Abstract
The gathering of crowd traffic data either from videos or from visual observation has different uses. In the social simulation context, one of them is validating crowd behavior models and match the resulting traffic in control points with the real ones. When such models have been already validated, the immediate use can be aiding managers of facilities to infer, from real time data, what crowd behavior they should expect in their facilities. However, the transformation of those measurements into actual behavior patterns has not been satisfactorily addressed in the literature. In particular, most papers take into account a single measurement point. This paper contributes with an algorithm that produces possible populations that reproduces real traffic data obtained from multiple measurement locations. The algorithm has been validated against data obtained in a real field experiment.
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Pax, R., Gómez-Sanz, J.J. (2016). A Greedy Algorithm for Reproducing Crowds. In: de la Prieta, F., et al. Trends in Practical Applications of Scalable Multi-Agent Systems, the PAAMS Collection. PAAMS 2016. Advances in Intelligent Systems and Computing, vol 473. Springer, Cham. https://doi.org/10.1007/978-3-319-40159-1_24
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DOI: https://doi.org/10.1007/978-3-319-40159-1_24
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