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QGIS-based Analysis of Traffic Accident Regions in Muratpasa District in Antalya City

Yıl 2024, Cilt: 11 Sayı: 4, 771 - 779, 30.12.2024

Mehmet Arıkan Yalçın , Sevil Köfteci

https://doi.org/10.54287/gujsa.1581268

Öz

Highway traffic accidents in general constitute a significant global issue, adversely affecting transportation, endangering life, and compromising the quality of life. To improve life quality, traffic accident types and regions should be analyzed in detail. The Geographical Information System (GIS) has been used as a valuable tool for addressing this challenge in recent years. By employing this approach, researchers can identify and visualize the locations where accidents are concentrated in a manner that is both comprehensive and readily comprehensible.
The objective of this study is to determine the regions within the Muratpaşa district of Antalya province that are at an increased risk of injury or fatality due to traffic accidents. Furthermore, the ages of the drivers involved in the accidents, the relationship between age and accident occurrence, the causes of the accidents and the distribution of accidents by year and month were examined by the way of statistics. For this aim, data obtained from the Turkish Republic General Directorate of Security for the years 2017-2021 were used. The Quantum Geographical Information Systems (QGIS) program was employed to determine the regions exhibiting elevated accident risk, and "Kernel Density Estimation" was used to categorize the accidents. A normality test was performed in SPSS to analyze the distribution of accidents by age. Accidents caused by rear-end collisions, failure to stop at red lights or stop signs, slowing or stopping in a disrupting traffic pattern, failure to slow at pedestrian and school crossings, or failure to yield to pedestrians were classified as traffic accidents caused by insufficient stopping visibility. In addition, traffic accidents caused by not following the rules of lane following and changing were considered as accidents caused by insufficient passing visibility. The study found that there was a significant risk of traffic accidents in three distinct regions within the Muratpaşa district. It was observed that individuals between the ages of 20 and 30 were predominantly involved in these incidents and that the main cause of these accidents was human errors made by the drivers. The overwhelming majority of traffic accidents (95%) resulted in injuries. The greatest number of traffic accidents was recorded in July.

Anahtar Kelimeler

Highway Traffic Accident Hotspots QGIS SPSS

Teşekkür

The authors thank the Turkish Republic General Directorate of Security for sharing data about traffic accidents. This paper is a part of the M.Sc. thesis of Yalcin Arıkan M., the first author.

Kaynakça

  • Abdulhafedh, A. (2020). Highway stopping sight distance, decision sight distance, and passing sight distance based on AASHTO models. Open Access Library Journal, 7(3), 1-24. http://doi.org/10.4236/oalib.1106095
  • 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. http://doi.org/10.3390/infrastructures7080103
  • Alam, M. S., & Tabassum, N. J. (2023). Spatial pattern identification and crash severity analysis of road traffic crash hot spots in Ohio. Heliyon, 9(5). http://doi.org/10.1016/j.heliyon.2023.e16303
  • Anderson, T. K. (2009). Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention, 41(3), 359-364. http://doi.org/10.1016/j.aap.2008.12.014
  • Clifton, K. J., & Kreamer-Fults, K. (2007). An examination of the environmental attributes associated with pedestrian–vehicular crashes near public schools. Accident Analysis & Prevention, 39(4), 708-715. http://doi.org/10.1016/j.aap.2006.11.003
  • Deng, K., Zhang, H., & Huang, Y. (2008, October 20-22). Safety analysis on road sight distance. In: Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA) (Vol. 2, pp. 461-465). Changsha, China. https://doi.org/10.1109/icicta.2008.226
  • Doğru, E., & Aydın, F. (2018, October 3-6). Coğrafi Bilgi Sistemleri Yardımıyla Trafik Kazalarının Analizi: Karabük Merkez İlçe Örneği. In: Proceedings of the International Geography Symposium on the 30th Anniversary of TÜCAUM, (pp. 355-369), Ankara.
  • Harirforoush, H., & Bellalite, L. (2019). A new integrated GIS-based analysis to detect hotspots: A case study of the city of Sherbrooke. Accident Analysis & Prevention, 130, 62-74. http://doi.org/10.1016/j.aap.2016.08.015
  • 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 Journal, 20(2), 123-132. http://doi.org/10.2478/ttj-2019-0011
  • Khan, S., & Mohiuddin, K. (2018). Evaluating the parameters of ArcGIS and QGIS for GIS Applications. International Journal of Advance Research in Science and Engineering, 7(3), 582-594. https://doi.org/10.1002/9781119457091.ch3
  • Le, K. G., Liu, P., & Lin, L.-T. (2020). 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. http://doi.org/10.1080/10095020.2019.1683437
  • Le, K. G., Tran, Q. H., & Do, V. M. (2023). Urban Traffic Accident Features Investigation to Improve Urban Transportation Infrastructure Sustainability by Integrating GIS and Data Mining Techniques. Sustainability, 16(1), 107. http://doi.org/10.3390/su16010107
  • Loo, B. P. Y. (2006). Validating crash locations for quantitative spatial analysis: a GIS-based approach. Accident Analysis & Prevention, 38(5), 879-886. http://doi.org/10.1016/j.aap.2006.02.012
  • Mesquitela, J., Elvas, L. B., Ferreira, J. C., & Nunes, L. (2022). Data analytics process over road accidents data—a case study of Lisbon city. ISPRS International Journal of Geo-Information, 11(2), 143. http://doi.org/10.3390/ijgi11020143
  • Moyroud, N., & Portet, F. (2018). Introduction to QGIS. In: N. Baghdadi, C. Mallet, & M. Zribi (Eds.), QGIS and Generic Tools, (pp. 1-17). Wiley. https://doi.org/10.1002/9781119457091.ch1
  • Sababhi, S., Aldala’in, S., Al Taani, A., Al Rawashdeh, S., Al Barari, T., Aladwan, Z., & Manan, T. S. B. A. (2024). Safety on Jordan's highways: A GIS-Based approach to identifying road accident hotspots. GeoJournal, 89(3), 105. http://doi.org/10.1007/s10708-024-11115-5
  • Sohaib, M., Najeeb, A., Umair, M., Khan, M. A., Zubair, M. U., Jehan, Z., & Khattak, A. (2024). Improving urban road infrastructure analysis and design using an integrated BIM-GIS and traffic microsimulation framework. Innovative Infrastructure Solutions, 9(7), 285. http://doi.org/10.1007/s41062-024-01609-z
  • 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. http://doi.org/10.3390/app11198828
  • Turkish Statistical Institute (nd). https://www.tuik.gov.tr/
  • Yayla, N. (2004). Karayolu Mühendisliği, Birsen Yayınevi, İstanbul.
  • Yigit Katanalp, B., Eren, E., & Alver, Y. (2023). An integrated solution to identify pedestrian-vehicle accident prone locations: GIS-based multicriteria decision approach. Journal of Transportation Safety & Security, 15(2), 137-176. http://doi.org/10.1080/19439962.2022.2048760
  • Zhang, J., & Shi, T. (2019). Spatial analysis of traffic accidents based on WaveCluster and vehicle communication system data. EURASIP Journal on Wireless Communications and Networking, 2019(1), 124. http://doi.org/10.1186/s13638-019-1450-0
  • Zhang, Y., Sun, X., Chen, J., & Cheng, C. (2021). Spatial patterns and characteristics of global maritime accidents. Reliability Engineering & System Safety, 206, 107310. http://doi.org/10.1016/j.ress.2020.107310

Yıl 2024, Cilt: 11 Sayı: 4, 771 - 779, 30.12.2024

Mehmet Arıkan Yalçın , Sevil Köfteci

https://doi.org/10.54287/gujsa.1581268

Öz

Kaynakça

  • Abdulhafedh, A. (2020). Highway stopping sight distance, decision sight distance, and passing sight distance based on AASHTO models. Open Access Library Journal, 7(3), 1-24. http://doi.org/10.4236/oalib.1106095
  • 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. http://doi.org/10.3390/infrastructures7080103
  • Alam, M. S., & Tabassum, N. J. (2023). Spatial pattern identification and crash severity analysis of road traffic crash hot spots in Ohio. Heliyon, 9(5). http://doi.org/10.1016/j.heliyon.2023.e16303
  • Anderson, T. K. (2009). Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention, 41(3), 359-364. http://doi.org/10.1016/j.aap.2008.12.014
  • Clifton, K. J., & Kreamer-Fults, K. (2007). An examination of the environmental attributes associated with pedestrian–vehicular crashes near public schools. Accident Analysis & Prevention, 39(4), 708-715. http://doi.org/10.1016/j.aap.2006.11.003
  • Deng, K., Zhang, H., & Huang, Y. (2008, October 20-22). Safety analysis on road sight distance. In: Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA) (Vol. 2, pp. 461-465). Changsha, China. https://doi.org/10.1109/icicta.2008.226
  • Doğru, E., & Aydın, F. (2018, October 3-6). Coğrafi Bilgi Sistemleri Yardımıyla Trafik Kazalarının Analizi: Karabük Merkez İlçe Örneği. In: Proceedings of the International Geography Symposium on the 30th Anniversary of TÜCAUM, (pp. 355-369), Ankara.
  • Harirforoush, H., & Bellalite, L. (2019). A new integrated GIS-based analysis to detect hotspots: A case study of the city of Sherbrooke. Accident Analysis & Prevention, 130, 62-74. http://doi.org/10.1016/j.aap.2016.08.015
  • 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 Journal, 20(2), 123-132. http://doi.org/10.2478/ttj-2019-0011
  • Khan, S., & Mohiuddin, K. (2018). Evaluating the parameters of ArcGIS and QGIS for GIS Applications. International Journal of Advance Research in Science and Engineering, 7(3), 582-594. https://doi.org/10.1002/9781119457091.ch3
  • Le, K. G., Liu, P., & Lin, L.-T. (2020). 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. http://doi.org/10.1080/10095020.2019.1683437
  • Le, K. G., Tran, Q. H., & Do, V. M. (2023). Urban Traffic Accident Features Investigation to Improve Urban Transportation Infrastructure Sustainability by Integrating GIS and Data Mining Techniques. Sustainability, 16(1), 107. http://doi.org/10.3390/su16010107
  • Loo, B. P. Y. (2006). Validating crash locations for quantitative spatial analysis: a GIS-based approach. Accident Analysis & Prevention, 38(5), 879-886. http://doi.org/10.1016/j.aap.2006.02.012
  • Mesquitela, J., Elvas, L. B., Ferreira, J. C., & Nunes, L. (2022). Data analytics process over road accidents data—a case study of Lisbon city. ISPRS International Journal of Geo-Information, 11(2), 143. http://doi.org/10.3390/ijgi11020143
  • Moyroud, N., & Portet, F. (2018). Introduction to QGIS. In: N. Baghdadi, C. Mallet, & M. Zribi (Eds.), QGIS and Generic Tools, (pp. 1-17). Wiley. https://doi.org/10.1002/9781119457091.ch1
  • Sababhi, S., Aldala’in, S., Al Taani, A., Al Rawashdeh, S., Al Barari, T., Aladwan, Z., & Manan, T. S. B. A. (2024). Safety on Jordan's highways: A GIS-Based approach to identifying road accident hotspots. GeoJournal, 89(3), 105. http://doi.org/10.1007/s10708-024-11115-5
  • Sohaib, M., Najeeb, A., Umair, M., Khan, M. A., Zubair, M. U., Jehan, Z., & Khattak, A. (2024). Improving urban road infrastructure analysis and design using an integrated BIM-GIS and traffic microsimulation framework. Innovative Infrastructure Solutions, 9(7), 285. http://doi.org/10.1007/s41062-024-01609-z
  • 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. http://doi.org/10.3390/app11198828
  • Turkish Statistical Institute (nd). https://www.tuik.gov.tr/
  • Yayla, N. (2004). Karayolu Mühendisliği, Birsen Yayınevi, İstanbul.
  • Yigit Katanalp, B., Eren, E., & Alver, Y. (2023). An integrated solution to identify pedestrian-vehicle accident prone locations: GIS-based multicriteria decision approach. Journal of Transportation Safety & Security, 15(2), 137-176. http://doi.org/10.1080/19439962.2022.2048760
  • Zhang, J., & Shi, T. (2019). Spatial analysis of traffic accidents based on WaveCluster and vehicle communication system data. EURASIP Journal on Wireless Communications and Networking, 2019(1), 124. http://doi.org/10.1186/s13638-019-1450-0
  • Zhang, Y., Sun, X., Chen, J., & Cheng, C. (2021). Spatial patterns and characteristics of global maritime accidents. Reliability Engineering & System Safety, 206, 107310. http://doi.org/10.1016/j.ress.2020.107310
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ulaşım ve Trafik, Ulaştırma Mühendisliği, Planlamada Coğrafi Bilgi Sistemleri (CBS)
Bölüm İnşaat Mühendisliği
Yazarlar

Mehmet Arıkan Yalçın 0000-0002-8916-1411

Sevil Köfteci 0000-0002-5096-2545

Yayımlanma Tarihi 30 Aralık 2024
Gönderilme Tarihi 11 Kasım 2024
Kabul Tarihi 14 Aralık 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 11 Sayı: 4

Kaynak Göster

APA Yalçın, M. A., & Köfteci, S. (2024). QGIS-based Analysis of Traffic Accident Regions in Muratpasa District in Antalya City. Gazi University Journal of Science Part A: Engineering and Innovation, 11(4), 771-779. https://doi.org/10.54287/gujsa.1581268
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