Short-term forecasting of origin-destination matrix in transit system via a deep learning approach

• Published in: Transportmetrica (Abingdon, Oxfordshire, UK) Oxfordshire, UK), 2023-03, Vol.19 (2)
• Main Authors: He, Yuxin, Zhao, Yang, Tsui, Kwok-Leung
• Format: Article
• Language: English
• Subjects: conv-based residual network units; Deep learning; Forecasting; Mathematical models; MF-ResNet; Short-term OD matrix forecasting; Spatial dependencies; spatiotemporal; Transportation networks; Transportation systems; Travel demand
• ISSN: 2324-9935; 2324-9943
• DOI: 10.1080/23249935.2022.2033348

Short-term travel demand forecasting is the critical first step to support transportation system management. Complex relevance among Origin-Destination (OD) pairs, temporal dependencies, and external factors bring challenges to it. An innovative deep learning approach, Multi-Fused Residual Network (MF-ResNet) is proposed to forecast travel demand. The complex relevance among OD pairs is converted into graphical-based spatial dependencies by treating OD matrix as the input of the model. The residual network units enable MF-ResNet to model not only near but also distant spatial correlations. Three conv-based residual network units model the temporal closeness, mid-term periodicity, as well as long-term periodicity features, and Fully-Connected (F-C) layers capture external factors. The fusion techniques coordinate all of the features. The proposed method is applied to the short-term forecasts of metro OD matrix in Shenzhen, China. The experimental results show that MF-ResNet can capture multiple complex dependencies robustly and outperforms traditional methods in terms of forecasting accuracy.