Social signal-driven knowledge automation: A focus on social transportation

Article

Lu, Hao, Zhu, Yifan, Yuan, Yong, Gong, Weichao, Li, Juanjuan, Shi, Kaize, Lv, Yisheng, Niu, Zhendong and Wang, Fei-Yue. 2021. "Social signal-driven knowledge automation: A focus on social transportation." IEEE Transactions on Computational Social Systems. 8 (3), pp. 737-753. https://doi.org/10.1109/TCSS.2021.3057332
Article Title

Social signal-driven knowledge automation: A focus on social transportation

ERA Journal ID212762
Article CategoryArticle
AuthorsLu, Hao, Zhu, Yifan, Yuan, Yong, Gong, Weichao, Li, Juanjuan, Shi, Kaize, Lv, Yisheng, Niu, Zhendong and Wang, Fei-Yue
Journal TitleIEEE Transactions on Computational Social Systems
Journal Citation8 (3), pp. 737-753
Number of Pages17
Year2021
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN2329-924X
Digital Object Identifier (DOI)https://doi.org/10.1109/TCSS.2021.3057332
Web Address (URL)https://ieeexplore.ieee.org/document/9374560
Abstract

Urban transportation systems are shaped by factors that include people, vehicles, roads, and the environment, forming a complex and giant system with dynamics, diversity, and uncertainty. Physical signal-driven intelligent transportation systems (ITSs) typically lack the ability to capture social behaviors or crowd willingness, and they achieve only information automation for transportation decision support. The crowdsourcing social signals consist of timely, extensive, comprehensive, and rich intelligence that concern urban dynamics, social behaviors, and traffic environments. Such social signals provide a new paradigm for operating ITS with unstructured semantic data, making knowledge automation for decision intelligence a possibility. This article reviews the knowledge automation paradigms for cyber-physical-social systems (CPSSs) compared with traditional information automation paradigms for cyber-physical systems (CPSs) in ITS, from the perspective of data-driven, modeling space, analytical methodologies, and decision support services. To investigate the key methodology in social spaces that enhance information automation into knowledge automation, we summarize the current research into a multisource heterogeneous social signal-based traffic decision knowledge automation framework and further exploit the computational paradigm and applications scenarios of this framework. Finally, we discuss future challenges for designing and realizing knowledge automation on CPSS in transportation.

Keywordscyber–physical–social systems (CPSSs); decision intelligence; intelligent transportation systems (ITSs); knowledge automation; social transportation
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
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Byline AffiliationsChinese Academy of Sciences, China
Beijing Institute of Technology, China
Renmin University of China, China
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