CIEN E4011: Big Data Analytics in Transportation
Dive into the world of big transportation data and learn how to turn real-world sensor inputs into actionable insights through hands-on projects, cutting-edge tools, and expert guidance.
Course Overview
Emerging communication technologies have made it possible to collect and access vast amounts of transportation data from sources such as surveillance cameras, mobile phones, and GPS trajectories.
With a strong emphasis on collaborative, project-based learning, this course will introduce you to the methods and analytical tools to extract and evaluate transportation data. You will be divided into different groups and assigned one transportation problem to focus on throughout the entire semester. Each week, you will be given a relevant paper to review ahead of class. Guest speakers will provide detailed explanations with contextual examples on each project. Lab sessions using Python programming will enable you to test and practice techniques. At the end of this course, you will deliver a final group presentation and technical report outlining your findings.
Basic probability, statistics and engineering economics concepts are applied during this course. You must be familiar with at least one computer language (MATLAB, Python etc.) as well as computer spreadsheets and common computer applications (word processor, latex etc.) to undertake this course.
Course Instructor
Xuan (Sharon) Di
Associate Professor
Dr Xuan (Sharon) Di is an Associate Professor in the Department of Civil Engineering and Engineering Mechanics at Columbia University, and co-chairs the Smart Cities Center in the Data Science Institute. Di directs the Data and innovative technology-driven Transportation Lab (DitecT), which focuses on transportation systems. Her overarching research mission is to empower mobility for all, emphasizing the use of technology for social good. She is currently focused on pioneering the development of digital twins for urban transportation management, leveraging cyber-physical systems technology. Within this framework, her research spans diverse areas, including multi-modal mobility optimization, autonomous vehicle control on shared roads with humans, and the intersection of transportation with health considerations.