AT-FAULT CMV CRASHES: Top Behavioral Factors And Outcomes

Analysis from 20 Texas Counties, 2011-2014

TEXAS TRUCKING ASSOCIATION SAFETY MANAGEMENT COUNCIL

Spring SeminarMarch 22, 2018

Waco, Texas

Project background/goals

Combine safety data Evaluate top crash factors Compare oilfield &

non-oilfield locations Visualize CMV crash risk Inform safety efforts Funding through FMCSA,

TTI, and the Center for Transportation Safety Nixon, Texas, ca. 2013. Image by D. Bierling, Texas A&M Transportation Institute.

What did project include?

2,780 CMV crashes Controlled for type,

configuration, weight ‘At-fault’ crashes only

20 counties across Texas Years 2011-2014

Polk County, Texas, ca. 2011. Image by Texas A&M Transportation Institute

Sample area…

Counties geographically distributed across Texas

County selection…

10 oilfield counties

10 non-oilfield counties

Matched by percentiles:• VMT• Lane Miles• Population• Area• ADT• Pop. density

• Also, network configuration

Data sources...

Road-Highway Inventory Network (RHiNo), TXDOT Crash Record Information System (CRIS), TXDOT Commercial vehicle inspection database, TXDPS CVE Well location data, Texas Railroad Commission (RRC) Drilling permit data, Texas RRC American Community Survey, U.S. Census Bureau

Excluded crashes…

Not on state road network No CMV ‘at-fault’ More than one CMV ‘at-fault’ Crashes between CMV and only non-motor vehicles Crashes involving 9+ passenger vans, school buses,

commercial buses, or bus body styles Railroad-related crashes Crashes where at-fault CMV was parkedAll other ‘at-fault’ CMV crashes on state roadways in sample counties were included: 2,780 crashes.

Crash severity measures…

Based on reported injuries/fatalities Estimated Crash Cost Using National Safety Council economic impact values

KABC/O

CMV and passenger vehicle crash on I-10, Sutton County, ca. 2012. Image by D. Bierling, Texas A&M Transportation Institute.

‘Stacked’ data…

1. Physical context – Location, geographic setting2. Roadway properties – Infrastructure,

management, administration3. Driving environment4. Driving apparatus – Vehicle characteristics5. Driver characteristics6. Driver behaviors7. Crash parameters

Driver behaviors…

Contributing factors, vehicle defects, and citations from crash reports

Grouped by frequency and type/similarity

Vehicle defects grouped in one category (low individual frequencies)

Also used for at-fault assignment

Variable Group VariableContributing factors

Backing-relatedParking-relatedPassing-relatedLane use-relatedStopping-relatedTurning-relatedDistracted driving-relatedSpeed-relatedFailure to yield right-of-way (FTYROW)Driver fatigued, ill, or impairedDangerous driving-relatedLoad securement or size-relatedOther moving contributing factorsVehicle defects-related

Citations Alcohol or drugsUnsafe backingDisregarded sign or signalDriving where prohibitedFTYROW - Driveway, intersection, stop sign, or signalFTYROW - Turning-relatedFTYROW - OtherIntoxicated assaultLoad securement, size, or weightOther moving violationsPassing or lane use-relatedSpeed-relatedVehicle or equipment defects Source: Polk County Today (www.polkcountytoday.com

Discussion: Contributing factorsSPEEDING was a factor in 20% of intersection crashes and increased estimated crash costs by 20%.

When a SPEEDING citation was issued in at-fault CMV crashes, there was a 170% increase in risk of injuries & fatalities.

IMPROPER LANE USE was a factor in 31% of single vehicle crashes, resulting in more than 30% higher injury/fatality risk per crash.

Oilfield effects (or not)…

Oilfield/non-oilfield measures did not retain significant relationships with at-fault CMV crash severity, accounting for other variables in models Unexpected result Crash frequencies in oilfield areas are higher,

but not severity per crash

Other factors…

Rural crashes more severe, even after accounting for higher speed limits

Interstate/freeway versus arterial crashes have different relationships for intersection/non-intersection

The following were also associated with increased at-fault CMV crash severity (depending on model): Lower socio-economic status of crash location Low/intermediate flex road surface (compared to high flex

road surface); load-limited roads Van cargo body style (compared to other styles) Heavier (5+ ton) CMV Dark or nighttime crashes (compared to light/day) Intrastate carrier (compared to interstate)

Application – Mapping examples

Atascosa County, Texashttp://www.arcgis.com/home/webmap/viewer.html?webmap=275a3ab6e5c84674a8ced486762f9b66&extent=-110.8984,24.9276,-89.2004,37.2227

For discussion…

Which crash factors (driver fatigue, stopping problems, lane use, speeding, overturns/rollovers) are most important for TXTA SMC members?

Which crash factors are ‘easiest’ to change? How are cost/risk data used to guide training? Are cost and injury fatality risk rates higher or lower

than expected? How are maps that visualize crash locations, causes,

and route risk profiles best used by carriers? By operators?

Integrating Datasets to Evaluate CMV Crash Severity and Risk on Texas Roads

Project Team: David Bierling, Boya Dai, Srinivas Geedipally, Katrina Korenek, Michael Martin, Zachary Palisch, Ashesh Pant, Marcie Perez, Sushant Sharma, Eva Shipp, Amber Trueblood, Ioannis Tsapakis

Project Supervisor:David Bierling, Ph.D.Research Scientist

Texas A&M Transportation Institutedhb@tamu.eduSH 72, Tilden, Texas, ca. 2013. Image by Debbie Jasek, Texas A&M Transportation Institute