@article{Lv2026, 
author = {Zhiqiang Lv and Qu Hao and Jianbo Li and Lei You},
title = {PSTGCN: A Taxi Flow Prediction Model Period-Based Spatial-Temporal Graph Convolution Network},
year = {2026},
journal = {Tsinghua Science and Technology},
volume = {31},
number = {3},
pages = {1706-1721},
keywords = {urban planning, Graph Convolution Network (GCN), taxi flow, periodic feature, spatial-temporal model},
url = {https://www.sciopen.com/article/10.26599/TST.2025.9010066},
doi = {10.26599/TST.2025.9010066},
abstract = {Efficient urban traffic management and intelligent urban planning are pivotal drivers for the development of modern cities. Among them, accurate prediction of taxi flow, as a core component of the urban transportation system, has significant implications for traffic management, resource allocation, and the daily lives of citizens. However, practical taxi flow prediction faces numerous challenges, particularly in fully utilizing periodic features. To address the issue of capturing periodic characteristics in urban taxi flow prediction research, this study proposes a prediction model named Period-based Spatial-Temporal Graph Convolution Network (PSTGCN). In the PSTGCN, graph convolutional techniques are employed in the spatial convolution module to capture both local and global spatial correlations of taxi flow, effectively integrating the two. This further enhances the understanding of flow interaction between different areas. Furthermore, in the temporal convolution module, PSTGCN transforms the one-dimensional time series data into two-dimensional data, enabling the simultaneous analysis of both the within-period changes in taxi flow and the patterns of variation between different periods. This significantly strengthens the capability to capture flow periodic features. Lastly, this study extensively validates the predictive performance of the PSTGCN model. Experimental results demonstrate its significant advantages over various existing benchmark models in terms of prediction effectiveness. Moreover, the model’s excellent generalization ability is showcased through validation using multiple real-world datasets.}
}