Programme Content
The course begins with an overview of econometrics and its applications in urban and transportation research. It then reviews key model assumptions, data types, and estimation approaches, including ordinary least squares and maximum likelihood. Participants will be introduced to commonly used models, including count data models, ordinal probit models, and discrete choice models. The workshop will also cover survey and experimental design for revealed- and stated-preference data. Hands-on sessions using R/GAUSS will allow participants to practice data preparation, model estimation, hypothesis testing, result interpretation, and model comparison. To strengthen practical learning, the course will incorporate real case studies from travel behaviour, transport demand, and road safety research, such as mode choice analysis, crash frequency modelling, ordinal injury severity analysis, and behavioral responses to transport policy or service changes. Through these applied examples, participants will learn how to select appropriate econometric methods, implement them with real datasets, and translate model results into meaningful academic and policy implications.
