IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v171y2020icp264-278.html
   My bibliography  Save this article

An interpretable model for short term traffic flow prediction

Author

Listed:
  • Wang, Wei
  • Zhang, Hanyu
  • Li, Tong
  • Guo, Jianhua
  • Huang, Wei
  • Wei, Yun
  • Cao, Jinde

Abstract

Predicting short term traffic flow to improve traffic control is a research problem attracting increased attention over the past 30 years. With increasing number of traffic data acquisition equipments coming into usage, it provides an opportunity to use deep neural network (DNN) to predict short-term traffic flow. Behind its considerable success, the DNN is weighed down by some problems, and here we focus on: 1. how to justify the number of input nodes employed by DNN; 2. how to explain the causality between the historical spatiotemporal information and the future traffic condition. In this paper, we propose a deep polynomial neural network combined with a seasonal autoregressive integrated moving average model. The new model has superior predicting accuracy as well as enhanced clarity on the spatiotemporal relationship in its deep architecture. Experimental results indicate that the proposed model has better explanation power and higher accuracy compared with the LSTM based model.

Suggested Citation

  • Wang, Wei & Zhang, Hanyu & Li, Tong & Guo, Jianhua & Huang, Wei & Wei, Yun & Cao, Jinde, 2020. "An interpretable model for short term traffic flow prediction," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 264-278.
    • Handle: RePEc:eee:matcom:v:171:y:2020:i:c:p:264-278
    DOI: 10.1016/j.matcom.2019.12.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475419303726
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2019.12.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dougherty, Mark S. & Cobbett, Mark R., 1997. "Short-term inter-urban traffic forecasts using neural networks," International Journal of Forecasting, Elsevier, vol. 13(1), pages 21-31, March.
    2. Whittaker, Joe & Garside, Simon & Lindveld, Karel, 1997. "Tracking and predicting a network traffic process," International Journal of Forecasting, Elsevier, vol. 13(1), pages 51-61, March.
    3. Okutani, Iwao & Stephanedes, Yorgos J., 1984. "Dynamic prediction of traffic volume through Kalman filtering theory," Transportation Research Part B: Methodological, Elsevier, vol. 18(1), pages 1-11, February.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    6. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    7. Kirby, Howard R. & Watson, Susan M. & Dougherty, Mark S., 1997. "Should we use neural networks or statistical models for short-term motorway traffic forecasting?," International Journal of Forecasting, Elsevier, vol. 13(1), pages 43-50, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ismail Shah & Izhar Muhammad & Sajid Ali & Saira Ahmed & Mohammed M. A. Almazah & A. Y. Al-Rezami, 2022. "Forecasting Day-Ahead Traffic Flow Using Functional Time Series Approach," Mathematics, MDPI, vol. 10(22), pages 1-16, November.
    2. Daniel Vélez-Serrano & Alejandro Álvaro-Meca & Fernando Sebastián-Huerta & Jose Vélez-Serrano, 2021. "Spatio-Temporal Traffic Flow Prediction in Madrid: An Application of Residual Convolutional Neural Networks," Mathematics, MDPI, vol. 9(9), pages 1-16, May.
    3. Wang, Ke & Ma, Changxi & Qiao, Yihuan & Lu, Xijin & Hao, Weining & Dong, Sheng, 2021. "A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Chang, Chia-Lin, 2015. "Modelling a latent daily Tourism Financial Conditions Index," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 113-126.
    3. Alagidede, Paul & Panagiotidis, Theodore, 2009. "Modelling stock returns in Africa's emerging equity markets," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 1-11, March.
    4. repec:wyi:journl:002087 is not listed on IDEAS
    5. Mai, Nhat Chi, 2022. "Tác động của lạm phát đến hoạt động của thị trường chứng khoán ở Việt Nam: Kiểm chứng bằng mô hình GARCH," OSF Preprints azcqd, Center for Open Science.
    6. Ataurima Arellano, Miguel & Rodríguez, Gabriel, 2020. "Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    7. Chia-Lin Chang & Yiying Li & Michael McAleer, 2018. "Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice," Energies, MDPI, vol. 11(6), pages 1-19, June.
    8. Dimitrakopoulos, Dimitris N. & Kavussanos, Manolis G. & Spyrou, Spyros I., 2010. "Value at risk models for volatile emerging markets equity portfolios," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(4), pages 515-526, November.
    9. Erie Febrian & Aldrin Herwany, 2009. "Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets," Working Papers in Economics and Development Studies (WoPEDS) 200911, Department of Economics, Padjadjaran University, revised Sep 2009.
    10. Altaf Muhammad & Zhang Shuguang, 2015. "Impact Of Structural Shifts on Variance Persistence in Asymmetric Garch Models: Evidence From Emerging Asian and European Markets," Romanian Statistical Review, Romanian Statistical Review, vol. 63(1), pages 57-70, March.
    11. P. Kearns & A.R. Pagan, 1993. "Australian Stock Market Volatility: 1875–1987," The Economic Record, The Economic Society of Australia, vol. 69(2), pages 163-178, June.
    12. Gerard H. Kuper & Daan P. van Soest, 2006. "Does Oil Price Uncertainty Affect Energy Use?," The Energy Journal, , vol. 27(1), pages 55-78, January.
    13. Levent, Korap, 2009. "Enflasyon ve enflasyon belirsizliği ilişkisi için G7 ekonomileri üzerine bir inceleme [An investigation for the inflation and inflation uncertainty relationship upon the G7 economies]," MPRA Paper 19478, University Library of Munich, Germany.
    14. Nour Meddahi, 2002. "A theoretical comparison between integrated and realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 479-508.
    15. Christensen, Bent Jesper & Nielsen, Morten Ørregaard & Zhu, Jie, 2010. "Long memory in stock market volatility and the volatility-in-mean effect: The FIEGARCH-M Model," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 460-470, June.
    16. Theodore Panagiotidis, 2010. "Market efficiency and the Euro: the case of the Athens stock exchange," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 37(3), pages 237-251, July.
    17. Lars Stentoft, 2008. "American Option Pricing Using GARCH Models and the Normal Inverse Gaussian Distribution," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 540-582, Fall.
    18. Mittnik, Stefan & Robinzonov, Nikolay & Spindler, Martin, 2015. "Stock market volatility: Identifying major drivers and the nature of their impact," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 1-14.
    19. Hunjra, Ahmed Imran & Azam, Muhammad & Niazi, Ghulam Shabbir Khan & Butt, Babar Zaheer & Rehman, Kashif-Ur- & Azam, Rauf i, 2010. "Risk and return relationship in stock market and commodity prices: a comprehensive study of Pakistani markets," MPRA Paper 40662, University Library of Munich, Germany.
    20. Sucarrat, Genaro & Grønneberg, Steffen & Escribano, Alvaro, 2016. "Estimation and inference in univariate and multivariate log-GARCH-X models when the conditional density is unknown," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 582-594.
    21. Kritika Mathur & Nidhi Kaicker & Raghav Gaiha & Katsushi S. Imai & Ganesh Thapa, 2014. "Financialisation of food commodity markets, price surge and volatility: new evidence," Chapters, in: Raghbendra Jha & Raghav Gaiha & Anil B. Deolalikar (ed.), Handbook on Food, chapter 7, pages 149-176, Edward Elgar Publishing.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:matcom:v:171:y:2020:i:c:p:264-278. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.