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Exploiting Multiple Correlations Among Urban Regions for Crowd Flow Prediction

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Abstract

Crowd flow prediction has become a strategically important task in urban computing, which is the prerequisite for traffic management, urban planning and public safety. However, due to variousness of crowd flows, multiple hidden correlations among urban regions affect the flows. Besides, crowd flows are also influenced by the distribution of Points-of-Interests (POIs), transitional functional zones, environmental climate, and different time slots of the dynamic urban environment. Thus, we exploit multiple correlations between urban regions by considering the mentioned factors comprehensively rather than the geographical distance and propose multi-graph convolution gated recurrent units (MGCGRU) for capturing these multiple spatial correlations. For adapting to the dynamic mobile data, we leverage multiple spatial correlations and the temporal dependency to build an urban flow prediction framework that uses only a little recent data as the input but can mine rich internal modes. Hence, the framework can mitigate the influence of the instability of data distributions in highly dynamic environments for prediction. The experimental results on two real-world datasets in Shanghai show that our model is superior to state-of-the-art methods for crowd flow prediction.

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References

  1. Zheng Y, Capra L, Wolfson O, Yang H. Urban computing: Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, 2014, 5(3): Article No. 38.

    Google Scholar 

  2. Zhang J B, Zheng Y, Qi D K. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proc. the 31st AAAI Conference on Artificial Intelligence, February 2017, pp.1655-1661.

  3. Zheng Z, Yang Y, Liu J et al. Deep and embedded learning approach for traffic flow prediction in urban informatics. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(10): 3927-3939.

    Google Scholar 

  4. Sun J, Zhang J, Li Q et al. Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks. arXiv:1903.07789, 2019. https://arxiv.org/abs/1903.07789, August 2019.

  5. Du B, Peng H, Wang S et al. Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2019.2900481.

    Google Scholar 

  6. Chai D, Wang L, Yang Q. Bike flow prediction with multigraph convolutional networks. In Proc. the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2018, pp.397-400.

  7. Geng X, Li Y, Wang L et al. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In Proc. the 33rd AAAI Conference on Artificial Intelligence, January 2019, pp.3656-3663.

  8. Ramaswami A, Russell A G, Culligan P J et al. Meta-principles for developing smart, sustainable, and healthy cities. Science, 2016, 352(6288): 940-943.

    Google Scholar 

  9. Ai Y, Li Z, Gan M et al. A deep learning approach on short-term spatiotemporal distribution forecasting of dock-less bike-sharing system. Neural Computing and Applications, 2019, 31(5): 1665-1677.

    Google Scholar 

  10. Shuman D I, Narang S K, Frossard P, Ortega A, Vandergheynst P. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Processing Magazine, 2013, 30(3): 83-98.

    Google Scholar 

  11. Li Y X, Zheng Y, Zhang H C, Chen L. Traffic prediction in a bike-sharing system. In Proc. the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2015, Article No. 33.

  12. Yao H, Wu F, Ke J, Tang X, Jia Y, Lu S, Gong P, Ye J, Li Z. Deep multi-view spatial-temporal network for taxi demand prediction. In Proc. the 32nd AAAI Conference on Artificial Intelligence, February 2018, pp.2588-2595.

  13. Holmgren J, Aspegren S, Dahlstroma J. Prediction of bicycle counter data using regression. Procedia Computer Science, 2017, 113: 502-507.

    Google Scholar 

  14. Kumar S V, Vanajakshi L. Short-term traffic flow prediction using seasonal ARIMA model with limited input data. European Transport Research Review, 2015, 7(3): Article No. 21.

  15. Abadi A, Rajabioun T, Ioannou P A. Traffic flow prediction for road transportation networks with limited traffic data. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(2): 653-662.

    Google Scholar 

  16. Li Y, Yu R, Shahabi C, Liu Y. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In Proc. the 6th International Conference on Learning Representations, April 2018.

  17. Cheng A Y, Jiang X, Li Y F, Zhang C, Zhu H. Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method. Physica A: Statistical Mechanics and its Applications, 2017, 466: 422-434.

    Google Scholar 

  18. Achar A, Bharathi D, Kumar B A et al. Bus arrival time prediction: A spatial Kalman filter approach. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2019.2909314.

    Google Scholar 

  19. Liu J M, Sun L L, Li Q, Ming J C, Liu Y C, Xiong H. Functional zone based hierarchical demand prediction for bike system expansion. In Proc. the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2017, pp.957-966.

  20. Liu J M, Sun L L, Chen W W, Xiong H. Rebalancing bike sharing systems: A multi-source data smart optimization. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, pp.1005-1014.

  21. Srivastava N, Mansimov E, Salakhutdinov R. Unsupervised learning of video representations using LSTMs. arXiv:1502.04681, 2015. https://arxiv.org/abs/1502.04681, August 2019.

  22. Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473, 2014. https://arxiv.org/abs/1409.0473, August 2019.

  23. Cho K, van Merrienboer B, Gulcehre C et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078, 2014. https://arxiv.org/abs/1406.1078, August 2019.

  24. Thirumalai C, Koppuravuri R. Bike sharing prediction using deep neural networks. JOIV: International Journal on Informatics Visualization, 2017, 1(3): 83-87.

    Google Scholar 

  25. Shi X J, Chen Z R, Wang H, Yeung D Y, Wong W K, WOO W C. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Proc. the 2015 Annual Conference on Neural Information Processing Systems, December 2015, pp.802-810.

  26. Bruna J, Zaremba W, Szlam A et al. Spectral networks and locally connected networks on graphs. arXiv:1312.6203, 2013. https://arxiv.org/abs/1312.6203, August 2019.

  27. Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. In Proc. the 2016 Annual Conference on Neural Information Processing Systems, December 2016, pp.3844-3852.

  28. Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. arXiv:1609.02907, 2016. https://arxiv.org/abs/1609.02907, August 2019.

  29. Zhang X, He L, Chen K, Luo Y, Zhou J, Wang F. Multi-view graph convolutional network and its applications on neuroimage analysis for Parkinson’s disease. arXiv:1805.08801, 2018. https://arxiv.org/abs/1805.08801, August 2019.

  30. Yao H, Tang X, Wei H, Zheng G, Yu Y, Li Z. Modeling spatial-temporal dynamics for traffic prediction. arXiv:1803.01254, 2018. https://arxiv.org/abs/1803.01254, August 2019.

  31. Yuan N J, Zheng Y, Xie X et al. Discovering urban functional zones using latent activity trajectories. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(3): 712-725.

    Google Scholar 

  32. Erman J, Arlitt M F, Mahanti A. Traffic classification using clustering algorithms. In Proc. the 2nd Annual ACM Workshop on Mining Network Data, September 2006, pp.281-286.

  33. Cho K, van Merrienboer B, Bahdanau D et al. On the properties of neural machine translation: Encoder-decoder approaches. arXiv:1409.1259, 2014. https://arxiv.org/abs/1409.1259, August 2019.

  34. Friedman J H. Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 2001, 29(5): 1189-1232.

    MathSciNet  MATH  Google Scholar 

  35. Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks. In Proc. the 2014 Annual Conference on Neural Information Processing Systems, December 2014, pp.3104-3112.

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Correspondence to Jing-Jing Gu.

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Zhou, Q., Gu, JJ., Ling, C. et al. Exploiting Multiple Correlations Among Urban Regions for Crowd Flow Prediction. J. Comput. Sci. Technol. 35, 338–352 (2020). https://doi.org/10.1007/s11390-020-9970-y

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