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

Abstract

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.

Keywords

• Highway
• Traffic Accident
• Hotspots
• QGIS
• SPSS

References

1. 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
2. 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
3. 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
4. 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
5. 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
6. 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
7. 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.
8. 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

9. 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
10. 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
11. 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
12. 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
13. 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
14. 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
15. 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
16. 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
17. 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
18. 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
19. Turkish Statistical Institute (nd). https://www.tuik.gov.tr/
20. Yayla, N. (2004). Karayolu Mühendisliği, Birsen Yayınevi, İstanbul.
21. 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
22. 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
23. 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