Abstract
Recently, people flow information has become necessary to mitigate secondary disasters following earthquakes, fires, or other major events, and to improve congestion at railway stations, roads, and public spaces. With the fast development of information technologies, nowadays the collection of people flow data becomes much easier and we can have different kinds of measurement data, such as train use data gotten by IC card, high way use data gotten by Electronic Toll Collection System, and so on. However, most of them have been used separately. In this research, we are trying to estimate people flow in an urban area by combining these different kinds of observation data together to make a more accurate estimation about people, based on data assimilation techniques. We propose an algorithm using Particle Filters for data assimilation of people flow data and estimate people flow in Tokyo metropolitan area, assuming that we can get the number of people who ride or drop trains at each station as observations and the number of people who use each main road in Tokyo metropolitan area. In this algorithm, we make a people flow estimation model from Person Trip Data in Tokyo metropolitan area, the actual people flow data gotten by 3 percent people of the area with questionnaires, and particles are made by this model. We evaluate the particles by the assumed observations. For the validation, we assume that only people who are included in Person Trip Data are in Kanto urban area and regard Person Trip Data as the complete people flow. We then select 3 percent of this to make the probabilistic people flow estimation model and estimate people flow of the assumed Kanto urban area. This assumption makes us possible to verify the estimation by comparing it with Person Trip Data.
