CIVL4100I Introduction to data analytics for smart transportation systems – Undergraduate course
This course covers the role of stochasticity in transport systems and the methods used to account for this within transport infrastructure assessment, with a particular focus on the application of data analysis methods. The course introduces how to analyze the performance of public transport systems and road network using classic queuing theory and travel time reliability concepts. The course will complement skills learnt in the other transport courses to provide a well-rounded knowledge of smart transport planning and management. The focus is on the application of transport models in real world settings using real data.
Students have the opportunity to work with large open-source data in two experiential-learning projects. The course also develops skills for working with data and managing collaborative projects.
CIVL4100L Public transport planning and operation – Undergraduate course
Public transport systems are recognized as a critical component in addressing urban mobility challenges, including congestion, air quality, and accessibility. This course focuses on approaches of planning, designing and operating public transport systems. It introduces traditional and innovative public transport modes, services and systems. It covers the demand modeling of public transport modes, the network planning of public transport routes and services, the operation optimization of public transport timetable setting and vehicle and crew scheduling, and the performance evaluation of public transport systems.
CIVL6100M Discrete choice experiments and data analysis – Postgraduate course
Discrete choice modeling framework and stated choice methods are widely applied across diverse fields to study the behavioral responses of individuals, households and organizations. This course is designed to provide both theory and practical experience in the building and estimating of advanced discrete choice models, as well as in generating stated choice experimental designs.
This course covers both traditional discrete choice models and future developments in the field of discrete choice analysis including risk attitude and perceptual conditioning. This course also places the focus on generating stated choice surveys for answering real-world research questions and building models using real data. The techniques gained in this course are transferable to diverse areas of researching, such as transportation, logistics, health services, marketing, economics, tourism, planning.