基于数字孪生的城市交通流智能预测与导引策略

廖熙雯; 冷甦鹏; 明昱君; 李天扬

doi:10.11959/j.issn.1000-0801.2023047

您当前的位置：

首页 >

文章列表页 >

基于数字孪生的城市交通流智能预测与导引策略

研究与开发 | 更新时间：2024-06-05

- 基于数字孪生的城市交通流智能预测与导引策略
- Digital twin based intelligent urban traffic forecasting and guidance strategy
- 电信科学 2023年39卷第3期页码：70-79
- 作者机构：
- 作者简介：
  
  [ "廖熙雯（1998- ），女，电子科技大学信息与通信工程学院博士生，主要研究方向为无线通信、交通路网与车联网协同优化" ]
  [ "冷甦鹏（1973- ），男，电子科技大学教授、博士生导师，主要研究方向为物联网、车联网、新一代宽带无线网络、无线自组织网、智能交通信息系统的资源管理、介质访问控制、路由、组网与互联、智能算法理论及技术应用等" ]
  [ "明昱君（1999- ），女，电子科技大学信息与通信工程学院硕士生，主要研究方向为无线网络及智能优化、交通路网与车联网" ]
  [ "李天扬（1997- ），女，电子科技大学信息与通信工程学院博士生，主要研究方向为无人机网络" ]
- 基金信息：
  
  国家重点研发计划项目;The National Key Research and Development Program of China(2018YFE0117500);国家自然科学基金资助项目;The National Natural Science Foundation of China(62171104)
- DOI：10.11959/j.issn.1000-0801.2023047
  中图分类号：
- 网络出版日期：2023-03，
  
  纸质出版日期：2023-03-20
- 稿件说明：
移动端阅览
廖熙雯, 冷甦鹏, 明昱君, 等. 基于数字孪生的城市交通流智能预测与导引策略[J]. 电信科学, 2023,39(3):70-79.

Xiwen LIAO, Supeng LENG, Yujun MING, et al. Digital twin based intelligent urban traffic forecasting and guidance strategy[J]. Telecommunications science, 2023, 39(3): 70-79.
廖熙雯, 冷甦鹏, 明昱君, 等. 基于数字孪生的城市交通流智能预测与导引策略[J]. 电信科学, 2023,39(3):70-79. DOI： 10.11959/j.issn.1000-0801.2023047.

Xiwen LIAO, Supeng LENG, Yujun MING, et al. Digital twin based intelligent urban traffic forecasting and guidance strategy[J]. Telecommunications science, 2023, 39(3): 70-79. DOI： 10.11959/j.issn.1000-0801.2023047.

摘要

物联网和人工智能等信息技术的快速发展极大地推动了交通系统的变革，同样也带来了机遇与挑战。针对现有导航系统忽略交通流时空特征而产生的策略重复性拥堵问题，对宏观交通流和微观车辆驾驶分别建模，并挖掘其耦合关系，进而提出一种基于数字孪生的城市智能交通流分层预测与导引策略，为减缓交通拥堵提供新思路。在该策略中，虚拟空间中的上层道路孪生通过扩散卷积递归神经网络预测时空交通流量，并显式作用于车辆路径规划决策。在此基础上，提出一种时空协同深度强化学习方法，用于实现车辆面向未来的协作式路径规划，指导虚拟空间中的下层车辆孪生选出最优策略反馈于真实世界。基于SUMO仿真平台进行了仿真验证。实验结果表明，本文所提方法在提高出行达成率、缓解拥堵等方面显著优于现有算法，能够有效提升城市交通出行效率。

Abstract

As the technology of ubiquitous Internet of things and artificial intelligence improves by leaps and bounds

the transportation system revolution is flourishing and bringing new opportunities and challenges.Considering the defect in the existing navigation system

and the neglect of the temporal and spatial characteristics of traffic flow

the macro traffic network and micro vehicle network were modeled and their coupling relationship was mined.Then

a digital twin based urban traffic forecasting and guidance method was proposed to alleviate the problem of traffic congestion.The spatial-temporal traffic flow information was predicted through the diffusion convolution recurrent neural network

which was explicitly applied to the vehicle path planning decision.On this basis

a spatial-temporal collaborative deep reinforcement learning method was proposed to implement the future-oriented collaborative path planning of vehicles.It also guided the underlying vehicle twins to select the optimal strategy for the real world.With SUMO for simulation verification

the experimental results show that the proposed method is significantly better than the existing algorithms in improving the travel completion ratio and congestion relief

and can improve the efficiency of urban traffic travel.

关键词

Keywords

references

齐旭 . 车路协同系统：解决交通拥堵是目的 [N ] . 中国电子报 , 2021 - 10 - 01 (7).

QI X . Vehicle-road collaboration system:solving traffic congestion is the goal [N ] . China Electronics News , 2021 - 10 - 01 (7).

RAHIMI-FARAHANI H , RASSAFI A A , MIRBAHA B . Forced-node route guidance system:incorporating both user equilibrium and system optimal benefits [J ] . IET Intelligent Transport Systems , 2019 , 13 ( 12 ): 1851 - 1859 .

LI Y , YU R , SHAHABI C , et al . Diffusion convolutional recurrent neural network:Data-driven traffic forecasting [J ] . arXiv preprint arXiv:1707.01926 , 2017 .

GUO S N , LIN Y F , FENG N , et al . Attention based spatial-temporal graph convolutional networks for traffic flow forecasting [C ] // Proceedings of the AAAI Conference on Artificial Intelligence [S.l.:s.n. ] , 2019 : 922 - 929 .

JIANG M R , CHEN W , LI X . S-GCN-GRU-NN:a novel hybrid model by combining a spatiotemporal graph convolutional network and a gated recurrent units neural network for short-term traffic speed forecasting [J ] . Journal of Data,Information and Management , 2021 , 3 ( 1 ): 1 - 20 .

PIASCIK R , VICKERS J , LOWRY D , et al . Technology area 12:materials,structures,mechanical systems,and manufacturing road map [J ] . NASA Office of Chief Technologist , 2010 : 15 - 88 .

BRUNNER P , DENK F , HUBER W , et al . Virtual safety performance assessment for automated driving in complex urban traffic scenarios [C ] // Proceedings of 2019 IEEE Intelligent Transportation Systems Conference (ITSC) . Piscataway:IEEE Press , 2019 : 679 - 685 .

HU C , FAN W C , ZENG E , et al . Digital twin-assisted real-time traffic data prediction method for 5g-enabled Internet of vehicles [J ] . IEEE Transactions on Industrial Informatics , 2022 , 18 ( 4 ): 2811 - 2819 .

ALAM K M , EL SADDIK A . C2PS:a digital twin architecture reference model for the cloud-based cyber-physical systems [J ] . IEEE Access , 2017 ( 5 ): 2050 - 2062 .

DU W J , ZHANG T F , ZHANG G G , et al . A digital twin framework and an implementation method for urban rail transit [C ] // Proceedings of 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) . Piscataway:IEEE Press , 2021 : 1 - 4 .

ZHAO L , HAN G J , LI Z H , et al . Intelligent digital twin-based software-defined vehicular networks [J ] . IEEE Network , 2020 , 34 ( 5 ): 178 - 184 .

PAN Y H , WU N Q , QU T , et al . Digital-twin-driven production logistics synchronization system for vehicle routing problems with pick-up and delivery in industrial park [J ] . International Journal of Computer Integrated Manufacturing , 2021 , 34 ( 7/8 ): 814 - 828 .

曹佳钰 , 冷甦鹏 , 张科 . 面向自动驾驶应用的车联多智能体信息融合协同决策机制研究 [J ] . 物联网学报 , 2020 , 4 ( 3 ): 69 - 77 .

CAO J Y , LENG S P , ZHANG K . Multi-agent driven collaborative decision mechanism of information fusion for autonomous driving vehicles [J ] . Chinese Journal on Internet of Things , 2020 , 4 ( 3 ): 69 - 77 .

詹全 . 闭环数字孪生智慧交通管理系统 [J ] . 中国交通信息化 , 2021 ( 10 ): 107 - 109 .

ZHAN Q . Closed-loop digital twin intelligent traffic management system [J ] . China ITS Journal , 2021 ( 10 ): 107 - 109 .

康宇 , 刘雅琼 , 赵彤雨 , 等 . AI 算法在车联网通信与计算中的应用综述 [J ] . 电信科学 , 2023 , 39 ( 1 ): 1 - 19 .

KANG Y , LIU Y Q , ZHAO T Y , et al . A survey on AI algorithms applied in communication and computation in Internet of vehicles [J ] . Telecommunications Science , 2023 , 39 ( 1 ): 1 - 19 .

ROZEMBERCZKI B , SCHERER P , HE Y X , et al . PyTorch geometric temporal:spatiotemporal signal processing with neural machine learning models [C ] // Proceedings of the 30th ACM International Conference on Information ＆ Knowledge Management . New York:ACM Press , 2021 : 4564 - 4573 .

NAHUM-SHANI I , QIAN M , ALMIRALL D , et al . Q-learning:a data analysis method for constructing adaptive interventions [J ] . Psychological Methods , 2012 , 17 ( 4 ): 478 - 494 .

LOWE R , WU Y , TAMAR A , et al . Multi-agent actor-critic for mixed cooperative-competitive environments [C ] // Proceedings of the 31st International Conference on Neural Information Processing Systems . New York:ACM Press , 2017 : 6382 - 6393 .

MNIH V , KAVUKCUOGLU K , SILVER D , et al . Human-level control through deep reinforcement learning [J ] . Nature , 2015 , 518 ( 7540 ): 529 - 533 .

唐慕尧 , 周大可 , 李涛 . 结合状态预测的深度强化学习交通信号控制 [J ] . 计算机应用研究 , 2022 : 1 - 6 .doi:10.19734/j.issn.1001-3695.2021.12.0704.

TANG M Y , ZHOU D K , LI T . Deep reinforcement learning traffic signal control combined with state prediction [J ] . Application Research of Computers , 2022 : 1 - 6 .doi:10.19734/j.issn.1001-3695.2021.12.0704.

PENG Z , LI Q , HUI K M , et al . Learning to simulate self- driven particles system with coordinated policy optimization [J ] . Advances in Neural Information Processing Systems , 2021 ( 34 ): 10784 - 10797 .

LOPEZ P A , BEHRISCH M , BIEKER-WALZ L , et al . Microscopic traffic simulation using SUMO [C ] // Proceedings of 2018 21st International Conference on Intelligent Transportation Systems (ITSC) . Piscataway:IEEE Press , 2018 : 2575 - 2582 .

浏览量

552

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于生成对抗网络的超宽带数字信道建模

基于虚拟网络嵌入的异构算力网络资源管理

5G承载网数字孪生典型场景与应用方案探索

SDCN中基于深度强化学习的移动边缘计算任务卸载算法研究

算网资源智能适配与融合调度方法