ASSIMILATING OF THE ACTIVITY-BASED SIMULATION AND OBSERVATION DATA IN A TOKYO METROPOLITAN AREA

Abstract

In recent years, social changes have accelerated due to the advancement of technology and the promotion of diverse work styles. In light of these circumstances, it is necessary to consider the aggregate amount of human behavior using the four-step estimation method as in the past and understand the behavioral characteristics of individuals and consider the actual situation in society. In this study, we proposed an estimation method that enables more accurate policy evaluation and demand forecasting at a lower cost, using activity simulations that can reproduce an individual's daily activities and take into account the linkage of trips, and observation data of traffic conditions, the types and amount of which have increased dramatically in recent years. By applying this method to the Tokyo metropolitan area, the forecasting accuracy was improved and the usefulness of supplementing observation data to activity simulation was confirmed.