A Short Course on
Advanced Choice Modelling Methods with
Applications in Transportation and Urban Systems

5th - 9th August 2024

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Centre for Continuing Education (CCE)

Indian Institute of Science (IISc), Bengaluru, India

Background: Effective management of urban transportation systems requires a thorough understanding of the choices and behaviour of the users of the system. The emergence of new mobility modes and technologies – mobility as a service (MaaS), electric vehicles (EVs), and novel public transit systems that integrate conventional transit with on-demand mobility modes – makes it more important to understand and model the travel choices people make in complex urban settings. A widely used methodological paradigm to understand transportation user behaviour is econometric choice modelling. Disaggregate choice models are at the heart of travel demand model systems that are used to forecast urban mobility patterns and system performance under alternative scenarios of land-use, transportation system characteristics, technology solutions, and policy strategies. In addition, choice models are employed to analyse user choices in a variety of fields, including marketing, environmental economics, geography, urban planning, and tourism. Examples include, but are not limited to, users’ valuation of non-market goods (e.g., air quality) in environmental economics, households’ residential location and employers’ firm location choices in geography and urban planning, and vacation destination and mode choices in tourism. In short, choice modelling is an important technique for analysing disaggregate, agent-level behaviour and for forecasting system-level outcomes relevant to urban planning, transportation engineering, regional science, and economics.

Course Objectives and Format: 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 EVs. 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, behavioural heterogeneity, and endogeneity. 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 traditional, chalk-and-board sessions and PowerPoint presentations. The lectures will be accompanied with software laboratory sessions to provide hands-on experience with model formulations and estimation methods. 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.

Course Contents:

• Brief review of utility-based choice theories, multinomial logit, and maximum likelihood estimation


• Multivariate extreme value and heteroscedastic choice models


• 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


• Methods to address endogeneity in choice models for causal inference


• Modelling lab sessions with estimation, specification, behavioural interpretation, and application exercises

Prerequisites:

Course participants are expected to be comfortable with matrix algebra; differential and integral calculus; probability and statistics; estimation and interpretation of linear regression models and hypothesis testing; maximum likelihood estimation; and the basic multinomial logit model.

Who will benefit from the course:

• The course is for those who are familiar with the basic choice modelling methods such as the multinomial logit and maximum likelihood estimation.
• Post graduate students and research scholars working in fields of Transportation, Urban Planning, Geography, Economics, Management Sciences, Data Analytics and Machine Learning.
• Faculty members and research staff from academic institutes and R&D centres.
• Practicing professionals from related fields.

Course Dates:

5th to 9th August 2024 (begins at 9 AM on 5th August and ends at 5 PM on 9th August)

Venue:

Centre for Continuing Education (CCE), Indian Institute of Science (IISc), Bengaluru

Registration:

Registration is necessary. Please use the following link (and select this course) to register and pay fees online: Register here

Please upload a PDF of your resume/CV at the end of the online registration form. Last date for registration is 31st July 2024. Number of participants for the course is limited to 40. Registration fees are:

• Students: Rs. 8,000/- (plus 18% GST).
• Non-students from academic institutions, R&D units, and government organizations: Rs. 15,000/- (plus 18% GST).
• Participants from industry: Rs. 20,000/- (plus 18% GST).

The fee includes expenses toward course material, refreshments, and lunch during the course. This does not include expenses toward travel, accommodation, breakfast, and dinner.

Accommodation: Limited on-campus accommodation is available on payment basis. Participants who need accommodation should request for it in advance (no later than 20th July 2024) through the registration website.

Course Faculty:

• Prof. Chandra Bhat, University of Texas at Austin. Profile
• Prof. Abdul Rawoof Pinjari, Indian Institute of Science (IISc). Profile
• Dr. Raja Gopalakrishnan, Indian Railways. Profile
• Dr. Sangram Krishna Nirmale, IIT Bombay. Profile

Note: Prof. Abdul Rawoof Pinjari is the course coordinator.

Other information:

Participants need to bring their own laptops for hands-on modelling exercises.