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Antalya Şehrinde Meydana Gelen Trafik Kazalarının Günlük Aktivite Alanları ile İlişkisi

Yıl 2022, Cilt: 10 Sayı: 2, 509 - 531, 28.10.2022

Hüseyin Zerenoğlu Tamer Özlü Himmet Haybat

https://doi.org/10.18795/gumusmaviatlas.1131907

Öz

Türkiye’de nüfusun artmasına bağlı olarak araç sayısında da artış yaşanmaktadır. Araç sayısındaki artışa bağlı olarak da şehir içi ulaşımda sorunların ve trafik kazalarının artmasına neden olmaktadır. Çalışma trafik kazalarının günlük aktivite alanları ile ilişkisini ortaya koymak ve tespit etmek amacıyla gerçekleştirilmiştir. Çalışma alanı olarak Antalya ilinin beş merkez ilçesi seçilmiştir. Çalışma alanının beş merkez ilçesinin seçilmesindeki nedenler arasında trafik kazalarının yoğunluğu, aktivite alanlarının yoğunluğu ve nüfusun büyük bir oranı bu alanda dağılım göstermesidir. Araştırma 2015-2019 yılları arasında trafik kaza tutanakları ile elde edilen verileri içermektedir. Çalışmada iki farklı analiz gerçekleştirilmiştir. Gerçekleştirilen analizler geliştirilmiş tampon analizi ve optimize edilmiş sıcak nokta analizidir. Analizler trafik kazalarının gerçekleştiği yoğun alanlarını tespit etmek ve günlük aktivite alanları arasındaki ilişkiyi saptamaktır. Analizleri uygulamak için ArcGIS 10.8 yazılımı kullanılmıştır. ArcGIS yazılımı kullanılarak özgün bir metot modeli olan geliştirilmiş tampon analiz aracı üretilmiştir. Geliştirilmiş tampon analiz yöntemi kullanılarak alışveriş-eğitim, ulaşım-eğitim ve ulaşım-alışveriş alanlarının kesişim alanları içerisindeki trafik kazaları ile ilişkisi incelenmiştir. Optimize edilmiş analiz yöntemi kullanılarak alışveriş-eğitim, ulaşım-alışveriş ve ulaşım-eğitim kesişim alanlarının sıcak nokta analizleri gerçekleştirilmiştir. Son analizde ulaşım, alışveriş, eğitim, konaklama ve yeme-içme alanlarının 150 metre çevresinde meydana gelen trafik kazaları incelenmiştir. Çalışma sonucunda trafik kazalarının günlük aktivite alanları içerisinde en çok ulaşım ve alışveriş alanlarının kesişim alanlarında meydana geldiği tespit edilmiştir.

Anahtar Kelimeler

Trafik Kazası Günlük Aktivite Tampon Analizi Sıcak Nokta Analizi Antalya

Kaynakça

  • 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. https://doi. org/10.1016/j.trpro.2017.05.409.
  • Andrey, J. (2010). Long-term trends in weather-related crash risks. Journal of Transport Geography, 18, 247–258. https://doi:10.1016/j.jtrangeo.2009.05.002.
  • Aronoff, S.(1989) Geographic information systems: A management perspective. Geocarto International, 4:4, 58-58. https://doi:10.1080/10106048909354237.
  • Bassani, M., Rossetti, L. & Catani, L. (2020). Spatial analysis of road crashes involving vulnerable road users in support of road safety management strategies. Transportation Research Procedia, 45, 394-401. https://doi.org/10.1016/j.trpro.2020.03.031.
  • Bekele, T. G. (2019). Road traffic accident cause and effect on socio economy of Addis Ababa city. Economics And Social Sciences Academic Journal, 1(4), 21-37.
  • Bhatia, S., Vira, V., Choksi, D. & Venkatachakam, P. (2013). An algorithm for generating geometric buffers for vector feature layers. Geo-spatial Information Science, 16, 130-138. https://doi.org/10.1080/10095020.2012.747643.
  • Bhavan, T. (2019). The economic ımpact of road accidents: the case of Sri Lanka. South Asia Economic Journal, 20(1), 124-137. https://doi.org/10.1177/1391561418822210.
  • Blazquez, C. A. & Celis, M. S. (2013). A spatial and temporal analysis of child pedestrian crashes in Santiago, Chile. Accident Analysis and Prevention, 50, 304-311. http://dx.doi.org/10.1016/j.aap.2012.05.001.
  • Briz-Redón, Á., Martínez-Ruiz, F. & Montes, F. (2019). Spatial analysis of traffic accidents near and between road intersections in a directed linear network. Accident Analysis and Prevention, 132, 105-252. https://doi.org/10.1016/j.aap.2019.07.013.
  • Burrough, P. A. (1986). Principles of geographical ınformation systems for land resources assement. Geocarto International, 1(3), 54. https://doi.org/10.1080/10106048609354060.
  • Carter, J. R. (1989). On defining the geographic ınformation system. In W. J. Ripple (Ed.), Advanced in fundamentals of geographic information systems: a compendium (ss. 3-7). Falls Church, Va: American Society of Photogrammetry and Remote Sensing.
  • Chen, S., Kuhn, M., Prettner, K. & Bloom, D. E. (2019). The global macroeconomic burden of road injuries: estimates and projections for 166 countries. Lancet Planet Health, 3, 390-398.
  • Chen, J., Shaw, S. L., Yu, H., Lu, F., Chai, Y. & Jia, Q. (2011). Exploratory data analysis of activity diary data: a space–time GIS approach. Journal of Transport Geography, 19, 394-404. https://doi.org/10.1016/j.jtrangeo.2010.11.002.
  • Chrisman, N. R. (1999). What does “GIS” mean?. Transactions in GIS, 3(2), 175-186.
  • Cowen, D. J. (1988). GIS versus CAD versus DBMS: what are the difference?. Photogrammetric Engineering and Remote Sensing, 54, 1551-1555.
  • 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, 103, 106-117. http://dx.doi.org/10.1016/j.tra.2017.05.031.
  • Devine, H. A. & Field, R. C. (1986). The gist of GIS. Journal of Forestry, 84(8), 17-22. https://doi.org/10.1093/jof/84.8.17.
  • Dezman, Z., De Andrade, L., Vissoci, J. R., El-Gabri, D., Johnson, A., Hirshon, J. M. & Staton, C. A. (2016). Hotspots and causes of motor vehicle crashes in Baltimore, Maryland: a geospatial analysis of five years of police crash and census data. Injury, 47, 2450-2458. http://dx.doi.org/10.1016/j.injury.2016.09.002.
  • Dong, P., Yang, C., Rui, X., Zhang, L. & Cheng, Q. (2003). An Effective Buffer Generation Method in GIS. IEEE International Geoscience and Remote Sensing Symposium (ss. 3706-3708), Toulouse, France.
  • Dueker, K. J. (1979). Land resource ınformation systems: a review of fifteen years experience. Geo-Processing, 1, 105-128.
  • Elvik, R., Høye, A., Vaa, T. & Sørensen, M. (2009), "List of abbreviations", The Handbook of Road Safety Measures, Emerald Group Publishing Limited, p. 1115. https://doi.org/10.1108/9781848552517-019.
  • Erdogan, S. (2009). Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey. Journal of Safety Research, 40, 341-351. https://doi.org/10.1016/j.jsr.2009.07.006.
  • Erdogan, S., Ilçi, V., Soysal, O. M. & Korkmaz, A. (2015). A Model Suggestıon For The Determınatıon Of The Traffıc Accıdent Hotspots On The Turkısh Hıghway Road Network: A Pılot Study. Bol. Ciênc. Geod., sec. Artigos, Curitiba, 21, 169-188. http://dx.doi.org/10.1590/S1982-21702015000100011.
  • Getis, A. & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 34, 189-206.
  • Goodchild, M. F. (1992). Geographical information science. International Journal of Geographical Information Systems, 6(1), 31-45.
  • Goodchild, M. F. (2004). GIScience, Geography, form and process. Annals of the Association of Amirican Geographers, 94(4), 709-714.
  • Goodchild, M. F. (2009). Geographic ınformation systems and science: today and tomorrow. Annals of GIS, 15(1), 3-9.
  • Goodchild, M. F. (2018). Reimagining the history of GIS. Annals of GIS, 24(1), 1-8.
  • Gudes, O., Varhol, R., Sun, Q. & Meuleners, L. (2017). Investigating articulated heavy-vehicle crashes in Western Australia using a spatial approach. Accident Analysis and Prevention, 106, 243-253. http://dx.doi.org/10.1016/j.aap.2017.05.026.
  • Gündoğdu, G. (2010). Coğrafi Bilgi Teknolojileri Kullanılarak Trafik Kaza Analizi: Adana Örneği. [Yüksek Lisans Tezi, Çukurova Üniversitesi]. YÖK Kurumsal Akademik Arşiv https://tez.yok.gov.tr/UlusalTezMerkezi/
  • Hashimoto, S., Yoshiki, S., Saeki, R., Mimura, Y., Ando, R., Nanba, S. (2016). Development and application of traffic accident density estimation models using kernel density estimation. Journal of Traffic and Transportation Engineering (English Edition), 3, 262-270. https://doi.org/10.1016/j.jtte.2016.01.005.
  • Haybat, H., & Karakaş, E. (2018). An analysis of traffic accidents with spatial statistical methods in Izmir Province. Social Science Development, 3, 599-617. https://doi.org/10.31567/ssd.126.
  • Haybat, H., & Karakaş, E. (2020). Relationship between daily activity areas and traffic accidents in İzmir city. International Journal of Geography and Geography Education (IGGE), 42, 429-454. https://doi.org/10.32003/igge.670506.
  • Hezaveh, A. M., Arvin, R. & Cherry, C. R. (2019). A geographically weighted regression to estimate the comprehensive cost of traffic crashes at a zonal level. Accident Analysis and Prevention, 131, 15-24. https://doi.org/10.1016/j.aap.2019.05.028.
  • Isıldar, S. (2006). Road Accıdents In Turkey 1995-2004. IATSS Research, 30, 115-118.
  • Jones, A. P., Langford, I. H. & Bentham, G. (1996). The Applıcatıon Of K-Functıon Analysıs To The Geographıcal Dıstrıbutıon Of Road Traffıc Accıdent Outcomes In Norfolk, England. Soc. Sci. Med., 42, 879-885.
  • Karacasu, M., Er, A., Bilgiç, S. & Barut, H. B. (2011). Variations in Traffic Accidents on Seasonal, Monthly, Daily and Hourly Basis: Eskisehir Case. Procedia Social and Behavioral Sciences, 20, 767-775. oi:10.1016/j.sbspro.2011.08.085.
  • Kaygisiz, Ö., Düzgün, Ş., Yildiz, A. & Senbil, M. (2015). Spatio-temporal accident analysis for accident prevention in relation to behavioral factors in driving: The case of South Anatolian Motorway. Transportation Research Part F, 33, 128-140. http://dx.doi.org/10.1016/j.trf.2015.07.002.
  • Kaygisiz, Ö., Senbil, M. & Yildiz, A. (2017). Influence of urban built environment on traffic accidents: The case of Eskisehir (Turkey). Case Studies on Transport Policy, 5, 306-313. https://doi.org/10.1016/j.cstp.2017.02.002.
  • Kemp, K. K., Goodchild, M. F. & Dodson, R. F. (1992). Teaching GIS in geography. The Professional Geographer, 44(2), 181-191.
  • Kingham, S., Sabel, C. E. & Bartie, P. (2011). The impact of the ‘school run’ on road traffic accidents: A spatio-temporal analysis. Journal of Transport Geography, 19, 705-711. https://doi.org/10.1016/j.jtrangeo.2010.08.011.
  • Kocatepe, A., Ulak, M. B., Ozguven, E. E. & Horner, M. W. (2017). Socioeconomic characteristics and crash injury exposure: A case study in Florida using two-step floating catchment area method. Applied Geography, 87, 207-221. http://dx.doi.org/10.1016/j.apgeog.2017.08.005.
  • Kuo, P., Lord, D. & Walden, T. D. (2013). Using geographical information systems to organize police patrol routes effectively by grouping hotspots of crash and crime data. Journal of Transport Geography, 30, 138-148. https://doi.org/10.1016/j.jtrangeo.2013.04.006.
  • Li, Y., Abdel-Aty, M., Yuan, J., Cheng, Z., & Lu, J. (2020). Analyzing traffic violation behavior at urban intersections: A spatiotemporal Kernel Density estimation approach using automated enforcement system data. Accident Analysis and Prevention, 141, 105-509. https://doi.org/10.1016/j.aap.2020.105509.
  • Li, L., Zhu, L. & Sui, D. Z. (2007). A GIS-based Bayesian approach for analyzing spatial–temporal patterns of intra-city motor vehicle crashes. Journal of Transport Geography, 15, 274-285. https://doi:10.1016/j.jtrangeo.2006.08.005.
  • Li, X., Zhang, L. & Liang, C. (2010). A GIS-based buffer gradient analysis on spatiotemporal dynamics of urban expansion in Shanghai and its major satellite cities. Procedia Environmental Sciences, 2, 1139-1156. https://doi.org/10.1016/j.proenv.2010.10.123.
  • Liu, C., Xiong, L., Hu, X. & Shan, J. (2015). A Progressive Buffering Method for Road Map Update Using OpenStreetMap Data. ISPRS Int. J. Geo-Inf., 4, 1246-1264. https://doi:10.3390/ijgi4031246.
  • Loidl, M., Traun, C. & Wallentin, G. (2016). Spatial patterns and temporal dynamics of urban bicycle crashes—A case study from Salzburg (Austria). Journal of Transport Geography, 52, 38-50. http://dx.doi.org/10.1016/j.jtrangeo.2016.02.008.
  • Loo, B. P. Y. & Yao, S. (2013). The identification of traffic crash hot zones under the link-attribute and event-based approaches in a network-constrained environment. Computers, Environment and Urban Systems, 41, 249-261. http://dx.doi.org/10.1016/j.compenvurbsys.2013.07.001
  • Lu, P., Bai, S., Tofani, V. & Casagli, N. (2019). Landslides detection through optimized hot spot analysis on persistent scatterers and distributed scatterers. ISPRS Journal of Photogrammetry and Remote Sensing, 156, 147-159. https://doi.org/10.1016/j.isprsjprs.2019.08.004.
  • Mafi, S., AbdelRazig, Y., Amirinia, G. Kocatepe, A., Ulak, M. B. & Ozguven, E. E. (2019). Investigating exposure of the population to crash injury using a spatiotemporal analysis: A case study in Florida. Applied Geography, 104, 42-55. https://doi.org/10.1016/j.apgeog.2019.02.001.
  • Mane, A. S. & Pulugurtha, S. S. (2018). Influence of on-network, traffic, signal, demographic, and land use characteristics by area type on red light violation crashes. Accident Analysis and Prevention, 120, 101-113. https://doi.org/10.1016/j.aap.2018.08.006.
  • Manner, H. & Wünsch-Ziegler, L. (2013). Analyzing the severity of accidents on the German Autobahn. Accident Analysis and Prevention, 57, 40-48. http://dx.doi.org/10.1016/j.aap.2013.03.022.
  • Marti-Henneberg, J. (2011). Geographical ınformation systems and the study of history. Journal of Interdisciplinary History, 42(1), 1-13.
  • Mitchell, A. (2005). The ESRI guide to GIS analysis volume 2: Spatial measurements. California. ESRI press.
  • Mukoko, K. K. & Pulugurtha, S. S. (2019). Examining the influence of network, land use, and demographic characteristics to estimate the number of bicycle-vehicle crashes on urban roads. IATSS Research, 44, 8-16. https://doi.org/10.1016/j.iatssr.2019.04.001.
  • Ouni, F. & Belloumi, M. (2019). Pattern of road traffic crash hot zones versus probable hot zones in Tunisia: A geospatial analysis. Accident Analysis and Prevention, 128, 185-196. https://doi.org/10.1016/j.aap.2019.04.008.
  • Özlü, T., Haybat, H., & Zerenoğlu, H. (2020). Temporal and spatial analysis of traffic accidents: The case of Eskişehir City. International Journal of Geography Education (IGGE), 43, 136-158. https://doi.org/10.32003/igge.746447.
  • Pan, Y., Chen, S., Niu, S., Ma, Y. & Tang, K. (2020). Investigating the impacts of built environment on traffic states incorporating spatial heterogeneity. Journal of Transport Geography, 83, 1-14. https://doi.org/10.1016/j.jtrangeo.2020.102663.
  • Parker, H. D. (1988). The Unique Qualities of a Geographic Information System: A Commentary. Photogrammetrıc Engıneerıng And Remote Sensıng, 54, 1547-1549.
  • Peuquet, D. J. & Marble, D. F. (1990). Introductory Readings in Geographic Information Systems. USA: Taylor & Francis.
  • Pljakić, M., Jovanović, D., Matović, B. & Mićić, S. (2019). Macro-level accident modeling in Novi Sad: A spatial regression approach. Accident Analysis and Prevention, 132, 1-12. https://doi.org/10.1016/j.aap.2019.105259.
  • 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. https://doi.org/10.1016/j.sbspro.2011.07.020.
  • Prato, C. G., Kaplan, S., Patrier, A. & Rasmussen, T. K. (2019). Integrating police reports with geographic information system resources for uncovering patterns of pedestrian crashes in Denmark. Journal of Transport Geography, 74, 10-23. https://doi.org/10.1016/j.jtrangeo.2018.10.018.
  • Rodrigues, D. S., Ribeiro, P. J. G. & Nogueira, I. C. (2015). Safety classification using GIS in decision-making process to define priority road interventions. Journal of Transport Geography, 43, 101-110. http://dx.doi.org/10.1016/j.jtrangeo.2015.01.007.
  • Shen, J., Chen, L., Wu, Y. & Jing, N. (2018). Approach to Accelerating Dissolved Vector Buffer Generation in Distributed In-Memory Cluster Architecture. ISPRS International Journal of Geo-Information, 7(1), 26. https://doi.org/10.3390/ijgi7010026.
  • Singh, S. K. (2017). Road Traffic Accidents in India: Issues and Challenges. Transportation Research Procedia, 25, 4708-4719. https://doi.org/10.1016/j.trpro.2017.05.484.
  • Smith, T. R., Menon, S., Starr, J. L. & Estes, J. E. (1987). Requirements and Principles for the implementation and construction of large-scale geographic information systems. International Journal of Geographical Information Systems, 1, 13-31.
  • Soltani, A. & Askari, S. (2014). Analysis of ıntra-urban traffic accidents using spatiotemporal visualızation techniques. Transport and Telecommunication, 15, 227-232. http://dx.doi.org/10.2478/ttj-2014-0020.
  • Suphanchaimat, R., Sornsrivichai, V., Limwattananon, S. & Thammawijaya, P. (2019). Economic development and road traffic ınjuries and fatalities in Thailand: an application of spatial panel data analysis, 2012–2016. BMC Public Health, 19(1), 1449. https://doi.org/10.1186/s12889-019-7809-7.
  • Tobler, W. R. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46(2), 234-240.
  • Ulak, M. B., Ozguven, E. E., Spainhour, L. & Vanli, O. A. (2017). Spatial investigation of aging-involved crashes: A GIS-based case study in Northwest Florida. Journal of Transport Geography, 58, 71-91. http://dx.doi.org/10.1016/j.jtrangeo.2016.11.011.
  • Ulak, M. B., Ozguven, E. E., Vanli, O. A. & Horner, M. W. (2019). Exploring alternative spatial weights to detect crash hotspots. Computers, Environment and Urban Systems, 78, 1-9. https://doi.org/10.1016/j.compenvurbsys.2019.101398.
  • Wang, C., Quddus, M. & Ison, S. (2009). The effects of area-wide road speed and curvature on traffic casualties in England. Journal of Transport Geography, 17, 385-395. https://doi.org/10.1016/j.jtrangeo.2008.06.003.
  • Wang, X., Zhou, Q., Yang, J., You, S., Song, Y. & Xue, M. (2019). Macro-level traffic safety analysis in Shanghai, China. Accident Analysis and Prevention, 125, 249-256. https://doi.org/10.1016/j.aap.2019.02.014.
  • Waters, N. (2017). The international encyclopedia of geography. New York: John Wiley & Sons.
  • Xie, Z. & Yan, J. (2013). Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach. Journal of Transport Geography, 31, 64-71. http://dx.doi.org/10.1016/j.jtrangeo.2013.05.009.
  • Yu, W. (2017). Assessing the implications of the recent community opening policy on The Street Centrality in China: a GIS-based method and case study. Applied Geography, 89, 61-76. http://dx.doi.org/10.1016/j.apgeog.2017.10.008.
  • Zhang, Y,, Lu, H. & Qu, W. (2020). Geographical detection of traffic accidents spatial stratified heterogeneity and influence factors. Int. J. Environ. Res. Public Health, 17(2), 572. https://doi.org/10.3390/ijerph17020572.
  • Zou, X. & Vu, H. L. (2019). Mapping the knowledge domain of road safety studies: a scientometric analysis. Accident Analysis and Prevention, 132, 105-243. https://doi.org/10.1016/j.aap.2019.07.019.
  • URL-1. Destatis Satistisches Bundesamt (2021). Almanya İstatistik Verisi. https://www.destatis.de/DE/Home/_inhalt.html
  • URL-2. Emniyet Genel Müdürlüğü Trafik Şube Başkanlığı (2021). Trafik Kaza Verisi. http://www.trafik.gov.tr/
  • URL-3. Organisation Internationale des Constructeurs d’Automobiles (2021). Araç Sayılarının Verisi. https://www.oica.net/category/sales-statistics/
  • URL-4. Türkiye İstatistik Kurumu (2021). İstatistik Verileri. https://data.tuik.gov.tr
  • URL-5. Türkiye Seyahat Acenteleri Birliği (2021). Türkiye Turizm Verisi. https://www.tursab.org.tr/turkiye-turizm-istatistikleri/diger-istatistikler
  • URL-6. Dünya Sağlık Örgütü (2021). Trafik Kazasında Meydana Gelen Ölüm Sayıları. https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries
  • URL-7. Milli Eğitim Bakanlığı (2022). Öğrenci Sayıları. https://antalya.meb.gov.tr/

Relationship of Traffic Accidents Occurring in Antalya City with Daily Activity Areas

Yıl 2022, Cilt: 10 Sayı: 2, 509 - 531, 28.10.2022

Hüseyin Zerenoğlu Tamer Özlü Himmet Haybat

https://doi.org/10.18795/gumusmaviatlas.1131907

Öz

Due to the increase in the population in Turkey, there is an increase in the number of vehicles. Depending on the increase in the number of vehicles, it causes an increase in problems and traffic accidents in urban transportation. The study was carried out to reveal and determine the relationship between traffic accidents and daily activity areas. Five central districts of Antalya were selected as the study area. Among the reasons for choosing the five central districts of the study area, the density of traffic accidents, the density of activity areas and the distribution of a large proportion of the population in this area. The research includes data obtained from traffic accident reports between 2015-2019. Two different analyzes were carried out in the study. Analyzes performed are enhanced buffer analysis and optimized hotspot analysis. The analyzes are to identify the dense areas where traffic accidents occur and to determine the relationship between daily activity areas. ArcGIS 10.8 software was used to implement the analyses. An improved buffer analysis tool, which is a unique method model, was produced using ArcGIS software. By using the developed buffer analysis method, the relationship between traffic accidents in the intersection areas of shopping-education, transportation-education and transportation-shopping areas was examined. Using the optimized analysis method, hot spot analyzes of shopping-education, transportation-shopping and transportation-education intersection areas were carried out. In the final analysis, traffic accidents occurring within 150 meters of transportation, shopping, education, accommodation and food and beverage areas were examined. As a result of the study, it has been determined that traffic accidents occur mostly in the intersection areas of transportation and shopping areas among the daily activity areas.

Anahtar Kelimeler

Traffic Accident Daily Activity Buffer Analysis Hot Spot Analysis Antalya.

Kaynakça

  • 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. https://doi. org/10.1016/j.trpro.2017.05.409.
  • Andrey, J. (2010). Long-term trends in weather-related crash risks. Journal of Transport Geography, 18, 247–258. https://doi:10.1016/j.jtrangeo.2009.05.002.
  • Aronoff, S.(1989) Geographic information systems: A management perspective. Geocarto International, 4:4, 58-58. https://doi:10.1080/10106048909354237.
  • Bassani, M., Rossetti, L. & Catani, L. (2020). Spatial analysis of road crashes involving vulnerable road users in support of road safety management strategies. Transportation Research Procedia, 45, 394-401. https://doi.org/10.1016/j.trpro.2020.03.031.
  • Bekele, T. G. (2019). Road traffic accident cause and effect on socio economy of Addis Ababa city. Economics And Social Sciences Academic Journal, 1(4), 21-37.
  • Bhatia, S., Vira, V., Choksi, D. & Venkatachakam, P. (2013). An algorithm for generating geometric buffers for vector feature layers. Geo-spatial Information Science, 16, 130-138. https://doi.org/10.1080/10095020.2012.747643.
  • Bhavan, T. (2019). The economic ımpact of road accidents: the case of Sri Lanka. South Asia Economic Journal, 20(1), 124-137. https://doi.org/10.1177/1391561418822210.
  • Blazquez, C. A. & Celis, M. S. (2013). A spatial and temporal analysis of child pedestrian crashes in Santiago, Chile. Accident Analysis and Prevention, 50, 304-311. http://dx.doi.org/10.1016/j.aap.2012.05.001.
  • Briz-Redón, Á., Martínez-Ruiz, F. & Montes, F. (2019). Spatial analysis of traffic accidents near and between road intersections in a directed linear network. Accident Analysis and Prevention, 132, 105-252. https://doi.org/10.1016/j.aap.2019.07.013.
  • Burrough, P. A. (1986). Principles of geographical ınformation systems for land resources assement. Geocarto International, 1(3), 54. https://doi.org/10.1080/10106048609354060.
  • Carter, J. R. (1989). On defining the geographic ınformation system. In W. J. Ripple (Ed.), Advanced in fundamentals of geographic information systems: a compendium (ss. 3-7). Falls Church, Va: American Society of Photogrammetry and Remote Sensing.
  • Chen, S., Kuhn, M., Prettner, K. & Bloom, D. E. (2019). The global macroeconomic burden of road injuries: estimates and projections for 166 countries. Lancet Planet Health, 3, 390-398.
  • Chen, J., Shaw, S. L., Yu, H., Lu, F., Chai, Y. & Jia, Q. (2011). Exploratory data analysis of activity diary data: a space–time GIS approach. Journal of Transport Geography, 19, 394-404. https://doi.org/10.1016/j.jtrangeo.2010.11.002.
  • Chrisman, N. R. (1999). What does “GIS” mean?. Transactions in GIS, 3(2), 175-186.
  • Cowen, D. J. (1988). GIS versus CAD versus DBMS: what are the difference?. Photogrammetric Engineering and Remote Sensing, 54, 1551-1555.
  • 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, 103, 106-117. http://dx.doi.org/10.1016/j.tra.2017.05.031.
  • Devine, H. A. & Field, R. C. (1986). The gist of GIS. Journal of Forestry, 84(8), 17-22. https://doi.org/10.1093/jof/84.8.17.
  • Dezman, Z., De Andrade, L., Vissoci, J. R., El-Gabri, D., Johnson, A., Hirshon, J. M. & Staton, C. A. (2016). Hotspots and causes of motor vehicle crashes in Baltimore, Maryland: a geospatial analysis of five years of police crash and census data. Injury, 47, 2450-2458. http://dx.doi.org/10.1016/j.injury.2016.09.002.
  • Dong, P., Yang, C., Rui, X., Zhang, L. & Cheng, Q. (2003). An Effective Buffer Generation Method in GIS. IEEE International Geoscience and Remote Sensing Symposium (ss. 3706-3708), Toulouse, France.
  • Dueker, K. J. (1979). Land resource ınformation systems: a review of fifteen years experience. Geo-Processing, 1, 105-128.
  • Elvik, R., Høye, A., Vaa, T. & Sørensen, M. (2009), "List of abbreviations", The Handbook of Road Safety Measures, Emerald Group Publishing Limited, p. 1115. https://doi.org/10.1108/9781848552517-019.
  • Erdogan, S. (2009). Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey. Journal of Safety Research, 40, 341-351. https://doi.org/10.1016/j.jsr.2009.07.006.
  • Erdogan, S., Ilçi, V., Soysal, O. M. & Korkmaz, A. (2015). A Model Suggestıon For The Determınatıon Of The Traffıc Accıdent Hotspots On The Turkısh Hıghway Road Network: A Pılot Study. Bol. Ciênc. Geod., sec. Artigos, Curitiba, 21, 169-188. http://dx.doi.org/10.1590/S1982-21702015000100011.
  • Getis, A. & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 34, 189-206.
  • Goodchild, M. F. (1992). Geographical information science. International Journal of Geographical Information Systems, 6(1), 31-45.
  • Goodchild, M. F. (2004). GIScience, Geography, form and process. Annals of the Association of Amirican Geographers, 94(4), 709-714.
  • Goodchild, M. F. (2009). Geographic ınformation systems and science: today and tomorrow. Annals of GIS, 15(1), 3-9.
  • Goodchild, M. F. (2018). Reimagining the history of GIS. Annals of GIS, 24(1), 1-8.
  • Gudes, O., Varhol, R., Sun, Q. & Meuleners, L. (2017). Investigating articulated heavy-vehicle crashes in Western Australia using a spatial approach. Accident Analysis and Prevention, 106, 243-253. http://dx.doi.org/10.1016/j.aap.2017.05.026.
  • Gündoğdu, G. (2010). Coğrafi Bilgi Teknolojileri Kullanılarak Trafik Kaza Analizi: Adana Örneği. [Yüksek Lisans Tezi, Çukurova Üniversitesi]. YÖK Kurumsal Akademik Arşiv https://tez.yok.gov.tr/UlusalTezMerkezi/
  • Hashimoto, S., Yoshiki, S., Saeki, R., Mimura, Y., Ando, R., Nanba, S. (2016). Development and application of traffic accident density estimation models using kernel density estimation. Journal of Traffic and Transportation Engineering (English Edition), 3, 262-270. https://doi.org/10.1016/j.jtte.2016.01.005.
  • Haybat, H., & Karakaş, E. (2018). An analysis of traffic accidents with spatial statistical methods in Izmir Province. Social Science Development, 3, 599-617. https://doi.org/10.31567/ssd.126.
  • Haybat, H., & Karakaş, E. (2020). Relationship between daily activity areas and traffic accidents in İzmir city. International Journal of Geography and Geography Education (IGGE), 42, 429-454. https://doi.org/10.32003/igge.670506.
  • Hezaveh, A. M., Arvin, R. & Cherry, C. R. (2019). A geographically weighted regression to estimate the comprehensive cost of traffic crashes at a zonal level. Accident Analysis and Prevention, 131, 15-24. https://doi.org/10.1016/j.aap.2019.05.028.
  • Isıldar, S. (2006). Road Accıdents In Turkey 1995-2004. IATSS Research, 30, 115-118.
  • Jones, A. P., Langford, I. H. & Bentham, G. (1996). The Applıcatıon Of K-Functıon Analysıs To The Geographıcal Dıstrıbutıon Of Road Traffıc Accıdent Outcomes In Norfolk, England. Soc. Sci. Med., 42, 879-885.
  • Karacasu, M., Er, A., Bilgiç, S. & Barut, H. B. (2011). Variations in Traffic Accidents on Seasonal, Monthly, Daily and Hourly Basis: Eskisehir Case. Procedia Social and Behavioral Sciences, 20, 767-775. oi:10.1016/j.sbspro.2011.08.085.
  • Kaygisiz, Ö., Düzgün, Ş., Yildiz, A. & Senbil, M. (2015). Spatio-temporal accident analysis for accident prevention in relation to behavioral factors in driving: The case of South Anatolian Motorway. Transportation Research Part F, 33, 128-140. http://dx.doi.org/10.1016/j.trf.2015.07.002.
  • Kaygisiz, Ö., Senbil, M. & Yildiz, A. (2017). Influence of urban built environment on traffic accidents: The case of Eskisehir (Turkey). Case Studies on Transport Policy, 5, 306-313. https://doi.org/10.1016/j.cstp.2017.02.002.
  • Kemp, K. K., Goodchild, M. F. & Dodson, R. F. (1992). Teaching GIS in geography. The Professional Geographer, 44(2), 181-191.
  • Kingham, S., Sabel, C. E. & Bartie, P. (2011). The impact of the ‘school run’ on road traffic accidents: A spatio-temporal analysis. Journal of Transport Geography, 19, 705-711. https://doi.org/10.1016/j.jtrangeo.2010.08.011.
  • Kocatepe, A., Ulak, M. B., Ozguven, E. E. & Horner, M. W. (2017). Socioeconomic characteristics and crash injury exposure: A case study in Florida using two-step floating catchment area method. Applied Geography, 87, 207-221. http://dx.doi.org/10.1016/j.apgeog.2017.08.005.
  • Kuo, P., Lord, D. & Walden, T. D. (2013). Using geographical information systems to organize police patrol routes effectively by grouping hotspots of crash and crime data. Journal of Transport Geography, 30, 138-148. https://doi.org/10.1016/j.jtrangeo.2013.04.006.
  • Li, Y., Abdel-Aty, M., Yuan, J., Cheng, Z., & Lu, J. (2020). Analyzing traffic violation behavior at urban intersections: A spatiotemporal Kernel Density estimation approach using automated enforcement system data. Accident Analysis and Prevention, 141, 105-509. https://doi.org/10.1016/j.aap.2020.105509.
  • Li, L., Zhu, L. & Sui, D. Z. (2007). A GIS-based Bayesian approach for analyzing spatial–temporal patterns of intra-city motor vehicle crashes. Journal of Transport Geography, 15, 274-285. https://doi:10.1016/j.jtrangeo.2006.08.005.
  • Li, X., Zhang, L. & Liang, C. (2010). A GIS-based buffer gradient analysis on spatiotemporal dynamics of urban expansion in Shanghai and its major satellite cities. Procedia Environmental Sciences, 2, 1139-1156. https://doi.org/10.1016/j.proenv.2010.10.123.
  • Liu, C., Xiong, L., Hu, X. & Shan, J. (2015). A Progressive Buffering Method for Road Map Update Using OpenStreetMap Data. ISPRS Int. J. Geo-Inf., 4, 1246-1264. https://doi:10.3390/ijgi4031246.
  • Loidl, M., Traun, C. & Wallentin, G. (2016). Spatial patterns and temporal dynamics of urban bicycle crashes—A case study from Salzburg (Austria). Journal of Transport Geography, 52, 38-50. http://dx.doi.org/10.1016/j.jtrangeo.2016.02.008.
  • Loo, B. P. Y. & Yao, S. (2013). The identification of traffic crash hot zones under the link-attribute and event-based approaches in a network-constrained environment. Computers, Environment and Urban Systems, 41, 249-261. http://dx.doi.org/10.1016/j.compenvurbsys.2013.07.001
  • Lu, P., Bai, S., Tofani, V. & Casagli, N. (2019). Landslides detection through optimized hot spot analysis on persistent scatterers and distributed scatterers. ISPRS Journal of Photogrammetry and Remote Sensing, 156, 147-159. https://doi.org/10.1016/j.isprsjprs.2019.08.004.
  • Mafi, S., AbdelRazig, Y., Amirinia, G. Kocatepe, A., Ulak, M. B. & Ozguven, E. E. (2019). Investigating exposure of the population to crash injury using a spatiotemporal analysis: A case study in Florida. Applied Geography, 104, 42-55. https://doi.org/10.1016/j.apgeog.2019.02.001.
  • Mane, A. S. & Pulugurtha, S. S. (2018). Influence of on-network, traffic, signal, demographic, and land use characteristics by area type on red light violation crashes. Accident Analysis and Prevention, 120, 101-113. https://doi.org/10.1016/j.aap.2018.08.006.
  • Manner, H. & Wünsch-Ziegler, L. (2013). Analyzing the severity of accidents on the German Autobahn. Accident Analysis and Prevention, 57, 40-48. http://dx.doi.org/10.1016/j.aap.2013.03.022.
  • Marti-Henneberg, J. (2011). Geographical ınformation systems and the study of history. Journal of Interdisciplinary History, 42(1), 1-13.
  • Mitchell, A. (2005). The ESRI guide to GIS analysis volume 2: Spatial measurements. California. ESRI press.
  • Mukoko, K. K. & Pulugurtha, S. S. (2019). Examining the influence of network, land use, and demographic characteristics to estimate the number of bicycle-vehicle crashes on urban roads. IATSS Research, 44, 8-16. https://doi.org/10.1016/j.iatssr.2019.04.001.
  • Ouni, F. & Belloumi, M. (2019). Pattern of road traffic crash hot zones versus probable hot zones in Tunisia: A geospatial analysis. Accident Analysis and Prevention, 128, 185-196. https://doi.org/10.1016/j.aap.2019.04.008.
  • Özlü, T., Haybat, H., & Zerenoğlu, H. (2020). Temporal and spatial analysis of traffic accidents: The case of Eskişehir City. International Journal of Geography Education (IGGE), 43, 136-158. https://doi.org/10.32003/igge.746447.
  • Pan, Y., Chen, S., Niu, S., Ma, Y. & Tang, K. (2020). Investigating the impacts of built environment on traffic states incorporating spatial heterogeneity. Journal of Transport Geography, 83, 1-14. https://doi.org/10.1016/j.jtrangeo.2020.102663.
  • Parker, H. D. (1988). The Unique Qualities of a Geographic Information System: A Commentary. Photogrammetrıc Engıneerıng And Remote Sensıng, 54, 1547-1549.
  • Peuquet, D. J. & Marble, D. F. (1990). Introductory Readings in Geographic Information Systems. USA: Taylor & Francis.
  • Pljakić, M., Jovanović, D., Matović, B. & Mićić, S. (2019). Macro-level accident modeling in Novi Sad: A spatial regression approach. Accident Analysis and Prevention, 132, 1-12. https://doi.org/10.1016/j.aap.2019.105259.
  • 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. https://doi.org/10.1016/j.sbspro.2011.07.020.
  • Prato, C. G., Kaplan, S., Patrier, A. & Rasmussen, T. K. (2019). Integrating police reports with geographic information system resources for uncovering patterns of pedestrian crashes in Denmark. Journal of Transport Geography, 74, 10-23. https://doi.org/10.1016/j.jtrangeo.2018.10.018.
  • Rodrigues, D. S., Ribeiro, P. J. G. & Nogueira, I. C. (2015). Safety classification using GIS in decision-making process to define priority road interventions. Journal of Transport Geography, 43, 101-110. http://dx.doi.org/10.1016/j.jtrangeo.2015.01.007.
  • Shen, J., Chen, L., Wu, Y. & Jing, N. (2018). Approach to Accelerating Dissolved Vector Buffer Generation in Distributed In-Memory Cluster Architecture. ISPRS International Journal of Geo-Information, 7(1), 26. https://doi.org/10.3390/ijgi7010026.
  • Singh, S. K. (2017). Road Traffic Accidents in India: Issues and Challenges. Transportation Research Procedia, 25, 4708-4719. https://doi.org/10.1016/j.trpro.2017.05.484.
  • Smith, T. R., Menon, S., Starr, J. L. & Estes, J. E. (1987). Requirements and Principles for the implementation and construction of large-scale geographic information systems. International Journal of Geographical Information Systems, 1, 13-31.
  • Soltani, A. & Askari, S. (2014). Analysis of ıntra-urban traffic accidents using spatiotemporal visualızation techniques. Transport and Telecommunication, 15, 227-232. http://dx.doi.org/10.2478/ttj-2014-0020.
  • Suphanchaimat, R., Sornsrivichai, V., Limwattananon, S. & Thammawijaya, P. (2019). Economic development and road traffic ınjuries and fatalities in Thailand: an application of spatial panel data analysis, 2012–2016. BMC Public Health, 19(1), 1449. https://doi.org/10.1186/s12889-019-7809-7.
  • Tobler, W. R. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46(2), 234-240.
  • Ulak, M. B., Ozguven, E. E., Spainhour, L. & Vanli, O. A. (2017). Spatial investigation of aging-involved crashes: A GIS-based case study in Northwest Florida. Journal of Transport Geography, 58, 71-91. http://dx.doi.org/10.1016/j.jtrangeo.2016.11.011.
  • Ulak, M. B., Ozguven, E. E., Vanli, O. A. & Horner, M. W. (2019). Exploring alternative spatial weights to detect crash hotspots. Computers, Environment and Urban Systems, 78, 1-9. https://doi.org/10.1016/j.compenvurbsys.2019.101398.
  • Wang, C., Quddus, M. & Ison, S. (2009). The effects of area-wide road speed and curvature on traffic casualties in England. Journal of Transport Geography, 17, 385-395. https://doi.org/10.1016/j.jtrangeo.2008.06.003.
  • Wang, X., Zhou, Q., Yang, J., You, S., Song, Y. & Xue, M. (2019). Macro-level traffic safety analysis in Shanghai, China. Accident Analysis and Prevention, 125, 249-256. https://doi.org/10.1016/j.aap.2019.02.014.
  • Waters, N. (2017). The international encyclopedia of geography. New York: John Wiley & Sons.
  • Xie, Z. & Yan, J. (2013). Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach. Journal of Transport Geography, 31, 64-71. http://dx.doi.org/10.1016/j.jtrangeo.2013.05.009.
  • Yu, W. (2017). Assessing the implications of the recent community opening policy on The Street Centrality in China: a GIS-based method and case study. Applied Geography, 89, 61-76. http://dx.doi.org/10.1016/j.apgeog.2017.10.008.
  • Zhang, Y,, Lu, H. & Qu, W. (2020). Geographical detection of traffic accidents spatial stratified heterogeneity and influence factors. Int. J. Environ. Res. Public Health, 17(2), 572. https://doi.org/10.3390/ijerph17020572.
  • Zou, X. & Vu, H. L. (2019). Mapping the knowledge domain of road safety studies: a scientometric analysis. Accident Analysis and Prevention, 132, 105-243. https://doi.org/10.1016/j.aap.2019.07.019.
  • URL-1. Destatis Satistisches Bundesamt (2021). Almanya İstatistik Verisi. https://www.destatis.de/DE/Home/_inhalt.html
  • URL-2. Emniyet Genel Müdürlüğü Trafik Şube Başkanlığı (2021). Trafik Kaza Verisi. http://www.trafik.gov.tr/
  • URL-3. Organisation Internationale des Constructeurs d’Automobiles (2021). Araç Sayılarının Verisi. https://www.oica.net/category/sales-statistics/
  • URL-4. Türkiye İstatistik Kurumu (2021). İstatistik Verileri. https://data.tuik.gov.tr
  • URL-5. Türkiye Seyahat Acenteleri Birliği (2021). Türkiye Turizm Verisi. https://www.tursab.org.tr/turkiye-turizm-istatistikleri/diger-istatistikler
  • URL-6. Dünya Sağlık Örgütü (2021). Trafik Kazasında Meydana Gelen Ölüm Sayıları. https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries
  • URL-7. Milli Eğitim Bakanlığı (2022). Öğrenci Sayıları. https://antalya.meb.gov.tr/
Toplam 87 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makalesi
Yazarlar

Hüseyin Zerenoğlu 0000-0001-7986-1273

Tamer Özlü 0000-0002-8847-7967

Himmet Haybat 0000-0001-6569-6617

Yayımlanma Tarihi 28 Ekim 2022
Gönderilme Tarihi 16 Haziran 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 10 Sayı: 2

Kaynak Göster

APA Zerenoğlu, H., Özlü, T., & Haybat, H. (2022). Antalya Şehrinde Meydana Gelen Trafik Kazalarının Günlük Aktivite Alanları ile İlişkisi. Mavi Atlas, 10(2), 509-531. https://doi.org/10.18795/gumusmaviatlas.1131907

Tarandığımız Dizinler:

19020 19017 1901824810 19019

e-ISSN: 2148-5232