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Analysis of HOS Rules briefing to the Motor Carrier Safety Advisory Committee

Analysis of HOS Rules briefing to the Motor Carrier Safety Advisory Committee . December 7, 2009 Mark Johnson, Economist, Analysis Division FMCSA Barry Galef, Senior Economist, ICF . Overview: Presenters and Topics. Mark Johnson, FMCSA: Data Sources for Analysis Crash Data

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Analysis of HOS Rules briefing to the Motor Carrier Safety Advisory Committee

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  1. Analysis of HOS Rulesbriefing to theMotor Carrier Safety Advisory Committee December 7, 2009 Mark Johnson, Economist, Analysis Division FMCSA Barry Galef, Senior Economist, ICF

  2. Overview:Presenters and Topics Mark Johnson, FMCSA: Data Sources for Analysis • Crash Data • Fatigue Data • Industry Data Barry Galef, ICF International: Use of the Data for Regulatory Analysis • Cost analysis • Benefit analysis • Impact analysis

  3. Crash Data • Motor Carrier Management Information System Crash Data (MCMIS) • Fatal, injury, and tow-away crashes, but no associated factor info. • Fatal Accident Reporting System (FARS) • Only fatal crashes, but has limited associated factor data • Trucks Involved in Fatal Accidents (TIFA) • Based on FARS, but supplemented with closer scrutiny of police accident reports and follow on questions • Has associated factor and hour of driving information • Large Truck Crash Causation Study (LTCCS) • Most comprehensive associated factor data • Limited collection period and smaller sample size than other data sources

  4. Data on Driving & On-duty Schedules • FMCSA Field Survey (mostly small carriers) • Average length of tour of duty (on-duty and drive time) • Weekly drive time, and use of 11th hour and 34 hour restart • Data on local vs. over-the-road drivers • Schneider Survey (large truck load carrier) • Use of 11th hour and 34 hour restart • Average daily and weekly duty and driving time • OOIDA Survey of Owner-Operators • Data on frequency of use of 11th hour and 34 hour restarts • Anonymous surveys give indications of compliance • UMTIP survey of truck drivers • IIHS anonymous survey of long-haul truck drivers

  5. Industry Profile Data • Truck driver wage and compensation data: Bureau of Labor Statistics • Revenue/Profitability data • TTS Blue Book of Trucking Companies for larger firms • Risk Management Association for smaller firms • Owner-Operator profile – Owner-Operator Independent Driver Association, MCMIS Census • Total size of the industry – MCMIS Census, OOIDA, TTS, ATA and Economic Census • Drivers and Power Units – VIUS, TTS, Economic Census, MCMIS and ATA

  6. RIA Overview: Answering These Questions • What is the baseline? • How do HOS options affect operations? • How do the operational changes affect … • Industry costs? • Crashes? • Given these changes, which options are cost-effective? • What impacts are there besides costs and benefits?

  7. Establishing an Industry Baseline • Profile of the Affected Industry • Divide into short vs. long-haul (LH) • Divide into private fleets vs. for-hire • Divide LH into TL (truckload) /LTL (less than truckload) and team/solo • Operational Patterns • Estimate distribution of freight hauls • Divide into regular/“random” patterns • Estimate current use of HOS provisions

  8. Estimating Cost Impacts of HOS Options • Analysis starts with impacts on schedules • Regular patterns can be assessed directly • Complex/irregular operations call for detailed modeling of HOS options • RIA for 2003 rules used commercial software • RIA for 2005 rules used a computer simulation of “drivers” choosing among randomly generated loads under various HOS constraints

  9. Valuing Changes in Productivity • Assuming the same freight needs to be delivered, lower productivity implies more drivers • Cost of hiring another driver is compared to the cost of using the same drivers slightly more • Examined the compensation of drivers working different schedules to compare costs • Found that a 1 percent drop in productivity increases industry costs by about $300 million

  10. Overview of Benefits Analysis • HOS options affect work schedules  change in amount and timing of rest  change in alertness/fatigue  changes in crashes  change in damages • To draw quantitative conclusions, we had to model most of these steps explicitly

  11. Effect of Work Schedules on Rest • Another hour off duty can mean more sleep – but not on a one-to-one basis • Walter Reed Field Study let us estimate relationship

  12. Driver Health Research Effect of Changes in Rest on Alertness • RIA for 2003 HOS rules used the Walter Reed Sleep Performance Model (SPM) (in Excel) • For 2005, we used SAFTE/FAST (related to SPM) Regular Schedule Irregular Schedule

  13. Driver Health Research Separate “Time on Task” Adder for 2005 and After Average Fatigue Involvement in TIFA 30% Cubic Logistic 20% 10% 0% 0 2 4 6 8 10 12 14 16 18 Hours of Driving • Allowed for an independent effect beyond effect of excessive time awake • Fit a polynomial, then a logistic, to TIFA data

  14. Driver Health Research Assessing the Results • We “monetized” the changes in crashes using a study of crash damages • Subtracting compliance costs from the dollar value of benefits yielded the net benefit of a proposal • Some benefits are hard to quantify, though, and are often left out of net benefit calculations • Other impacts – mode shifts, jobs losses, hardships for small entities – are often important to decision makers, and need to measured

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