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Yapay Sinir Ağları ile Trafik Yoğunluğu Tahmini

Yıl 2020, Cilt: 22 Sayı: 4, 1020 - 1034, 31.12.2020
https://doi.org/10.32709/akusosbil.746349

Öz

Şehirlerin büyük problemlerden olan trafik yoğunluğu, insan hayatını birçok yönden etkilemektedir. Daha yaşanabilir bir kent hayatı için; mevcut yoğunluğun belirlenerek araç trafiğinin kontrol edilebilmesi ve ileride yaşanabilecek sıkışıklıklar için gerekli önlemlerin alınabilmesi gerekmektedir. Trafik yoğunluğunu arttırabilecek muhtemel parametreler dikkate alınarak gerçekleşecek bir yoğunluk tahminin, kent sakinleri açısından olduğu kadar sürücüler ve bir şehrin mümkün olan en az trafik yoğunluğuna maruz kalmasından sorumlu olan yetkili kurum ve kuruluşlar için önemli bir yeri vardır. Çalışma, İstanbul’ a Tuzla ilçesinden giriş yönünde, E-5 karayolu üzerinde yapılmıştır. Veriler, Yandex Trafik ve Wunderground internet sitelerinden elde edilmiştir. Hafta içi günler, mesai saatleri, havanın genel durumu, sıcaklık, rüzgârın hızı ve nem seviyeleri bağımsız değişken olarak alınmıştır. Gün, saat ve hava koşulu değişkenleri kategoriktir ve bu değişkenler kukla değişken olarak belirlenmiştir. Bağımsız değişkenlerden hareketle hız tahmini yapılmış ve trafik yoğunluğu seviyesi belirlenmeye çalışılmıştır. Diğer bağımsız değişkenlere kıyasla, rüzgar hızı ve sıcaklık değişkenlerinin hız üzerinde negatif ve yüksek oranda etkili olduğu, olası bir değişiklik durumunda sıcaklığın hızı en yüksek düzeyde etkileyeceği görülmüştür. İlgili güzergahta genel olarak 80 km ortalama hız düzeyi elde edilmiş ve Serbest Akım Hızı, C ve D hizmet seviyeleri için incelendiğinde, en fazla yoğunluk düzeyinin 16- 22 aralığında, en fazla hacim/ kapasite oranının % 64- 85 aralığında olacağı tespit edilmiştir.

Kaynakça

  • Abdel-Aty, M., Ekram, A.-A., Huang, H., & Choi, K. (2011). A study on crashes related to visibility obstruction due to fog and smoke. Accident Analysis & Prevention, 43(5), 1730-1737. https://doi.org/10.1016/j.aap.2011.04.003
  • Aküzüm, Prof. Dr. T., Kodal, Doç. Dr. S., Beyribey, Doç. Dr. M., Erözel, Doç. Dr. A. Z., Tokgöz, Doç. Dr. A., Selenay, Yard. Doç. Dr. F., … Yurtsever, Yard. Doç. Dr. E. (1994). Meteoroloji I. Ankara: Ankara Üniversitesi Ziraat Fakültesi Yayınları.
  • Andreescu, M. P., & Frost, D. B. (1998). Weather and traffic accidents in montreal, Canada. Climate Research, 9(3), 225-230. https://doi.org/10.3354/cr009225
  • Andrey, J., & Yagar, S. (1993). A Temporal Analysis of rain-related crash risk. Accident Analysis and Prevention, 25(4), 465-472. https://doi.org/10.1016 /0001-4575(93)90076-9
  • Andrey, J., Hambly, D., Mills, B., & Afrin, S. (2013). Insights into driver adaptation to ınclement weather in Canada. Journal of Transport Geography, 28, 192-203. https://doi.org /10.1016/j.jtrangeo.2012.08.014
  • Choi, S., & Oh, C. (2016). Proactive strategy for variable speed limit operations on freeways under foggy weather conditions. Transportation Research Record: Journal of the Transportation Research Board, 2551, 29-36. https://doi.org/10.3141/2551-04
  • Cools, M., Moons, E., & Wets, G. (2007). Investigating the Effect of Holidays on Daily Traffic Counts: A Time Series. 19. https://doi.org/10.3141/2019-04
  • Cools, M., Moons, E., & Wets, G. (2010). Assessing the ımpact of weather on traffic ıntensity. Weather Climate and Society, 2(1), 60-68. https://doi.org/10.1175 /2009WCAS1014.1
  • Datla, S., & Sharma, S. (2008). Impact of cold and snow on temporal and spatial variations of highway traffic volumes. Journal of Transport Geography, 16(5), 358-372. https://doi.org/10.1016/j.jtrangeo.2007.12.003
  • Dey, K. C., Mishra, A., & Chowdhury, M. (2015). Potential of ıntelligent transportation systems in mitigating adverse weather ımpacts on road mobility: a review. Ieee Transactions on Intelligent Transportation Systems, 16(3), 1107-1119. https://doi.org/10.1109/TITS.2014.237145
  • Eken, M., Ulupınar, Y., Demircan, M., Nadaroğlu, Y., Aydın, B., & Özhan, Ü. (2008). Klimatoloji rasat el kitabı, Çevre ve Orman Bakanlığı Devlet Meteoroloji İşleri Genel Müdürlüğü. Ankara: DMİ Genel Müdürlüğü Matbaası.
  • Gültekin, Y., Demircan, M., Ulupınar, Y., & Bulut, E. (2005). Klimatoloji I. Geliş tarihi gönderen https://www.mgm.gov.tr/FILES/genel/kitaplar/klimatoloji1.pdf
  • Haykin, S. (1998). Neural Networks A Comprehensive Foundation (Second Edition). Pearson Education.
  • HCM. (2000). Highway Capacity Manual 2000. United States of America: Transportation research board, National Research Council.
  • Janik, M., Kurihara, O., Bossew, P., (2018). Machine Learning methods as a tool to analyse ıncomplete or ırregularly sampled radon time series data, Sci. Total Environ., 630, 1155–1167, https://doi.org/10.1016/j.scitotenv.2018.02.233.
  • Jung, S., Jang, K., Yoon, Y., & Kang, S. (2014). Contributing Factors to Vehicle to Vehicle Crash Frequency and Severity Under Rainfall. Journal of Safety Research, 50, 1-10. https://doi.org/10.1016/j.jsr.2014.01.001
  • Keay, K., & Simmonds, I. (2005). The association of rainfall and other weather variables with road traffic volume in Melbourne, Australia. Accident Analysis and Prevention, 37(1), 109-124. https://doi.org/10.1016/j.aap.2004.07.005
  • Kopal, B. (2011). Boğaziçi Köprüsü üzerindeki trafik sıkışıklığının hız yönetimi yöntemiyle azaltılması (Yüksek lisans tezi). Bahçeşehir Üniversitesi Fen Bilimleri Fakültesi, İstanbul.
  • Kyte, M., Khatib, Z., Shannon, P., & Kitchener, F. (2001). Effect of weather on free- flow speed. traffic flow theory and highway capacity 2001: highway operations, Capacity, and Traffic Control (ss. 60-68). Transportation Research Board Natl Research Council.
  • Levine, N., Kim, K. E., & Nitz, L. H. (1995). Daily fluctuations in honolulu motor vehicle accidents. Accident Analysis And Prevention, 27(6), 785-796. https://doi.org/10.1016 /0001-4575(95)00038-0
  • Maind, Sonali. B., & Wankar, P. (2014). Research paper on basic of artificial neural network |- Academia.edu. International Journal on Recent and Innovation Trends in Computing and Communication, 2(1), 96-100.
  • Liu, A., Soneja, S. I., Jiang, C., Huang, C., Kerns, T., Beck, K., Mitchell, C., & Sapkota, A. (2017). Frequency of extreme weather events and ıncreased risk of motor vehicle collision in maryland, Science of The Total Environment, 580, 550-555. https://doi.org/10.1016/j.scitotenv.2016.11.211
  • Maze, T. H., Agarwal, M., & Burchett, G. (2006). Whether weather matters to traffic demand, traffic safety, and traffic operations and flow. management and delivery of maintenance and operations services (ss. 170-176). Transportation Research Board Natl Research Council.
  • MGM. (2020). Meteoroloji Genel Müdürlüğü. https://www.mgm.gov.tr (Son Erişim: 13 Şubat 2020)
  • Nofal, F. H., & Saeed, A. a. W. (1997). Seasonal variation and weather effects on road traffic accidents in riyadh city. Public Health, 111(1), 51-55. https://doi.org/10.1038 /sj.ph.1900297
  • Öztemel, E. (2016). Yapay Sinir Ağları (4. bs). İstanbul: Papatya Yayıncılık Eğitim.
  • Öztürk, K., & Şahin, M. E. (2018). Yapay sinir ağları ve yapay zekâ’ya genel bir bakış. (6/ 2), 25-36.
  • Turner, S. M., Eisele, W. L., Benz, R. J., & Holdener, D. J. (1998). Travel time data collection handbook. texas: texas transportation ınstitute and the texas a&m university system.
  • TÜİK. (2020). Türkiye İstatistik Kurumu. http://www.tuik.gov.tr/Start.do Son Erişim: 13 Şubat 2020)
  • Unrau, D., & Andrey, J. (2006). Driver Response to rainfall on urban expressways. ıçinde driver behavior, older drivers, Simulation, User Information Systems, and Visualization (ss. 24-+). Washington: Transportation Research Board Natl Research Council.
  • Usman, T., Fu, L., & Miranda-Moreno, L. F. (2012). A Disaggregate model for quantifying the safety effects of winter road maintenance activities at an operational level. Accident Analysis & Prevention, 48, 368-378. https://doi.org/10.1016/j.aap.2012.02.005
  • Yan, X., Li, X., Liu, Y., & Zhao, J. (2014). Effects of foggy conditions on drivers’ speed control behaviors at different risk levels. Safety Science, 68, 275-287. https://doi.org /10.1016/j.ssci.2014.04.013
  • Yılmaz, E. (2006). Karayolu trafik simülasyonu (Yüksek lisans tezi). Karadeniz Teknik Üniversitesi Fen Bilimleri Enstitüsü, Trabzon.

Traffic Density Estimation with Artificial Neural Networks

Yıl 2020, Cilt: 22 Sayı: 4, 1020 - 1034, 31.12.2020
https://doi.org/10.32709/akusosbil.746349

Öz

Traffic density, which is one of the major problems of cities, affects human life in many ways. For a more livable urban life, by determining the current density, it is necessary to control the vehicle traffic and take necessary measures for future congestion. An estimation of density to be made taking into account the possible parameters that may increase traffic density has an important place for drivers and authorized institutions and organizations responsible for exposure of a city to the least possible traffic density for city residents. The study was carried out on the E-5 highway in the direction of entrance from Tuzla district to Istanbul. The data was obtained from Yandex Traffic and Wunderground websites. Weekdays, working hours, general condition of the weather, temperature, wind speed and humidity levels were taken as independent variables. Day, hour and weather conditions are categorical and these variables are determined as dummy variables. An average speed level of 80 km was obtained on the relevant route, and when Free Flow Speed was examined for C and D service levels, it was determined that the highest density level would be in the range of 16-22, and the maximum volume / capacity ratio would be in the range of 64-85%.

Kaynakça

  • Abdel-Aty, M., Ekram, A.-A., Huang, H., & Choi, K. (2011). A study on crashes related to visibility obstruction due to fog and smoke. Accident Analysis & Prevention, 43(5), 1730-1737. https://doi.org/10.1016/j.aap.2011.04.003
  • Aküzüm, Prof. Dr. T., Kodal, Doç. Dr. S., Beyribey, Doç. Dr. M., Erözel, Doç. Dr. A. Z., Tokgöz, Doç. Dr. A., Selenay, Yard. Doç. Dr. F., … Yurtsever, Yard. Doç. Dr. E. (1994). Meteoroloji I. Ankara: Ankara Üniversitesi Ziraat Fakültesi Yayınları.
  • Andreescu, M. P., & Frost, D. B. (1998). Weather and traffic accidents in montreal, Canada. Climate Research, 9(3), 225-230. https://doi.org/10.3354/cr009225
  • Andrey, J., & Yagar, S. (1993). A Temporal Analysis of rain-related crash risk. Accident Analysis and Prevention, 25(4), 465-472. https://doi.org/10.1016 /0001-4575(93)90076-9
  • Andrey, J., Hambly, D., Mills, B., & Afrin, S. (2013). Insights into driver adaptation to ınclement weather in Canada. Journal of Transport Geography, 28, 192-203. https://doi.org /10.1016/j.jtrangeo.2012.08.014
  • Choi, S., & Oh, C. (2016). Proactive strategy for variable speed limit operations on freeways under foggy weather conditions. Transportation Research Record: Journal of the Transportation Research Board, 2551, 29-36. https://doi.org/10.3141/2551-04
  • Cools, M., Moons, E., & Wets, G. (2007). Investigating the Effect of Holidays on Daily Traffic Counts: A Time Series. 19. https://doi.org/10.3141/2019-04
  • Cools, M., Moons, E., & Wets, G. (2010). Assessing the ımpact of weather on traffic ıntensity. Weather Climate and Society, 2(1), 60-68. https://doi.org/10.1175 /2009WCAS1014.1
  • Datla, S., & Sharma, S. (2008). Impact of cold and snow on temporal and spatial variations of highway traffic volumes. Journal of Transport Geography, 16(5), 358-372. https://doi.org/10.1016/j.jtrangeo.2007.12.003
  • Dey, K. C., Mishra, A., & Chowdhury, M. (2015). Potential of ıntelligent transportation systems in mitigating adverse weather ımpacts on road mobility: a review. Ieee Transactions on Intelligent Transportation Systems, 16(3), 1107-1119. https://doi.org/10.1109/TITS.2014.237145
  • Eken, M., Ulupınar, Y., Demircan, M., Nadaroğlu, Y., Aydın, B., & Özhan, Ü. (2008). Klimatoloji rasat el kitabı, Çevre ve Orman Bakanlığı Devlet Meteoroloji İşleri Genel Müdürlüğü. Ankara: DMİ Genel Müdürlüğü Matbaası.
  • Gültekin, Y., Demircan, M., Ulupınar, Y., & Bulut, E. (2005). Klimatoloji I. Geliş tarihi gönderen https://www.mgm.gov.tr/FILES/genel/kitaplar/klimatoloji1.pdf
  • Haykin, S. (1998). Neural Networks A Comprehensive Foundation (Second Edition). Pearson Education.
  • HCM. (2000). Highway Capacity Manual 2000. United States of America: Transportation research board, National Research Council.
  • Janik, M., Kurihara, O., Bossew, P., (2018). Machine Learning methods as a tool to analyse ıncomplete or ırregularly sampled radon time series data, Sci. Total Environ., 630, 1155–1167, https://doi.org/10.1016/j.scitotenv.2018.02.233.
  • Jung, S., Jang, K., Yoon, Y., & Kang, S. (2014). Contributing Factors to Vehicle to Vehicle Crash Frequency and Severity Under Rainfall. Journal of Safety Research, 50, 1-10. https://doi.org/10.1016/j.jsr.2014.01.001
  • Keay, K., & Simmonds, I. (2005). The association of rainfall and other weather variables with road traffic volume in Melbourne, Australia. Accident Analysis and Prevention, 37(1), 109-124. https://doi.org/10.1016/j.aap.2004.07.005
  • Kopal, B. (2011). Boğaziçi Köprüsü üzerindeki trafik sıkışıklığının hız yönetimi yöntemiyle azaltılması (Yüksek lisans tezi). Bahçeşehir Üniversitesi Fen Bilimleri Fakültesi, İstanbul.
  • Kyte, M., Khatib, Z., Shannon, P., & Kitchener, F. (2001). Effect of weather on free- flow speed. traffic flow theory and highway capacity 2001: highway operations, Capacity, and Traffic Control (ss. 60-68). Transportation Research Board Natl Research Council.
  • Levine, N., Kim, K. E., & Nitz, L. H. (1995). Daily fluctuations in honolulu motor vehicle accidents. Accident Analysis And Prevention, 27(6), 785-796. https://doi.org/10.1016 /0001-4575(95)00038-0
  • Maind, Sonali. B., & Wankar, P. (2014). Research paper on basic of artificial neural network |- Academia.edu. International Journal on Recent and Innovation Trends in Computing and Communication, 2(1), 96-100.
  • Liu, A., Soneja, S. I., Jiang, C., Huang, C., Kerns, T., Beck, K., Mitchell, C., & Sapkota, A. (2017). Frequency of extreme weather events and ıncreased risk of motor vehicle collision in maryland, Science of The Total Environment, 580, 550-555. https://doi.org/10.1016/j.scitotenv.2016.11.211
  • Maze, T. H., Agarwal, M., & Burchett, G. (2006). Whether weather matters to traffic demand, traffic safety, and traffic operations and flow. management and delivery of maintenance and operations services (ss. 170-176). Transportation Research Board Natl Research Council.
  • MGM. (2020). Meteoroloji Genel Müdürlüğü. https://www.mgm.gov.tr (Son Erişim: 13 Şubat 2020)
  • Nofal, F. H., & Saeed, A. a. W. (1997). Seasonal variation and weather effects on road traffic accidents in riyadh city. Public Health, 111(1), 51-55. https://doi.org/10.1038 /sj.ph.1900297
  • Öztemel, E. (2016). Yapay Sinir Ağları (4. bs). İstanbul: Papatya Yayıncılık Eğitim.
  • Öztürk, K., & Şahin, M. E. (2018). Yapay sinir ağları ve yapay zekâ’ya genel bir bakış. (6/ 2), 25-36.
  • Turner, S. M., Eisele, W. L., Benz, R. J., & Holdener, D. J. (1998). Travel time data collection handbook. texas: texas transportation ınstitute and the texas a&m university system.
  • TÜİK. (2020). Türkiye İstatistik Kurumu. http://www.tuik.gov.tr/Start.do Son Erişim: 13 Şubat 2020)
  • Unrau, D., & Andrey, J. (2006). Driver Response to rainfall on urban expressways. ıçinde driver behavior, older drivers, Simulation, User Information Systems, and Visualization (ss. 24-+). Washington: Transportation Research Board Natl Research Council.
  • Usman, T., Fu, L., & Miranda-Moreno, L. F. (2012). A Disaggregate model for quantifying the safety effects of winter road maintenance activities at an operational level. Accident Analysis & Prevention, 48, 368-378. https://doi.org/10.1016/j.aap.2012.02.005
  • Yan, X., Li, X., Liu, Y., & Zhao, J. (2014). Effects of foggy conditions on drivers’ speed control behaviors at different risk levels. Safety Science, 68, 275-287. https://doi.org /10.1016/j.ssci.2014.04.013
  • Yılmaz, E. (2006). Karayolu trafik simülasyonu (Yüksek lisans tezi). Karadeniz Teknik Üniversitesi Fen Bilimleri Enstitüsü, Trabzon.
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Hukuk
Yazarlar

Nurullah Taş 0000-0001-6221-0204

Bülent Sezen 0000-0001-7485-3194

Yayımlanma Tarihi 31 Aralık 2020
Gönderilme Tarihi 9 Haziran 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 22 Sayı: 4

Kaynak Göster

APA Taş, N., & Sezen, B. (2020). Yapay Sinir Ağları ile Trafik Yoğunluğu Tahmini. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 22(4), 1020-1034. https://doi.org/10.32709/akusosbil.746349