Optimized Multicar Dynamic Route Planning Based on Time-Hierarchical Graph Model
文献类型:期刊论文
作者 | Song, Qing5; Li, Xiaolei6; Gao, Chao1; Shen, Zhen4![]() ![]() |
刊名 | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
![]() |
出版日期 | 2023-08-21 |
页码 | 15 |
关键词 | Automobiles Planning Roads Vehicles Optimization Computer architecture Real-time systems |
ISSN号 | 1939-1390 |
DOI | 10.1109/MITS.2023.3302330 |
通讯作者 | Li, Xiaolei(qylxl@sdu.edu.cn) |
英文摘要 | Traffic congestion has become a major concern in most cities all over the world. The proper guidance of cars with an effective route planning method has become a fundamental and smart way to alleviate congestion under existing urban road facilities. Current route planning methods mainly focus on a single car, but ignoring the dynamic effect between cars may lead to severe congestion during the actual driving guidance. In this article, we extend the study of route planning to the case of multiple cars and present a novel multicar shortest travel-time routing problem. The objective is to minimize the average travel time by considering the dynamic effect of the induced traffic congestion on travel speed, while ensuring that each car's travel distance is within an acceptable range. We construct a time-hierarchical graph model for structuring the spatiotemporal dynamic properties of the urban road network and then develop a two-level multicar route planning optimization method for complex problem solving. The experimental results show that our path recommendations reduce the average travel time by 51.74% and 38.87% on average compared to two representative methods. Our research will become more important in the years ahead as self-driving cars become more commonplace. |
WOS关键词 | PATHS ; NETWORKS |
资助项目 | National Natural Science Foundation of China[U19B2029] ; National Natural Science Foundation of China[U1909204] ; National Natural Science Foundation of China[92267103] ; Open Research Fund Program of the Key Laboratory of Industrial Internet and Big Data, China National Light Industry, Beijing Technology and Business University ; Guangdong Basic and Applied Basic Research[2021B1515140034] ; China Academy of Railway Sciences Corporation[RITS2021KF03] ; China State Railway Group Co., Ltd.[L2022X002] ; Key-Area Research and Development Program of Guangdong Province[2020B0909050001] |
WOS研究方向 | Engineering ; Transportation |
语种 | 英语 |
WOS记录号 | WOS:001063649400001 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
资助机构 | National Natural Science Foundation of China ; Open Research Fund Program of the Key Laboratory of Industrial Internet and Big Data, China National Light Industry, Beijing Technology and Business University ; Guangdong Basic and Applied Basic Research ; China Academy of Railway Sciences Corporation ; China State Railway Group Co., Ltd. ; Key-Area Research and Development Program of Guangdong Province |
源URL | [http://ir.ia.ac.cn/handle/173211/53173] ![]() |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Li, Xiaolei |
作者单位 | 1.Beijing Technol & Business Univ, Key Lab Ind Internet & Big Data, China Natl Light Ind, Beijing 100048, Peoples R China 2.Chinese Acad Sci, Cloud Comp Ctr, Dongguan 523808, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing Engn Res Ctr Intelligent Syst & Technol, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 5.Univ Jinan, Sch Elect Engn, Jinan 250022, Peoples R China 6.Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Peoples R China |
推荐引用方式 GB/T 7714 | Song, Qing,Li, Xiaolei,Gao, Chao,et al. Optimized Multicar Dynamic Route Planning Based on Time-Hierarchical Graph Model[J]. IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE,2023:15. |
APA | Song, Qing,Li, Xiaolei,Gao, Chao,Shen, Zhen,&Xiong, Gang.(2023).Optimized Multicar Dynamic Route Planning Based on Time-Hierarchical Graph Model.IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE,15. |
MLA | Song, Qing,et al."Optimized Multicar Dynamic Route Planning Based on Time-Hierarchical Graph Model".IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE (2023):15. |
入库方式: OAI收割
来源:自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。