What is the Student Learning Hub?
In the fall of 2020, C2SMART launched the “C2SMART Student Learning Hub”, free for all consortium member students. Students were able to access learning from a variety of course domains, including data science, computer science, and traffic simulation. The Hub is designed to offer students hands-on experience to learn the tools and skills they will need as they advance their careers, whether in academia, industry, or within government agencies. To accomplish this, the Hub operates using four primary pillars of work:
The Student Hub brings together all members of the C2SMART community: students, researchers, and industry partners.
The Student Hub provides students with skills and tips for conducting effective, sound research.
The Student Hub connects students to hands-on projects to accelerate applied learning.
The Student Hub helps students master research- or job-ready skills to ready them for life after graduation.
What have we done so far?
Throughout Fall 2020 and Spring 2021, the Student Learning Hub offered a variety of courses, taught by a range of experts in the transportation field. This semester, we have attracted 105 students, across 14 universities, from 6 states in the US and 7 countries internationally. We worked with agency and industry partners to deliver programs, provided our students with access to researchers and professionals to learn both professional and academic skills. To learn more about past programs, or to request recordings of past lectures, email us.
Instructor: Zhengbo Zou
This session provided students with a foundational understanding of the use of virtual reality in construction, with a focus on construction safety at work zones. It focused on state-of-the-art implementations of virtual reality in the construction domain, and how it could be used to carry out user experiments when dangerous situations are to be simulated for construction safety studies. Finally, students learned through example of how to create a virtual reality model from an existing building information model.
Instructors: Fan Zuo & Sha Di
Traffic simulation is the mathematical modeling of transportation systems through the application of computer software to better help plan, design, and operate transportation systems. In this course, students got to know the extensive functions of an open-source, highly portable, microscopic, and continuous road traffic simulation package, Simulation of Urban MObility” (SUMO), which is designed to handle large road networks.
Instructor: Suzana Duran Bernardes, NYU
This session was intended for newcomers to data visualization. The program demonstrated best practices for data visualization and data storytelling with examples from real world cases. Students generated powerful visualizations and dashboards of common data analyses that will help people understand and make decisions based on their data.
Instructor: Gyugeun Yoon, NYU
Transit systems are essential to modern urban communities to fulfill the travel demand within or between regions. This course covered two aspects of how transit systems have developed: 1) a description of different types of transit operation systems, and 2) an introduction to how to use the open-source simulation (written in MATLAB) shared with the public via Github (https://github.com/BUILTNYU/
Instructor: Chan Yang, Rutgers University, Rutgers Infrastructure Monitoring and Evaluation (RIME) Group
Nowadays, bridges are everywhere, establishing connections between different lands and expediting communications. In the field of bridge engineering, designing a new bridge and evaluating an existing bridge are equally important. This session provided students with a fundamental understanding of structural health monitoring (SHM), with a focus on the modeling technique using Abaqus software.
Instructor: Dr. Yueshuai (Brian) He, New York University
This workshop provided a detailed introduction to the MATSim-NYC model developed by C2SMART Center and taught students how to extend the base model to incorporate new scenarios as well as how to duplicate the development of the model to other cities. The MATSim-NYC model is a city-scale simulation test-bed to evaluate emerging technologies and policies with a common platform. Participants will gain hands-on example data and scripts from the model and practice input preparation and output analysis.
Instructor: Suzana Duran Bernardes, New York University
The session introduced learners to data science through the Python programming language and fundamental programming concepts including data structures, basic operations in Python, Pandas library for data analysis, and Matplotlib for data visualization. Students used Jupyter Notebook to create their own programs for data retrieval, processing, and visualization.
Instructor: Zilin Bian, New York University
This session will provided students with a foundational understanding of machine learning models (isolation forest, decision tree, neural network etc.) as well as demonstrate how these models can solve complex problems for smart cities.
Instructor: Jingxing Wang, University of Washington
This session introduced approaches to collect open-sourced transportation data for related research. Students used Google API travel time data collection as an example to demonstrate how such real time travel time data was collected and used for traffic performance analysis in the greater Seattle area during the COVID-19 pandemic.