Using a data-driven framework to reduce transit worker fatalities

Transit workers across the U.S. are facing an epidemic of violence and injury, with assaults on these essential frontline employees having tripled since 2008, according to data from the National Transit Database. More recent trends show the rate of assaults on transit operators increased by 232% between 2014 and 2024. In 2023 alone, U.S. transit agencies reported over 2,200 major assault injuries sustained on public transit vehicles or within transit environments. 

Beyond assaults, injuries remain a persistent threat. The Bureau of Labor Statistics reports an annual average of 2,460 nonfatal injuries among urban transit workers (NAICS 4851) from 2013 to 2017, with an incidence rate of 6.7 cases per 100 full-time workers; more than double the 3.3 rate for all industries. Fatalities compound this crisis, with an annual average of 15.6 fatal occupational injuries in the transit and ground passenger transportation sector from 2013 to 2017. 

This issue has garnered significant federal attention. Safety is one of the U.S. Department of Transportation’s top priorities, and the Federal Transit Administration issued General Directive 24-1, mandating agencies to assess assault risks, implement mitigations and monitor effectiveness. 

Why most agencies remain reactive 

Despite these mandates, most transit agencies still analyze incidents only after they occur. The primary barrier is data silos. GPS tracking, incident reports, staffing schedules and maintenance logs often live in disconnected systems, preventing integrated analysis and proactive intervention. 

Transit agencies can shift to predictive safety by adopting a three-layer framework that has already delivered 20 to 37% incident reductions in benchmarked transit and rail systems. 

Building the foundation  

The cornerstone is a modern extract, load, transform (ELT) pipeline, which is preferred over traditional ETL for scalability in cloud environments like Snowflake or AWS Redshift. Using Python, Apache Spark and real-time streaming via Kafka, agencies can unify thousands of daily GPS records, incident logs and maintenance data into a governed data lakehouse. For example, one large U.S. transit agency engineered an end-to-end ELT pipeline for over 1,200 buses, handling more than 4,000 daily GPS records (1.4 million annually), which automated on-time performance reporting and eliminated 40-plus hours of monthly manual processing. 

Finding the signals (AI and predictive modeling) 

Integrated data enables artificial intelligence (AI) models (random forests, isolation forests) to identify leading indicators such as route-time-staffing correlations, equipment failures, weather or crowd events that precede assaults or injuries. Continuous retraining loops routinely achieve 85% or better accuracy, with proven 20 to 30% incident reductions in rail and metro environments. 

Delivering actionable insights (real-time dashboards) 

Interactive Tableau or Power BI dashboards with geographic information system (GIS) heatmaps, mobile push alerts and role-based views turn predictions into immediate action, including dynamic staffing, route adjustments or added security. In one major agency implementation, a fatality tracking dashboard reduced manual report generation from eight hours to 30 minutes—a 94% time savings. 

Phased implementation roadmap: 

1. Months one through three: Audit silos and pilot ELT on one route.   
2. Months four through nine: Train models on historical + live IoT data.   
3. Month 10 onward: Deploy dashboards with user training.   
4. Quarterly: Measure KPIs and report to FTA per directive 24-1. 

National impact: Lives and dollars saved 

A 20% reduction across urban transit would prevent approximately 492 nonfatal injuries and save $171 million annually in medical and productivity costs, plus $37 million from fewer fatalities (using USDOT’s $11.8 million Value of a Statistical Life) At the upper end of proven benchmarks (37%), savings exceeded $350 million per year while strengthening national infrastructure resilience.