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Daisuke Watanabe is an Associate Professor at the Department of Logistics and Information Engineering, Tokyo University of Marine Science and Technology, Japan. He holds MSc and Ph.D. degrees in Policy and Planning Sciences from the University of Tsukuba. He has worked as a researcher at the National Maritime Research Institute, Japan from 2006 to 2007 and a visiting scholar at the University of California, Santa Barbara, the United States from 2015 to 2016. He has numerous academic publications and extensive research and technical experience in the development and management of logistics systems. His research interests include logistics system engineering, particularly transport network analysis, and facility location analysis, logistics risk management, shipping environment analysis, and shipping digitalization in maritime and port logistics. He was awarded the Annual Best Paper Award in 2017 by the City Planning Institute of Japan. He is one of the organizers of the International Conference on Transportation and Logistics (TLOG Network).
In this study, we propose an effective method using deep learning to strengthen real-time vessel carbon dioxide emission management. We propose a method to predict real-time carbon dioxide emissions of the vessel in three steps: (1) convert the trajectory data of the fixed time interval into a spatial–temporal sequence, (2) apply a long short-term memory (LSTM) model to predict the future trajectory and vessel status data of the vessel, and (3) predict the carbon dioxide emissions. Automatic identification system (AIS) database of a liquefied natural gas (LNG) vessel were selected as the sample and we reconstructed the trajectory data with a fixed time interval using cubic spline interpolation. Applying the interpolated AIS data, the carbon dioxide emissions of the vessel were calculated based on the International Towing Tank Conference (ITTC) recommended procedures. The experimental results are twofold. First, it reveals that vessel emissions are currently underestimated. This study clearly indicates that the actual carbon dioxide emissions are higher than those reported. The finding offers insight into how to accurately measure the emissions of vessels, and hence, better execute a greenhouse gases (GHGs) reduction strategy. Second, the LSTM model has a better trajectory prediction performance than the recurrent neural network (RNN) model. The errors of the trajectory endpoint and carbon dioxide emissions were small, which shows that the LSTM model is suitable for spatial–temporal data prediction with excellent performance. Therefore, this study offers insights to strengthen the real-time management and control of vessel greenhouse gas emissions and handle those in a more efficient way.
Yongpeng Wang; Daisuke Watanabe; Enna Hirata; Shigeki Toriumi. Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning. Journal of Marine Science and Engineering 2021, 9, 871 .
AMA StyleYongpeng Wang, Daisuke Watanabe, Enna Hirata, Shigeki Toriumi. Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning. Journal of Marine Science and Engineering. 2021; 9 (8):871.
Chicago/Turabian StyleYongpeng Wang; Daisuke Watanabe; Enna Hirata; Shigeki Toriumi. 2021. "Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning." Journal of Marine Science and Engineering 9, no. 8: 871.
In the present world, with the recent advances in the globalization of trade and economic activity, research on the logistics issue should be approached from more global or international viewpoints, to achieve sustainable economic development
Ryuichi Shibasaki; Daisuke Watanabe; Tomoya Kawasaki. Global and International Logistics. Sustainability 2021, 13, 5610 .
AMA StyleRyuichi Shibasaki, Daisuke Watanabe, Tomoya Kawasaki. Global and International Logistics. Sustainability. 2021; 13 (10):5610.
Chicago/Turabian StyleRyuichi Shibasaki; Daisuke Watanabe; Tomoya Kawasaki. 2021. "Global and International Logistics." Sustainability 13, no. 10: 5610.
Truck platooning involves a small convoy of freight vehicles using electronic coupling as an application in automated driving technology, and it is expected to represent a major solution for improving efficiency in truck transportation in the near future. Recently, there have been several trials regarding truck platooning with major truck manufacturers and logistics companies on public roads in the United States, European countries and Japan. There is a need to locate a facility for the formation of truck platooning to realize the unmanned operation of trucks following in a platoon. In this study, we introduce the current status of truck platooning in Japan and present the optimal location model for truck platooning using the continuous approximation model with a numerical experiment, considering the case in Japan. We derived the optimal locational strategy for the combination of the long-haul ratio and the cost factor of platooning. With parameters estimated for several scenarios for the deployment of truck platooning in Japan, the numerical results show that the optimal locational strategy for a platoon of manned vehicles and a platoon with unmanned following vehicles is the edge of the local region, and that for a platoon of fully automated vehicles is the center of the region.
Daisuke Watanabe; Takeshi Kenmochi; Keiju Sasa. An Analytical Approach for Facility Location for Truck Platooning—A Case Study of an Unmanned Following Truck Platooning System in Japan. Logistics 2021, 5, 27 .
AMA StyleDaisuke Watanabe, Takeshi Kenmochi, Keiju Sasa. An Analytical Approach for Facility Location for Truck Platooning—A Case Study of an Unmanned Following Truck Platooning System in Japan. Logistics. 2021; 5 (2):27.
Chicago/Turabian StyleDaisuke Watanabe; Takeshi Kenmochi; Keiju Sasa. 2021. "An Analytical Approach for Facility Location for Truck Platooning—A Case Study of an Unmanned Following Truck Platooning System in Japan." Logistics 5, no. 2: 27.
Many states are actively working toward regulating CO2 emissions from a wide range of industries. However, due to the international characteristic of shipping, the emissions from shipping have not yet been strictly controlled. Using Automatic Identification System (AIS) data acquired through satellites, this study estimates the emission inventory, such as, CO2, CH4, CH4, N2O, NOx, CO and non-methane volatile organic compounds (NMVOCs) around the world and bunker consumption from a liquified natural gas (LNG) fleet under the assumption that a LNG fleet uses LNG as fuel. Using position data calculated from an AIS database, we made comparisons regarding the LNG trade amount and bunker consumption of LNG fleet, as well as the total CO2 inventory and CO2 emissions from LNG fleet in the vicinity of the coasts of relevant countries. The result provides insights into (1) how the emissions and bunker consumption from LNG fleet is distributed, (2) which countries are taking relatively more advantages of LNG trade, and (3) which countries are suffering possible harmful effects.
Hoegwon Kim; Daisuke Watanabe; Shigeki Toriumi; Enna Hirata. Spatial Analysis of an Emission Inventory from Liquefied Natural Gas Fleet Based on Automatic Identification System Database. Sustainability 2021, 13, 1250 .
AMA StyleHoegwon Kim, Daisuke Watanabe, Shigeki Toriumi, Enna Hirata. Spatial Analysis of an Emission Inventory from Liquefied Natural Gas Fleet Based on Automatic Identification System Database. Sustainability. 2021; 13 (3):1250.
Chicago/Turabian StyleHoegwon Kim; Daisuke Watanabe; Shigeki Toriumi; Enna Hirata. 2021. "Spatial Analysis of an Emission Inventory from Liquefied Natural Gas Fleet Based on Automatic Identification System Database." Sustainability 13, no. 3: 1250.
Digital transformation is a topical theme in shipping research and professional practice, today. Our paper aims at developing a comprehensive framework of digitalization technologies and their maritime business implications, grounded on available evidence. In order to understand the full complexity of shipping digitalization activities, we addressed technology and management aspects in a coalesced framework. Our research follows a qualitative, case study approach. Five cases of advanced, shipping incumbents’ digitalization activities were investigated. Our research outcome is an overarching theoretical model, which systematizes the technological components (technology typology), the prevailing management rationales (strategic drivers) and determinant factors (practices) of shipping digitalization.
Maria Lambrou; Daisuke Watanabe; Junya Iida. Shipping digitalization management: conceptualization, typology and antecedents. Journal of Shipping and Trade 2019, 4, 1 -17.
AMA StyleMaria Lambrou, Daisuke Watanabe, Junya Iida. Shipping digitalization management: conceptualization, typology and antecedents. Journal of Shipping and Trade. 2019; 4 (1):1-17.
Chicago/Turabian StyleMaria Lambrou; Daisuke Watanabe; Junya Iida. 2019. "Shipping digitalization management: conceptualization, typology and antecedents." Journal of Shipping and Trade 4, no. 1: 1-17.
This paper focuses on the passenger traffic bottlenecks occurred in the bus route network in disaster situations and proposes the multi-agent based bus route optimization method to resolve such bottlenecks by generating the networks which can effectively transport many stranded persons including ones who wait around the station as the passenger traffic bottlenecks. For this purpose, the proposed method modifies the bus route networks generated as usual conditions to suitably pass many bus lines to and redistribute the buses among the bus lines according to the number of passengers. The intensive simulations have revealed the following implications: (1) the proposed bus route network optimization method generates the route network which is suitable for passenger traffic bottlenecks; (2) the proposed method decreases a risk of the bottlenecks; and (3) our method transports the passengers faster than those by the conventional one in various virtual disaster situations.
Sayaka Morimoto; Takahiro Jinba; Hiroto Kitagawa; Keiki Takadama; Takahiro Majima; Daisuke Watanabe; Mitsujiro Katsuhara. Multi-agent Based Bus Route Optimization for Restricting Passenger Traffic Bottlenecks in Disaster Situations. Proceedings in Adaptation, Learning and Optimization 2015, 415 -428.
AMA StyleSayaka Morimoto, Takahiro Jinba, Hiroto Kitagawa, Keiki Takadama, Takahiro Majima, Daisuke Watanabe, Mitsujiro Katsuhara. Multi-agent Based Bus Route Optimization for Restricting Passenger Traffic Bottlenecks in Disaster Situations. Proceedings in Adaptation, Learning and Optimization. 2015; ():415-428.
Chicago/Turabian StyleSayaka Morimoto; Takahiro Jinba; Hiroto Kitagawa; Keiki Takadama; Takahiro Majima; Daisuke Watanabe; Mitsujiro Katsuhara. 2015. "Multi-agent Based Bus Route Optimization for Restricting Passenger Traffic Bottlenecks in Disaster Situations." Proceedings in Adaptation, Learning and Optimization , no. : 415-428.