March 18, 2021 4:00 PM, EDT

FMCSA Begins Large-Truck Crash Causal Factors Study

Trucks on highwayWendellandCarolyn/Getty Images

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Federal trucking regulators are progressing on plans for a $30 million Large-Truck Crash Causal Factors study intended to gather new critical information and data in an effort to reduce crashes nationwide.

The study, an update to research conducted more than 17 years ago, aims to provide critical insight specifically into how changes in technology, as well as driver behavior, roadway designs and vehicle safety, affect the likelihood of a crash, according to documents used in a recent Federal Motor Carrier Safety Administration presentation.

The research, which is in the very early stages of development, calls for “an evolutionary focus moving from crashworthiness to crash avoidance,” according to Jenny Guarino, an FMCSA statistician.

Jenny Guarino


In a March 10 FMCSA virtual presentation, Guarino said enhancing crash avoidance would be transformative in terms of lives saved. She said it will provide vital data on the role of pre-crash factors like driving behaviors and novel technologies unavailable through other means, and could be used to identify, develop and deploy countermeasures to keep large-truck crashes from occurring. This data could also help understand the role of new automation and fleet technologies, drive potential rulemaking activities, and potentially be reused for additional crash causal factors studies focusing on passenger vehicles.

The study is needed because fatal crashes involving large trucks have steadily increased since 2009, and there is a need to update data and analysis about how changes in technology, vehicle safety, driver behavior and roadway designs impact changes in commercial motor vehicle safety, Guarino said.

“The data will greatly increase our knowledge about causation and related factors sufficient to create countermeasures through legislation, regulation, enforcement and education,” she said.

DOT-FMCSA-2020-00557 by Transport Topics

In January 2020, the agency sent out a request for information seeking input on how the study should be designed. There were 167 comments received from industry, academia and a variety of stakeholders, indicating strong support for a nationally representative sampling design. The information request also sought information on how best to balance sample representativeness, comprehensive data sources, ranges of crash types and cost efficiency.

The agency is on the first of four phases. The second phase calls for study planning, information technology development and Office of Management and Budget clearance. The third phase will be the actual collection of data related to 2,000 crashes studied at 32 sites nationwide. The collection effort is expected to begin in January and take two years to complete.

The final phase calls for detailed analysis of the data and writing the final report.

The previous crash causation study examined large truck crashes in detail that occurred between April 2001 and December 2003 to create a nationally representative sample. Each crash in the study sample involved at least one large truck and resulted in a fatality or injury.

Ryan Smith


The 2003 study was a representative crash study sample of 963 crashes involving 1,123 large trucks and 959 motor vehicles that were not large trucks. The 963 crashes resulted in 249 fatalities and 1,654 injuries. Of the 1,123 large trucks in the sample, 77% were tractors pulling a single semi-trailer, and 5% were trucks carrying hazardous materials. A total of 73% of the crashes involved a large truck colliding with at least one other vehicle.

Speaking at a Jan. 13, 2020, Truck and Bus Data Subcommittee session of the Transportation Research Board’s annual meeting, Ryan Smith, a project manager for the National Transportation Safety Board, outlined the formidable challenges of finding quality data and understanding data on impaired truck and car drivers using marijuana. Smith said researchers have actually warned about using the federal Fatality Analysis Reporting System data to understand drug impairment.

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