Toward multi-granularity spatiotemporal simulation modeling of crowd movement for dynamic assessment of tourist carrying capacity

Abstract

Dynamic process simulation and prediction of crowd movement are effective approaches to understanding the complex human behavior system in GIScience. At present, obtaining full-sample individual trajectory data still faces challenges because of privacy and cost constraints, thereby resulting in difficulty solving geographic modeling problems that require full-sample individual data. In this paper, a general model for crowd movement simulation is proposed by taking the dynamic evaluation of tourist carrying capacity as an example. Such method is a multi-granularity coupling model, which considers behavioral process and spatiotemporal heterogeneity of tourists. First, a secrete event-based logic model of tourist behavior is proposed. Second, a social force-based inference method of tourist path is designed. Finally, the simulation and evaluation model of remaining spatial carrying capacity of tourists based on a behavioral dynamic system is achieved. In addition, the correctness and applicability of the model are demonstrated through a case study. The proposed model will positively affect time- and space-sharing analysis and assessment of crowd flow within a specific area of activity.