Mao, TianluWang, JiMeng, RuoyuYan, QinyuanLiu, ShaohuaWang, ZhaoqiDominik L. MichelsSoeren Pirk2022-08-102022-08-1020221467-8659https://doi.org/10.1111/cgf.14630https://diglib.eg.org:443/handle/10.1111/cgf14630This paper proposes a novel crowd simulation method which integrates not only modelling ideas but also advantages from both data-driven methods and crowd dynamics methods. To seamlessly integrate these two different modelling ideas, first, a fusion crowd motion model is developed. In this model the motion of crowd are driven dynamically by different forces. Part of the forces are modeled under a universal interaction mechanism, which describe the common parts of crowd dynamics. Others are modeled by examples from real data, which describe the personality parts of the agent motion. Second, a construction method for example dataset is proposed to support the fusion model. In the dataset, crowd trajectories captured in the real world are decomposed and re-described under the structure of the fusion model. Thus, personality parts hidden in the real data could be locked and extracted, making the data understandable and migratable for our fusion model. A comprehensive crowd motion generation workflow using the fusion model and example dataset is also proposed. Quantitative and qualitative experiments and user studies are conducted. Results show that the proposed fusion crowd simulation method can generate crowd motion with the great motion fidelity, which not only match the macro characteristics of real data, but also has lots of micro personality showing the diversity of crowd motion.CCS Concepts: Computing methodologies --> Multi-agent planning; Applied computing --> Law, social and behavioral sciencesComputing methodologiesMultiagent planningApplied computingLawsocial and behavioral sciences10.1111/cgf.14630131-14212 pages