Multi Agent Flow Estimation Based on Bayesian Optimization with Time Delay and Low Dimensional Parameter Conversion

Abstract

Forming comprehensive security plans is essential to ensure safety at large events. The Multi Agent Simulator (MAS) is widely used for preparing security plans that will guide responses to accidents at large events. For forming security plans, it is necessary that we simulate crowd behaviors that reflect real world observations. However, crowd behavior simulations require OD information (departure time, place of Origin, and Destination) of each agent. Moreover, from the viewpoint of protecting personal information, it is difficult to observe the complete and detailed trajectories of all pedestrians. Therefore, the OD information should be estimated from the data observed at several points, usually the number of people passing fixed points. In this paper, we propose an accurate method for estimating OD information; it has three features. First, by using Bayesian optimization (BO) which is widely used to find optimal hyper parameters in the machine learning fields, the OD information is efficiently and accurately estimated using fewer parameter searches. Second, by dividing the time window and ignoring the identity of the observed people, the parameter dimension of the OD information is reduced to yield a solvable search space. Third, by considering the time delay created by the physical separation of the observation points, we develop a more accurate objective function. Experiments evaluate the proposed method using the data collected at three events (University festival, projection-mapping event, and music live), and the accuracy with which reproduction MAS can reproduce the people flows is assessed. We also show an example of the MAS-based process used in making guidance plans to reduce crowd congestion.