Urban Multimodal Transportation Generative Pretrained Transformer Foundation Model:Hierarchical Techniques and Application Scenarios of Spot-corridor-network Decomposition
The urban multimodal transportation system is a highly complex and diverse transportation network designed to efficiently meet the mobility needs of people,goods,and services within a city.Its complexity originates from many factors including the coupling between different transportation modes,complex interactions between transportation demand and supply,and intrinsic stochasticity and self-organization of an open,heterogeneous,and adaptive system.Therefore,understanding and managing such a complex system is a nontrivial task.However,with the increasing availability of multisource big data in multimodal transportation and other sectors,enhanced computational hardware capabilities,and rapid development of machine learning models,the concept of large models has been applied in various fields,including computer vision and natural language processing.In this study,a conceptual framework,multimodal transportation generative pretrained transformer(MT-GPT),of a data-driven foundation model for multifaceted decision-making in complex multimodal transportation systems was conceived.Considering the characteristics of different transportation modes,the core technologies and their integration methods were investigated to realize this conceptual framework.An expansive data paradigm is envisioned for a foundation model tailored to transportation,along with improvements in hierarchical multitask learning,hierarchical federated learning,hierarchical transfer learning,and hierarchical transformer framework.Application cases of MT-GPT within the"spots-corridors-networks"three-layer large model framework are discussed by constructing"task islands"and"coupling bridges".MT-GPT aims to provide an intelligent support for tasks such as multiscale multimodal transportation planning,network design,infrastructure construction,and traffic management.
NETL
NSTL
万方数据
