2023 Project Descriptions
Social Science Curriculum
In the My Streetscape Summer Research Institute, students are trained as youth community researchers by learning about social sciences research methods, such as surveys, interviews, and ethnography. In teams, students use what they have learned about these methods to design a social science research project to get community input on the engineering projects and technologies they are working on.
Students design data gathering tools, go on field trips where they go into the streetscape to engage with community members and gather data through conversations and observations, and analyze the data they collected before writing their final Research Reports. In the Research Reports, students share what their projects were, how they collected their original data, what they learned from community members, and how the findings informed their engineering project and technologies.
Additionally, the students create Photovoice Exhibits, another method for community engagement and way of communicating visually and through narratives what they learned in the engineering sessions and social science sessions and how they think interdisciplinary about their projects technologies.
SMART INTERSECTIONS
Faculty Advisor:
Andrew Smyth
Engineering Mentor:
Sevin Mohammadi
Social Science Mentor:
Cristian Capotescu
This project explores how advanced technologies can enhance safety, efficiency, and sustainability at urban road intersections. Students will investigate the integration of sensors, cameras, communication systems, and data analytics to create intelligent intersections capable of adaptive traffic control and hazard detection. The syllabus emphasizes understanding intersection safety challenges, conducting a literature review on current research and innovations, and examining real-world case studies. Through hands-on experience with data analytics tools and traffic simulation software such as SUMO, students will develop and test a safety-focused hypothesis to optimize intersection performance. By the end of the project, participants will present their findings, demonstrating how smart intersection technologies and intelligent systems can reduce accidents, improve traffic flow, and support the integration of connected and autonomous vehicles.
Mentees
Columbia Secondary School
Manhattan Center for Science and Mathematics
COLLISION AVOIDANCE SYSTEMS
Faculty Advisor:
Sharon Di
Engineering Mentor:
Zhaobin Mo
Social Science Mentor:
Cristian Capotescu
In transportation, collision avoidance systems play a crucial role in reducing accidents and enhancing overall traffic safety. These systems employ a combination of sensors, algorithms, and real-time data analysis to monitor the surrounding environment and assess potential collision risks. By continuously analyzing factors such as relative speed, distance, trajectory, and the behavior of nearby objects, collision avoidance systems provide timely warnings, alerts, or automated interventions to help drivers or operators take evasive action.
This project is to build a collision avoidance system in the CS3 project. Given the real-time data of the trajectory of the car and pedestrian that is collected from the CS3 sensor, our goal is to send a real-time warning to the pedestrian that a collision is likely to happen. The students will focus on the collision prediction part, using mathematical models and Python language, and visualize the future trajectory in Python.
Mentees
Regis High School
NYC Lab High School
AI MODELS FOR DIGITAL TWINS
Faculty Advisor:
Zoran Kostic
Engineering Mentor:
Mehmet K. Turkcan
Social Science Mentor:
Cristian Capotescu
This project is focused on applying artificial intelligence and computer vision to understand and improve street intersections. The program begins with an introduction to object detection, tracking, and machine learning fundamentals, followed by hands-on workshops in annotation, model training, and traffic simulations. Participants then develop and submit individual research proposals exploring machine learning or simulation-based approaches to traffic analysis. In subsequent weeks, students engage in independent research, applying learned methods, analyzing data, and refining their hypotheses through continuous feedback, discussions, and reflection sessions. As the program progresses, emphasis shifts to analysis, refinement, and report writing, culminating in peer reviews, instructor consultations, and iterative improvements. The final weeks focus on reviewing and presenting findings, with students submitting a comprehensive research report and delivering final presentations summarizing their contributions to advancing intelligent traffic systems and smart intersection research.
Mentees
Columbia Secondary School
Harry S Truman High School
The Beacon School