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Farklı Yöntemler Kullanılarak GeliĢtirilen Trafik Kaza Tahmin Modelleri ve Analizi

Yıl 2010, Cilt: 2 Sayı: 1, 16 - 22, 15.01.2010

Öz

Bu çalışmada, regresyon analizi, yapay sinir ağları (YSA) ve genetik algoritma (GA) yöntemleri kullanılarak İzmir ili için trafik kaza tahmin modelleri geliştirilmiştir Modeller geliştirilirken nüfus, araç sayısı ve kaza sayısı model parametreleri olarak kullanılmış ve bu parametrelere ait 1986-2005 yılları arasındaki verilerden faydalanılmıştır. Regresyon analizi kullanılarak geliştirilen kaza modellerinde Smeed ve Andreassen kaza model formları kullanılmıştır. YSA modelinde 2-5-1 ağ mimarisi en uygun mimari olarak belirlenmiş, ağların gizli katmanında sigmoid, çıkış katmanında da doğrusal fonksiyon kullanılmıştır. Ağın eğitiminde ise ileri beslemeli geri yayılım algoritmasından yararlanılmıştır. GA tekniği ile modeller oluşturulurken farklı formdaki modeller denenmiş ancak bu çalışma için en başarılı modelin üstel model olduğu görülmüştür. Geliştirilen bütün modellerin performansları ortalama mutlak yüzde hata (OMYH) ortalama mutlak hata (OMH) ve ortalama karesel hataların karekökü (OKHK) ölçütleri içinde değerlendirilmiştir.

Kaynakça

  • [1] Smeed, R.J. “Some statistics aspects of road safety research”, Journal of the Royal Statistical Society, Series A, Part I, 1-34, 1949 [2] Andreassen., D.C. “Linking deaths with vehicles and population”, Traffic Engineering & Control, Vol.26 No.11 pp. 547-549, 1985 [3] Mekky, A. “Effect of rapid increase in motorization levels on road fatality rates in some rich developing countries” Accident Analysis and Prevention, Vol.17 No.2 pp. 101-109 ,1985 [4] Chakraborty S, Roy S K. “Traffic accident characteristics of Kolkata” Transport and Communications Bulletin for Asia and the Pacific, No.74, pp.75-86, 2005. [5] Valli., P.P. “Road accident models for large metropolitan cities of India” IATSS Research, Vol.29, No.1, pp.57-65, 2005 [6] Akgungor, A P, Dogan E. “Smeed ve Andreassen kaza modellerinin Türkiye uygulaması: farklı senaryo analizleri” Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, cilt.23, No.4, s. 821-827, 2008. [7] Mussone L, Ferrari A, Oneta, M. “An analysis of urban collision using an artificial intelligence model” Accident Analysis and Prevention, Vol.3, No.8, pp.705-718, 1999 [8] Abdelwahab H T, Abdel-Aty M A. “Development of artificial neural network models to predict driver injury severity in traffic accident at signalized intersection” Transportation Research Record 1746, pp.6-13, 2001 [9] Delen D, Sharda R, Besson M. “Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks” Accident Analysis and Prevention Vol.38, No.3, pp.434-444, 2006 [10] Chiou Y.C. “An artificial network-based expert system for appraisal of two-car crash accidents” Accident Analysis and Prevention Vol.38, No.4, pp.777-785, 2006. [11] Akgungor A P, Dogan E. “Estimating road accidents of Turkey based on regression analysis and artificial neural network approach” Advances in Transportation Studies, An International Journal Section A 16, pp.11-22, 2008. [12] Holland, J. H. Adaptation in Natural Artificial Systems, University of Michigan Press, Ann Arbor, Michigan, 211 p. 1992. [13] Haldenbilen, S., Ceylan H.. “Genetic algorithm approach to estimate transport energy demand in Turkey” Energy Policy, Vol. 33, Issue 1, pp. 89-98, 2005. [14] Gündoğdu, Ö., Gökdağ, M., Yüksel F. “A traffic noise prediction method based on vehicle composition using genetic algorithms” Applied Acoustics Vol.66, No.7, pp. 799-809, 2005. [15] Ceylan, H., ve Haldenbilen, S. “Genetik algoritma yaklaĢımı ile Avrupa Birliği üyeliği sürecinde Türkiye de beklenen ulaĢım talebi ve yönetimi üzerine bir yaklaĢım” SDÜ, Fen Bilimleri Enstitüsü Dergisi, cilt. 9, sayı 1, s. 153–159, 2005. [16] Akgüngör A. P., Doğan E., “An artificial intelligent approach to traffic accident estimation: Model development and application” Transport, Vol. 24 No. 2, pp.135-142, 2009. [17] Goldberg, D. E. Genetic algorithms in search, optimization and machine learning, Addison-Wesley, Harlow, England. 432 p., 1989. [18] Gen, M.; Cheng, R. Genetic Algorithms and Engineering Design. New York: John Wiley and Sons. 432 p.,1997. [19] Coley, D.A. An introduction to genetic algorithms for scientists and engineers, World Scientific Publishing Company, England 227 p, 1997. [20] Mitchell, M. An introduction to genetic algorithms, Cambridge, MA: The MIT Press. 205 p, 1996. [21] Elmas, Ç. Yapay zeka uygulamaları, Seçkin Kitabevi, s.425, 2007. [22] Türkiye Ġstatistik Kurumu, Karayolları Kaza Ġstatistikleri 1986-2005. [23] Emniyet Genel Müdürlüğü, Trafik Kaza Ġstatistikleri, 1986-2005.

Farklı Yöntemler Kullanılarak GeliĢtirilen Trafik Kaza Tahmin Modelleri ve Analizi

Yıl 2010, Cilt: 2 Sayı: 1, 16 - 22, 15.01.2010

Öz

In this study, traffic accident prediction models were developed using regression analysis, artificial neural networks (ANNs) and genetic algorithm (GA) methods for the city of Izmir. In the development of the models, population, the number of the vehicles and accidents were used as model parameters with data between 1986 and 2005. Smeed and Andreassen accident model structures were utilized in the development of the accident models by using regression analysis. In the ANN model, 2-5-1 network architecture was determined as the best suitable network architecture. The sigmoid and pureline functions were used as activation functions with feed forward-back propagation algorithm. In the GA approach, genetic algorithm models in different forms were developed but the exponential model form had the best performance for this study. The performances of all developed models were evaluated by the use of mean absolute percent errors (MAPE), mean absolute errors (MAE) and root mean square errors (RMSE).

Kaynakça

  • [1] Smeed, R.J. “Some statistics aspects of road safety research”, Journal of the Royal Statistical Society, Series A, Part I, 1-34, 1949 [2] Andreassen., D.C. “Linking deaths with vehicles and population”, Traffic Engineering & Control, Vol.26 No.11 pp. 547-549, 1985 [3] Mekky, A. “Effect of rapid increase in motorization levels on road fatality rates in some rich developing countries” Accident Analysis and Prevention, Vol.17 No.2 pp. 101-109 ,1985 [4] Chakraborty S, Roy S K. “Traffic accident characteristics of Kolkata” Transport and Communications Bulletin for Asia and the Pacific, No.74, pp.75-86, 2005. [5] Valli., P.P. “Road accident models for large metropolitan cities of India” IATSS Research, Vol.29, No.1, pp.57-65, 2005 [6] Akgungor, A P, Dogan E. “Smeed ve Andreassen kaza modellerinin Türkiye uygulaması: farklı senaryo analizleri” Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, cilt.23, No.4, s. 821-827, 2008. [7] Mussone L, Ferrari A, Oneta, M. “An analysis of urban collision using an artificial intelligence model” Accident Analysis and Prevention, Vol.3, No.8, pp.705-718, 1999 [8] Abdelwahab H T, Abdel-Aty M A. “Development of artificial neural network models to predict driver injury severity in traffic accident at signalized intersection” Transportation Research Record 1746, pp.6-13, 2001 [9] Delen D, Sharda R, Besson M. “Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks” Accident Analysis and Prevention Vol.38, No.3, pp.434-444, 2006 [10] Chiou Y.C. “An artificial network-based expert system for appraisal of two-car crash accidents” Accident Analysis and Prevention Vol.38, No.4, pp.777-785, 2006. [11] Akgungor A P, Dogan E. “Estimating road accidents of Turkey based on regression analysis and artificial neural network approach” Advances in Transportation Studies, An International Journal Section A 16, pp.11-22, 2008. [12] Holland, J. H. Adaptation in Natural Artificial Systems, University of Michigan Press, Ann Arbor, Michigan, 211 p. 1992. [13] Haldenbilen, S., Ceylan H.. “Genetic algorithm approach to estimate transport energy demand in Turkey” Energy Policy, Vol. 33, Issue 1, pp. 89-98, 2005. [14] Gündoğdu, Ö., Gökdağ, M., Yüksel F. “A traffic noise prediction method based on vehicle composition using genetic algorithms” Applied Acoustics Vol.66, No.7, pp. 799-809, 2005. [15] Ceylan, H., ve Haldenbilen, S. “Genetik algoritma yaklaĢımı ile Avrupa Birliği üyeliği sürecinde Türkiye de beklenen ulaĢım talebi ve yönetimi üzerine bir yaklaĢım” SDÜ, Fen Bilimleri Enstitüsü Dergisi, cilt. 9, sayı 1, s. 153–159, 2005. [16] Akgüngör A. P., Doğan E., “An artificial intelligent approach to traffic accident estimation: Model development and application” Transport, Vol. 24 No. 2, pp.135-142, 2009. [17] Goldberg, D. E. Genetic algorithms in search, optimization and machine learning, Addison-Wesley, Harlow, England. 432 p., 1989. [18] Gen, M.; Cheng, R. Genetic Algorithms and Engineering Design. New York: John Wiley and Sons. 432 p.,1997. [19] Coley, D.A. An introduction to genetic algorithms for scientists and engineers, World Scientific Publishing Company, England 227 p, 1997. [20] Mitchell, M. An introduction to genetic algorithms, Cambridge, MA: The MIT Press. 205 p, 1996. [21] Elmas, Ç. Yapay zeka uygulamaları, Seçkin Kitabevi, s.425, 2007. [22] Türkiye Ġstatistik Kurumu, Karayolları Kaza Ġstatistikleri 1986-2005. [23] Emniyet Genel Müdürlüğü, Trafik Kaza Ġstatistikleri, 1986-2005.
Toplam 1 adet kaynakça vardır.

Ayrıntılar

Bölüm Makaleler
Yazarlar

Ali Payidar Akgüngör

Erdem Doğan

Yayımlanma Tarihi 15 Ocak 2010
Gönderilme Tarihi 23 Ekim 2017
Yayımlandığı Sayı Yıl 2010 Cilt: 2 Sayı: 1

Kaynak Göster

APA Akgüngör, A. P., & Doğan, E. (2010). Farklı Yöntemler Kullanılarak GeliĢtirilen Trafik Kaza Tahmin Modelleri ve Analizi. International Journal of Engineering Research and Development, 2(1), 16-22.
Tüm hakları saklıdır. Kırıkkale Üniversitesi, Mühendislik Fakültesi.