DATA SYSTEM
Utilizing technology to analyze big data, we are conducting research on suitable transportation system relaed maritime transport, database of international logistics, and forecasting shipbuilding demand.
(◎: Head of the Group)
Utilizing detailed data concerning shipping and shipbuilding, alongside technologies like simulation and AI, the project endeavors to develop methods for reducing greenhouse gas (GHG) emissions from international shipping. Additionally, it aims to assess the shipping and shipbuilding market while integrating various types of data to construct databases, thus advancing transport systems.
(Left) System Dynamics (SD) Model for Evaluating GHG Reduction Targets
(Right) Example of Database for Shipping and Shipbuilding Markets
Efficient utilization of multiple transportation methods is crucial during disasters. We focus on visualization technology to aid disaster transportation, advanced information sharing, and evaluation analysis to bolster Japan's national resilience measures.
This group supports the development and advancement of eE-NaviPlan .eE-NaviPlan consists of a voyage planning support system, an information sharing system on HP, etc. The system aims to reduce fuel consumption and CO2 emissions and is in operation by Marine Technologists, a non-profit organization. For more information, please visit the NPO Marine Technologists website .
LinerViewer Ver.1.0 2017 data is now available. For sales information, please visit Ocean Commerce, Inc.'s website .
This group has developed LinerViewer , a program for ocean liner route visualization, in collaboration with Ocean Commerce Co. This software reads the LinerViewer data based on the data in the Liner Shipping System on CD (compiled from the International Shipping Handbook) sold by Ocean Commerce and allows you to perform the following operations( Sample image ).
We are pleased to announce the completion of the official version, which reflects the opinions and requests we received for the trial version, and it will be available for sale from August 1, 2016, from Ocean Commerce Inc . For information on the features of LinerViewer Ver. 1.0, please refer to this page . For sales information, please visit Ocean Commerce, Inc.'s website .
In the Great East Japan Earthquake, Tokyo was hit by a tremor with an intensity of 5 or higher on the Japanese seismic scale, causing railroad lines to suspend service, and the long-feared difficulty for people to return home became a reality. For those who have difficulty returning home, a system has been well-maintained to avoid going home en masse and to support people walking home afterward. However, if the railroad service is suspended for a prolonged period, commuting to work and school and returning home may be disrupted. Here, we present a fundamental study on building a route network for alternative transportation by water buses and buses.
The Great East Japan Earthquake caused extensive damage to infrastructure systems such as electricity, water, and gas. But it also disrupted relief supply transportation systems, causing problems with supplies not reaching evacuation centers. This problem has occurred not only in the Great East Japan Earthquake but also in the Great Hanshin-Awaji Earthquake and the Niigata Chuetsu Earthquake and is a recurring problem in large-scale earthquakes. Here, we describe a simulator for disaster transportation.
We can represent shipping transportation activities as a network.
For example, container ship voyages are schemed in advance. Utilizing
this data, we can draw a network by representing ports as dots and ship
movements between ports as lines. Here, we analyzed international
container shipping route networks using methods from a relatively new
research field called complex networks.
Small World Networks and Scale-free Networks represent complex
networks. Still, we found that the network formed by international
container shipping routes can have the characteristics of both networks.
Although the functional capability to transport containers worldwide
with fewer voyages, the dysfunctional nature of a hub port can lead to
large-scale disruptions at the level of network fragmentation.