Dynamic management of next-generation transportation system for post-big data era

2019 Fiscal Year Final Research Report

• Project/Area Number: 16H02368
• Research Category: Grant-in-Aid for Scientific Research (A)
• Allocation Type: Single-year Grants
• Section: 一般
• Research Field: Civil engineering project/Traffic engineering
• Research Institution: Kobe University
• Principal Investigator: Iryo Takamasa 神戸大学, 工学研究科, 教授 (10362758)
• Co-Investigator(Kenkyū-buntansha): 和田 健太郎 筑波大学, システム情報系, 准教授 (20706957) ; 日下部 貴彦 東京大学, 空間情報科学研究センター, 講師 (80604610) ; 赤松 隆 東北大学, 情報科学研究科, 教授 (90262964) ; 原 祐輔 東京大学, 大学院工学系研究科(工学部), 助教 (50647683)
• Project Period (FY): 2016-04-01 – 2020-03-31
• Keywords: 交通工学 / 交通ビッグデータ / 交通マネジメント

Outline of Final Research Achievements

Transport big-data is often used to obtain various kinds of insights on transport systems and travel behaviour, but ultimately it should be used for a better management of a transport system. The purpose of this research is to construct dynamic future prediction methods based on the assumptions of dynamic data obtained by transport big-data, and to construct dynamic management methods that include them. The dynamic future prediction methods were constructed by dynamic extension based on the concept of equilibrium, which is the mainstream in existing research. As for the dynamic management methods, in consideration of the characteristics of transport big-data, we have proposed management methods by quantity management, price management, and information provision. We analysed their characteristics and confirmed the usefulness of the proposed methods.

Free Research Field

交通工学

Academic Significance and Societal Importance of the Research Achievements

本研究の成果は，交通工学分野でも普及しているビッグデータの活用方法をシステマティックに提案する．ビッグデータによる交通システムの現況理解はここ数年で多くの事例が出るようになったが，それを用いた交通システムのマネジメントの方法論は学術的にも途上である．本研究の成果は，この課題に学術的な進展を加えたという点で学術的意義が大きいほか，実務におけるデータ活用の方策を提案するという点で社会的意義も大きい．