基于大语言模型的交通异常事件检测与辅助决策

李炎英; 王新宇; 王晓; 孙长银

doi:10.11959/j.issn.2096-6652.202410

您当前的位置：

首页 >

文章列表页 >

基于大语言模型的交通异常事件检测与辅助决策

学术论文 | 更新时间：2024-11-04

- 基于大语言模型的交通异常事件检测与辅助决策
- Traffic anomaly event detection and auxiliary decision-making based on large language models
- 智能科学与技术学报 2024年6卷第3期页码：347-355
- 作者机构：
  
  安徽大学人工智能学院，安徽合肥 230031
- 作者简介：
  
  [ "李炎英（2000- ），女，安徽大学人工智能学院硕士生，主要研究方向为社会计算、生成式人工智能、交通事件检测。" ]
  [ "王新宇（2003- ），女，安徽大学人工智能学院本科生，主要研究方向为交通事件预测、生成式人工智能。" ]
  [ "王晓（1988- ），女，安徽大学人工智能学院教授，主要研究方向为社会计算、群体行为建模、无人自主系统及其平行测试。" ]
  [ "孙长银（1975- ），男，安徽大学人工智能学院教授，主要研究方向为智能控制与优化、强化学习、神经网络和数据驱动控制。" ]
- 基金信息：
  
  国家自然科学基金项目(62173329)
- DOI：10.11959/j.issn.2096-6652.202410
  中图分类号： TP39
- 收稿：2023-11-03，
  
  修回：2024-04-28，
  
  纸质出版：2024-09-15
- 稿件说明：
移动端阅览
李炎英,王新宇,王晓等.基于大语言模型的交通异常事件检测与辅助决策[J].智能科学与技术学报,2024,06(03):347-355.

LI Yanying,WANG Xinyu,WANG Xiao,et al.Traffic anomaly event detection and auxiliary decision-making based on large language models[J].Chinese Journal of Intelligent Science and Technology,2024,06(03):347-355.
李炎英,王新宇,王晓等.基于大语言模型的交通异常事件检测与辅助决策[J].智能科学与技术学报,2024,06(03):347-355. DOI： 10.11959/j.issn.2096-6652.202410.

LI Yanying,WANG Xinyu,WANG Xiao,et al.Traffic anomaly event detection and auxiliary decision-making based on large language models[J].Chinese Journal of Intelligent Science and Technology,2024,06(03):347-355. DOI： 10.11959/j.issn.2096-6652.202410.

摘要

大语言模型卓越的数据分析和逻辑推理能力为实时交通管理和辅助决策提供了新思路。ChatGPT能够高效地处理和分析公开的社交媒体数据，检测数据中包含的城市、道路信息及发生的交通事件，可用于辅助交通管理人员进行实时询问、追溯起因以及探讨应对措施。构建了一个融合社交媒体与ChatGPT的智能问答框架TMGPT（traffic management GPT），研究如何基于大语言模型进行交通异常事件的快速检测并辅助交通管理部门进行决策。该框架通过对社交媒体数据的获取、处理与分析，精准检测交通异常事件并生成针对性应对策略。该框架通过反馈机制持续优化系统性能，为交通管理和政策制定部门提供决策依据，从而提升城市交通运行效率和安全性。实验结果表明，相较于传统方法，TMGPT框架显著提高了交通异常事件检测的准确率并缩短了响应时间，展现出大语言模型在复杂城市交通管理中的应用潜力。

Abstract

The superior data analytics and logical reasoning capabilities of big language models provide new ideas for real-time traffic management and assisted decision-making. ChatGPT efficiently processes and analyzes publicly available social media data to detect city and roadway information and traffic events contained in the data

which can be used to assist traffic managers in making real-time inquiries

tracing causes and exploring countermeasures. This paper constructs an intelligent Q&A framework

TMGPT (traffic management GPT)

which integrates social media data with ChatGPT

to explore how large language models can be leveraged to quickly detect traffic anomalies and provide decision support for traffic management departments. Through the acquisition

processing and analysis of social media data

the framework achieves accurate detection of traffic anomalies and the generation of targeted response strategies

and continuously optimizes the system performance through the feedback mechanism

providing a decision basis for traffic management and policy-making departments to improve the efficiency and safety of urban traffic operation. The results show that compared with traditional methods

TMGPT significantly improves the accuracy of detection and reduced response time in the detection and assisted decision-making of abnormal traffic events

which demonstrates the application potential of large language models in complex urban traffic management.

关键词

Keywords

references

WANG X, TANG K, DAI X Y, et al. S4TP: social-suitable and safety-sensitive trajectory planning for autonomous vehicles[J]. IEEE Transactions on Intelligent Vehicles, 2024, 9(2): 3220-3231.

郑治豪, 吴文兵, 陈鑫, 等. 基于社交媒体大数据的交通感知分析系统[J]. 自动化学报, 2018, 44(4): 656-666.

ZHENG Z H, WU W B, CHEN X, et al. A traffic sensing and analyzing system using social media data[J]. Acta Automatica Sinica, 2018, 44(4): 656-666.

郑南宁. 人工智能新时代[J]. 智能科学与技术学报, 2019, 1(1): 1-3.

ZHENG N N. The new era of artificial intelligence[J]. Chinese Journal of Intelligent Science and Technology, 2019, 1(1): 1-3.

RADFORD A, NARASIMHAN K, SALIMANS T, et al. Improving language understanding by generative pre-training[J]. Computer Science, 2018: 1-12.

王飞跃, 缪青海, 张军平, 等. 探讨AI for Science的影响与意义: 现状与展望[J]. 智能科学与技术学报, 2023, 5(1): 1-6.

WANG F Y, MIAO Q H, ZHANG J P, et al. The DAOs to AI for Science by DeSci: the state of the art and perspective[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(1): 1-6.

FUTTERER T, FISCHER C, ALEKSEEVA A, et al. ChatGPT in education: global reactions to AI innovations[J]. Scientific Reports, 2023, 13: 15310.

BAIDOO-ANU D, OWUSU ANSAH L. Education in the era of generative artificial intelligence(AI): understanding the potential benefits of ChatGPT in promoting teaching and learning[J]. SSRN Electronic Journal. 2023, 7(1): 52-62.

ZHANG X Y, YANG Q, XU D L. XuanYuan 2.0: a large Chinese financial chat model with hundreds of billions parameters[EB ] . arXiv preprint, 2023, arXiv: 2305.12002 .

XU C W, GUO D Y, DUAN N, et al. Baize: an open-source chat model with parameter-efficient tuning on self-chat data[EB ] . arXiv preprint, 2023, arXiv: 2304.01196 .

WU C F, YIN S M, QI W Z, et al. Visual ChatGpt: talking, drawing and editing with visual foundation models[EB ] . arXiv preprint, 2023, arXiv: 2303.04671 .

GAO Y B, TONG W, WU E Q, et al. Chat with ChatGPT on interactive engines for intelligent driving[J]. IEEE Transactions on Intelligent Vehicles, 2023, 8(3): 2034-2036.

WANG X, HUANG J, TIAN Y L,et al. Parallel driving with big models and foundation intelligence in cyber-physical-social spaces[J]. Research, 2024, 7: 349.

WAKE N, KANEHIRA A, SASABUCHI K, et al. ChatGPT empowered long-step robot control in various environments: a case application[J]. IEEE Access, 2023, 11: 95060-95078.

LI X, LIU E L, SHEN T Y, et al. ChatGPT-based scenario engineer: a new framework on scenario generation for trajectory prediction[J]. IEEE Transactions on Intelligent Vehicles, 2024, 9(3): 1-10.

VILLARREAL M, POUDEL B, LI W Z. Can ChatGPT enable ITS? the case of mixed traffic control via reinforcement learning[EB ] . arXiv preprint, 2023, arXiv: 2306.08094 .

DA L C, GAO M, MEI H, et al. LLM powered sim-to-real transfer for traffic signal control[EB ] . arXiv preprint, 2023, arXiv: 2308.14284 .

ZHENG O, ABDEL-ATY M, WANG D D, et al. ChatGPT is on the horizon: could a large language model be suitable for intelligent traffic safety research and applications? [EB ] . arXiv preprint, 2023, arXiv: 2303.05382 .

邴其春, 龚勃文, 林赐云, 等. 城市快速路交通事件自动检测算法[J]. 中南大学学报（自然科学版）, 2017, 48(6): 1682-1687.

BING Q C, GONG B W, LIN C Y, et al. Traffic incident automatic detection algorithm for urban expressway[J]. Journal of Central South University (Science and Technology), 2017, 48(6): 1682-1687.

万福才, 尹承祥, 韩晓微, 等. 城市道路交通异常事件自动检测方法[J]. 沈阳大学学报（自然科学版）, 2015, 27(1): 30-33, 43.

WAN F C, YIN C X, HAN X W, et al. Automatic detection method of abnormal events in city road traffic[J]. Journal of Shenyang University (Natural Science), 2015, 27(1): 30-33, 43.

陈苑文, 王晓, 李灵犀, 等. 基于社会媒体数据增强的交通态势感知研究及进展[J]. 智能科学与技术学报, 2022, 4(1): 1-13.

CHEN Y W, WANG X, LI L X, et al. Traffic situational awareness research and development enhanced by social media data: the state of the art and prospects[J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(1): 1-13.

CHANG H L, LI L S, HUANG J X, et al. Tracking traffic congestion and accidents using social media data: a case study of Shanghai[J]. Accident Analysis and Prevention, 2022, 169: 106618.

陈虹宇, 艾红, 王晓, 等. 社会交通中的社会信号分析与感知[J]. 自动化学报, 2021, 47(6): 1256-1272.

CHEN H Y, AI H, WANG X, et al. Analysis and perception of social signals in social transportation Acta Automatica Sinica, 2021, 47(6): 1256-1272.

XU S S, LI S N, HUANG W, et al. Detecting spatiotemporal traffic events using geosocial media data[J]. Computers, Environment and Urban Systems, 2022, 94: 101797.

ALI F, ALI A, IMRAN M, et al. Traffic accident detection and condition analysis based on social networking data[J]. Accident Analysis and Prevention, 2021, 151: 105973.

JONNALAGADDA J, HASHEMI M. A deep learning-based traffic event detection from social media[C]//Proceedings of 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI). Piscataway: IEEE Press, 2021: 1-8.

LU J W, GUO C, DAI X Y, et al. The ChatGPT after: opportunities and challenges of very large scale pre-trained models[J]. Acta Automatica Sinica, 2023, 49(4): 705-717.

CHEN Y Y, LYU Y S, WANG X, et al. Detecting traffic information from social media texts with deep learning approaches[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(8): 3049-3058.

蒋伟进, 王扬. 基于词相关性特征的社交网络突发事件检测方法[J].软件学报, 已录用, doi: 10.13328/j.cnki.jos.006351.

JIANG W J, WANG Y. Social network emergency detection method based on word correlation characteristics[J]. Journal of Software, accepted, doi: 10.13328/j.cnki.jos.006351.

WANG X, WANG Y T, YANG J, et al. The survey on multi-source data fusion in cyber-physical-social systems: foundational infrastructure for industrial metaverses and industries 5.0[J]. Information Fusion, 2024, 107: 102321.

李亚玲, 覃缘琪, 魏阙. 人工智能生成内容的潜在风险及治理对策[J]. 智能科学与技术学报, 2023, 5(3): 415-423.

LI Y L, QIN Y Q, WEI Q. Potential risks and governance strategies of artificial intelligence generated content technology[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(3): 415-423.

王晓, 张翔宇, 周锐, 等. 基于平行测试的认知自动驾驶智能架构研究[J]. 自动化学报, 2024, 50(2): 356-371.

WANG X, ZHANG X Y, ZHOU R, et al. An intelligent architecture for cognitive autonomous driving based on parallel testing[J]. Acta Automatica Sinica, 2024, 50(2): 356-371.

WANG X, TANG K, DAI X Y, et al. Safety-balanced driving-style aware trajectory planning in intersection scenarios with uncertain environment[J]. IEEE Transactions on Intelligent Vehicles, 2023, 8(4): 2888-2898.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

平行智能数字警察构建平行安全新格局：从平行警务到平安中国

数字教师与平行教育：关于ChatGPT之后教学变革的探讨

人工智能生成内容的潜在风险及治理对策

基于社会媒体数据增强的交通态势感知研究及进展

Cerberus：基于深度学习的跨站社交网络机器人检测系统