New report: Distracted driving raises crash risk by 240% — a new action plan for states to reduce risk (GHSA)

Drivers who use their phones behind the wheel are 240% more likely to crash, according to a new report released by the Governors Highway Safety Association (GHSA) and Cambridge Mobile Telematics (CMT). The report, A Data-Driven Action Plan for Safer Roads, urges public officials to support and advance predictive analytics to prevent traffic deaths before they happen, marking a fundamental shift in how road safety is approached.

With more than 200,000 deaths on U.S. roads since 2020, the report presents one of the clearest cases yet for moving from reactive safety strategies to proactive ones powered by data and AI. As the report underscores, crashes can be both predicted and prevented with the right resources and data. This action plan emphasizes that these tools — which the insurance industry has proven accurate through decades of refinement — can now be leveraged to make roads safer for everyone.

"This is an action plan to prevent crashes — not just respond to them," said Jonathan Adkins, GHSA’s chief executive officer. "We have the tools to save thousands of lives. What we need now is action. The advanced analytics we outline are validated, predictive and provide a level of foresight that past generations of safety leaders could only imagine. This is a call for government, community and corporate leaders to work together to help us move boldly into a new era of road safety."

Key Insights from the Report

• Predict and prevent crashes: Unlike traditional systems that react to historical trends, new analytics methods using physics and AI-based tools are able to analyze roadway behaviors such as phone handling, speeding, hard braking and aggressive cornering. This allows officials to assess risk as it emerges, enabling them to spot dangerous conditions before they result in injuries or fatalities. Proactive instead of reactive decision making marks a fundamental shift in road safety strategy.
• Proven, validated models: These analytical sources are built on the same actuarially validated risk models that insurers have used and regulators have approved for decades to predict crash risk. Studies have confirmed that one key method, telematics risk assessment (TRA) is built on procedures that can accurately predict crashes. The method uses key risk metrics to help predict areas for further analysis. For example, drivers with the highest levels of phone distraction are 240% more likely to crash, while high rates of hard braking are associated with 103% higher expected losses, and excessive speeding with a 71% increase in predicted losses.
• Fast, affordable safety wins: Road safety officials are already using predictive risk tools to uncover solvable problems that traditional crash data might miss, such as faded paint, poor signage or obstructed sightlines. These are rapid, data-informed interventions that reduce road risk within days.
  Protects individual privacy: Any data source used must be grounded in privacy, safeguarding individuals by aggregating and anonymizing patterns of risk. No personal or trip-level information should be used, and all data collection practices must preserve the rights and privacy of individual drivers.
• Rapid evaluation of effectiveness: A significant advantage of modern data analytics is the ability to rapidly evaluate an intervention’s effectiveness. This leads to continuous improvement of safety programs and builds effectiveness based on improvements in risk, allowing for quicker responses and resource allocation for effective interventions.

Read “New Report: Distracted Driving Raises Crash Risk by 240% — A New Action Plan for States to Reduce Risk” to learn more.