フライヤーpdf

日時：2022年11月5日（土）13:30～11月6日（日）14:50
数理経済学会の会員でない方で参加を希望される方は、hstnk(at)waseda.jpにメールしてください。

要旨：With the rapid increase in the availability of passive data in the field of transportation, combining machine learning with transportation models has emerged as an important research topic in recent years. This study proposes an entropy Tucker model that integrates (1) a Tucker decomposition technique, i.e., an existing machine learning method, and (2) an entropy maximizing model, i.e., an existing model used for modeling trip distribution in the field of transportation. In addition, an optimization algorithm is presented to empirically identify the proposed model. The proposed model provides a solid theoretical foundation for the machine learning method, substantially improves prediction performance, and provides richer behavioral implications through empirical parameter estimation of travel impedance. We empirically apply the proposed method to evaluate the impacts of changes in the public transport fare structure on the destination choice of public transport users in Hiroshima by using the smart card data collected in Hiroshima, Japan. The estimated values of travel time range from 1.146 to 14.44 JPY/min, which is consistent with that reported in existing studies. The results of scenario analysis with different public transport fare structures suggest that identified changes in trip patterns, revenues, and users’ benefits for the public transport operator are considerably different between the conventional entropy model and the proposed entropy Tucker model. Further, we confirm that the users’ benefits vary based on the time of day. These obtained results confirm the importance of considering the heterogeneous preferences of users in economic appraisals.