Eskişehir Kent Merkezindeki Trafik Kazalarının Zamana Bağlı Konumsal Analizi

Year 2023, Volume: 4 Issue: 1, 17 - 32, 28.03.2023

Vural Yıldırım , Erdem Yurdakul , Gökben Adana Karaağaç , Merve Koçer , Hakan Uyguçgil

https://doi.org/10.48123/rsgis.1167844

Cited By: 2

Abstract

Trafik kazalarının önlenmesinde ilk yapılması gereken işlem kazaların yoğunlaştığı noktaların belirlenmesidir. Bu amaçla 2010-2019 yılları arasında Eskişehir kent merkezinde meydana gelen trafik kazaları istatistiksel olarak benzer, yaklaşım olarak farklı iki yöntem kullanılarak analiz edilmiştir. Çalışmada önce klasik sıcak nokta analizi kullanılmış ve 15 sıcak nokta tespit edilmiştir. Daha sonra aynı veri seti konum-zaman küpü kullanılarak zamana bağlı sıcak nokta yöntemi ile analiz edilmiş, 50 aralıklı, 10 yeni, 7 ardışık, 4 sürekli, 1 azalan ve 1 yoğunlaşan olmak üzere toplam 73 sıcak nokta bulunmuştur. İki yöntemin sonuçları kıyaslandığında, zamana bağlı sıcak nokta analizi ile 1. bölgedeki sıcak nokta sayısının 6'dan 19'a, 2. bölgedeki sıcak nokta sayısının 2'den 20'ye, 3. bölgedeki sıcak nokta sayısının 3'den 12'ye, 4. bölgedeki sıcak nokta sayısının 3'den 11'e ve 5. bölgedeki sıcak nokta sayısının 1'den 11'e çıktığı görülmüştür. Klasik sıcak nokta analizine kıyasla zamana bağlı sıcak nokta analizi ile farklı konumlarda ve farklı desenlerde daha çok trafik kazası sıcak noktalarının tespit edilmesi, konumun ve zamanın bir arada kullanılmasının önemini ortaya koymaktadır. Çalışma sonucunda zamana bağlı sıcak nokta analizinin klasik sıcak nokta analizine göre daha detaylı sonuçlar verdiği gözlemlenmiştir.

Keywords

Coğrafi Bilgi Sistemleri, Trafik kazaları, Konum-zaman küpü yöntemi, Klasik sıcak nokta analizi, Zamana bağlı sıcak nokta analizi

Spatiotemporal Analysis of Traffic Accidents in Eskişehir City Center

Abstract

The first thing to do in preventing traffic accidents is to determine the spots where the accidents are concentrated. For this purpose, traffic accidents in Eskişehir city center that occurred between 2010-2019 were analyzed using two methodologies those are statistically similar but different in approaches. Firstly, classical hot spot analysis was performed, and 15 hot spots were found. Subsequently, the same data set was analyzed with the emerging hot spot using the space-time cube method, and a total of 73 hot spots were detected, including 50 sporadic, 10 new, 7 consecutive, 4 persistent, 1 diminishing, and 1 intensifying. A comparison of the results of the two methodologies shows an increase in the number of hot spots in the first region from 6 to 19, in the second region from 2 to 20, in the third region from 3 to 12, in the fourth region from 3 to 11 and in the fifth region from 1 to 11. Finding more hot spots in different locations and patterns with emerging hot spot analysis proportional to classical hot spot analysis reveals the importance of using location and time together. As a result of the study, it was observed that the emerging hot spot analysis provides more detailed outcomes whence the classical hot spot analysis.

Keywords

Geographic Information Systems, Traffic accidents, Space-time cube method, Classical hot spot analysis, Emerging hot spot analysis

References

• 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. doi: 10.3390/infrastructures7080103.
• Amiri, A. M., Nadimi, N., Khalifeh, V., & Shams, M. (2021). GIS-based crash hotspot identification: a comparison among mapping clusters and spatial analysis techniques. International Journal of Injury Control and Safety Promotion, 28(3), 325-338.
• Anderson, T. K. (2009). Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention, 41(3), 359-364.
• Bil, M., Andrasik, R., & Sedonik, J. (2019). A detailed spatiotemporal analysis of traffic crash hotspots. Applied Geography, 107, 82-90.
• Cheng, Z., Zu, Z., & Lu, J. (2018). Traffic crash evolution characteristic analysis and spatiotemporal hotspot identification of urban road intersections. Sustainability, 11(1), 160. doi: 10.3390/su11010160.
• De Silva, V., Tharindra, H., Vissoci, J. R. N., Andrade, L., Mallawaarachchi, B. C., Ostbye, T., & Staton, C. A. (2018). Road traffic crashes and built environment analysis of crash hotspots based on local police data in Galle, Sri Lanka. International Journal of Injury Control and Safety Promotion, 25(3), 311-318.
• ESRI. (2022a, June 8). ArcGIS Pro Resources, How create space sime cube works. Retrieved from https://pro.arcgis.com/en/pro-app/2.8/tool-reference/space-time-pattern-mining/learnmorecreatecube.htm
• ESRI. (2022b, June 8) ArcGIS Pro Resources, How emerging hot spot analysis works. Retrieved from https://pro.arcgis.com/en/pro-app/2.8/tool-reference/space-time-pattern-mining/learnmoreemerging.htm
• Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206.
• Gudes, O., Varhol, R., Sun, Q., & Meuleners, L. B. (2017). Investigating articulated heavy-vehicle crashes in Western Australia using a spatial approach. Accident Analysis and Prevention, 106, 243-253.
• Hayidso, T. H., Gemeda, D. O., & Abraham, A. M. (2019). Identifying road traffic accidents hotspots areas using GIS in Ethiopia: A case study of Hosanna Town. Transport and Telecommunication, 20(2), 123-132.
• 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. doi: 10.3390/ijgi11040260.
• Kang, Y., Cho, N., & Son, S. (2018). Spatiotemporal characteristics of elderly population’s traffic accidents in Seoul using space-time cube and space-time Kernel density estimation. PLoS ONE, 13(5), e0196845. doi: 10.1371/journal.pone.0196845.
• Kang, Y., Son, S., & Nahye, C. (2017). Analysis of traffic accidents injury severity in Seoul using decision trees and spatiotemporal data visualization. Journal of Cadastre & Land InformatiX, 47(2), 233-254.
• Li, Y., Zhang, L., Yan, J., Wang, P., Hu, N., Cheng, W., & Fu, B. (2017). Mapping the hotspots and coldspots of ecosystem services in conservation priority setting. Journal of Geographical Sciences, 27(6), 681-696.
• Moons, E., Brijs, T., & Wets, G. (2009). Improving Moran’s Index to identify hot spots in traffic safety. In B. Murgante, G. Borruso, & A. Lapucci (Eds.), Geocomputation and Urban Planning: Studies in Computational Intelligence (pp. 117-132), Heidelberg: Springer.
• Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis, 27(4), 286-306.
• Özmal, M., Küçükönder, M. Karabulut, M., ve Göksu, G. (2014, Haziran). Coğrafi bilgi sistemleri kullanarak Kahramanmaraş trafik kaza analizi. Coğrafyacılar Derneği Uluslararası Kongresi. (pp. 867-875).
• Tamakloe, R. & Park, D. (2022). Factors influencing fatal vehicleinvolved crash consequence metrics at spatio-temporal hotspots in South Korea: application of GIS and machine learning techniques. International Journal of Urban Sciences, doi: 10.1080/12265934.2022.2134182.
• Thomas, I. (1995). Spatial data aggregation: Exploratory analysis of road accidents. Accident Analysis and Prevention, 28(2), 251-264.
• Tobler, W. R. (1970). A Computer movie simulating urban growth in the Detroit region. Economic Geography, 46(sub1), 234-240.
• 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. doi: 10.3390/app11198828.
• TÜİK. (2022, Haziran 8). Adrese dayalı nüfus kayıt istatistikleri. Retrieved from https://data.tuik.gov.tr/Kategori/Get Kategori?p=Nufus-ve-Demografi-109
• Uğur Özçelik, M., Gökçen, H. ve Dağdeviren, M. (2013). Ankara şehir içi otobüs kazalarının analizi ve bölge risklerinin belirlenmesi için birçok ölçütlü karar modeli. Dumlupınar Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 30, 33-55.
• TCK. (2021). Trafik kazaları özeti. Ulaştırma ve Altyapı Bakanlığı Karayolları Genel Müdürlüğü. Retrieved from https://www. kgm.gov.tr/Sayfalar/KGM/SiteTr/Trafik/TrafikKazalariOzeti.aspx
• TCK. (2022). Durma ve intikal süreleri. Ulaştırma ve Altyapı Bakanlığı Karayolları Genel Müdürlüğü. Retrieved from https://www.kgm.gov.tr/Sayfalar/KGM/SiteTr/Trafik/DurmaIntikal.aspx
• Vale, F. (2018, March). Spatial data mining II: A deep dive into space-time analysis. ESRI Federal GIS Conference. Washington DC: USA.
• WHO. (2022, June 20). Road traffic injuries. World Health Organization. Retrieved from https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries
• Wu, P., Meng, X., & Song, L. (2021). Identification and spatiotemporal evolution analysis of high-risk crash spots in urban roads at the microzonelevel: Using the space-time cube method. Journal of Transportation Safety&Security. doi: 10.1080/19439962.2021.1938323
• Xie, Z., & Yan, J. (2008). Kernel density estimation of traffic accidents in a network space. Computers, Environment and Urban Systems, 32(5), 396-406, 2008.
• Yıldırım, V., & Mert Kantar, Y. (2020). Spatial analysis of the road traffic accidents statistics in the provinces of Turkey. SIGMA Journal of Engineering and Natural Sciences, 38(4), 1667-1680.
• Yohannes, A. Y. W., and Minale, A. S. (2015). Identifying the hot spot areas of road traffic accidents. Jordan Journal of Civil Engineering, 9(3), 358-370.
• Yoon, J., & Lee, S. (2022). Spatio-temporal patterns in pedestrian crashes and their determining factors: Application of a space-time cube analysis model. Accident Analysis & Prevention, 161, 106291. doi: 10.1016/j.aap.2021.106291.