Title: Harnessing Data Analytics in Urban Public Transport Operation and Planning
Speaker: Dr. Sun LiJun (MIT Media Lab, USA)
Time: Friday, Jun 9, 2017, 03:00 p.m.
Venue: 604, Jiaotong Building, Wushan Campus
Contents: In recent years, the emergence of massive individual-based datasets and advances in informatics and data science have transformed our understanding in a variety of fields in transportation research. It also motivates a new way of data-driven transport approach through conducting extensive analyses and building realistic models. For example, behavior pattern inference from spatial-temporal data set has facilitated the development of urban public transport in both day-to-day operation and long-term planning. However, in a large-scale and highly-congested city, the application of these technologies remains prone to operational pitfalls and obstacles. In this talk, I will present about harnessing various data analytics and computational models to tackle resilience issues in public transport operation and planning, by integrating machine learning, operations research and behavioral economics. I will mainly discuss a typical topic about passenger behavior inference to show the basic concept of combining data analytics and computational models, and illustrate how it can be used to improve the resilience of public transport systems. The presented methodologies can be integrated with an agent-based modeling/simulation framework, and further help plan and evaluate future transportation systems (e.g., on-demand mobility services) for “resilient smart cities”.