Data assimilation approach for addressing incompleteness in pedestrian flow measurement techniques using particle filter

Abstract

Understanding the dynamics of pedestrian flow in urban areas is crucial for decision-making in urban planning and marketing strategies. Previous methods for analyzing pedestrian flow can be divided into data-driven approaches and simulation-driven approaches. While data-driven approaches effectively capture actual patterns of pedestrian flow, they face the challenge of data incompleteness. On the other hand, simulation-driven approaches can generate complete data on a computer, but they only consider some of the factors determining human behavior, resulting in deviations from actual pedestrian flow. Each approach has its own limitations, yet combining them can mutually resolve these shortcomings. This paper proposes a method that applies data assimilation, a fusion technique of data-driven and simulation-driven approaches, to agent-based simulation. Combining these approaches allows for the collection of more comprehensive pedestrian flow data that better represents real-world human behavior. We conducted an evaluation experiment to assess the effectiveness of the proposed method in addressing three types of incompleteness in pedestrian flow data. The results indicate that the proposed method can effectively address data incompleteness. These findings provide guidelines for supplementing sparse measurement data in real-world environments.