Automated speed and red light enforcement systems help reduce speeding, motor vehicle injuries and fatalities. Despite their effectiveness in advancing public safety, however, when not properly deployed and administered these technologies may impact underprivileged communities and further social inequities.

Understanding the impacts

For many communities, the absence of robust data science practices in the implementation and oversight of traffic systems often exacerbates social inequity in three major areas:

  • Overallocation of cameras and systems in underprivileged communities
  • Over-issuance of citations to Black and Latino motorists
  • Inequitable financial burdens for disadvantaged motorists

Some discrepancies stem from road design. BIPOC neighborhoods, for example, often have wider streets with more lanes and less trac-calming infrastructure. Historic redlining and other practices have placed many communities near expressways, industrial corridors and arterial streets.

Tools to drive change and social equity Data science and analytics is helping agencies optimize the benefits of red light and photo enforcement technologies while curbing other unintended consequences. Leveraging decades of experience, Trellint helps deliver fairer speed and red light enforcement through:

  • Camera allocation: Proper deployment of enforcement systems ensures geographical and socioeconomic diversity, providing the necessary data to ensure defensible decision-making and to meet reporting requirements. Considerations include speeding, road design and overall safety.
  • Annual data-driven assessments: Data is monitored, parsed and contextualized to include speed reduction (speeding events and average speed reduction), accident reduction (including the type of accidents and injuries), and racial and economic diversity (the racial and economic composition of residents and violators). When behaviors fail to improve due to road design, we make recommendations for relocating cameras and the potential for trac calming measures.
  • Fine administration and optimization: Trellint makes data-driven recommendations for payment plans, compliance programs, fine amounts and adjustments, and even the implementation of “day fines,” which provide for fair, graduated penalties based on a motorist’s income

Our Experience

City planners have access to unparalleled amounts of data as they look to improve fairness but often lack time to sort through that information. Our data scientists work with cities to improve fairness and equity by optimizing fine schedules, rethinking enforcement priorities, measuring and improving hearing fairness, and implementing demand pricing recommendations that improve access and affordability for motorists.