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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

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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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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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