Service-demand-forecasting method using multiple data sources

A service-demand-forecasting method that uses multiple data sources for improved accuracy is presented in this paper. We presented a advanced scenario simulation framework to analyze each customer service choice behavior and total service demand under an assumed condition. To estimate the demand on service, especially for a new service, scenario simulation is executed based on stated preference data (SP). However, there is usually a considerable gap between an estimate based on SP and the actual outcome. Furthermore, the distribution of attributes of users collected in market surveys is not equal to that of actual market users, and there is a bias between collected data and attributes of users in the market. In this paper, we propose an adjusting method that can re-estimate demands by analyzing the difference between actual and estimated results. We consider the problem of choosing an Internet access line service to evaluate the method. An application example shows that distribution differences between areas with respect to customer attributes strongly affect actual market shares