99 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Oak Ridge National LaboratoryUS20 papers
- University of Tennessee at KnoxvilleUS7 papers
- National Institute of Advanced Industrial Science and TechnologyJP2 papers
- RIKEN Center for Computational ScienceJP2 papers
- Shenzhen Institutes of Advanced TechnologyCN2 papers
- Tokyo Institute of TechnologyJP2 papers
- Virginia TechUS2 papers
- Arts et MétiersFR1 paper
- Bar-Ilan UniversityIL1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- Clemson UniversityUS1 paper
- Florida A&M University - Florida State University College of EngineeringUS1 paper
5 papers · 1 filter
Hazard-Responsive Digital Twin for Climate-Driven Urban Resilience and Equity
Zhenglai Shen, Hongyu Zhou
Compounding climate hazards, such as wildfire-induced outages and urban heatwaves, challenge the stability and equity of cities. We present a Hazard-Responsive Digital Twin (H-RDT)…
Improving the accuracy of freight mode choice models: A case study using the 2017 CFS PUF data set and ensemble learning techniques
Diyi Liu, Hyeonsup Lim, Majbah Uddin +4
The US Census Bureau has collected two rounds of experimental data from the Commodity Flow Survey, providing shipment-level characteristics of nationwide commodity movements, publi…
Exploring the Effects of Population and Employment Characteristics on Truck Flows: An Analysis of NextGen NHTS Origin-Destination Data
Majbah Uddin, Yuandong Liu, Hyeonsup Lim
Truck transportation remains the dominant mode of US freight transportation because of its advantages, such as the flexibility of accessing pickup and drop-off points and faster de…
Modeling Freight Mode Choice Using Machine Learning Classifiers: A Comparative Study Using the Commodity Flow Survey (CFS) Data
Majbah Uddin, Sabreena Anowar, Naveen Eluru
This study explores the usefulness of machine learning classifiers for modeling freight mode choice. We investigate eight commonly used machine learning classifiers, namely Naive B…
An Interpretable Machine Learning Framework to Understand Bikeshare Demand before and during the COVID-19 Pandemic in New York City
Majbah Uddin, Ho-Ling Hwang, Md Sami Hasnine
In recent years, bikesharing systems have become increasingly popular as affordable and sustainable micromobility solutions. Advanced mathematical models such as machine learning a…