Research Article
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Year 2021, Volume: 1 Issue: 2, 136 - 143, 30.12.2021

Sümeyye Aydın Murat Taşyürek Celal Öztürk

Abstract

References

  • [1] D. Ravì, C. Wong, F. Deligianni, M. Berthelot; J. Andreu-Perez, B. Lo and G. Yanget, "Deep Learning for Health Informatics," in IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 4-21, doi: 10.1109/JBHI.2016.2636665, Jan. 2017
  • [2] T. Young, D. Hazarika, S. Poria and E. Cambria, "Recent Trends in Deep Learning Based Natural Language Processing [Review Article]," in IEEE Computational Intelligence Magazine, vol. 13, no. 3, pp. 55-75, doi: 10.1109/MCI.2018.2840738, Aug. 2018.
  • [3] A. Şeker, B. Diri ve H. H. Balık, "Derin Öğrenme Yöntemleri Ve Uygulamaları Hakkında Bir İnceleme", Gazi Mühendislik Bilimleri Dergisi (GMBD), c. 3, sayı. 3, ss. 47-64, Aralık 2017
  • [4] A. J. P. Samarawickrama, T. G. I. Fernando, "A recurrent neural network approach in predicting daily stock prices an application to the Sri Lankan stock market," 2017 IEEE International Conference on Industrial and Information Systems (ICIIS), pp. 1-6, doi: 10.1109/ICIINFS.2017.8300345, 2017
  • [5] N. Buduma and N. Locascio, Fundamentals of Deep Learning. Designing Next-Generation Machine Intelligence Algorithms, O'Reilly Media, 172-217, 2017.
  • [6] S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," in Neural Computation, vol. 9, no. 8, pp. 1735-1780, 15 Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.
  • [7] X. Ma, Z. Tao, Y. Wang, H. Yu, and Y. Wang, “Long short-term memory neural network for traffic speed prediction using remote microwave sensor data” Transportation Research Part C: Emerging Technologies, 54, pp. 187–197, 2015.
  • [8] Y. X. Tian and P. Li, "Predicting Short-term Traffic Flow by Long Short Term Memory Recurrent Neural Network", 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), pp. 153-158, 2015.
  • [9] R. Fu, Z. Zhang and L. Li, "Using LSTM and GRU neural network methods for traffic flow prediction", Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 324-328, 2016.
  • [10] TUİK, Adrese Dayalı Nüfus Kayıt Sistemi Sonuçları 2013, Türkiye İstatistik Kurumu Haber Bülteni, Sayı: 37210, 04.Şubat.2021.
  • [11] TUİK, “İllere göre motorlu kara taşıtları sayısı”, Available: https://data.tuik.gov.tr/Bulten/Index?p=Road-Motor-Vehicles- December-2020-37410, [Accessed Aralık, 2020].
  • [12] A Liaw, M. Wiener, 2002, Classification And Regression ByRandom Forest, R News, Vol.2/3, December.
  • [13] L. Breiman, “Random forests”, Machine Learning, Volume 45, pp. 5-32, 2001.
  • [14] N. Kriegeskorte, T. Golan, Neural network models and deep learning, Current Biology, 29(7), R231–R236, 2019.
  • [15] L. Deng and D. Yu, “Deep Learning: Methods and Applications,” Found. Trends® Signal Process., vol. 7, no. 3–4, pp. 197–387, 2014.
  • [16] K. Chakraborty, K. Mehrotra, C. K. Mohan and S. Ranka, “Forecasting The Behavior of Multivariate Time Series Using Neural Networks”, Neural Networks 5(6):961-970, 1992.
  • [17] Y. Duan, Y. L.V. and F. Wang, "Travel time prediction with LSTM neural network," 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 2016, pp. 1053-1058, doi: 10.1109/ITSC.2016.7795686, 2016.
  • [18] S. Samui, I. Chakrabarti, S.K. Ghosh, “Tensor-Train Long Short-Term Memory for Monaural Speech Enhancement” arXiv preprint arXiv:1812.10095, 2018
  • [19] F. A. Gers, J. Schmidhuber, F. A. Cummins, “Learning to forget: Continual prediction with LSTM” Neural Computation, 12 (10), pp. 2451-2471, 2000.
  • [20] Ulaşım Daire Başkanlığı, “Saatlik Trafik Yoğunluk Veri Seti, Ağustos 2020 Trafik Yoğunluk Verisi” 13 Aralık, 2020.

Traffic Density Estimation using Machine Learning Methods

Year 2021, Volume: 1 Issue: 2, 136 - 143, 30.12.2021

Sümeyye Aydın Murat Taşyürek Celal Öztürk

Abstract

In cities where population density is high and transportation systems are widely used, it is necessary to manage traffic systems more effectively not to affect the daily planned works. The Intelligent Transportation System (AUS) is expressed as a system that provides users with better information and safer, more coordinated, and smarter use of transportation networks with different transportation modes and traffic management. One of the most important components of AUS models is the determination of traffic density. The traffic density of intersections is a difficult problem as it affects other interconnected intersections and varies in time. Deep learning method is a widely used method in traffic density estimation in recent years. In this study, the long- term short memory network (LSTM) model, one of the deep learning methods, is proposed to estimate the traffic density of a certain region using open data of Istanbul Metropolitan Municipality. The performance of the proposed LSTM-based model is compared with machine learning methods such as linear regression, decision tree, random forest, and the classical deep learning method (DL). Experimental evaluations show that the proposed LSTM method is more successful in traffic density estimation than the compared methods.

Keywords

Deep learning Intelligent transportation systems Istanbul traffic density prediction LSTM Machine Learning

Thanks

We would like to thank IMM for sharing the hourly traffic density data used in this study.

References

  • [1] D. Ravì, C. Wong, F. Deligianni, M. Berthelot; J. Andreu-Perez, B. Lo and G. Yanget, "Deep Learning for Health Informatics," in IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 4-21, doi: 10.1109/JBHI.2016.2636665, Jan. 2017
  • [2] T. Young, D. Hazarika, S. Poria and E. Cambria, "Recent Trends in Deep Learning Based Natural Language Processing [Review Article]," in IEEE Computational Intelligence Magazine, vol. 13, no. 3, pp. 55-75, doi: 10.1109/MCI.2018.2840738, Aug. 2018.
  • [3] A. Şeker, B. Diri ve H. H. Balık, "Derin Öğrenme Yöntemleri Ve Uygulamaları Hakkında Bir İnceleme", Gazi Mühendislik Bilimleri Dergisi (GMBD), c. 3, sayı. 3, ss. 47-64, Aralık 2017
  • [4] A. J. P. Samarawickrama, T. G. I. Fernando, "A recurrent neural network approach in predicting daily stock prices an application to the Sri Lankan stock market," 2017 IEEE International Conference on Industrial and Information Systems (ICIIS), pp. 1-6, doi: 10.1109/ICIINFS.2017.8300345, 2017
  • [5] N. Buduma and N. Locascio, Fundamentals of Deep Learning. Designing Next-Generation Machine Intelligence Algorithms, O'Reilly Media, 172-217, 2017.
  • [6] S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," in Neural Computation, vol. 9, no. 8, pp. 1735-1780, 15 Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.
  • [7] X. Ma, Z. Tao, Y. Wang, H. Yu, and Y. Wang, “Long short-term memory neural network for traffic speed prediction using remote microwave sensor data” Transportation Research Part C: Emerging Technologies, 54, pp. 187–197, 2015.
  • [8] Y. X. Tian and P. Li, "Predicting Short-term Traffic Flow by Long Short Term Memory Recurrent Neural Network", 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), pp. 153-158, 2015.
  • [9] R. Fu, Z. Zhang and L. Li, "Using LSTM and GRU neural network methods for traffic flow prediction", Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 324-328, 2016.
  • [10] TUİK, Adrese Dayalı Nüfus Kayıt Sistemi Sonuçları 2013, Türkiye İstatistik Kurumu Haber Bülteni, Sayı: 37210, 04.Şubat.2021.
  • [11] TUİK, “İllere göre motorlu kara taşıtları sayısı”, Available: https://data.tuik.gov.tr/Bulten/Index?p=Road-Motor-Vehicles- December-2020-37410, [Accessed Aralık, 2020].
  • [12] A Liaw, M. Wiener, 2002, Classification And Regression ByRandom Forest, R News, Vol.2/3, December.
  • [13] L. Breiman, “Random forests”, Machine Learning, Volume 45, pp. 5-32, 2001.
  • [14] N. Kriegeskorte, T. Golan, Neural network models and deep learning, Current Biology, 29(7), R231–R236, 2019.
  • [15] L. Deng and D. Yu, “Deep Learning: Methods and Applications,” Found. Trends® Signal Process., vol. 7, no. 3–4, pp. 197–387, 2014.
  • [16] K. Chakraborty, K. Mehrotra, C. K. Mohan and S. Ranka, “Forecasting The Behavior of Multivariate Time Series Using Neural Networks”, Neural Networks 5(6):961-970, 1992.
  • [17] Y. Duan, Y. L.V. and F. Wang, "Travel time prediction with LSTM neural network," 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 2016, pp. 1053-1058, doi: 10.1109/ITSC.2016.7795686, 2016.
  • [18] S. Samui, I. Chakrabarti, S.K. Ghosh, “Tensor-Train Long Short-Term Memory for Monaural Speech Enhancement” arXiv preprint arXiv:1812.10095, 2018
  • [19] F. A. Gers, J. Schmidhuber, F. A. Cummins, “Learning to forget: Continual prediction with LSTM” Neural Computation, 12 (10), pp. 2451-2471, 2000.
  • [20] Ulaşım Daire Başkanlığı, “Saatlik Trafik Yoğunluk Veri Seti, Ağustos 2020 Trafik Yoğunluk Verisi” 13 Aralık, 2020.
There are 20 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Research Articles
Authors

Sümeyye Aydın

Murat Taşyürek

Celal Öztürk

Publication Date December 30, 2021
Submission Date November 29, 2021
Published in Issue Year 2021 Volume: 1 Issue: 2

Cite

IEEE S. Aydın, M. Taşyürek, and C. Öztürk, “Traffic Density Estimation using Machine Learning Methods”, Journal of Artificial Intelligence and Data Science, vol. 1, no. 2, pp. 136–143, 2021.
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