Data assimilation approach for addressing imperfections in people flow measurement techniques using particle filter

TL;DR

Data assimilation is applied to address imperfections in people flow measurement by fusing agent-based simulations with particle filtering. The method defines per-agent location as the state and per-location inflow counts as observations, using a particle filter to adjust simulated roaming to reflect observed trends. Experiments in a virtual 18-store facility show that incorporating attribute information and partial transition sequences improves estimation of transition tendencies and reduces the discrepancy in origin-destination patterns. The work demonstrates the potential of data assimilation to compensate for missing data, limited attribute information, and biased sampling in urban mobility measurements.

Abstract

Understanding and predicting people flow in urban areas is useful for decision-making in urban planning and marketing strategies. Traditional methods for understanding people flow can be divided into measurement-based approaches and simulation-based approaches. Measurement-based approaches have the advantage of directly capturing actual people flow, but they face the challenge of data imperfection. On the other hand, simulations can obtain complete data on a computer, but they only consider some of the factors determining human behavior, leading to a divergence from actual people flow. Both measurement and simulation methods have unresolved issues, and combining the two can complementarily overcome them. This paper proposes a method that applies data assimilation, a fusion technique of measurement and simulation, to agent-based simulation. Data assimilation combines the advantages of both measurement and simulation, contributing to the creation of an environment that can reflect real people flow while acquiring richer data. The paper verifies the effectiveness of the proposed method in a virtual environment and demonstrates the potential of data assimilation to compensate for the three types of imperfection in people flow measurement techniques. These findings can serve as guidelines for supplementing sparse measurement data in physical environments.

Figures (6)

• Figure 1: The basic process of particle filtering. Initially, a large number of particles are generated for each entity, with the model projecting the state for the next step. The weight of each particle is determined by the likelihood calculated from observational data. A resampling process based on this likelihood is then conducted to establish the distribution of the predicted state for the next step.
• Figure 2: The application of particle filtering in agent-based simulation. Particles are generated for each agent, and the model predicts their next location. Each particle's likelihood is determined based on anonymous inflow count data, and the next transition location is determined through resampling based on this likelihood.
• Figure 3: Comparison of observational and simulated transition tendencies in case 1. (a) represents observational values of the OD (Origin-Destination) matrix, and (b) shows the estimated values based on data assimilation. The axis values represent store numbers, with the vertical axis indicating the store of departure and the horizontal axis indicating the arrival store. (c) compares the top 20 most frequent three-store transition sequences observed and estimated. The horizontal axis numbers represent the ranking of transition frequency, and the vertical axis shows their frequencies.
• Figure 4: Comparison of the observational and simulated OD matrices for each attribute (from (a) to (d)), along with the combined OD matrix (e), in Case 2. The OD matrices from (a) to (d) are derived from data assimilation, utilizing inflow count data that can distinguish between attributes.
• Figure 5: Comparison of observational OD matrix (a) and the estimated OD matrices when data assimilation was performed (b) and not performed (c) in case 3.