Skip to main content
SpringerLink
Log in

Using Deep Learning to Predict Short Term Traffic Flow: A Systematic Literature Review

  • Conference paper
  • First Online:
Intelligent Transport Systems – From Research and Development to the Market Uptake (INTSYS 2017)

Included in the following conference series:

Abstract

This paper systematically reviews Deep Learning-based methods for traffic flow prediction. We extracted 26 articles using a concrete methodology and reviewed them from two perspectives: first, the deep learning architecture used; and second, the datasets and data dimensions incorporated. Recent big data explosion caused by sensors, IoV, IoT and GPS technology needs traffic analytics using deep architectures. This survey reveals that the LSTM (Long Short-Term Memory) Neural Networks are the most commonly used architecture for short term traffic flow prediction due to their inherent ability to handle sequential data. Among the datasets, PeMS is the most commonly used for traffic flow prediction task. Today, Intelligent Transport Systems (ITS) are not limited to temporal data; spatial dimension is also incorporated along with weather data, and traffic sentiments from twitter, Facebook and Instagram to get better results. In the authors’ knowledge, this is the first deep learning review in ITS domain.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular flow as a seasonal ARIMA process: theoretical basis and empirical results. J. Transp. Eng. 129(6), 664–672 (2003)

    Article  Google Scholar 

  2. Zhao, Z., Chen, W.: LSTM network: a deep learning approach for short-term traffic forecast. IET Intell. Transp. Syst. 11(2), 68–75 (2017)

    Article  MathSciNet  Google Scholar 

  3. Huang, W., Hong, H., Li, M., Hu, W., Song, G., Xie, K.: Deep architecture for traffic flow prediction. In: ADMA, pp. 165–176 (2013)

    Chapter  Google Scholar 

  4. Khalid, K.S., Kunz, R., Kleijn, J., Antes, G.: Five steps to conducting a systemic review. J. R. Soc. Med. 96(3), 118–121 (2003)

    Article  Google Scholar 

  5. Huang, W., Song, G., Hong, H., Xie, K.: Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IET Intell. Transp. Syst. 15(5), 2191–2201 (2014)

    Article  Google Scholar 

  6. Hinton, G.E.: Scholarpedia (2009). www.scholarpedia.org/article/Deep_belief_networks

  7. PeMS Data Source - Caltrans - State of California. http://www.dot.ca.gov/trafficops/mpr/source.html

  8. Koesdwiady, A., Soua, R., Karray, F.: Improving traffic flow prediction with weather information in connected cars: a deep learning approach. IEEE Trans. Veh. Technol. 65(12), 9508–9517 (2016)

    Article  Google Scholar 

  9. Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT’2010. Physica-Verlag HD, Heidelberg (2010)

    Chapter  Google Scholar 

  10. Wei, L.-Y., Peng, W.-C., Lin, C.-S., Jung, C.-H.: Exploring spatio-temporal features for traffic estimation on road networks. In: Mamoulis, N., Seidl, T., Pedersen, T.B., Torp, K., Assent, I. (eds.) SSTD 2009. LNCS, vol. 5644, pp. 399–404. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02982-0_28

    Chapter  Google Scholar 

  11. Soua, R., Koesdwiady, A., Karray, F.: Big-data-generated traffic flow prediction using deep learning and Dampster-Shafer theory. In: IEEE IJCNN, pp. 3195–3202 (2016)

    Google Scholar 

  12. Beynon, M., Curry, B., Morgan, P.: The Dampster-Shafer theory of evidence: an alternative approach to multicriteria decision modeling. OMEGA 28, 37–50 (2000)

    Article  Google Scholar 

  13. Jia, Y., Wu, J., Du, Y.: Traffic speed prediction using deep learning method. In: 19th IEEE International Conference on Intelligent Transportation Systems, Brazil, pp. 1217–1222 (2016)

    Google Scholar 

  14. Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2014)

    Google Scholar 

  15. Leelavathi, M.V., Sahana Devi, K.J.: An architecture of deep learning method to predict traffic flow in big data. IJRET 5(4), 461–468 (2016)

    Google Scholar 

  16. Duan, Y., Lv, Y., Wang, F.: Performance evaluation of the deep learning approach for traffic flow prediction at different times. In: IEEE International Conference on Service Operations and Logistics, and Informatics, Beijing, China, pp. 223–227 (2016)

    Google Scholar 

  17. Hao-Fan, Y., Dillon, S.: Optimized structure of the traffic flow forecasting model with a deep learning approach. IEEE Trans. Neural Netw. Learn. Syst. 1–11 (2016)

    Google Scholar 

  18. Duan, Y., Lv, Y., Liu, Y.-L., Wang, F.-Y.: An efficient realization of deep learning for traffic data imputation. Transp. Res. Part C Emerg. Technol. 72, 168–181 (2016)

    Article  Google Scholar 

  19. Ma, X., Tao, Z.: LSTM neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. Part C 54, 187–197 (2015)

    Article  Google Scholar 

  20. Yu, R., Li, Y.: Deep learning: a generic approach for extreme condition traffic forecasting. http://roseyu.com/Papers/sdm2017.pdf

  21. Chen, Y.-y., Lv, Y., Li, Z., Wang, F.-Y.: Long short-term memory model for traffic congestion prediction with online open data. In: IEEE 19th International Conference on Intelligent Transportation Systems, pp. 132–137 (2016)

    Google Scholar 

  22. Shao, H., Soong, B.-H.: Traffic flow prediction with Long Short-Term Memory Networks (LSTMs). In: IEEE Region 10 Conference, pp. 2986–2989 (2016)

    Google Scholar 

  23. Fu, R., Zhang, Z., Li, L.: Using LSTM and GRU neural network methods for traffic flow prediction. In: 31st Youth Academic Annual Conference of Chinese Association of Automation, pp. 324–328 (2016)

    Google Scholar 

  24. Niu, X., Zhu, Y., Zhang, X.: DeepSense: a novel learning mechanism for traffic prediction with taxi GPS traces. In: 2014 IEEE Global Communications Conference, Austin, TX, pp. 2745–2750 (2014)

    Google Scholar 

  25. Ma, X., Yu, H.: Large scale transportation network congestion evolution prediction using deep learning theory. PLoS ONE. https://doi.org/10.1371/journal.pone.0119044

  26. Wang, J., Gu, Q., Wu, J., Liu, G., Xiong, Z.: Traffic speed prediction and congestion source exploration: a deep learning method. In: IEEE 16th International Conference on Data Mining, pp. 499–508 (2016)

    Google Scholar 

  27. Wu, Y., Tan, H.: Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework (2016). http://arxiv.org/abs/1612.01022

  28. Flex Kunde, F., Sauer, P.: Traffic prediction using a deep learning paradigm (2017). http://ceur-ws.org/Vol-1810/BIGQP_paper_03

  29. Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction (2016). http://arxiv.org/abs/1610.00081

  30. Yi, H., Jung, H., Bae, S.: Deep Neural Networks for traffic flow prediction. In: IEEE International Conference on Big Data and Smart Computing, pp. 328–331 (2017)

    Google Scholar 

  31. Ma, X., Dai, Z.: Learning traffic as images: a deep convolution neural network for large-scale transportation network speed prediction (2017). http://arxiv.org/abs/1701.04245

  32. Surya, S., Babu, R.V.: TraCount: a deep CNN for highly overlapping vehicle counting. In: 10th Indian Conference on Computer Vision, Graphics and Image Processing, p. 46. ACM (2016)

    Google Scholar 

  33. Polson, N.G., Sokolov, V.O.: Deep learning for short-term traffic flow prediction (2017). https://arxiv.org/pdf/1604.04527.pdf

  34. Beijing Traffic Management Bureau. http://www.bjjtgl.gov.cn/english/Roads/index.html

  35. National Weather service. http://www.weather.gov

  36. City Pulse Dataset Collection. http://iot.ee.surrey.ac.uk:8080/

  37. AMAP: A Web Based Service Provider in China. https://ditu.amap.com/,accessed:2016-09-01

  38. https://tu-dresden.de/bu/verkehr/vis/vlp/forschung/forschungsprojekte

  39. Shafer, G., et al.: A Mathematical Theory of Evidence, vol. 1. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  40. Moré, J.J.: The Levenberg-Marquardt algorithm: implementation and theory. In: Watson, G.A. (ed.) Numerical Analysis. LNM, vol. 630. Springer, Heidelberg (1978)

    Chapter  Google Scholar 

  41. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  42. Alpaydin, E.: Introduction to Machine Learning, 3rd edn. MIT Press, Cambridge (2014)

    MATH  Google Scholar 

  43. Intelligent Transportation System. https://en.wikipedia.org/wiki/Intelligent_transportation_system

  44. Intelligent Transportation systems, Benefits, Costs, Deployment and Lessons Learned. https://ntl.bts.gov/lib/30000/30400/30466/14412.pdf

  45. Bellman, R.E.: Dynamic Programming. Princeton University Press, Rand Corporation, Princeton (1957)

    MATH  Google Scholar 

  46. Verleysen, M., François, D.: The curse of dimensionality in data mining and time series prediction. In: Cabestany, J., Prieto, A., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 758–770. Springer, Heidelberg (2005). https://doi.org/10.1007/11494669_93

    Chapter  Google Scholar 

  47. Hinton, G.E., Osindero, S.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  Google Scholar 

  48. Hyndmana, R., Koehler, A.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2006)

    Article  Google Scholar 

  49. Chai, T., Draxler, R.: (RMSE) or (MAE)? –Arguments against avoiding RMSE in the literature

    Google Scholar 

  50. Tofallis, C.J.: A better measure of relative prediction accuracy for model selection and model estimation. Oper. Res. Soc. 66(8), 1352–1362 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Business Administration (IBA), Karachi, Pakistan

    Usman Ali & Tariq Mahmood

Authors
  1. Usman Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tariq Mahmood
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Usman Ali .

Editor information

Editors and Affiliations

  1. University of Žilina, Žilina, Slovakia

    Tatiana Kováčiková

  2. University of Žilina, Žilina, Slovakia

    Ľuboš Buzna

  3. University of Žilina, Žilina, Slovakia

    Ghadir Pourhashem

  4. University of Žilina, Žilina, Slovakia

    Giuseppe Lugano

  5. University of Žilina, Žilina, Slovakia

    Yannick Cornet

  6. University of Žilina, Žilina, Slovakia

    Nathalie Lugano

Rights and permissions

Reprints and permissions

Copyright information

© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ali, U., Mahmood, T. (2018). Using Deep Learning to Predict Short Term Traffic Flow: A Systematic Literature Review. In: Kováčiková, T., Buzna, Ľ., Pourhashem, G., Lugano, G., Cornet, Y., Lugano, N. (eds) Intelligent Transport Systems – From Research and Development to the Market Uptake. INTSYS 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 222. Springer, Cham. https://doi.org/10.1007/978-3-319-93710-6_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93710-6_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93709-0

  • Online ISBN: 978-3-319-93710-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation