Data-Driven Street Safety Plan to Reduce Vehicle Crashes | NYU Tandon School of Engineering

Data-Driven Street Safety Plan to Reduce Vehicle Crashes

Center for Urban Science + Progress
Transportation & Infrastructure,
Urban
Project Sponsor:

Bandana Parajuli, City of Paterson, New Jersey

Authors

Sofia Usmani, Johnny Xie, Mirzonabot Mirzonabotov

Research Question

Why are vehicle crashes in Passaic County so heavily concentrated in Paterson, and is it driven by street design or other factors? The project analyzes NJDOT crash data and police crash reports to identify high-risk locations and conditions, moving past a general sense of "dangerous intersections" toward a defensible, data-driven street safety strategy.

Background

Between 2019 and 2026, Passaic County recorded 28,322 crashes, and 27,839 of them (98%) occurred in the City of Paterson, even though Paterson holds only a third of the county's population. Reducing crashes saves lives and reduces costs for the city, for residents' auto insurance and for emergency response.

The city has secured a $500,000 grant to develop a citywide street safety plan, which will support a later $10M grant application to carry it out. What is missing is the analytical work to turn the data into a clear, prioritized plan. This capstone provides that foundation and feeds directly into a live, funded policy process.

Methodology

The project involves:

  • Analyzing city-specific NJDOT crash data to compare Paterson with other NJ municipalities.
  • Analyzing Police Department crash reports for patterns in location, timing, severity, contributing factors, road users involved (pedestrians, cyclists, vehicles) and infrastructure conditions.
  • Identifying which corridors and intersections account for a disproportionate share of severe crashes.
  • Testing whether Paterson's crash rates differ from comparable NJ cities after controlling for population and road-miles.
  • Examining which contributing factors correlate most with high- versus low-severity crashes, and whether behavioral factors such as non-adherence to traffic rules play a role.
  • Mapping crash density and severity and connecting patterns to design factors and enforcement data.

Deliverables

  • Data analysis / statistical report with a comparison to similar cities
  • Crash hotspot mapping / GIS visualization
  • Prioritized recommendations for the street safety plan
  • Dashboard or visualization

Data Sources

  • NJDOT city-specific crash data
  • Paterson Police Department crash reports
  • Moving-violation and pedestrian-violation data