Subojit Debnath
Postgraduate Student (IIEST, Shibpur)
Howrah
Summary
Prospective PhD applicant with strong interests in travel behavior modelling, sustainable urban mobility, and data-driven transportation systems. My research focuses on integrating behavioral theory with machine learning methods to explain and predict mode choice and electric vehicle adoption. I am particularly interested in behaviorally grounded, policy-relevant transport modelling for complex urban contexts, with an emphasis on developing-country settings.
Overview
1
1
year of professional experience
3
3
Certifications
3
3
Languages
Work History
Research Student – Transportation Behaviour & Modelling
Indian Institute of Engineering Science and Technology, Shibpur (IIEST)
08.2024 - Current
- Designed and analyzed stated-preference survey data capturing socio-demographic and perceptual determinants of travel behavior.
- Applied discrete choice modeling and machine learning techniques, including Multinomial Logit and Ensemble models.
- Used SHAP-based explainable AI tools to interpret behavioral drivers in mode choice and EV adoption.
- Conducted a comparative analysis between traditional econometric models and machine learning approaches.
- Developed policy-relevant insights for sustainable urban transport planning.
Education
Master of Technology (M.Tech) - Transportation Engineering
Indian Institute of Engineering Science And Technology (IIEST)
Howrah, India06-2026
Bachelor of Technology (B.Tech) - Civil Engineering
Narula Institute of Technology
Kolkata, India06-2022
Skills
- Discrete choice analysis
- Proficient in ensemble methods for data analysis
- Deep learning specialization
- Survey Design and Likert-Scale Data Coding
- Explainable AI methodologies
- Behavioral data interpretation
Publications & Manuscripts
Journal Articles
Debnath, S., Roy, S.K. “Assessing Modal Shift Readiness for Sustainable Mobility among Potential Private Mobility Adopters Using Machine Learning and Scenario Simulation.”
Status: Under Review
Debnath, S., Roy, S.K. “From Discrete Choice Models to Machine Learning: A Review of Mode Choice Modelling in Congested Urban Areas.”
Status: Under Review
Conference Articles
ICPCCE 2025 – International Conference on Pollution Control and Clean Environment
Psychological and Perceptual Feature Engineering for Electric Vehicle Purchase Willingness: A Principal Component and Explainable AI Approach
Status: Accepted
Master’s Research Summary
My research addresses the methodological gap between traditional discrete choice models and modern machine learning approaches in transportation behaviour analysis. While discrete choice models provide interpretability, they struggle to capture non-linearities and latent psychological mechanisms. Conversely, machine learning models offer strong predictive power but limited behavioural transparency. My work proposes an integrated behavioural and machine learning framework for analysing sustainable mobility readiness, specifically modal shift behaviour and electric vehicle adoption, using psychological and perception based variables, statistical and machine learning modelling, and explainable AI tools for behavioural interpretation. The objective is to develop modelling approaches that support evidence-based and behaviourally grounded transport policy.
Certification
Sustainable Transportation Systems, IIT Roorkee - November 23, 2025
References
Dr. Sudip Kumar Roy, Professor, sudip@civil.iiests.ac.in, Indian Institute of Engineering Science and Technology (IIEST), Shibpur, (Additional referees available upon request)
Timeline
Research Student – Transportation Behaviour & Modelling
Indian Institute of Engineering Science and Technology, Shibpur (IIEST)
08.2024 - Current
Master of Technology (M.Tech) - Transportation Engineering
Indian Institute of Engineering Science And Technology (IIEST)
Bachelor of Technology (B.Tech) - Civil Engineering
Narula Institute of Technology