A Virtual Reality and Questionnaire Approach to Gathering Real-World Data for Agent-Based Crowd Simulation Models

Abstract

Abstract Gathering real-world data is a crucial process in developing realistic, agent-based crowd simulation models. In order to gather real-world data, three types of data need to be considered: physical, mental, and visual. Existing data gathering methods do not collect all three data types, but they provide a limited amount of data for agent-based simulations. This article proposes using a combination of Virtual Reality and Questionnaires as a means to gathering real-world data. This hybrid method collects all three data types and is validated by comparing it to data collected from the real world. Two data gathering experiments (real world and our proposed method) were conducted to collect all three types of data for comparison. Experimental results show that the proposed method can collect similar data to the real-world experiment, in particular for mental and visual data. The Chi-Square Goodness-of-Fit Test proves that there is no significant difference between the real world and our proposed method for mental and visual data, whilst the test shows there is significant difference in physical data, in particular, completed time. We propose an adjustment factor for the completed time data that mitigates the gap between virtual space and real space, and allows the results collected to be input into agent-based simulations as real-world data. Overall, the proposed method is cost effective, time efficient, reproducible, ecologically valid, and able to collect three types of data for agent-based crowd simulation models.