An Analysis of CMV Driver Traffic Conviction Data to Identify High Safety Risk Motor Carriers

1. An Analysis of CMV Driver Traffic Conviction Data to Identify High Safety Risk Motor Carriers Brenda Lantz (Brenda.Lantz@ndsu.nodak.edu)

2. Overview • Prior Research • Motivation / Background • Methodology • Results • Discussion – Next Steps

3. Prior Research • Driver/Carrier Relationship Project • Used 1994 citation data from IN and MI • Conclusions: • Violation rates differ among carriers • Higher violation rates associated with higher crash rates

4. Motivation / Background • Driver citation information linked to carriers may be useful, but problems collecting it • Similar correlation with conviction data? • Background of CDLIS • Created through CMVSA, operational since 1992 • How it works – pointer system to each state DMV • Problem: It doesn’t identify the employing carrier • Background of MCMIS • Use crash/inspection reports to link drivers to carriers

5. Study Methodology MCMIS - Database Contains: • Carrier DOT# • Driver CDL# • Safety Data CMV Inspection Data CMV Crash Data State DMV Traffic Records CDLIS Search Combined CDH Records – 82% • Contains: • Driver CDL# • Conviction data • 13,829 carriers • 64,711 associated drivers

6. Methodology • Goal: To obtain about 75,000 driver records • Stratified random sample of carriers, then drivers, from 70 groups • Accident and inspection reports from 9/99 to 9/00 • Sample of 15,829 carriers -- 79,244 drivers • Sent to TML to obtain driver histories • 64,711 driver records retrieved (82%) -- 13,829 carriers • Data included state, DOB, conviction information • Obtained MCMIS census and safety information for carriers as of 9/00 • Created Carrier Driver History Measure (CDHM)

7. Create Driver History Measure (DHM) • 3*(disqualifying offense) + • 2*(serious offense) + • 1*(any other offense) = DHM Create Carrier Driver History Measure (CDHM) Sum of severity weighted # of convictions (DHM) # of drivers for carrier

8. Results • Correlation analysis of CDHM with OOS rates, crash rates, and SEA values revealed significant positive linear correlations • Highest correlation coefficients with driver SEA value, accident SEA value, and driver OOS rate • Correlations held across all size groups and regions • Further analysis of drivers matched with carriers from non-OOS inspections yielded similar results

11. Results (cont) • Creating a CDHI -- 35% (4,604) of carriers in study • Adds 4x more carriers to the Safety Management SEA (4,604 new – 899 existing = 3,705 new) • Provides additional data on smaller carriers that is not being captured by other SEA values • 516 carriers have CDHI but no other SEA • 84% (435) of these had less than 6 drivers • May provide valuable risk information not being captured by other SEA values

12. Create Carrier Driver History Indicator (CDHI) • If sum of DHMs < 2 then do not use • If sum of DHMs = 2-3 then = group 1 • If sum of DHMs = 4-6 then = group 2 • If sum of DHMs = 7-14 then = group 3 • If sum of DHMs > 14 then = group 4 For each group: rank CDHM values & transform into percentiles (0-100) Result is Carrier Driver History Indicator (CDHI)

13. Discussion – Next Steps • Carrier driver conviction data serves as an indicator for carriers with safety problems • Further test the statistical relationships and construction of the indicator • Build a process and add CDHI to SafeStat and/or ISS • Improve accessibility of ISS information • Survey of states • PDA and Query Central