Course Dates

3rd to 6th August 2026

Duration

4 Days

Last day to apply: 15th JULY 2026

Mode of Instruction

Offline (IISc Campus)

Who will benefit?

Bachelor’s degree holders, Masters students, PhD Students, faculty, and industry professionals in the fields of transportation, urban planning, marketing research, economics, statistics and econometrics, geography, regional science, civil engineering, management/business studies, etc.

Prerequisites: Calculus, Applied Probability and Statistics, Matrix Algebra

— INR 8,500 + GST for students.
— INR 18,000 + GST for non-students from academic institutions and R&D units.
— INR 25,000 + GST for participants from industry.
— INR 45,000 + GST for participants from institutions outside India.

This course covers the theory and advanced methods of choice modelling, with applications drawn from travel behaviour analysis in emerging urban settings with new modes such as shared mobility, ride hailing, integrated multimodal transit, and electric vehicles. Assuming familiarity with the basic choice modelling methods such as the multinomial logit and maximum likelihood estimation, the course will quickly delve into advanced model formulations and estimation methods. Equal emphasis will be given to empirical model specification and behavioural interpretation issues, including causality, and behavioural heterogeneity. In addition to theories and modelling methods, importance is given to hands-on estimation, specification, and interpretation of choice models on real-life empirical datasets.

Lectures in the course will include a mix of PowerPoint presentations and chalk-and-board sessions. The lectures will be accompanied by software laboratory sessions to provide hands-on experience with model formulations and estimation methods. Open-source codes written in Python and other statistical packages will be provided for the software sessions.

Course participants need to bring their laptops for hands-on software laboratory sessions involving estimation and interpretation of choice models. No computers or laptops will be provided at the site.

• Brief review of utility-based choice theories, multinomial logit, and maximum likelihood estimation
• Mixed logit and multinomial probit models, models with stochastic variables
• Multivariate dependent variable models (e.g., multivariate ordered response models)
• Parameter identification in basic and advanced choice models
• Discrete-continuous choice models and multiple discrete-continuous choice models (MDCEV, etc.)
• Advanced estimation methods
• Modelling lab sessions with estimation, specification, behavioural interpretation, and application exercises