Analysis and comparison of long short-term memory networks short-term traffic prediction performance

• Autorzy: Dogan Erdem

Identyfikatory

DOI

10.20858/sjsutst.2020.107.2

• Języki publikacji: EN

Abstrakty

EN

Long short-term memory networks (LSTM) produces promising results in the prediction of traffic flows. However, LSTM needs large numbers of data to produce satisfactory results. Therefore, the effect of LSTM training set size on performance and optimum training set size for short-term traffic flow prediction problems were investigated in this study. To achieve this, the numbers of data in the training set was set between 480 and 2800, and the prediction performance of the LSTMs trained using these adjusted training sets was measured. In addition, LSTM prediction results were compared with nonlinear autoregressive neural networks (NAR) trained using the same training sets. Consequently, it was seen that the increase in LSTM's training cluster size increased performance to a certain point. However, after this point, the performance decreased. Three main results emerged in this study: First, the optimum training set size for LSTM significantly improves the prediction performance of the model. Second, LSTM makes short-term traffic forecasting better than NAR. Third, LSTM predictions fluctuate less than the NAR model following instant traffic flow changes.

Słowa kluczowe

EN

deep learning; traffic flow; short-term; prediction; LSTM; nonlinear autoregressive; training set size

PL

uczenie głębokie; ruch uliczny; krótki termin; prognoza; LSTM; autoregresja nieliniowa

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Transport / Politechnika Śląska
• Rocznik: 2020
• Tom: z. 107
• Strony: 19--32
• Opis fizyczny: Bibliogr. 28 poz.

Twórcy

autor

Dogan Erdem

• Department of Civil Engineering, Faculty of Engineering, Kirikkale University, Yahsihan, 71451, Kirkkale, Turkey

Bibliografia

• 1. Ahmed M.S., A.R. Cook. 1979. „Analysis of freeway traffic time-series data by using box-jenkins techniques”. Transportation Research Record. DOI: https://doi.org/10.3141/2024-03.
• 2. Çetiner B., M. Sari, Borat O. 2010. „A neural network based traffic-flow prediction model”. Mathematical and Computational Applications 15(2): 269-278.
• 3. Chen C., J. Hu, Q. Meng, Y. Zhang. 2011. „Short-time traffic flow prediction with ARIMA-GARCH model”. IEEE Intelligent Vehicles Symposium. Proceedings 100084(Iv): 607-612. DOI: https://doi.org/10.1109/IVS.2011.5940418.
• 4. Dai X., R. Fu, Y. Lin, L. Li, F.-Y. Wang. 2017. „Deeptrend: A deep hierarchical neural network for traffic flow prediction”. ArXiv Preprint ArXiv 1707.03213.
• 5. Doğan E. 2018. „Developing short-term traffic forecasting models using seasonal ARIMA method for D-200 highway”. Sakarya University Journal of Science 22(2): 1-1. DOI: https://doi.org/10.16984/saufenbilder.308997.
• 6. Doğan E. 2020. „Short-term traffic flow prediction using artificial intelligence with periodic clustering and elected set”. Promet-Traffic&Transportation 32(1): 65-78.
• 7. Dunne S., B. Ghosh. 2012. „Regime-based short-term multivariate traffic condition forecasting algorithm”. Journal of Transportation Engineering 138: 455-466. DOI: https://doi.org/10.1061/(ASCE)TE.1943-5436.0000337.
• 8. Fu R., Z. Zhang, L. Li. 2016. „Using LSTM and GRU neural network methods for traffic flow prediction”. 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC): 324-328. IEEE.
• 9. Fulari S., A. Thankappan, L. Vanajakshi, S. Subramanian. 2019. „Traffic flow estimation at error prone locations using dynamic traffic flow modeling”. Transportation Letters 11(1): 43-53. DOI: https://doi.org/10.1080/19427867.2016.1271761.
• 10. Hecht-Nielsen R. 1992. „Theory of the Backpropagation Neural Network”. Proceedings of the International Joint Conference on Neural Networks 1: 593-611. June 1989. IEEE. Academic Press. DOI: https://doi.org/https://doi.org/10.1016/B978-0-12-741252-8.50010-8.
• 11. Hochreiter S. 1998. „The vanishing gradient problem during learning recurrent neural nets and problem solutions”. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 6(02): 107-116.
• 12. Jiang X., H. Adeli, H.M. Asce. 2005. „Dynamic wavelet neural network model for traffic flow forecasting”. Journal of Transportation Engineering 131(10): 771-779. DOI: https://doi.org/10.1061/(ASCE)0733-947X(2005)131:10(771).
• 13. Kumar K., M. Parida, V.K. Katiyar. 2015. „Short term traffic flow prediction in heterogeneous condition using artificial neural network”. Transport 30(4): 397-405.
• 14. Levenberg K. 1944. „A method for the solution of certain non-linear problems in least squares”. Quarterly Journal of Applied Mathematics 2(2): 164-168. DOI: https://doi.org/10.1017/CBO9781107415324.004.
• 15. Lin S.-L.L.S.-L., H.-Q.H.H.-Q. Huang, D.-Q.Z.D.-Q. Zhu, T.-Z.W.T.-Z. Wang. 2009. „The application of space-time ARIMA model on traffic flow forecasting”. International Conference on Machine Learning and Cybernetics 6: 12-15. DOI: https://doi.org/10.1109/ICMLC.2009.5212785.
• 16. Marquardt D.W. 1963. „An algorithm for least-squares estimation of nonlinear parameters”. Journal of the Society for Industrial and Applied Mathematics 11(2): 431-441. DOI: https://doi.org/10.1137/0111030.
• 17. Mccorbin Web Site. (n.d.). Available at: http://www.mhcorbin.com/portable-trafficanalyzer/.
• 18. Polson N.G., V.O. Sokolov. 2017. „Deep learning for short-term traffic flow prediction”. Transportation Research Part C: Emerging Technologies 79: 1-17. DOI: https://doi.org/10.1016/j.trc.2017.02.024.
• 19. Shekhar S., B.M. Williams. 2008. „Adaptive seasonal time series models for forecasting short-term traffic flow”. Transportation Research Record: Journal of the Transportation Research Board 2024(1): 116-125. DOI: https://doi.org/10.3141/2024-14.
• 20. Sheu J.-B., L.W. Lan, Y.-S. Huang 2009. „Short-term prediction of traffic dynamics with real-time recurrent learning algorithms”. Transportmetrica 5(1): 59-83. DOI: https://doi.org/10.1080/18128600802591681.
• 21. Tian Y., K. Zhang, J. Li, X. Lin, B. Yang. 2018. „LSTM-based traffic flow prediction with missing data”. Neurocomputing 318: 297-305. DOI: https://doi.org/https://doi.org/10.1016/j.neucom.2018.08.067.
• 22. Van Lint J.W.C., C. Van Hinsbergen. 2012. „Short-term traffic and travel time prediction models”. Artificial Intelligence Applications to Critical Transportation Issues 22: 22-41.
• 23. Williams B.M., L. Hoel. 2003. „Modeling and Forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results”. Journal of Transportation Engineering 129(6): 664-672. DOI: https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(664).
• 24. Xiao Y., J.J. Liu, J. Xiao, Y. Hu, H. Bu, S. Wang, 2015. „Application of multiscale analysis-based intelligent ensemble modeling on airport traffic forecast”. Transportation Letters 7(2): 73-79.
• 25. Yu R., Y. Li, C. Shahabi, U. Demiryurek, Y. Liu. 2017. „Deep learning: a generic approach for extreme condition traffic forecasting”. Proceedings of the 2017 SIAM International Conference on Data Mining: 777-785. SIAM.
• 26. Zargari S.A., S.Z. Siabil, A.H. Alavi, A.H. Gandomi. 2010. „A computational intelligence-based approach for short-term traffic flow prediction”. Expert Systems 29(2). DOI: https://doi.org/10.1111/j.1468-0394.2010.00567.x.
• 27. Zeng D., J. Xu, J. Gu, L. Liu, G. Xu. 2008. „Short term traffic flow prediction using hybrid ARIMA and ANN models”. Proceedings 2008 Workshop on Power Electronics and Intelligent Transportation System. PEITS 2008. DOI: https://doi.org/10.1109/PEITS.2008.135.
• 28. Zhou B., D. He, Z. Sun. 2006. „Traffic predictability based on ARIMA/GARCH model”. 2nd Conference on Next Generation Internet Design and Engineering, NGI 2006: 200-207. DOI: https://doi.org/10.1109/NGI.2006.1678242.