Vuban Chowdhury
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Researcher
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Biography and Research Information
OverviewAI-generated summary
Vuban Chowdhury's research investigates the adoption of low-carbon transportation technologies, focusing on the factors influencing the transition to electric vehicles (EVs) in multi-vehicle households within the United States. His work employs machine learning techniques to analyze vehicle choice patterns and understand user preferences. Additionally, Chowdhury has explored the drivers for adopting low-carbon transportation in California's heavy-duty and off-road sectors, aiming to decode behavioral catalysts for this green transition. His research also extends to practical applications, including the development of real-time helmet violation detection systems using ensemble learning and YOLOv5, and the prediction of noise levels in urban environments using artificial neural networks and regression models. Chowdhury has co-authored several publications with collaborators at the University of Arkansas at Fayetteville, including Suman Mitra, Sarah Hernandez, and Farzana Mehzabin Tuli.
Metrics
- h-index: 3
- Publications: 8
- Citations: 38
Selected Publications
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Electric Vehicle Usage Patterns in Multi-Vehicle Households in the US: A Machine Learning Study (2024)
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Unraveling Electric Vehicle Preference: A Machine Learning Analysis of Vehicle Choice in Multi-Vehicle Households in the United States (2024)
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Sustainable Shift: Analyzing Drivers for Low-Carbon Transportation Adoption in California’s Heavy-Duty and Off-Road Sectors (2024)
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Decoding Green Transition: Unraveling Behavioral Catalysts in the Adoption of Low-Carbon Transportation for Heavy-Duty Vehicles and Off-Road Equipment in California (2023)
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Unraveling Electric Vehicle Preference: A Machine Learning Analysis of Vehicle Choice in Multi-Vehicle Households in the United States (2023)
Collaboration Network
Top Collaborators
- Sustainable Shift: Analyzing Drivers for Low-Carbon Transportation Adoption in California’s Heavy-Duty and Off-Road Sectors
- Electric Vehicle Usage Patterns in Multi-Vehicle Households in the US: A Machine Learning Study
- Decoding Green Transition: Unraveling Behavioral Catalysts in the Adoption of Low-Carbon Transportation for Heavy-Duty Vehicles and Off-Road Equipment in California
- Unraveling Electric Vehicle Preference: A Machine Learning Analysis of Vehicle Choice in Multi-Vehicle Households in the United States
- Unraveling Electric Vehicle Preference: A Machine Learning Analysis of Vehicle Choice in Multi-Vehicle Households in the United States
- Electric Vehicle Usage Patterns in Multi-Vehicle Households in the US: A Machine Learning Study
- Unraveling Electric Vehicle Preference: A Machine Learning Analysis of Vehicle Choice in Multi-Vehicle Households in the United States
- Unraveling Electric Vehicle Preference: A Machine Learning Analysis of Vehicle Choice in Multi-Vehicle Households in the United States
- Sustainable Shift: Analyzing Drivers for Low-Carbon Transportation Adoption in California’s Heavy-Duty and Off-Road Sectors
- Decoding Green Transition: Unraveling Behavioral Catalysts in the Adoption of Low-Carbon Transportation for Heavy-Duty Vehicles and Off-Road Equipment in California
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