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Multi-perspective convolutional neural networks for citywide crowd flow prediction

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Abstract

Crowd flow prediction is an important problem of urban computing with many applications, such as public security. Inspired by the success of deep learning, various deep learning models have been proposed to solve this problem. Although existing methods have achieved good prediction performance, they cannot effectively capture richer spatial-temporal correlations that are important for crowd flow prediction. To address the limitation of existing methods, we propose a novel 2D CNN-based (convolutional neural networks) model via multiple perspectives called the MPCNN to capture richer spatial-temporal correlations. In particular, three perspective CNNs are included in the MPCNN: the front CNN, the side CNN and the top CNN. Then, we propose a fusion layer to combine the results of the three CNNs. In addition, in the MPCNN, we use external factors to enhance prediction performance. Based on four real-world datasets, we performed a series of experiments to compare the proposed method with existing methods, and experimental results demonstrate the effectiveness and efficiency of the proposed method.

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Notes

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  2. https://www1.nyc.gov/site/tlc/about/tlc-triprecord-data.page

References

  1. Belhadi A, Djenouri Y, Djenouri D et al (2020) A recurrent neural network for urban long-term traffic flow forecasting. Appl Intell 50(10):3252–3265

    Article  Google Scholar 

  2. Chen C, Li K, Teo SG et al (2018) Exploiting spatio-temporal correlations with multiple 3d convolutional neural networks for citywide vehicle flow prediction. In: IEEE international conference on data mining, ICDM, pp 893–898

  3. Dai G, Hu X, Ge Y, et al. (2021) Attention based simplified deep residual network for citywide crowd flows prediction. Front Comput Sci 15(2):152,317

    Article  Google Scholar 

  4. Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in neural information processing systems, pp 3844–3852

  5. Feng J, Lin Z, Xia T et al (2020) A sequential convolution network for population flow prediction with explicitly correlation modelling. In: Bessiere C (ed) Proceedings of the twenty-ninth international joint conference on artificial intelligence, IJCAI, pp 1331-1337

  6. Guo S, Lin Y, Feng N et al (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting

  7. Hoang MX, Zheng Y, Singh AK (2016) Fccf: forecasting citywide crowd flows based on big data. In: Proceedings of the 24th ACM SIGSPATIAL international conference on advances in geographic information systems, pp 6:1–6:10

  8. Huang F, Yi P, Wang J et al (2022) A dynamical spatial-temporal graph neural network for traffic demand prediction. Inf Sci 594:286–304

    Article  Google Scholar 

  9. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: 5th international conference on learning representations

  10. Kumar SV (2017) Traffic flow prediction using kalman filtering technique. Procedia Eng 187:582–587

    Article  Google Scholar 

  11. LeCun Y, Bottou L, Bengio Y, et al. (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  12. Li Y, Yu R, Shahabi C et al (2018) Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: 6th international conference on learning representations, ICLR

  13. Liang Y, Ouyang K, Jing L et al (2019) Urbanfm: inferring fine-grained urban flows. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, KDD, pp 3132–3142

  14. Liang Y, Ouyang K, Wang Y et al (2020) Revisiting convolutional neural networks for citywide crowd flow analytics. In: Machine learning and knowledge discovery in databases - European conference, pp 578–594

  15. Lin Z, Feng J, Lu Z et al (2019) Deepstn+: Context-aware spatial-temporal neural network for crowd flow prediction in metropolis. In: The thirty-third AAAI conference on artificial intelligence, AAAI, pp 1020–1027

  16. Pan Z, Liang Y, Wang W et al (2019a) Urban traffic prediction from spatio-temporal data using deep meta learning. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1720–1730

  17. Pan Z, Wang Z, Wang W et al (2019b) Matrix factorization for spatio-temporal neural networks with applications to urban flow prediction. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 2683–2691

  18. Poon KH, Wong PK, Cheng JCP (2022) Long-time gap crowd prediction using time series deep learning models with two-dimensional single attribute inputs. Adv Eng Inform 51:101,482

    Article  Google Scholar 

  19. Singh U, Determe J, Horlin F, et al. (2020) Crowd forecasting based on wifi sensors and LSTM neural networks. IEEE Trans Instrum Meas 69(9):6121–6131

    Article  Google Scholar 

  20. Song C, Lin Y, Guo S et al (2020) Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. In: The thirty-fourth AAAI conference on artificial intelligence, AAAI, pp 914–921

  21. Su H, Maji S, Kalogerakis E et al (2015) Multi-view convolutional neural networks for 3d shape recognition. In: 2015 IEEE international conference on computer vision, pp 945–953

  22. Sun J, Zhang J, Li Q, et al. (2022) Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks. IEEE Trans Knowl Data Eng 34(5):2348–2359

    Article  Google Scholar 

  23. Tian C, Zhu X, Hu Z, et al. (2020) Deep spatial-temporal networks for crowd flows prediction by dilated convolutions and region-shifting attention mechanism. Appl Intell 50(10):3057–3070

    Article  Google Scholar 

  24. Tu Y, Lin S, Qiao J, et al. (2021) Deep traffic congestion prediction model based on road segment grouping. Appl Intell 51(11):8519–8541

    Article  Google Scholar 

  25. Wang J, Zhu W, Sun Y, et al. (2021) An effective dynamic spatiotemporal framework with external features information for traffic prediction. Appl Intell 51(6):3159–3173

    Article  Google Scholar 

  26. Wang S, Miao H, Chen H et al (2020) Multi-task adversarial spatial-temporal networks for crowd flow prediction. In: The 29th ACM international conference on information and knowledge management, pp 1555–1564

  27. Wu C, Yin T, Ge S et al (2017) Ensemble learning for crowd flows prediction on campus. In: Proceedings of international conference on smart computing and communication, pp 103–113

  28. Xia T, Lin J, Li Y et al (2021) dgcn: 3-dimensional dynamic graph convolutional network for citywide crowd flow prediction. ACM Trans Knowl Discov Data (TKDD) 15(6):110:1–110:21

    Google Scholar 

  29. Xu J, Zhang X, Li W et al (2020) Joint multi-view 2d convolutional neural networks for 3d object classification. In: Proceedings of the twenty-ninth international joint conference on artificial intelligence, pp 3202–3208

  30. Yabe T, Tsubouchi K, Sudo A et al (2016) Predicting irregular individual movement following frequent mid-level disasters using location data from smartphones. In: Proceedings of the 24th ACM SIGSPATIAL international conference on advances in geographic information systems, GIS. ACM, pp 54:1–54:4

  31. Yang B, Sun S, Li J et al (2019) Traffic flow prediction using LSTM with feature enhancement. Neurocomputing 332:320–327

    Article  Google Scholar 

  32. Yao H, Wu F, Ke J, et al (2018) Deep multi-view spatial-temporal network for taxi demand prediction. In: Proceedings of the thirty-second AAAI conference on artificial intelligence, pp 2588–2595

  33. Yao H, Tang X, Wei H et al (2019) Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction. In: The Thirty-third AAAI conference on artificial intelligence, pp 5668–5675

  34. Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of international joint conferences on artificial intelligence, pp 3634–3640

  35. Yuan H, Zhu X, Hu Z, et al. (2020) Deep multi-view residual attention network for crowd flows prediction. Neurocomputing 404:198–212

    Article  Google Scholar 

  36. Zhang J, Zheng Y, Qi D et al (2016) Dnn-based prediction model for spatio-temporal data. In: Proceedings of the 24th ACM SIGSPATIAL international conference on advances in geographic information systems, pp 92:1–92:4

  37. Zhang J, Zheng Y, Qi D (2017) Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Proceedings of AAAI conference on artificial intelligence, pp 1655–1661

  38. Zhang X, Huang C, Xu Y et al (2020) Spatial-temporal convolutional graph attention networks for citywide traffic flow forecasting. In: The 29th ACM international conference on information and knowledge management, pp 1853–1862

  39. Zhang Y, Yang Y, Zhou W et al (2021) Multi-city traffic flow forecasting via multi-task learning. Appl Intell 51(10):6895–6913

    Article  Google Scholar 

  40. Zhou Q, Gu J, Ling C, et al. (2020) Exploiting multiple correlations among urban regions for crowd flow prediction. J Comput Sci Technol 35(2):338–352

    Article  Google Scholar 

  41. Zhou X, Shen Y, Zhu Y et al (2018) Predicting multi-step citywide passenger demands using attention-based neural networks. In: Proceedings of the eleventh acm international conference on web search and data mining, WSDM, pp 736–744

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Authors and Affiliations

  1. School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, 510006, China

    Genan Dai, Weiyang Kong, Yubao Liu, Youming Ge & Sen Zhang

  2. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, 510006, China

    Yubao Liu

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  1. Genan Dai
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  2. Weiyang Kong
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  3. Yubao Liu
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  4. Youming Ge
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  5. Sen Zhang
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Correspondence to Genan Dai or Yubao Liu.

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Dai, G., Kong, W., Liu, Y. et al. Multi-perspective convolutional neural networks for citywide crowd flow prediction. Appl Intell 53, 8994–9008 (2023). https://doi.org/10.1007/s10489-022-03980-9

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