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  Development of simulators as analytical platforms

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  Long-term urban structural changes and logistics land use

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  Logistics policies and solutions related to e-commerce

Continuous development and improvement of urban and transportation simulators while incorporating the state-of-the-art behavioral models and data science methods. The simulator will be updated in response to new data sources including big data (GPS data, sensing data, camera data, delivery records, etc.), newly proposed measures and solutions, and planning needs.

The transformation of logistics systems over the past several decades has had a significant impact on logistics land use, and the need for related logistics facilities (e.g., distribution centers) is expected to increase further as e-commerce takes root. We will analyze and evaluate the long-term changes in urban structure and their impact on traffic flow and the environment, mainly from the perspective of logistics, and identify issues that should be considered in urban and regional planning.

E-commerce is transforming the existing logistics system, making it more convenient for consumers, while the increase in parcel deliveries is making freight flows inefficient. Given that telecommuting and online lectures have become commonplace and demand for e-commerce has skyrocketed, and this trend is expected to continue, we propose and evaluate next-generation joint delivery systems.

Recent Publications

Kodera, R., Sakai, T., & Hyodo, T. (2025). Development of a Delivery Time-Period Selection Model for Urban Freight Using GPS Data. Smart Cities, 8(1), 31.

Developing policy instruments related to urban freight, such as congestion pricing, urban consolidation schemes, and off-hours delivery, requires an understanding of the distribution of shipment delivery times. Furthermore, agent-based urban freight simulators use relevant information (shipment delivery time distribution or vehicle tour start time distribution) as input to simulate tour generation. However, studies focusing on shipment delivery time-period selection modeling are very limited. In this study, we propose a method using GPS trajectory data from the Tokyo Metropolitan Area to estimate a shipment delivery time-period selection model based on pseudo-shipment records inferred from GPS data. The results indicate that shipment distance, size, and destination attributes can explain the delivery times of goods. Moreover, we demonstrate the practicality of the model by comparing the simulation result with the observed data for three areas with distinct characteristics, concluding that the model could be applied to urban freight simulation models for accurately reproducing spatial heterogeneity in shipment delivery time periods. This study contributes to promoting smart city development and management by proposing a method to use big data to better understand deliveries and support the development of relevant advanced city logistics solutions.

Motojima, R., Sakai, T., & Hyodo, T. (2024). Development of an online daily goods shopping demand model using internet-based consumption behavior survey data. Transportation Research Record, 03611981241270172.

With the rapid growth of the e-commerce market, how to cater to the demand for last-mile deliveries to households is an important challenge for both policymakers and practitioners. Therefore, the incorporation of e-commerce-driven delivery demand into a freight transportation analysis is required. In this research, we conduct an internet-based survey in Japan to collect data on past in-person and online shopping behaviors and develop a household-based e-commerce demand model that simultaneously estimates e-commerce delivery demand in regard to the total value and order size (or order frequency) using a framework proposed by Sakai et al. Using the estimated model parameters, we conduct a sensitivity analysis using the model to understand the impact of changes in delivery service on e-commerce delivery demand. Focusing on groceries and household goods, we successfully demonstrate the ability of the model framework and obtain insights into the impact of delivery service characteristics on households’ reliance on e-commerce using the model developed based on real-world data.

Book Chapters

Handbook on City Logistics and Urban Freight, Edoardo Marcucci, Valerio Gatta, Michela Le Pira (ed.) Edward Elgar. 2023.

Chapter 5: Evaluating city logistics solutions with agent-based microsimulation
Takanori Sakai, Peiyu Jing, André Romano Alho, Ravi Seshadri, and Moshe Ben-Akiva. https://doi.org/10.4337/9781800370173.00013

The Routledge Handbook of Urban Logistics, Jason Monios, Lucy Budd, Stephen Ison (ed.) Routledge. 2023.

Chapter 14: Facility Locations in Urban Logistics
Takanori Sakai, Adrien Beziat, and Adeline Heitz. https://doi.org/10.4324/9781003241478

We have international collaborations with other research groups at the following institutions:

MIT-ITS Lab
University Gustave Eiffel/IFSTTAR (French Institute of Science and Technology for Transport)
Harbin Institutte of Technology, Shenzhen
Rensselaer Polytechnic institute
NYU Tandon School of Engineering

Institute for City Logistics

Tokyo University of Marine Science and Technology

Regional Planning Lab@TUMSAT

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