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:


Skill Building

Applied Learning

Job Preparation

join our student cohort

Receive updates on new curriculum, sign up for future courses, watch videos of past programming, and connect with C2SMART's wider work.

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.


Upcoming Courses

The Introduction to Cybersecurity and its application in Transportation will help you to discover essential knowledge, skills, key elements and topics in cybersecurity. We will briefly discuss the history of security analysis of modern automobiles and the need for cybersecurity including typical threats and potential solutions.

As blockchain continues to grow in popularity, another type of distributed ledger is gaining traction as well: enterprise grade distributed ledgers. This session will give an overview on enterprise grade distributed ledgers, answering the questions on how they work, and when to use them. A tutorial using Hyperledger Fabric will also show how we can create and interact with our own traffic specialized distributed ledger, to demonstrate a real use case.

Past Programming

Instructor: Srushti Rath, New York University

In this session, students learned the basics of text representation in natural language processing (NLP) and various state-of-the-art NLP techniques for (semantic) textual similarity tasks. The session walked through data preparation and processing steps with language modeling tools in Python (e.g., Doc2Vec, Sentence-BERT) for computing text/document similarity and discussed several practical applications of such NLP techniques in transportation related downstream tasks.

  • Click Here to view the Video Recording

Instructor: Chenxi Liu, University of Washington

This session provided students with a foundational understanding of Computer Vision (CV) technology including the basic knowledge about image processing. Also, a simple Convolutional Neural Network (CNN) was mentioned in the session to demonstrate traffic sensing based on image data. The session required basic knowledge about python.

  • Click Here to View the Video Recording 

Instructor: Alex Wen, New York University

This series provided a brief introduction to ArcGIS by teaching the basics of geographical visualization and preliminary spatial analysis. The first session introduced the user interface and taught the basics of map design; the spatial relationship between pedestrian traffic and pavement quality was explored. The second taught students about the basics of data management (e.g., data cleaning, attribute filtering) and briefly introduced ArcPy (Python in ArcGIS).

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.

Register for access to video recording.

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.

Register for access to video recording of Session 1.

Register for access to video recording of Session 2.

Register for access to video recording of Session 3.

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.

Register for access to video recording.

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/FTA_TransitSystems).

Register for access to video recording of Session 1.

Register for access to video recording of Session 2.

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.

Register for access to video recording.

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.

Request access to video recordings of sessions 1-5.

Request access to video recording of session 6.

Request access to video recording of session 7.

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.

Register for access to video recording of Session 1.

Register for access to video recording of Session 2.

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. 

Register for access to video recording.

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.

Register to access to video recording.

Student Hub Coordinator

Jingqin Gao

Senior Research Associate

Jingqin (Jannie) Gao completed her Ph.D. in Transportation Planning and Engineering at NYU Tandon, where she works with C2SMART Director Kaan Ozbay. She studied Science and Technology of Optical Information and received her B.S. from Tongji University in China and her M.S in Transportation Planning and Engineering from New York University. Her research interests lie in offline and real-time simulation modeling, big data and machine learning approach for transportation, and transportation economics.