A crowdsourcing–geocomputational framework of mobile crowd simulation and estimation

Abstract

Population estimation typically involves counting a group of individuals at a specific place and time. Estimating people on the move, i.e. a mobile population, is an intellectual challenge both in theory and in practice. The research objective is to develop a framework to estimate mobile population by simulating crowd behaviors based on crowdsourced data. The framework includes the following steps: (1) collecting and analyzing small area population movements (i.e. density and walking speed) from crowdsourced pictures and trajectories; (2) reconstructing the mobile population dynamics during a rally using a GIS; and (3) simulating the mobile population by using agent-based modeling. In this model, each individual is encoded as an agent with associated rules to define their crowd behaviors in walking, stopping, and personal spacing. Each agent moves toward the cell with next lowest distance if the occupying capacity of the cell area has not exceeded the crowd density at that time. By simulating crowd movements and behaviors, crowd size can be estimated by comparing the simulation time with observed rally time taken by the real crowd. The intellectual merits and research findings shed useful insights to improve mobile population estimation, and leverage alternative data sources to support related scientific applications.

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