[Stay on top of transportation news: Get TTNews in your inbox.]
The Federal Motor Carrier Safety Administration on Jan. 14 issued a formal request for public comment on how best to design and conduct a major study to identify factors contributing to all large-truck fatal, injury and tow-away crashes.
The Large Truck Crash Causal Factors Study would replace a 15-year-old crash causation study that the agency has used to buttress some of its policy decisions.
The information request seeks information on how best to balance sample representativeness, comprehensive data sources, ranges of crash types and cost efficiency.
In Episode 23 of RoadSigns, we look ahead to trucking's future by looking back. Hear a snippet from host Seth Clevenger, above, and get the full program by going to RoadSigns.TTNews.com.
The request also notes that the study’s methodology should address the use of onboard electronic systems that can generate information about speeding, lane departure and hard braking. In addition, the study should be designed to yield information that will help FMCSA and the truck safety community identify activities and other measures likely to lead to significant reductions in the frequency, severity and crash rate involving commercial motor vehicles, the pre-publication announcement said.
“In the more than 15 years since the original study, many changes in technology, vehicle safety, driver behavior and roadway design have occurred that affect how a driver performs,” the announcement said. “Since the study ended in 2003, fatal crashes involving large trucks decreased until 2009, when they hit their lowest point in recent years (2,893 fatal crashes). Since 2009, fatal crashes involving large trucks have steadily increased to 4,415 fatal crashes in 2018, a 52.6% increase when compared to 2009. Over the last three years (2016-2018), fatal crashes involving large trucks increased 5.7%.”
FMCSA said it would accept comments on the information request for 60 days after publication in the Federal Register, expected to be posted Jan. 15.
Speaking Jan. 13 at a Truck and Bus Data Subcommittee session of the Transportation Research Board’s 2020 Annual meeting, Bill Bannister, chief of FMCSA’s Analysis Division, said the study is currently in the development phase.
“There is a lot to be determined,” Bannister told the subcommittee. “It’s in the developmental concept stage.”
The previous crash causation study examined in detail 120,000 large-truck crashes 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.
The representative crash study sample of 963 crashes involved 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. Of the 963 crashes in the sample, 73% involved a large truck colliding with at least one other vehicle.
Bannister also told the committee that the agency is embarking on a “completing the picture of crashes” study that will use FMCSA’s crash datasets along with additional sources of information to complete a picture of large-truck and bus crashes to better analyze trends and root causes.
“We plan to integrate our data with other datasets,” Bannister said. “This will allow us to drill down into the types of circumstances surrounding crashes, the differences among the types of crashes, and whether it’s the vehicles involved or the roadway that’s involved. This sort of information might provide predictors of crashes.”
Bannister said Jim Mullen, acting head of FMCSA, has challenged the entire agency to focus on the things that will reduce crashes, and particularly fatal crashes, over the next year.
“In particular he wants to see a reduction in 2020,” Bannister said. “It’s not like we haven’t focused on that in the past, but as you know large truck crashes actually continue to increase over the last several years. The emphasis is what can we do, where are the problems, and how do we address them.”
In a separate presentation to the subcommittee, Ryan Smith, a project manager for the National Transportation Safety Board, outlined the formidable challenges finding quality data and understanding data on impaired truck and car drivers using marijuana. In fact, Smith said researchers have actually warned about using the federal Fatality Analysis Reporting System data to understand drug impairment.
NTSB's Ryan Smith discusses toxicology reporting challenges. (Eric Miller/Transport Topics)
Unlike drivers impaired by alcohol, those using marijuana may fail a drug test, but still not actually be impaired to drive, Smith said. And a person who used marijuana may not be impaired until some time after they actually used the drug, he said.
“A lot of people will say that marijuana actually makes you drive more safely,” Smith said. “There is some data to support that.”
On the other hand, alcohol is the only drug that makes drivers much more likely to be involved in a crash, he said.
Smith cautioned that there are a number of problems in assessing impairment issues via data, including:
- Drugs impact each individual differently.
- Not all drugs are included in drug testing panels. For example, there are about 100 different cannabinoids.
- Some testing for alcohol provides results about other drugs.
- Drug databases may be missing critical information about drug matrixes and time between driving arrest and specimen collection.
“There is a lot of data out there that is not properly being discussed,” Smith said. “The concern is that people are using data improperly and coming up with these conclusions that are not in journals but are in news reports. Some of the findings are being twisted and can actually be doing more harm.”
There is good research being done, he said, but he added, it “is hard to interpret unless you’re trained to understand it.”
Want more news? Listen to today's daily briefing: