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Yoğunluk tabanlı kümeleme yöntemiyle karakteristiği oluşturulan yollar için RNN yöntemi ile kısa zamanlı trafik hız tahmini

Year 2022, Volume: 37 Issue: 2, 581 - 594, 28.02.2022

Murat Akın , Şeref Sağıroğlu

https://doi.org/10.17341/gazimmfd.921035

Abstract

Büyük şehirlerin gelişimi ve buna bağlı olarak artan araç sayısı şehirler için şehir trafiğini arttırmakta, ulaşım sorununu ön plana çıkarmaktadır. Şehir trafiğini yönetmek için kamu ve özel kurumlar tarafından akıllı ulaşım ve yönetim sistemleri geliştirilmekte, bu sistemleri kullanarak trafik bileşenlerinden trafik akış, yoğunluk ve hız parametreleri tahmin edilmektedir. Bu çalışma, trafik hız tahmini için 9 aşamadan oluşan yeni bir tahmin modeli sunmaktadır. Sunulan modelde gerçek araç verileri, veri filtreleme ve harita eşleme işlemlerinden geçirilmiş, yoğunluk tabanlı kümeler oluşturulmuş, küme öznitelikleri üretilmiş, anlık trafik gösterimi yapılmış ve trafik hız tahmini yapay sinir ağı RNN modeli ile gerçekleştirilmiştir. Daha önce yapılan çalışmalarda, trafik hız tahmini sabit veri kaynakları ile belirli bir yolda veya dağıtık GPS kayıtları ile farklı günlerde yapılabilmekte iken, geliştirilen model ile istenilen ve belirlenen bölge için yoğunluk tabanlı kümeler ve kümelere ait öznitelikleri üretilerek ilgilenilen yol için karakteristik oluşturulmuş ve trafiğin kendi olasılığı içinde aynı gün içerisinde kısa zamanlı ve veri odaklı hız tahmini yapılmıştır. Hız tahmini Ankara iline ait Eskişehir yolu ve İstanbul yolu güzergâhlarında gerçekleştirilmiş, hız tahmini için RNN modeli varyantı olan LSTM ve GRU modelleri kullanılarak hata oranları tespit edilmiş, Eskişehir yolu güzergâhında LSTM-GRU modelleri hata oranları sırasıyla 8,589-8,507, İstanbul yolu güzergâhında model hata oranları 7,370-8,201 olarak ölçülmüştür. Trafiğin olasılıklı ve değişken yapısı için geliştirilen model ile gerçek zaman için başarılı sonuçlar elde edilmiştir. Önerilen modelin, gelecekte yapılacak olan trafik parametrelerinin tahmininde farklı ve yeni çözümler sunacağı, katkılar sağlayacağı, süreçleri hızlandıracağı ve en önemlisi ise kullanıcılara daha doğru ve hızlı hizmet verilmesine katkılar sağlayacağı değerlendirilmektedir.

Keywords

Kısa zamanlı trafik hız tahmini trafik tahmin modeli akıllı ulaşım sistemleri tekrarlı sinir ağları yoğunluk tabanlı kümeleme yöntemi

Supporting Institution

TÜBİTAK

Project Number

3191873

Thanks

Yazarlar, sağladığı kıymetli destekler için TÜBİTAK'a teşekkür ederler.

References

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  • S. George and A. K. Santra, “Traffic Prediction Using Multifaceted Techniques: A Survey,” Wirel. Pers. Commun., vol. 115, no. 2, pp. 1047–1106, Nov. 2020, doi: 10.1007/s11277-020-07612-8.
  • M. A. Silgu and H. B. Çelikoğlu, “K-Means Clustering Method to Classify Freeway Traffic Flow Patterns,” Pamukkale Univ. J. Eng. Sci., vol. 20, no. 6, pp. 232–239, 2014, doi: 10.5505/pajes.2014.36449.
  • L. N. N. Do, H. L. Vu, B. Q. Vo, Z. Liu, and D. Phung, “An effective spatial-temporal attention based neural network for traffic flow prediction,” Transp. Res. Part C Emerg. Technol., vol. 108, pp. 12–28, Nov. 2019, doi: 10.1016/j.trc.2019.09.008.
  • Z. Li, D. P. Filev, I. Kolmanovsky, E. Atkins, and J. Lu, “A New Clustering Algorithm for Processing GPS-Based Road Anomaly Reports With a Mahalanobis Distance,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 7, pp. 1980–1988, Jul. 2017, doi: 10.1109/TITS.2016.2614350.
  • A. C. Diker and E. Nasibov, “Estimation of traffic congestion level via FN-DBSCAN algorithm by using GPS data,” 2012, doi: 10.1109/ICPCI.2012.6486279.
  • Z. Cui, R. Ke, Z. Pu, X. Ma, and Y. Wang, “Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction,” Transp. Res. Part C Emerg. Technol., vol. 115, p. 102620, Jun. 2020, doi: 10.1016/j.trc.2020.102620.
  • M. Akin, S. Sagiroglu, and A. Degirmenci, “Traffic Flow Forecasting Model with Density Based Clustering Algorithm,” 1st Int. Informatics Softw. Eng. Conf. Innov. Technol. Digit. Transform. IISEC 2019 - Proc., 2019, doi: 10.1109/UBMYK48245.2019.8965527.
  • E. I. Vlahogianni, M. G. Karlaftis, and J. C. Golias, “Short-term traffic forecasting: Where we are and where we’re going,” Transp. Res. Part C Emerg. Technol., vol. 43, pp. 3–19, Jun. 2014, doi: 10.1016/j.trc.2014.01.005.
  • T. Seo, A. M. Bayen, T. Kusakabe, and Y. Asakura, “Traffic state estimation on highway: A comprehensive survey,” Annu. Rev. Control, vol. 43, pp. 128–151, 2017, doi: 10.1016/j.arcontrol.2017.03.005.
  • I. Lana, J. Del Ser, M. Velez, and E. I. Vlahogianni, “Road Traffic Forecasting: Recent Advances and New Challenges,” IEEE Intell. Transp. Syst. Mag., vol. 10, no. 2, pp. 93–109, 2018, doi: 10.1109/MITS.2018.2806634.
  • B. Jiang and Y. Fei, “Vehicle Speed Prediction by Two-Level Data Driven Models in Vehicular Networks,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 7, pp. 1793–1801, Jul. 2017, doi: 10.1109/TITS.2016.2620498.
  • Y. Hou, J. Chen, and S. Wen, “The effect of the dataset on evaluating urban traffic prediction,” Alexandria Eng. J., Oct. 2020, doi: 10.1016/j.aej.2020.09.038.
  • M. T. Asif et al., “Spatiotemporal patterns in large-scale traffic speed prediction,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 2, pp. 794–804, 2014, doi: 10.1109/TITS.2013.2290285.
  • S. Jeon and B. Hong, “Monte Carlo simulation-based traffic speed forecasting using historical big data,” Futur. Gener. Comput. Syst., vol. 65, pp. 182–195, 2016, doi: 10.1016/j.future.2015.11.022.
  • 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,” Transp. Res. Part C Emerg. Technol., vol. 54, pp. 187–197, 2015, doi: 10.1016/j.trc.2015.03.014.
  • Z. Cheng, M.-Y. Chow, D. Jung, and J. Jeon, “A big data based deep learning approach for vehicle speed prediction,” in 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), Jun. 2017, pp. 389–394, doi: 10.1109/ISIE.2017.8001278.
  • K. Niu, H. Zhang, T. Zhou, C. Cheng, and C. Wang, “A Novel Spatio-Temporal Model for City-Scale Traffic Speed Prediction,” IEEE Access, vol. 7, pp. 30050–30057, 2019, doi: 10.1109/ACCESS.2019.2902185.
  • J. Zhao et al., “Truck Traffic Speed Prediction Under Non-Recurrent Congestion: Based on Optimized Deep Learning Algorithms and GPS Data,” IEEE Access, vol. 7, pp. 9116–9127, 2019, doi: 10.1109/ACCESS.2018.2890414.
  • T. L. C. Da Silva, A. C. Aráujo Neto, R. Pires Magalhaes, V. A. E. De Farias, J. A. F. De Macêdo, and J. C. Machado, “Efficient and distributed DBScan algorithm using mapreduce to detect density areas on traffic data,” ICEIS 2014 - Proc. 16th Int. Conf. Enterp. Inf. Syst., vol. 1, pp. 52–59, 2014, doi: 10.5220/0004891700520059.
  • M. Ankerst, M. M. Breunig, H. Kriegel, and J. Sander, “OPTICS : Ordering Points To Identify the Clustering Structure,” ACM SIGMOD Int. Conf. Manag. data, pp. 49–60, 1999.
  • X. X. Martin Ester, Hans-Peter Kriegel, Jörg Sander, “A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise,” Proc. Second Int. Conf. Knowl. Discov. Data Min., pp. 226–231, 1996.
  • Z. Cui, R. Ke, Z. Pu, and Y. Wang, “Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction,” arXiv, Jan. 2018, [Online]. Available: http://arxiv.org/abs/1801.02143.
  • Y. Wang, D. Zhang, Y. Liu, B. Dai, and L. H. Lee, “Enhancing transportation systems via deep learning: A survey,” Transp. Res. Part C Emerg. Technol., vol. 99, pp. 144–163, Feb. 2019, doi: 10.1016/j.trc.2018.12.004.
  • G. Işık and H. Artuner, “Turkish dialect recognition in terms of prosodic by long short-term memory neural networks,” J. Fac. Eng. Archit. Gazi Univ., vol. 35, no. 1, pp. 213–224, Oct. 2020, doi: 10.17341/gazimmfd.453677.
  • G. Fusco, C. Colombaroni, and N. Isaenko, “Short-term speed predictions exploiting big data on large urban road networks,” Transp. Res. Part C Emerg. Technol., vol. 73, pp. 183–201, Dec. 2016, doi: 10.1016/j.trc.2016.10.019.
  • E. Winarno, W. Hadikurniawati, and R. N. Rosso, “Location based service for presence system using haversine method,” in 2017 International Conference on Innovative and Creative Information Technology (ICITech), Nov. 2017, pp. 1–4, doi: 10.1109/INNOCIT.2017.8319153.
  • N. Chopde and M. Nichat, “Landmark Based Shortest Path Detection by Using A* and Haversine Formula,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 1, no. 2, pp. 298–302, 2013, [Online]. Available: http://www.ijircce.com/upload/2013/april/17_V1204030_Landmark_H.pdf.
  • P. Sondhi, “Feature construction methods: a survey,” Sifaka. Cs. Uiuc. Edu, vol. 69, pp. 70–71, 2010.

Year 2022, Volume: 37 Issue: 2, 581 - 594, 28.02.2022

Murat Akın , Şeref Sağıroğlu

https://doi.org/10.17341/gazimmfd.921035

Abstract

Project Number

3191873

References

  • A. Chesterton, “How many cars are there in the world?,” Carsguide, Nov. 2018. https://web.archive.org/web/20210322032308/https://www.carsguide.com.au/car-advice/how-many-cars-are-there-in-the-world-70629 (Erişim Tarihi: Mart 22, 2021).
  • S. Djahel, R. Doolan, G.-M. Muntean, and J. Murphy, “A Communications-Oriented Perspective on Traffic Management Systems for Smart Cities: Challenges and Innovative Approaches,” IEEE Commun. Surv. Tutorials, vol. 17, no. 1, pp. 125–151, 2015, doi: 10.1109/COMST.2014.2339817.
  • A. Boukerche and J. Wang, “Machine Learning-based traffic prediction models for Intelligent Transportation Systems,” Comput. Networks, vol. 181, 2020, doi: 10.1016/j.comnet.2020.107530.
  • G. M. Lingani, D. B. Rawat, and M. Garuba, “Smart traffic management system using deep learning for smart city applications,” in 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019, Mar. 2019, pp. 101–106, doi: 10.1109/CCWC.2019.8666539.
  • N. Lanke and S. Koul, “Smart Traffic Management System,” Int. J. Comput. Appl., vol. 75, no. 7, pp. 19–22, Aug. 2013, doi: 10.5120/13123-0473.
  • G. Dimitrakopoulos and P. Demestichas, “Intelligent Transportation Systems,” IEEE Veh. Technol. Mag., vol. 5, no. 1, pp. 77–84, Mar. 2010, doi: 10.1109/MVT.2009.935537.
  • M. S. Mahdavinejad, M. Rezvan, M. Barekatain, P. Adibi, P. Barnaghi, and A. P. Sheth, “Machine learning for internet of things data analysis: a survey,” Digital Communications and Networks, vol. 4, no. 3. Chongqing University of Posts and Telecommunications, pp. 161–175, Aug. 01, 2018, doi: 10.1016/j.dcan.2017.10.002.
  • K. V. K. Rao, “Fundamental parameters of traffic flow,” Transportation Engineering, 2007. https://web.archive.org/web/20210406205237/https://nptel.ac.in/content/storage2/courses/105101087/downloads/Lec-30.pdf (Erişim Tarihi: Nisan 06, 2021).
  • M. M. El Sherief, I. M. I. Ramadan, and A. M. Ibrahim, “Development of traffic stream characteristics models for intercity roads in Egypt,” Alexandria Eng. J., vol. 55, no. 3, pp. 2765–2770, Sep. 2016, doi: 10.1016/j.aej.2016.04.031.
  • K. Kovačić, E. Ivanjko, and N. Jelušić, “Measurement of Road Traffic Parameters based on Multi-Vehicle Tracking,” Oct. 2015, pp. 3–8, doi: 10.20532/ccvw.2015.0002.
  • J. Barros, M. Araujo, and R. J. F. Rossetti, “Short-term real-time traffic prediction methods: A survey,” 2015 Int. Conf. Model. Technol. Intell. Transp. Syst. MT-ITS 2015, pp. 132–139, 2015, doi: 10.1109/MTITS.2015.7223248.
  • M. Akin and S. Sagiroglu, “Traffic Prediction Based on Big Data Perspective,” 1st Int. Informatics Softw. Eng. Conf. Innov. Technol. Digit. Transform. IISEC 2019 - Proc., 2019, doi: 10.1109/UBMYK48245.2019.8965545.
  • S. George and A. K. Santra, “Traffic Prediction Using Multifaceted Techniques: A Survey,” Wirel. Pers. Commun., vol. 115, no. 2, pp. 1047–1106, Nov. 2020, doi: 10.1007/s11277-020-07612-8.
  • M. A. Silgu and H. B. Çelikoğlu, “K-Means Clustering Method to Classify Freeway Traffic Flow Patterns,” Pamukkale Univ. J. Eng. Sci., vol. 20, no. 6, pp. 232–239, 2014, doi: 10.5505/pajes.2014.36449.
  • L. N. N. Do, H. L. Vu, B. Q. Vo, Z. Liu, and D. Phung, “An effective spatial-temporal attention based neural network for traffic flow prediction,” Transp. Res. Part C Emerg. Technol., vol. 108, pp. 12–28, Nov. 2019, doi: 10.1016/j.trc.2019.09.008.
  • Z. Li, D. P. Filev, I. Kolmanovsky, E. Atkins, and J. Lu, “A New Clustering Algorithm for Processing GPS-Based Road Anomaly Reports With a Mahalanobis Distance,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 7, pp. 1980–1988, Jul. 2017, doi: 10.1109/TITS.2016.2614350.
  • A. C. Diker and E. Nasibov, “Estimation of traffic congestion level via FN-DBSCAN algorithm by using GPS data,” 2012, doi: 10.1109/ICPCI.2012.6486279.
  • Z. Cui, R. Ke, Z. Pu, X. Ma, and Y. Wang, “Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction,” Transp. Res. Part C Emerg. Technol., vol. 115, p. 102620, Jun. 2020, doi: 10.1016/j.trc.2020.102620.
  • M. Akin, S. Sagiroglu, and A. Degirmenci, “Traffic Flow Forecasting Model with Density Based Clustering Algorithm,” 1st Int. Informatics Softw. Eng. Conf. Innov. Technol. Digit. Transform. IISEC 2019 - Proc., 2019, doi: 10.1109/UBMYK48245.2019.8965527.
  • E. I. Vlahogianni, M. G. Karlaftis, and J. C. Golias, “Short-term traffic forecasting: Where we are and where we’re going,” Transp. Res. Part C Emerg. Technol., vol. 43, pp. 3–19, Jun. 2014, doi: 10.1016/j.trc.2014.01.005.
  • T. Seo, A. M. Bayen, T. Kusakabe, and Y. Asakura, “Traffic state estimation on highway: A comprehensive survey,” Annu. Rev. Control, vol. 43, pp. 128–151, 2017, doi: 10.1016/j.arcontrol.2017.03.005.
  • I. Lana, J. Del Ser, M. Velez, and E. I. Vlahogianni, “Road Traffic Forecasting: Recent Advances and New Challenges,” IEEE Intell. Transp. Syst. Mag., vol. 10, no. 2, pp. 93–109, 2018, doi: 10.1109/MITS.2018.2806634.
  • B. Jiang and Y. Fei, “Vehicle Speed Prediction by Two-Level Data Driven Models in Vehicular Networks,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 7, pp. 1793–1801, Jul. 2017, doi: 10.1109/TITS.2016.2620498.
  • Y. Hou, J. Chen, and S. Wen, “The effect of the dataset on evaluating urban traffic prediction,” Alexandria Eng. J., Oct. 2020, doi: 10.1016/j.aej.2020.09.038.
  • M. T. Asif et al., “Spatiotemporal patterns in large-scale traffic speed prediction,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 2, pp. 794–804, 2014, doi: 10.1109/TITS.2013.2290285.
  • S. Jeon and B. Hong, “Monte Carlo simulation-based traffic speed forecasting using historical big data,” Futur. Gener. Comput. Syst., vol. 65, pp. 182–195, 2016, doi: 10.1016/j.future.2015.11.022.
  • 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,” Transp. Res. Part C Emerg. Technol., vol. 54, pp. 187–197, 2015, doi: 10.1016/j.trc.2015.03.014.
  • Z. Cheng, M.-Y. Chow, D. Jung, and J. Jeon, “A big data based deep learning approach for vehicle speed prediction,” in 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), Jun. 2017, pp. 389–394, doi: 10.1109/ISIE.2017.8001278.
  • K. Niu, H. Zhang, T. Zhou, C. Cheng, and C. Wang, “A Novel Spatio-Temporal Model for City-Scale Traffic Speed Prediction,” IEEE Access, vol. 7, pp. 30050–30057, 2019, doi: 10.1109/ACCESS.2019.2902185.
  • J. Zhao et al., “Truck Traffic Speed Prediction Under Non-Recurrent Congestion: Based on Optimized Deep Learning Algorithms and GPS Data,” IEEE Access, vol. 7, pp. 9116–9127, 2019, doi: 10.1109/ACCESS.2018.2890414.
  • T. L. C. Da Silva, A. C. Aráujo Neto, R. Pires Magalhaes, V. A. E. De Farias, J. A. F. De Macêdo, and J. C. Machado, “Efficient and distributed DBScan algorithm using mapreduce to detect density areas on traffic data,” ICEIS 2014 - Proc. 16th Int. Conf. Enterp. Inf. Syst., vol. 1, pp. 52–59, 2014, doi: 10.5220/0004891700520059.
  • M. Ankerst, M. M. Breunig, H. Kriegel, and J. Sander, “OPTICS : Ordering Points To Identify the Clustering Structure,” ACM SIGMOD Int. Conf. Manag. data, pp. 49–60, 1999.
  • X. X. Martin Ester, Hans-Peter Kriegel, Jörg Sander, “A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise,” Proc. Second Int. Conf. Knowl. Discov. Data Min., pp. 226–231, 1996.
  • Z. Cui, R. Ke, Z. Pu, and Y. Wang, “Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction,” arXiv, Jan. 2018, [Online]. Available: http://arxiv.org/abs/1801.02143.
  • Y. Wang, D. Zhang, Y. Liu, B. Dai, and L. H. Lee, “Enhancing transportation systems via deep learning: A survey,” Transp. Res. Part C Emerg. Technol., vol. 99, pp. 144–163, Feb. 2019, doi: 10.1016/j.trc.2018.12.004.
  • G. Işık and H. Artuner, “Turkish dialect recognition in terms of prosodic by long short-term memory neural networks,” J. Fac. Eng. Archit. Gazi Univ., vol. 35, no. 1, pp. 213–224, Oct. 2020, doi: 10.17341/gazimmfd.453677.
  • G. Fusco, C. Colombaroni, and N. Isaenko, “Short-term speed predictions exploiting big data on large urban road networks,” Transp. Res. Part C Emerg. Technol., vol. 73, pp. 183–201, Dec. 2016, doi: 10.1016/j.trc.2016.10.019.
  • E. Winarno, W. Hadikurniawati, and R. N. Rosso, “Location based service for presence system using haversine method,” in 2017 International Conference on Innovative and Creative Information Technology (ICITech), Nov. 2017, pp. 1–4, doi: 10.1109/INNOCIT.2017.8319153.
  • N. Chopde and M. Nichat, “Landmark Based Shortest Path Detection by Using A* and Haversine Formula,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 1, no. 2, pp. 298–302, 2013, [Online]. Available: http://www.ijircce.com/upload/2013/april/17_V1204030_Landmark_H.pdf.
  • P. Sondhi, “Feature construction methods: a survey,” Sifaka. Cs. Uiuc. Edu, vol. 69, pp. 70–71, 2010.
There are 40 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Murat Akın 0000-0003-0001-1036

Şeref Sağıroğlu 0000-0003-0805-5818

Project Number 3191873
Publication Date February 28, 2022
Submission Date April 19, 2021
Acceptance Date June 4, 2021
Published in Issue Year 2022 Volume: 37 Issue: 2

Cite

APA Akın, M., & Sağıroğlu, Ş. (2022). Yoğunluk tabanlı kümeleme yöntemiyle karakteristiği oluşturulan yollar için RNN yöntemi ile kısa zamanlı trafik hız tahmini. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 37(2), 581-594. https://doi.org/10.17341/gazimmfd.921035
AMA Akın M, Sağıroğlu Ş. Yoğunluk tabanlı kümeleme yöntemiyle karakteristiği oluşturulan yollar için RNN yöntemi ile kısa zamanlı trafik hız tahmini. GUMMFD. February 2022;37(2):581-594. doi:10.17341/gazimmfd.921035
Chicago Akın, Murat, and Şeref Sağıroğlu. “Yoğunluk Tabanlı kümeleme yöntemiyle karakteristiği oluşturulan Yollar için RNN yöntemi Ile kısa Zamanlı Trafik hız Tahmini”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 37, no. 2 (February 2022): 581-94. https://doi.org/10.17341/gazimmfd.921035.
EndNote Akın M, Sağıroğlu Ş (February 1, 2022) Yoğunluk tabanlı kümeleme yöntemiyle karakteristiği oluşturulan yollar için RNN yöntemi ile kısa zamanlı trafik hız tahmini. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 37 2 581–594.
IEEE M. Akın and Ş. Sağıroğlu, “Yoğunluk tabanlı kümeleme yöntemiyle karakteristiği oluşturulan yollar için RNN yöntemi ile kısa zamanlı trafik hız tahmini”, GUMMFD, vol. 37, no. 2, pp. 581–594, 2022, doi: 10.17341/gazimmfd.921035.
ISNAD Akın, Murat - Sağıroğlu, Şeref. “Yoğunluk Tabanlı kümeleme yöntemiyle karakteristiği oluşturulan Yollar için RNN yöntemi Ile kısa Zamanlı Trafik hız Tahmini”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 37/2 (February 2022), 581-594. https://doi.org/10.17341/gazimmfd.921035.
JAMA Akın M, Sağıroğlu Ş. Yoğunluk tabanlı kümeleme yöntemiyle karakteristiği oluşturulan yollar için RNN yöntemi ile kısa zamanlı trafik hız tahmini. GUMMFD. 2022;37:581–594.
MLA Akın, Murat and Şeref Sağıroğlu. “Yoğunluk Tabanlı kümeleme yöntemiyle karakteristiği oluşturulan Yollar için RNN yöntemi Ile kısa Zamanlı Trafik hız Tahmini”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 37, no. 2, 2022, pp. 581-94, doi:10.17341/gazimmfd.921035.
Vancouver Akın M, Sağıroğlu Ş. Yoğunluk tabanlı kümeleme yöntemiyle karakteristiği oluşturulan yollar için RNN yöntemi ile kısa zamanlı trafik hız tahmini. GUMMFD. 2022;37(2):581-94.