarim, I. A. (2022). Road traffic crashes and emergency response optimization: a geo-spatial analysis using closest facility and location-allocation methods. Geomatics, Natural Hazards and Risk, 13(1), 1535-1555.‏
Geographical Engineering of Territory
Volume 8, Issue 2 - Serial Number 20
Summer 2024
Pages 117-134

  • Receive Date 07 February 2021
  • Revise Date 07 September 2022
  • Accept Date 29 September 2023
  • Publish Date 22 August 2024