Simultaneous Multimodal Demand Imputation and Forecasting via Graph-Guided Generative and Adversarial Network

Can Li (), Wei Liu (), Wanjing Ma () and Hai Yang ()
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Can Li: The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 200070, China
Wei Liu: Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Wanjing Ma: The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 200070, China
Hai Yang: Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Hong Kong, China

Transportation Science, 2025, vol. 59, issue 4, 763-781

Abstract: Accurate prediction of multimodal transport demand is essential for effective transport planning and management and enables service optimization based on historical and (predicted) future demand. However, dealing with missing data remains a common challenge in multimodal demand analytics. Furthermore, the potential benefits of knowledge sharing across different modes for simultaneous imputation and forecasting have not been thoroughly explored. This study introduces the Graph-guided Generative-Adversarial Imputation and Forecasting Network (GIF) to tackle these challenges. GIF utilizes a Generative Adversarial Network with a generator and a discriminator. The generator simultaneously fills in missing values and predicts future demand, whereas the discriminator differentiates between synthetic and real data. An Encoder-Decoder framework is employed to reconstruct the generated data to the original data to ensure that the important information is preserved. Spatiotemporal features of each mode’s demand are captured via Transformer-encoder layers, whereas the knowledge sharing among multiple modes is facilitated by graph-guided feature fusion of different modes. The proposed method is evaluated on three real-world transport data sets, demonstrating its potential to address forecasting tasks with missing data in multimodal transport systems. Overall, this study provides insights into the effectiveness of cross-modal knowledge sharing and joint imputation and prediction in enhancing the accuracy of multimodal demand prediction.

Keywords: multimodal transport demand; imputation and forecasting; generative adversarial network (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:59:y:2025:i:4:p:763-781

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