Abstract
Advances in artificial intelligence and data acquisition technology are growing, the research on deep learning algorithm has gone deep into various fields. At this stage, the demand supply matching model under the comprehensive passenger transport hub travel system is obtained by analyzing the travel mode data. This paper takes traffic time as the research direction, uses machine learning and complex network theory to conduct in-depth learning algorithm research, respectively, discusses, explores and forecasts the traffic supply and demand mode data, and explores the traffic mode supply and demand model under the comprehensive passenger transport hub travel service system. It is shown in traffic data that using the prediction form of deep learning to predict traffic conditions and travel pressure can enable traffic managers to master traffic dynamics and lead the direction for future traffic development. Finally, from the perspective of MaaS system, the paper uses big data and information processing methods to explore the supply and demand matching model in terms of transportation modes. The research shows that MaaS system can make overall planning in terms of traffic resources, provide reasonable travel modes for traffic travelers and match corresponding travel services. It not only makes overall planning and guarantee for travel services, but also promotes the sustainable development of the transportation supply and demand system.
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Funding
This study was supported by “Research on the Theory and Methodology of Mobility as a Service (MaaS) System Planning for Integrated Passenger Transport Hub, Sichuan Science and Technology Plan Project (Grant No. 2021YJ0076).”
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Liang, Y., Lan, C., Dan, T. et al. Research on supply and demand matching model of transportation modes in MaaS system of integrated passenger transport hub based on deep learning. Soft Comput 27, 5973–5983 (2023). https://doi.org/10.1007/s00500-023-08065-4
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DOI: https://doi.org/10.1007/s00500-023-08065-4