| Building Urban Public Traffic Dynamic Network Based on CPSS: An Integrated Approach of Big Data and AI | |
Xiong, Gang1,2,6 ; Li, Zhishuai1,3; Wu, Huaiyu1; Chen, Shichao1,4,5; Dong, Xisong1; Zhu, Fenghua1,2; Lv, Yisheng1
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| Source Publication | APPLIED SCIENCES-BASEL
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| 2021-02-01 | |
| Volume | 11Issue:3Pages:14 |
| Corresponding Author | Wu, Huaiyu(huaiyu.wu@ia.ac.cn) |
| Abstract | The extensive proliferation of urban transit cards and smartphones has witnessed the feasibility of the collection of citywide travel behaviors and the estimation of traffic status in real-time. In this paper, an urban public traffic dynamic network based on the cyber-physical-social system (CPSS-UPTDN) is proposed as a universal framework for advanced public transportation systems, which can optimize the urban public transportation based on big data and AI methods. Firstly, we introduce three modules and two loops which composes of the novel framework. Then, the key technologies in CPSS-UPTDN are studied, especially collecting and analyzing traffic information by big data and AI methods, and a particular implementation of CPSS-UPTDN is discussed, namely the artificial system, computational experiments, and parallel execution (ACP) method. Finally, a case study is performed. The data sources include both traffic congestion data from physical space and cellular data from social space, which can improve the prediction performance for traffic status. Furthermore, the service quality of urban public transportation can be promoted by optimizing the bus dispatching based on the parallel execution in our framework. |
| Keyword | urban public transportation cyber-physical-social system advanced public transportation systems big data |
| DOI | 10.3390/app11031109 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[61773381] ; National Natural Science Foundation of China[U1909204] ; National Natural Science Foundation of China[U1811463] ; National Natural Science Foundation of China[61872365] ; Chinese Guangdong's ST project[2019B1515120030] ; Dongguan's Innovation Talents Project |
| Funding Organization | National Natural Science Foundation of China ; Chinese Guangdong's ST project ; Dongguan's Innovation Talents Project |
| WOS Research Area | Chemistry ; Engineering ; Materials Science ; Physics |
| WOS Subject | Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied |
| WOS ID | WOS:000614973300001 |
| Publisher | MDPI |
| Sub direction classification | 人工智能+交通 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/42865 |
| Collection | 复杂系统管理与控制国家重点实验室_平行智能技术与系统团队 |
| Corresponding Author | Wu, Huaiyu |
| Affiliation | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Cloud Comp Ctr, Dongguan 523808, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China 5.Macau Univ Sci & Technol, Collaborat Lab Intelligent Sci & Syst, Macau 999078, Peoples R China 6.Zhongguancun East Rd, Beijing, Peoples R China |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Xiong, Gang,Li, Zhishuai,Wu, Huaiyu,et al. Building Urban Public Traffic Dynamic Network Based on CPSS: An Integrated Approach of Big Data and AI[J]. APPLIED SCIENCES-BASEL,2021,11(3):14. |
| APA | Xiong, Gang.,Li, Zhishuai.,Wu, Huaiyu.,Chen, Shichao.,Dong, Xisong.,...&Lv, Yisheng.(2021).Building Urban Public Traffic Dynamic Network Based on CPSS: An Integrated Approach of Big Data and AI.APPLIED SCIENCES-BASEL,11(3),14. |
| MLA | Xiong, Gang,et al."Building Urban Public Traffic Dynamic Network Based on CPSS: An Integrated Approach of Big Data and AI".APPLIED SCIENCES-BASEL 11.3(2021):14. |
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