Urban Multimodal Transportation Generative Pretrained Transformer Foundation Model: Hierarchical Techniques and Application Scenarios of Spot-corridor-network Decomposition

被引：0
|
作者：
Zhou Z. [1 ]
Gu Z.-Y. [1 ]
Qu X.-B. [2 ]
Liu P. [1 ]
Liu Z.-Y. [1 ]
机构：
[1] School of Transportation, Southeast University, liangsu, Nanjing
[2] School of Vehicle and Mobility, Tsinghua University, Beijing
来源：
Zhongguo Gonglu Xuebao/China Journal of Highway and Transport | 2024年 / 37卷 / 02期
基金：
中国国家自然科学基金;
关键词：
federated learning; foundation model; multi-task learning; multimodal transportation; traffic engineering; transfer learning; transformer; transportation decision-making task;
D O I：
10.19721/j.cnki.1001-7372.2024.02.020
中图分类号：
学科分类号：
摘要：
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. © 2024 Chang'an University. All rights reserved.
引用
收藏
页码：253 / 274
页数：21
相关论文
共 62 条
  • [1] FEI N Y, LU Z W, GAO Y Z, Et al., Towards artificial general intelligence via a multimodal foundation model[J], Nature Communications, 13, (2022)
  • [2] ZHENG O, ABDEL-ATY M, WANG D D, Et al., ChatGPT is on the horizon: Could a large language model be all we need for intelligent transportation[J/OL]
  • [3] Bing-feng SI, YANG Xiao-bao, GAO Liang, Et al., Urban multimodal traffic assignment model based on travel demand[J], China Journal of Highway and Transport, 23, 6, pp. 85-91, (2010)
  • [4] CHEN Jun, Collaborative optimization of multi-modal transportation networks and generalized hubs: A review[J], Technology & Economy in Areas of Communications, 25, 2, pp. 1-10, (2023)
  • [5] LIU Z Y, LIU Y, MENG Q, Et al., A tailored machine learning approach for urban transport network flow estimation, Transportation Research Part C: Emerging Technologies, 108, pp. 130-150, (2019)
  • [6] ZHAO Cong, ZHANG Xin-yuan, LI Xing-hua, Et al., Intelligent delay matching method for parking allocation system via multi-agent deep reinforcement learning[J], China Journal of Highway and Transport, 35, 7, pp. 261-272, (2022)
  • [7] MIKOLOV T, CHEN K, CORRADO G, Et al., Efficient estimation of word representations in vector space
  • [8] SUTSKEVER I, VINYALS O, LE Q V., Sequence to sequence learning with neural networks[J], Advances in Neural Information Processing Systems, 4, 2, pp. 3104-3112, (2014)
  • [9] VASWANI A, SHAZEER N, PARMAR N, Et al., Attention is all you need, Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000-6010, (2017)
  • [10] DEVLIN J, CHANG M W, LEE K, Et al., BERT: Pre-training of deep bidirectional transformers for language understanding[J/OL]
1234567