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.
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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.0000000041
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.2017.0284
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.2017.10.010
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/s17040818
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)
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)
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
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)
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)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv:1707.01926 [cs, stat] (2017)
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
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
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)
Liao, T.W.: Clustering of time series data—a survey. Pattern Recogn. 38, 1857–1874 (2005). https://doi.org/10.1016/j.patcog.2005.01.025
Borg, I., Groenen, P.: Modern Multidimensional Scaling. Springer, New York (2014)
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”.
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Pavlyuk, D. (2020). Make It Flat: Multidimensional Scaling of Citywide Traffic Data. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2019. Lecture Notes in Networks and Systems, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-030-44610-9_9
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DOI: https://doi.org/10.1007/978-3-030-44610-9_9
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