Data pre-integration in graph-transformer networks for enhanced spatiotemporal ridership prediction

Linmu Zou; Zijia Wang; Lu Zhao

doi:10.1117/12.3054693

20 December 2024 Data pre-integration in graph-transformer networks for enhanced spatiotemporal ridership prediction

Linmu Zou, Zijia Wang, Lu Zhao

Author Affiliations +

Proceedings Volume 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024); 1342147 (2024) https://doi.org/10.1117/12.3054693
Event: Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 2024, Dalian, China

Abstract

Accurate ridership prediction is vital for optimizing urban rail transit operations, yet it remains a complex challenge due to the spatiotemporal dynamics influenced by factors like weather, holidays, and station attributes. This study proposes an advanced prediction model that integrates Graph Convolutional Networks (GCNs) with Transformer-based architectures to address these complexities. Our model incorporates a priori knowledge and comprehensive data pre-processing to capture both spatial dependencies and temporal dynamics effectively. The dataset comprises hourly ridership records, weather data, and ticket type distributions from 329 stations across Beijing’s urban rail network in 2019. We employed periodic clustering and autocorrelation analyses to pre-process the data, which were then used to train our GraphTransformer model. The proposed model outperforms traditional statistical and machine learning models, demonstrating superior performance. Furthermore, our model is resilient to fluctuations during holidays, outperforming baseline models at stations with varying attributes. This study underscores the importance of integrating domain-specific knowledge into deep learning models and highlights the potential of Transformer networks for enhancing the accuracy and robustness of ridership predictions in complex urban transit environments.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Linmu Zou, Zijia Wang, and Lu Zhao "Data pre-integration in graph-transformer networks for enhanced spatiotemporal ridership prediction", Proc. SPIE 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 1342147 (20 December 2024); https://doi.org/10.1117/12.3054693

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