Make It Flat: Multidimensional Scaling of Citywide Traffic Data
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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.
KeywordsSpatiotemporal models Machine learning Image processing Urban traffic modelling
The first author was financially supported by the specific support objective activity 188.8.131.52. “Post-doctoral Research Aid” (Project id. N. 184.108.40.206/16/I/001) of the Republic of Latvia, funded by the European Regional Development Fund. Dmitry Pavlyuk’s research project No. 220.127.116.11/VIAA/1/16/112 “Spatiotemporal urban traffic modelling using big data”.
- 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.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.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.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.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.Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv:1707.01926 [cs, stat] (2017)
- 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.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.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)
- 15.Borg, I., Groenen, P.: Modern Multidimensional Scaling. Springer, New York (2014)Google Scholar