Derin Öğrenme Tabanlı Trafik Yoğunluğu Tahmini: İstanbul İçin Bir Vaka Çalışması

Year 2023, , 1584 - 1598, 31.07.2023

Anıl Utku

https://doi.org/10.29130/dubited.1139534

Cited By: 1

Abstract

Trafik yoğunluk tahmini, kullanıcıların daha iyi seyahat kararları verebilmeleri, trafik sıkışıklığının hafifletilmesi, zaman ve yakıt tasarrufu sağlanması ile trafik işlem verimliliğinin arttırılması açısından önemlidir. Akıllı ulaşım sistemlerinin gelişmesi ve yaygınlaşmasıyla birlikte trafik yoğunluğunun tahmin edilmesi giderek daha fazla ilgi görmeye başladı. Trafik yoğunluk tahmini, büyük ölçüde geçmiş ve gerçek zamanlı trafik verilerine bağlıdır. Sensörler, kameralar, mobil cihazlar ve sosyal medya gibi kaynaklarından anlık olarak büyük miktarlarda trafik verileri elde edilmektedir. Giderek artan trafik verileri, trafik yönetimi sorununu çözebilmek amacıyla yapay zekâ teknolojilerinin kullanımını ön plana çıkarmaktadır. Bu çalışmada, trafik yoğunluk tahminine yönelik LSTM tabanlı bir tahmin modeli geliştirilmiştir. Geliştirilen tahmin modeli LR, RF, SVM, MLP, CNN ve Recurrent Neural Network (RNN) ile İstanbul’un trafik verileri kullanılarak test edilmiştir. Deneysel sonuçlar, geliştirilen LSTM tabanlı modelin karşılaştırılan modellere göre daha başarılı sonuçlar ürettiğini ve kavşaktan geçen araç sayısı tahmininde 0,897 R2 değerine, kavşaktan geçen araçların ortalama hızlarının tahmininde ise 0,883 R2 değerine sahip olduğunu göstermiştir.

Keywords

Trafik yoğunluk tahmini, Makine öğrenmesi, Derin öğrenme, LSTM

References

• [1] M. Shahidehpour, Z. Li, and M. Ganji, “Smart cities for a sustainable urbanization: Illuminating the need for establishing smart urban infrastructures,” IEEE Electrification Magazine, vol. 6, no. 2, pp. 16-33, 2018.
• [2] A. Sumalee, and H. W. Ho, “Smarter and more connected: Future intelligent transportation system,” Iatss Research, vol. 42, no. 2, pp. 67-71, 2018.
• [3] X. Yin, G. Wu, J. Wei, Y. Shen, H. Qi, and B. Yin, “Deep learning on traffic prediction: Methods, analysis and future directions,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 4927 – 4943, 2021.
• [4] C. Benevolo, R. P. Dameri, and B. D’auria, “Smart mobility in smart city. In Empowering organizations,” Empowering Organizations, 2016, pp. 13-28.
• [5] A. Thiagarajan, L. Ravindranath, K. LaCurts, S. Madden, H. Balakrishnan, S. Toledo, and J. Eriksson, “Vtrack: accurate, energy-aware road traffic delay estimation using mobile phones,” In Proceedings of the 7th ACM conference on embedded networked sensor systems, 2009, pp. 85-98.
• [6] Z. Liu, Z., Li, K. Wu, and M. Li, “Urban traffic prediction from mobility data using deep learning,” Ieee network, vol. 32, no. 4, pp. 40-46, 2018.
• [7] E. Ozus, S. S.Turk, and V. Dokmeci, “Urban restructuring of Istanbul,” European Planning Studies, vol. 19, no. 2, pp. 331-356, 2011.
• [8] R. Fu, Z. Zhang, and L. Li, “Using LSTM and GRU neural network methods for traffic flow prediction,” In 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), 2016, pp. 324-328.
• [9] Y. Liu, H. Zheng, X. Feng, and Z. Chen, “Short-term traffic flow prediction with Conv-LSTM,” In 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP), 2017, pp. 1-6.
• [10] Z. Duan, Y. Yang, K. Zhang, Y. Ni, S. Bajgain, “Improved deep hybrid networks for urban traffic flow prediction using trajectory data,” Ieee Access, vol. 6, pp. 31820-31827, 2018.
• [11] F. Lin, Y. Xu, Y. Yang, and H. Ma, “A spatial-temporal hybrid model for short-term traffic prediction,” Mathematical Problems in Engineering, vol. 19, 2019.
• [12] O. Mohammed, and 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), 2018, pp. 1-7.
• [13] W. Zhang, Y. Yu, Y. Qi, F. Shu, and Y. Wang, “Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning,” Transportmetrica A: Transport Science, vol. 15, no. 2, pp. 1688-1711, 2019.
• [14] C. Bratsas, K. Koupidis, J. M. Salanova, K. Giannakopoulos, A. Kaloudis, and G. Aifadopoulou, “A comparison of machine learning methods for the prediction of traffic speed in urban places,” Sustainability, vol. 12, no. 1, 2019.
• [15] R. Zhu, X. Hu, J. Hou, and X. Li, “Application of machine learning techniques for predicting the consequences of construction accidents in China,” Process Safety and Environmental Protection, vol. 145, pp. 293-302, 2021.
• [16] N. Fumo, and M. R. Biswas, “Regression analysis for prediction of residential energy consumption,” Renewable and sustainable energy reviews, vol. 47, pp. 332-343, 2015.
• [17] J. K. Jaiswal, and R. Samikannu, “Application of random forest algorithm on feature subset selection and classification and regression,” In 2017 world congress on computing and communication technologies (WCCCT), 2017, pp. 65-68.
• [18] Q. Zou, K. Qu, Y. Luo, D. Yin, Y. Ju, and H. Tang, “Predicting diabetes mellitus with machine learning techniques,” Frontiers in genetics, vol. 9, 2018.
• [19] Z. K. Şentürk, and N. Çekiç, “A machine learning based early diagnosis system for mesothelioma disease,” Düzce Üniversitesi Bilim ve Teknoloji Dergisi, c. 8, s. 2, ss. 1604-1611, 2020.
• [20] L. Piyathilaka, and S. Kodagoda, “Affordance-map: Mapping human context in 3d scenes using cost-sensitive svm and virtual human models,” In 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2015, pp. 2035-2040.
• [21] D. H. Lee, Y. T. Kim, and S. R. Lee, “Shallow landslide susceptibility models based on artificial neural networks considering the factor selection method and various non-linear activation functions,” Remote Sensing, vol. 12, no. 7, 2020.
• [22] A. İ. Taş, P. Gülüm, and G. Tulum, “Finansal Piyasalarda Hisse Fiyatlarının Derin Öğrenme ve Yapay Sinir Ağı Yöntemleri ile Tahmin Edilmesi; S&P 500 Endeksi Örneği,” Düzce Üniversitesi Bilim ve Teknoloji Dergisi, c. 9, s. 3, ss. 446-460, 2021.
• [23] X. Wan, H. Song, L. Luo, Z. Li, G. Sheng, and X. Jiang, “Pattern recognition of partial discharge image based on one-dimensional convolutional neural network,” In 2018 Condition Monitoring and Diagnosis (CMD), 2018, pp. 1-4.
• [24] M. Volpi, and D. Tuia, “Dense semantic labeling of subdecimeter resolution images with convolutional neural networks,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 2, pp. 881-893, 2016.
• [25] Y. Tian, and L. Pan, “Predicting short-term traffic flow by long short-term memory recurrent neural network,” In 2015 IEEE international conference on smart city/SocialCom/SustainCom (SmartCity), 2015, pp. 153-158.
• [26] L. Mou, P. Ghamisi, and X. X. Zhu, “Deep recurrent neural networks for hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 7, pp. 3639-3655, 2017.
• [27] P. Bahad, P. Saxena, and R. Kamal, “Fake news detection using bi-directional LSTM-recurrent neural network,” Procedia Computer Science, vol. 165, pp. 74-82, 2019.
• [28] S. Patil, V. M. Mudaliar, P. Kamat, S. Gite, “LSTM based Ensemble Network to enhance the learning of long-term dependencies in chatbot,” International Journal for Simulation and Multidisciplinary Design Optimization, vol. 11, no. 25, 2020.
• [29] İstanbul Büyükşehir Belediyesi. (2022, 25 Mayıs). Saatlik Trafik Yoğunluk Veri Seti [Çevrimiçi]. Erişim: https://data.ibb.gov.tr/dataset/hourly-traffic-density-data-set