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A period-extracted multi-featured dynamic graph convolution network for traffic demand prediction

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Abstract

Urban online car-hailing demand prediction poses a significant challenge in developing intelligent transportation systems due to its intricate and dynamic spatio-temporal correlation. Prior research has demonstrated promising outcomes in demand forecasting by employing graph neural networks. However, these studies either solely rely on static prior information or allow the model to independently capture spatial associations. In terms of temporal considerations, effectively modeling both long-term and short-term dependencies remains a crucial factor that significantly impacts overall performance. To tackle these challenges, we propose a novel Period-Extracted Multi-featured Dynamic Graph Convolution Network (PE-MDGCN) for traffic demand prediction. Specifically, our proposed model introduces the Period Dynamic Arrival Learning module and the Static Feature Dynamic Adaptation module, to effectively capture shorter-term relations based on time intervals and arrival connections, as well as the dynamic changes based on static multi-featured graphs. Furthermore, our proposed spatio-temporal multi-graph learning framework leverages a temporal contextual gated mechanism and multi-visual field convolution to efficiently capture global, long-term, and short-term information. By conducting comprehensive experiments on two real-world traffic demand datasets, our model consistently surpasses all baseline models in terms of various evaluation metrics.

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Data availability and access

The datasets utilized and analyzed in the present study can be accessed on the official websites of the New York City Taxi & Limousine Commission and citibike. The corresponding URLs for these datasets are https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page and https://ride.citibikenyc.com/system-data, respectively.

Notes

  1. https://www.openstreetmap.org/

  2. https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page

  3. https://ride.citibikenyc.com/system-data

References

  1. Ke J, Qin X, Yang H, Zheng Z, Zhu Z, Ye J (2021) Predicting origin destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network. Transportation Research Part C: Emerging Technologies 122:102858

    Article  Google Scholar 

  2. Li Z, Richard YF, Yige W, Bin N, Tao T (2019) Big data analytics in intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst 20(1):383–398

    Article  Google Scholar 

  3. Yu B, Yin H, Zhu Z (2017) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. arXiv:1709.04875

  4. Jiang R, Yin D, Wang Z, Wang Y, Deng J, Liu H, Cai Z, Deng J, Song X, Shibasaki R (2021) Dl-traff: survey and benchmark of deep learning models for urban traffic prediction. In: Proceedings of the 30th ACM international conference on information & knowledge management. CIKM’21, pp 4515-4525. Association for Computing Machinery, New York, USA

  5. Lv Y, Duan Y, Kang W, Li Z, Wang FY (2014) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865–873

    Google Scholar 

  6. Wang X, Ma Y, Wang Y, Jin W, Wang X, Tang J, Jia C, Yu J (2020) Traffic flow prediction via spatial temporal graph neural network. Proceedings of the Web Conference 2020:1082–1092

    Google Scholar 

  7. Zeng H, Peng Z, Huang X, Yang Y, Hu R (2022) Deep spatio-temporal neural network based on interactive attention for traffic flow prediction. Applied Intelligence 1–12

  8. Wang J, Chen R, He Z (2019) Traffic speed prediction for urban transportation network: a path based deep learning approach. Transportation Research Part C: Emerging Technologies 100:372–385

    Article  Google Scholar 

  9. Zheng C, Fan X, Wang C, Qi J (2020) Gman: a graph multi-attention network for traffic prediction. Proceedings of the AAAI Conference on Artificial Intelligence 34(01):1234–1241

    Article  Google Scholar 

  10. Xu C, Zhang A, Xu C, Chen Y (2022) Traffic speed prediction: spatiotemporal convolution network based on long-term, short-term and spatial features. Appl Intell 52(2):2224–2242

    Article  Google Scholar 

  11. Ye J, Sun L, Du B, Fu Y, Xiong H (2021) Coupled layer-wise graph convolution for transportation demand prediction. Proceedings of the AAAI Conference on Artificial Intelligence 35:4617–4625

    Article  Google Scholar 

  12. Jin G, Sha H, Feng Y, Cheng Q, Huang J (2021) Gsen: An ensemble deep learning benchmark model for urban hotspots spatiotemporal prediction. Neurocomputing 455:353–367

    Article  Google Scholar 

  13. Wu Y, Zhang H, Li C, Tao S, Yang F (2023) Urban ride-hailing demand prediction with multi-view information fusion deep learning framework. Appl Intell 53(8):8879–8897

    Article  Google Scholar 

  14. Tedjopurnomo DA, Bao Z, Zheng B, Choudhury FM, Qin AK (2022) A survey on modern deep neural network for traffic prediction: Trends, methods and challenges. IEEE Trans Knowl Data Eng 34(4):1544–1561

    Google Scholar 

  15. Yao H, Tang X, Wei H, Zheng G, Li Z (2019) Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction. Proceedings of the AAAI Conference on Artificial Intelligence 33:5668–5675

    Article  Google Scholar 

  16. Ke J, Yang H, Zheng H, Chen X, Jia Y, Gong P, Ye J (2018) Hexagon based convolutional neural network for supply-demand forecasting of ride-sourcing services. IEEE Trans Intell Transp Syst 20(11):4160–4173

    Article  Google Scholar 

  17. Jiang R, Song X, Huang D, Song X, Xia T, Cai Z, Wang Z, Kim K, Shibasaki R (2019) Deepurbanevent: a system for predicting citywide crowd dynamics at big events pp 2114–2122

  18. Yang B, Sun S, Li J, Lin X, Tian Y (2019) Traffic flow prediction using lstm with feature enhancement. Neurocomputing 332:320–327

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Li Y, Yu R, Shahabi C, Liu Y (2018) Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: International conference on learning representations (ICLR’18)

  21. Zhao L, Song Y, Zhang C, Liu Y, Wang P, Lin T, Deng M, Li H (2020) T-gcn: a temporal graph convolutional network for traffic prediction. IEEE Trans Intell Transp Syst 21(9):3848–3858

    Article  Google Scholar 

  22. Zhang J, Zheng Y, Qi D, Li R, Yi X, Li T (2018) Predicting city wide crowd flows using deep spatio-temporal residual networks. Artif Intell 259:147–166

    Article  Google Scholar 

  23. Bai L, Yao L, Kanhere SS, Wang X, Sheng QZ (2019) Stg2seq: spatial temporal graph to sequence model for multi-step passenger demand forecasting. In: Proceedings of the 28th international joint conference on artificial intelligence pp 1981–1987

  24. Geng X, Li Y, Wang L, Zhang L, Yang Q, Ye J, Liu Y (2019) Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 33(01):3656–3663

    Article  Google Scholar 

  25. Li M, Zhu Z (2021) Spatial-temporal fusion graph neural networks for traffic flow forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35:4189–4196

    Article  Google Scholar 

  26. Zhang D, Xiao F, Shen M, Zhong S (2021) Dneat: a novel dynamic node-edge attention network for origin-destination demand prediction. Transportation Research Part C: Emerging Technologies 122:102851

    Article  Google Scholar 

  27. Li F, Feng J, Yan H, Jin G, Yang F, Sun F, Jin D, Li Y (2023) Dynamic graph convolutional recurrent network for traffic prediction: benchmark and solution. ACM Trans Knowl Discov Data 17(1):1–21

    Google Scholar 

  28. Liang J, Tang J, Gao F, Wang Z, Huang H (2023) On region-level travel demand forecasting using multi-task adaptive graph attention network. Inf Sci 622:161–177

    Article  Google Scholar 

  29. Huang Z, Zhang W, Wang D, Yin Y (2022) A gan framework-based dynamic multi-graph convolutional network for origin-destination-based ride-hailing demand prediction. Inf Sci 601:129–146

    Article  Google Scholar 

  30. Huang B, Ruan K, Yu W, Xiao J, Xie R, Huang J (2023) Odformer: spatial-temporal transformers  for long sequence origin-destination matrix forecasting against cross application scenario. Expert Syst Appl 222:119835

  31. Zhang S, Tong H, Xu J, Maciejewski R (2019) Graph convolutional networks: a comprehensive review. Computational Social Networks 6(1):1–23

    Article  Google Scholar 

  32. Bruna J, Zaremba W, Szlam A, LeCun Y (2013) Spectral networks and locally connected networks on graphs. arXiv:1312.6203

  33. Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems 29

  34. Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv:1710.10903

  35. Huang F, Yi P, Wang J, Li M, Peng J, Xiong X (2022) A dynamical spatial-temporal graph neural network for traffic demand prediction. Inf Sci 594:286–304

    Article  Google Scholar 

  36. Zhang W, Zhu F, Lv Y, Tan C, Liu W, Zhang X, Wang FY (2022) Adapgl: an adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks. Transportation Research Part C: Emerging Technologies 139:103659

    Article  Google Scholar 

  37. Guo S, Lin Y, Feng N, Song C, Wan H (2019) Attention based spatial temporal graph convolutional networks for traffic flow forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 33:922–929

    Article  Google Scholar 

  38. Han L, Du B, Sun L, Fu Y, Lv Y, Xiong H (2021) Dynamic and multi faceted spatio-temporal deep learning for traffic speed forecasting. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. KDD’21, pp 547–555. Association for Computing Machinery, New York, USA

  39. Colombo P, Dadalto E, Staerman G, Noiry N, Piantanida P (2022) Beyond mahalanobis distance for textual ood detection. Adv Neural Inf Process Syst 35:17744–17759

    Google Scholar 

  40. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:1609.02907

  41. Lan S, Ma Y, Huang W, Wang W, Yang H, Li P (2022) Dstagnn: dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In: International conference on machine learning, pp 11906–11917. PMLR

  42. Jin G, Xi Z, Sha H, Feng Y, Huang J (2022) Deep multi-view graph-based network for citywide ride-hailing demand prediction. Neurocomputing 510:79–94

    Article  Google Scholar 

  43. Bai L, Yao L, Li C, Wang X, Wang C (2020) Adaptive graph convolutional recurrent network for traffic forecasting. Adv Neural Inf Process Syst 33:17804–17815

    Google Scholar 

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Acknowledgements

This paper is supported by the National Natural Science Foundation of China (12273003).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, 211189, China

    Yuntian Zhu & Qingjian Ni

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  1. Yuntian Zhu
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  2. Qingjian Ni
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Contributions

Yuntian Zhu: Methodology, Validation, Formal analysis, Writing - original draft, Visualization. Qingjian Ni: Investigation, Data curation, Writing - review & editing.

Corresponding author

Correspondence to Qingjian Ni.

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The authors declare that they have no conflict of interest.

Ethical and informed consent for data used

This paper hereby declares that all data utilized within this study is obtained in a manner that adheres to ethical standards and informed consent protocols. The dataset employed in this research is publicly available, accessible for download by anyone. The use of this public dataset for academic or research purposes does not violate any copyright, intellectual property, or data protection regulations.

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Cite this article

Zhu, Y., Ni, Q. A period-extracted multi-featured dynamic graph convolution network for traffic demand prediction. Appl Intell 54, 722–737 (2024). https://doi.org/10.1007/s10489-023-05226-8

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