- Data-Driven Security Measurements to improve Safety in NYC and NJ Mass Transit
- Project Year:
2022
- REU Student (s):
Michael Bsales | University of Notre Dame IN
| Nithya Nalluri | The College of New Jersey NJ
- Student 1 Institution:
University of Notre Dame
- Student 2 Institution:
The College of New Jersey
- Project Mentor:
Christie Nelson
- Project Mentor Area:
Masters of Business and Science in Analytics and CCICADA
- Project Abstract:
Public transit in America in recent years has suffered from attacks and crimes since they are very vulnerable to terrorist/mass casualty attacks. These vulnerabilities are due to the lack of strict screening and content policing, unlike security at airports. Although current public transit is designed to efficiently allow a way that it allows for passengers to quickly travel as needed, there is not a strong security system in place. Utilizing metro station security check systems (SCS) can achieve great scrutiny by transit authorities and the public due to their high throughput, risk factors, and a demand for safety. Modern SCS achieves safety through many layers of active and passive security checks. In order to implement strong security around public transit around America, it is important to understand the different types of transit stations and how they are operated, and the types of passengers that utilize them. This paper aims to develop an understanding of the current state of security check systems as applicable to high-traffic subway stations and what must change in the near future to establish effective security check systems in metro systems. By working toward creating a proof-of-concept risk analysis model using crime and other types of publicly available data on the NYC and NJ regions was used to make predictions on how transit stations are more at risk than others. With these predictions, it would be up to local governments and stations to implement more appropriate security measures.
