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A knowledge-informed deep learning paradigm for generaliz-able and stability-optimized car-following models

Chengming Wang Dongyao Jia Wei Wang Dong Ngoduy Bei Peng Jianping Wang

交通研究通讯（英文）2025，Vol.5Issue(3)：236-251,16.

交通研究通讯（英文）2025，Vol.5Issue(3)：236-251,16.DOI:10.1016/j.commtr.2025.100211

A knowledge-informed deep learning paradigm for generaliz-able and stability-optimized car-following models

Chengming Wang ¹Dongyao Jia ¹Wei Wang ¹Dong Ngoduy ²Bei Peng ³Jianping Wang⁴

作者信息

1. School of Advanced Technology,Xi'an Jiaotong-Liverpool University,Suzhou,215123,China
2. Institute of Transport Studies,Monash University,Clayton,3800,Australia
3. Department of Computer Science,University of Liverpool,Liverpool,L69 7ZX,UK
4. Department of Computer Science,City University of Hong Kong,Hong Kong,999077,China
折叠

摘要

关键词

Car-following models(CFMs)/Large language models(LLMs)/Knowledge distillation/Stability analysis/Deep learning

Key words

Car-following models(CFMs)/Large language models(LLMs)/Knowledge distillation/Stability analysis/Deep learning

引用本文复制引用

Chengming Wang,Dongyao Jia,Wei Wang,Dong Ngoduy,Bei Peng,Jianping Wang..A knowledge-informed deep learning paradigm for generaliz-able and stability-optimized car-following models[J].交通研究通讯（英文）,2025,5(3):236-251,16.

基金项目

This work was supported in part by the National Natural Science Foundation of China(No.62372384)and in part by Suzhou Science and Technology Development Planning Programme(No.ZXL2024342).Dong Ngoduy was partially funded by LK Engineering Ltd. （No.62372384）

交通研究通讯（英文）

ISSN：2772-4247

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