Work

Origin-destination flow generation for metro network expansion using spatiotemporal gated graph neural networks

Year: 2026

Type: article

Abstract: Rapid urbanization drives continuous expansion of metro networks to meet rising mobility demand. Due to the large investment, such expansion requires careful planning, including detailed assessment of potential Origin-Destination (OD) passenger flows. However, forecasting OD flows becomes particularly challenging in evolving metro networks; new stations and lines introduce OD pairs without historical ridership data, while travel patterns shift as passengers adapt to network changes. Traditional gravity-based models rely on oversimplified assumptions and overlook complex network dependencies within metro systems. Meanwhile, recent deep learning methods designed for regional-scale mobility often struggle to capture the fine-grained dynamics and structural evolution unique to metro networks. To address these limitations, we propose a Spatiotemporal Gated Graph Neural Network (STG-GNN) to address the OD flow generation for metro network expansion problem. STG-GNN integrates diverse urban contextual data and dynamically evolving metro network structures. Spatial dependencies are modeled using graph attention networks built on both transfer-aware travel time and geographic distance, while temporal patterns are captured via Gated Recurrent Units (GRUs). A novel gated fusion module adaptively combines spatial and temporal outputs, weighting their contributions based on OD pair characteristics. Additionally, an age-aware weighted loss function is used to reflect the maturation process of new OD pairs. We validate STG-GNN with extensive experiments using multi-year metro ridership data from Shenzhen, China. Results show that STG-GNN consistently outperforms existing state-of-the-art models, improving Common Part of Commute (CPC) by 13.6% and reducing RMSE and MAE by 11.3% and 4.7% for new OD pairs. The proposed model is general and can be adapted to other evolving transportation networks. The source code and a synthetic dataset are made publicly available for transparency and reproducibility.

Source: Transportation Research Part C Emerging Technologies

DOI: 10.1016/j.trc.2026.105677

Authors: Fangyi Ding, Zhan Zhao, Yamin Wang, Min Namgung, JangHyeon Lee, Tianhao Li

Institutions: University of Hong Kong, University of Minnesota

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,040