期刊
ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION
卷 32, 期 1, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3481299
关键词
Crowd simulation; crowd model validation; agent-based crowd modeling; data-driven crowd modeling
资金
- National Natural Science Foundation of China [62076098]
- Guangdong Natural Science Foundation Research Team [2018B030312003]
- Fundamental Research Funds for the Central Universities [D2191200]
This article provides a comprehensive survey of popular and effective data-driven crowd modeling techniques, including commonly used datasets, discussions on different methods, validation techniques, and six promising research topics.
Data-driven crowd modeling has now become a popular and effective approach for generating realistic crowd simulation and has been applied to a range of applications, such as anomaly detection and game design. In the past decades, a number of data-driven crowd modeling techniques have been proposed, providing many options for people to generate virtual crowd simulation. This article provides a comprehensive survey of these state-of-the-art data-driven modeling techniques. We first describe the commonly used datasets for crowd modeling. Then, we categorize and discuss the state-of-the-art data-driven crowd modeling methods. After that, data-driven crowd model validation techniques are discussed. Finally, six promising future research topics of data-driven crowd modeling are discussed.
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