Abstract
This study proposes a modeling framework based on few radio frequency (RF) tracking devices, such as smartphones or beacon. The proposed framework aims to estimate crowd density continuously where vision analysis is unreliable and the relation between pedestrian speed and density can be at least specified. The crowd density estimated through the modelling framework can not only be used for evacuation commanding at emergency times, but also can be used for commercial usage at a normal time and building/facility layout improvement during design time. In the proposed framework, the application level maps input data spaces into feature spaces. The model level applies multiple data models to increase the accuracy of the estimated states. Moreover, the abstract level fuses the heterogeneous parameters estimated from the model level. The models we included in the framework are cellular automata models, ferromagnetic models, social force models, and complexity models. The model parameters are estimated by Markov Chain Monte Carlo (MCMC) and particle swarm optimization (PSO) methods. The fusion algorithm factory instantiates a data assimilation approach and a continuous receiver operating characteristic (ROC) estimator.
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Chen, Y.J., Yang, A.JF. (2017). Crowd Density Estimation from Few Radio-Frequency Tracking Devices: I. A Modelling Framework. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_39
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DOI: https://doi.org/10.1007/978-3-319-61845-6_39
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