Courses
CIV1599 - CIV1599 S: Analytics for Transit and Mobility Systems
University of Toronto Summer 2024
I assisted Prof. Amer Shalaby in developing and teaching a unique graduate-level course that focuses on the most advanced techniques used in public transit. This course covers topics such as AI models and advanced time series analysis. It emphasizes principles of data analysis, modeling, and visualization, with applications in transit reliability, equity, access to opportunities, demand forecasting, and real-time data analysis.
Course Description
Transit agencies around the world witness a growing trend of data abundance and diversity, presenting opportunities to enhance transit system effectiveness but requiring specialized knowledge and experience with analytics for harnessing such data. Transportation agencies and companies overseeing other modes and emerging mobility services are faced with the same challenges. This course provides students with in-depth exposure to emerging data types, sources and standards for transit and other mobility systems . The course will cover a range of analytics for harnessing diverse data in a variety of planning and management applications. While special focus will be given to transit applications, other mobility modes and services will be considered as appropriate.
Learning Objectives
By the end of this course, students will:
- gain in-depth knowledge in various transit and mobility data types, sources and standards;
- demonstrate competence in data analysis and modelling;
- apply appropriate analytical methods to different types of transit problems and selected mobility applications;
- gain hands-on experience in commercial and open-source tools; and
- communicate results in attractive visual and textual forms.
Course Schedule
| Week # | Lectures + Lab | Tasks |
|---|---|---|
| 1 | Course introduction, transit data, building the toolbox,exploratory data analysis | Assignment 1 released |
| 2 | Reliability analysis, origin-destination demand estimation | Project start |
| 3 | Spatial analysis, accessibility and equity analysis | Assignment 2 released |
| 4 | Advanced AI and time-series methods and applications to transit/mobility systems | |
| 5 | Ridership analysis and modelling using advanced analytics | Assignment 3 released |
| 6 | Ridership analysis and modelling using advanced analytics | |
| 7 | Operational control using reinforcement learning | |
| 8 | Course wrap-up | Project presentations |