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Kentsel Trafik Tahminine Yönelik Derin Öğrenme Tabanlı Verimli Bir Hibrit Model

Yıl 2023, , 107 - 117, 30.04.2023
https://doi.org/10.17671/gazibtd.1167140

Öz

Trafik yoğunluğu problemi, kentsel hayatın en önemli sorunlarından biri haline gelmiştir. Trafik yoğunluğu sebebiyle harcanan zaman ve yakıt, araç kullanıcıları ve ülkeler için önemli bir kayıptır. Trafikte geçen zamanı azaltmak amacı ile geliştirilen uygulamalar, uzun vadeli trafik yoğunluğu hakkında başarılı tahminlerde bulunamamaktadır. Kameralar, sensörler ve mobil cihazlar üzerinden elde edilen trafik verileri, trafik yönetimi sorununu çözebilmek amacıyla yapay zekâ teknolojilerinin kullanımını ön plana çıkarmaktadır. Bu çalışmada, trafik yoğunluk tahminine yönelik Convolutional Neural Network (CNN) ve Recurrent Neural Network (RNN) modelleri kullanılarak hibrit bir tahmin modeli geliştirilmiştir. Çalışmada, CNN ve RNN'in öne çıkan özelliklerinden faydalanmak amaçlanmıştır. CNN, özellik çıkarma aşamasında, RNN ise sıralı zaman serisi verileri üzerinde öğrenme ve tahmin için etkili bir modeldir. Bu yöntemler hibrit bir şekilde kullanılarak tahmin doğruluğunun arttırılması amaçlanmıştır. İstanbul Büyükşehir Belediyesi tarafından sunulan saatlik trafik yoğunluğu veri seti kullanılmıştır. Kullanılan veriseti 2321 farklı nokta için 2020 Ocak ile 2020 Aralık tarihleri arasındaki trafik yoğunluk bilgisini içermektedir. Geçen araç sayısı, Bağcılar Avrupa Otoyolu kavşağında daha yüksek olduğu için bu konum deneysel çalışmalarda kullanılmıştır. Seçilen konum için 9379 satır araç bilgisi bulunmaktadır. Geliştirilen hibrit model Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), CNN, RNN ve Long-Short Term Memory (LSTM) ile İstanbul’un 2020 yılına ait trafik verileri kullanılarak test edilmiştir. Deneysel sonuçlar, önerilen hibrit modelin karşılaştırılan modellere göre daha başarılı sonuçlara sahip olduğunu göstermiştir. Önerilen model kavşaktan geçen araç sayısı tahmininde 0,929 R2 değerine, kavşaktan geçen araçların ortalama hızlarının tahmininde ise 0,934 R-Squared (R2) değerine sahip olmuştur.

Kaynakça

  • W. Broere, “Urban underground space: Solving the problems of today’s cities”. Tunnelling and Underground Space Technology, 55, 245-248, 2016.
  • G. Firdaus, & A. Ahmad, “Noise pollution and human health: a case study of municipal corporation of Delhi”, Indoor and built environment, 19(6), 648-656, 2010.
  • D. Muley, M. Shahin, C. Dias, & M. Abdullah, “Role of transport during outbreak of infectious diseases: evidence from the past”, Sustainability, 12(18), 7367, 2020.
  • Ş. İmre, & D. Çelebi, “Measuring comfort in public transport: a case study for İstanbul”, Transportation Research Procedia, 25, 2441-2449, 2017.
  • J. Zhang, F.Y. Wang, K. Wang, W.H. Lin, X. Xu, & C. Chen, “Data-driven intelligent transportation systems: A survey”, IEEE Transactions on Intelligent Transportation Systems, 12(4), 1624-1639, 2011.
  • S. Du, T. Li, X. Gong, Y. Yang, & S.J. Horng, “Traffic flow forecasting based on hybrid deep learning framework”, In 2017 12th international conference on intelligent systems and knowledge engineering (ISKE), 1-6, 2017.
  • O. Mohammed, & J. Kianfar, “A machine learning approach to short-term traffic flow prediction: A case study of interstate 64 in Missouri,” In 2018 IEEE International Smart Cities Conference (ISC2), Kansas City, Missouri, USA, 1-7, 2018.
  • W. Zhang, Y. Yu, Y. Qi, F. Shu, & Y. Wang, “Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning”, Transportmetrica A: Transport Science, 15(2), 1688-1711, 2019.
  • Y. Gu, W. Lu, L. Qin, M. Li, & Z. Shao, “Short-term prediction of lane-level traffic speeds: A fusion deep learning model”, Transportation research part C: emerging technologies, 106, 1-16, 2019.
  • J. Wang, R. Chen, & Z. He, “Traffic speed prediction for urban transportation network: A path based deep learning approach”, Transportation Research Part C: Emerging Technologies, 100, 372-385, 2019.
  • İ.C. Taş, & A.A. Müngen, “Regional Traffic Density Estimation with Artificial Neural Networks and Support Vector Machines Methods”, Adıyaman University Journal of Engineering Sciences, 8(15), 378-390, 2021.
  • İ. Takak, H. Görümez, H.İ. Türkmen, & M.A. Güvensan, “Short, Medium and Long Term Traffic Flow Rate Estimation and Visualization Tool”, International Journal of Advances in Engineering and Pure Sciences, 33(4), 568-580, 2021.
  • A. Essien, I. Petrounias, P. Sampaio, & S. Sampaio, S. “A deep-learning model for urban traffic flow prediction with traffic events mined from twitter”, World Wide Web, 24(4), 1345-1368, 2021.
  • K. Wang, C. Ma, Y. Qiao, X. Lu, W. Hao, & S. Dong, “A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction”, Physica A: Statistical Mechanics and its Applications, 583, 126293, 2021.
  • G. Zheng, W.K. Chai, J.L. Duanmu, & V. Katos, “Hybrid deep learning models for traffic prediction in large-scale road networks”, Information Fusion, 92, 93-114, 2023.
  • Internet: İstanbul Büyükşehir Belediyesi, Saatlik trafik yoğunluk veriseti, https://data.ibb.gov.tr/dataset/hourly-traffic-density-data-set
  • G.Y. Oukawa, P. Krecl, & A.C. Targino, “Fine-scale modeling of the urban heat island: A comparison of multiple linear regression and random forest approaches”, Science of the total environment, 815, 152836, 2022.
  • F. Rustam, A.A. Reshi, A. Mehmood, S. Ullah, B.W. On, W.Aslam, & G.S. Choi, “COVID-19 future forecasting using supervised machine learning models”, IEEE access, 8, 101489-101499, 2020.
  • M. Kayakuş, & M. Terzioğlu, “Yapay sinir ağları ve çoklu doğrusal regresyon kullanarak emeklilik fonu net varlık değerlerinin tahmin edilmesi”, Bilişim Teknolojileri Dergisi, 14(1), 95-103, 2021.
  • A.L. Balogun, & A. Tella, “Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear regression, and support vector regression”, Chemosphere, 299, 134250, 2022.
  • Y. Yang, & W. Chen, “Taiga: performance optimization of the C4. 5 decision tree construction algorithm”, Tsinghua Science and Technology, 21(4), 415-425, 2016.
  • M. Peker, O. Özkaraca, & B. Kesimal, “Enerji tasarruflu bina tasarımı için isıtma ve soğutma yüklerini regresyon tabanlı makine öğrenmesi algoritmaları ile modelleme”, Bilişim Teknolojileri Dergisi, 10(4), 443-449, 2017.
  • V.A. Kumari, & R. Chitra, R. “Classification of diabetes disease using support vector machine”, International Journal of Engineering Research and Applications, 3(2), 1797-1801, 2013.
  • A.A. Heidari, H. Faris, S. Mirjalili, I. Aljarah, & M. Mafarja, “Ant lion optimizer: theory, literature review, and application in multi-layer perceptron neural networks”, Nature-inspired optimizers, 23-46, 2020.
  • I. Namatēvs, “Deep convolutional neural networks: Structure, feature extraction and training”, Information Technology and Management Science, 20(1), 40-47, 2017.
  • Z. Qiao, N. Sun, X. Li, E. Xia, S. Zhao, & Y. Qin, “Using machine learning approaches for emergency room visit prediction based on electronic health record data”, In Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth, IOS Press, 111-115, 2018.
  • A. Shewalkar, “Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU”, Journal of Artificial Intelligence and Soft Computing Research, 9(4), 235-245, 2019.
  • Y. Tian, R. Lai, X. Li, L. Xiang, & J. Tian, “A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter”, Applied Energy, 265, 114789, 2020.
  • A. Farzad, H. Mashayekhi, & H. Hassanpour, “A comparative performance analysis of different activation functions in LSTM networks for classification”, Neural Computing and Applications, 31(7), 2507-2521, 2019.
  • X.H. Le, H.V. Ho, G. Lee, S. Jung, “Application of long short-term memory (LSTM) neural network for flood forecasting”, Water, 11(7), 1387, 2019.

Deep Learning Based an Efficient Hybrid Model for Urban Traffic Prediction

Yıl 2023, , 107 - 117, 30.04.2023
https://doi.org/10.17671/gazibtd.1167140

Öz

The traffic density problem has become one of the most important problems of urban life. The time and fuel spent due to traffic density is a significant loss for vehicle users and countries. Applications developed to reduce the time spent in traffic cannot make successful predictions about long-term traffic density. Traffic data obtained from cameras, sensors and mobile devices highlights the use of artificial intelligence technologies in order to solve the traffic management problem. In this study, a hybrid prediction model has been proposed by using CNN and RNN models for traffic density prediction. The proposed hybrid model has been tested using LR, RF, SVM, MLP, CNN, RNN and LSTM and Istanbul's traffic data for 2020. Experimental results showed that the proposed hybrid model has more successful results than the compared models. The proposed model has 0.929 R2 in the prediction of the number of vehicles passing through the junction, and 0.934 R2 in the prediction of the average speed of the vehicles passing through the junction.

Kaynakça

  • W. Broere, “Urban underground space: Solving the problems of today’s cities”. Tunnelling and Underground Space Technology, 55, 245-248, 2016.
  • G. Firdaus, & A. Ahmad, “Noise pollution and human health: a case study of municipal corporation of Delhi”, Indoor and built environment, 19(6), 648-656, 2010.
  • D. Muley, M. Shahin, C. Dias, & M. Abdullah, “Role of transport during outbreak of infectious diseases: evidence from the past”, Sustainability, 12(18), 7367, 2020.
  • Ş. İmre, & D. Çelebi, “Measuring comfort in public transport: a case study for İstanbul”, Transportation Research Procedia, 25, 2441-2449, 2017.
  • J. Zhang, F.Y. Wang, K. Wang, W.H. Lin, X. Xu, & C. Chen, “Data-driven intelligent transportation systems: A survey”, IEEE Transactions on Intelligent Transportation Systems, 12(4), 1624-1639, 2011.
  • S. Du, T. Li, X. Gong, Y. Yang, & S.J. Horng, “Traffic flow forecasting based on hybrid deep learning framework”, In 2017 12th international conference on intelligent systems and knowledge engineering (ISKE), 1-6, 2017.
  • O. Mohammed, & J. Kianfar, “A machine learning approach to short-term traffic flow prediction: A case study of interstate 64 in Missouri,” In 2018 IEEE International Smart Cities Conference (ISC2), Kansas City, Missouri, USA, 1-7, 2018.
  • W. Zhang, Y. Yu, Y. Qi, F. Shu, & Y. Wang, “Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning”, Transportmetrica A: Transport Science, 15(2), 1688-1711, 2019.
  • Y. Gu, W. Lu, L. Qin, M. Li, & Z. Shao, “Short-term prediction of lane-level traffic speeds: A fusion deep learning model”, Transportation research part C: emerging technologies, 106, 1-16, 2019.
  • J. Wang, R. Chen, & Z. He, “Traffic speed prediction for urban transportation network: A path based deep learning approach”, Transportation Research Part C: Emerging Technologies, 100, 372-385, 2019.
  • İ.C. Taş, & A.A. Müngen, “Regional Traffic Density Estimation with Artificial Neural Networks and Support Vector Machines Methods”, Adıyaman University Journal of Engineering Sciences, 8(15), 378-390, 2021.
  • İ. Takak, H. Görümez, H.İ. Türkmen, & M.A. Güvensan, “Short, Medium and Long Term Traffic Flow Rate Estimation and Visualization Tool”, International Journal of Advances in Engineering and Pure Sciences, 33(4), 568-580, 2021.
  • A. Essien, I. Petrounias, P. Sampaio, & S. Sampaio, S. “A deep-learning model for urban traffic flow prediction with traffic events mined from twitter”, World Wide Web, 24(4), 1345-1368, 2021.
  • K. Wang, C. Ma, Y. Qiao, X. Lu, W. Hao, & S. Dong, “A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction”, Physica A: Statistical Mechanics and its Applications, 583, 126293, 2021.
  • G. Zheng, W.K. Chai, J.L. Duanmu, & V. Katos, “Hybrid deep learning models for traffic prediction in large-scale road networks”, Information Fusion, 92, 93-114, 2023.
  • Internet: İstanbul Büyükşehir Belediyesi, Saatlik trafik yoğunluk veriseti, https://data.ibb.gov.tr/dataset/hourly-traffic-density-data-set
  • G.Y. Oukawa, P. Krecl, & A.C. Targino, “Fine-scale modeling of the urban heat island: A comparison of multiple linear regression and random forest approaches”, Science of the total environment, 815, 152836, 2022.
  • F. Rustam, A.A. Reshi, A. Mehmood, S. Ullah, B.W. On, W.Aslam, & G.S. Choi, “COVID-19 future forecasting using supervised machine learning models”, IEEE access, 8, 101489-101499, 2020.
  • M. Kayakuş, & M. Terzioğlu, “Yapay sinir ağları ve çoklu doğrusal regresyon kullanarak emeklilik fonu net varlık değerlerinin tahmin edilmesi”, Bilişim Teknolojileri Dergisi, 14(1), 95-103, 2021.
  • A.L. Balogun, & A. Tella, “Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear regression, and support vector regression”, Chemosphere, 299, 134250, 2022.
  • Y. Yang, & W. Chen, “Taiga: performance optimization of the C4. 5 decision tree construction algorithm”, Tsinghua Science and Technology, 21(4), 415-425, 2016.
  • M. Peker, O. Özkaraca, & B. Kesimal, “Enerji tasarruflu bina tasarımı için isıtma ve soğutma yüklerini regresyon tabanlı makine öğrenmesi algoritmaları ile modelleme”, Bilişim Teknolojileri Dergisi, 10(4), 443-449, 2017.
  • V.A. Kumari, & R. Chitra, R. “Classification of diabetes disease using support vector machine”, International Journal of Engineering Research and Applications, 3(2), 1797-1801, 2013.
  • A.A. Heidari, H. Faris, S. Mirjalili, I. Aljarah, & M. Mafarja, “Ant lion optimizer: theory, literature review, and application in multi-layer perceptron neural networks”, Nature-inspired optimizers, 23-46, 2020.
  • I. Namatēvs, “Deep convolutional neural networks: Structure, feature extraction and training”, Information Technology and Management Science, 20(1), 40-47, 2017.
  • Z. Qiao, N. Sun, X. Li, E. Xia, S. Zhao, & Y. Qin, “Using machine learning approaches for emergency room visit prediction based on electronic health record data”, In Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth, IOS Press, 111-115, 2018.
  • A. Shewalkar, “Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU”, Journal of Artificial Intelligence and Soft Computing Research, 9(4), 235-245, 2019.
  • Y. Tian, R. Lai, X. Li, L. Xiang, & J. Tian, “A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter”, Applied Energy, 265, 114789, 2020.
  • A. Farzad, H. Mashayekhi, & H. Hassanpour, “A comparative performance analysis of different activation functions in LSTM networks for classification”, Neural Computing and Applications, 31(7), 2507-2521, 2019.
  • X.H. Le, H.V. Ho, G. Lee, S. Jung, “Application of long short-term memory (LSTM) neural network for flood forecasting”, Water, 11(7), 1387, 2019.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Anıl Utku 0000-0002-7240-8713

Yayımlanma Tarihi 30 Nisan 2023
Gönderilme Tarihi 26 Ağustos 2022
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Utku, A. (2023). Deep Learning Based an Efficient Hybrid Model for Urban Traffic Prediction. Bilişim Teknolojileri Dergisi, 16(2), 107-117. https://doi.org/10.17671/gazibtd.1167140