Presenter: FATOUMATA BARROW

Faculty Sponsor: Komalpreet Kaur

School: Salem State University

Research Area: Computer Science

Session: Poster Session 2, 11:30 AM - 12:15 PM, Concourse, B9

ABSTRACT

Over the past several years, Salem has experienced a noticeable rise in traffic incidents, especially during the fall when the city becomes packed with visitors. With the streets more crowded and people constantly moving between major attractions, certain areas consistently become more dangerous than others. The city has plenty of crash data, but there isn’t a clear way to analyze patterns, understand what triggers these incidents, or see how nearby points of interest contribute to higher risk.

The project focuses on using machine learning and geospatial tools to study the last five years of Salem’s crash records and identify where and when accidents are most likely to occur. By combining crash locations, POI proximity, and time-based factors, the aim is to highlight high-risk zones that the city could target for improvements. This is important because better insight into why crashes cluster in certain places can support safer street design, smarter traffic control, and more proactive planning, especially during busy seasons like October.

The project also includes exploratory data analysis, feature engineering, and predictive modeling to better understand patterns in crash severity and frequency. Visualizations such as hotspot maps and seasonal trend graphs will be used to clearly communicate findings to city planners and community stakeholders. By transforming raw crash data into actionable insights, this research aims to contribute to safer urban mobility and data-driven decision-making in Salem.