Advertisement

Make It Flat: Multidimensional Scaling of Citywide Traffic Data

Conference paper
  • 297 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 117)

Abstract

Citywide urban traffic forecasting is widely acknowledged as beneficial yet challenging approach. One of the main obstacles for discovering and utilising relationships of traffic flows at a city level is an extreme complexity and high-dimensionality of the resulting data structure. In this paper we propose multidimensional scaling of actual spatiotemporal traffic data into regular image-like (two-dimensional) and video-like (three-dimensional) structures. Further we adopted existing approaches to image and video processing for making conclusions on the predictability of scaled traffic data. Spatial correlation and filtering were used for analysis of image-like traffic representation and an artificial neural network of a specific architecture – for prediction of video-like traffic representation. The proposed approach was empirically tested on a large real-world urban traffic data set and demonstrated its practical utility for traffic forecasting. In addition, we analysed the effects of different distance definitions (geographical, travel time-based, cross correlation-based, and dynamic time wrapping distance) and concluded the preference of travel time-based and cross correlation-based distances for discovering the spatiotemporal structure of traffic flows.

Keywords

Spatiotemporal models Machine learning Image processing Urban traffic modelling 

Notes

Acknowledgements

The first author was financially supported by the specific support objective activity 1.1.1.2. “Post-doctoral Research Aid” (Project id. N. 1.1.1.2/16/I/001) of the Republic of Latvia, funded by the European Regional Development Fund. Dmitry Pavlyuk’s research project No. 1.1.1.2/VIAA/1/16/112 “Spatiotemporal urban traffic modelling using big data”.

References

  1. 1.
    He, Z., He, S., Guan, W.: A figure-eight hysteresis pattern in macroscopic fundamental diagrams and its microscopic causes. Transp. Lett. 7, 133–142 (2015).  https://doi.org/10.1179/1942787514Y.0000000041CrossRefGoogle Scholar
  2. 2.
    Song, T.J., Williams, B.M., Rouphail, N.M.: Data-driven approach for identifying spatiotemporally recurrent bottlenecks. IET Intel. Transport Syst. 12, 756–764 (2018).  https://doi.org/10.1049/iet-its.2017.0284CrossRefGoogle Scholar
  3. 3.
    Zhang, Z., Wang, Y., Chen, P., He, Z., Yu, G.: Probe data-driven travel time forecasting for urban expressways by matching similar spatiotemporal traffic patterns. Transp. Res. Part C: Emerg. Technol. 85, 476–493 (2017).  https://doi.org/10.1016/j.trc.2017.10.010CrossRefGoogle Scholar
  4. 4.
    Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., Wang, Y.: Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17, 818 (2017).  https://doi.org/10.3390/s17040818CrossRefGoogle Scholar
  5. 5.
    Lotter, W., Kreiman, G., Cox, D.: Deep predictive coding networks for video prediction and unsupervised learning. In: Presented at the 5th International Conference on Learning Representations (ICLR), Toulon, France (2017)Google Scholar
  6. 6.
    Villegas, R., Yang, J., Hong, S., Lin, X., Lee, H.: Decomposing motion and content for natural video sequence prediction. In: Presented at the 5th International Conference on Learning Representations (ICLR), Toulon, France (2017)Google Scholar
  7. 7.
    Liang, X., Lee, L., Dai, W., Xing, E.P.: Dual motion GAN for future-flow embedded video prediction. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 1762–1770. IEEE, Venice (2017).  https://doi.org/10.1109/ICCV.2017.194
  8. 8.
    Wang, Y., Long, M., Wang, J., Gao, Z., Yu, P.S.: PredRNN: Recurrent neural networks for predictive learning using spatiotemporal LSTMs. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30. pp. 879–888. Curran Associates, Inc., New York (2017)Google Scholar
  9. 9.
    Vondrick, C., Pirsiavash, H., Torralba, A.: Generating videos with scene dynamics. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29. pp. 613–621. Curran Associates, Inc., New York (2016)Google Scholar
  10. 10.
    Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv:1707.01926 [cs, stat] (2017)
  11. 11.
    Cheng, X., Zhang, R., Zhou, J., Xu, W.: Deeptransport: learning spatial-temporal dependency for traffic condition forecasting. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE, Rio de Janeiro (2018).  https://doi.org/10.1109/IJCNN.2018.8489600
  12. 12.
    Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, pp. 3634–3640. International Joint Conferences on Artificial Intelligence Organization, Stockholm, Sweden (2018).  https://doi.org/10.24963/ijcai.2018/505
  13. 13.
    Cui, Z., Henrickson, K., Ke, R., Wang, Y.: High-order graph convolutional recurrent neural network: a deep learning framework for network-scale traffic learning and forecasting. arXiv:1802.07007 [cs, stat] (2018)
  14. 14.
    Liao, T.W.: Clustering of time series data—a survey. Pattern Recogn. 38, 1857–1874 (2005).  https://doi.org/10.1016/j.patcog.2005.01.025CrossRefzbMATHGoogle Scholar
  15. 15.
    Borg, I., Groenen, P.: Modern Multidimensional Scaling. Springer, New York (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Transport and Telecommunication InstituteRigaLatvia

Personalised recommendations