Motor Carrier Safety: Additional Research Standards and Truck Drivers' Schedule Data Could Allow More Accurate Assessments of the Hours of Service Rule
Published: 2015-07-29T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: FMCSA—within the Department of Transportation (DOT)—issues rules to address safety concerns of the motor carrier industry, including on truck drivers' HOS. In July 2013, FMCSA began to enforce three new provisions of its HOS rule. GAO was asked to review a 2014 FMCSA study on the rule, as well as the rule's assumptions and effects. This report (1) compares the study to generally accepted research standards, and (2) identifies the assumptions used to estimate the rule's costs and benefits and the rule's driver-operation, economic, safety, and health effects.
GAO identified research standards that professional associations, academics, and GAO's prior work have used. GAO evaluated the 2014 FMCSA study against these standards. GAO also compared FMCSA's assumptions about how drivers would be affected by the HOS rule against actual drivers' schedule data from 16 for-hire carriers that cover the years 2012 through 2014. These data include information on over 15,000 drivers per year, but are not generalizable to the motor carrier industry as a whole.
section_title: What GAO Found
paragraphs: GAO found that the January 2014 study issued by the Federal Motor Carrier Safety Administration (FMCSA) to examine the efficacy of its hours of service (HOS) rule—a regulation that governs how many hours truck drivers transporting freight can work—followed most generally accepted research standards. However, FMCSA did not completely meet certain research standards such as reporting limitations and linking the conclusions to the results. For example, by not adhering to these standards, FMCSA's conclusion in the study about the extent to which crash risk is reduced by the HOS rule may be overstated. GAO found that FMCSA has not adopted guidance on the most appropriate methods for designing, analyzing, and reporting the results of scientific research. Without such guidance, FMCSA may be at risk for excluding critical elements in research it undertakes to evaluate the safety of its rules, leaving itself open to criticism.
FMCSA made several assumptions and anticipated certain effects of the HOS rule in the regulatory impact analysis. Specifically, to estimate the economic costs of the rule, FMCSA assumed that some drivers would lose a certain amount of driving and on-duty time and then estimated the amount and cost of the work time lost. Further, FMCSA assumed that reduced work time could increase a driver's opportunity to sleep, leading to safety and health benefits. Assessing the effectiveness of the HOS rule is difficult because of the limited availability of representative driver schedule data (i.e., records of drivers' work hours). Nevertheless, GAO's analysis of a limited sample of available data provides some insight into the rule's effects and the extent to which they aligned with FMCSA's assumptions and estimates. For example, according to GAO's analysis, some drivers at a sample of 16 for-hire carriers who worked the longest hours (over 65 hours per work week) reduced their work hours after the rule went into effect, a finding consistent with FMCSA's assumptions that drivers working over 65 hours were more likely to be affected. However, GAO's analysis found that drivers who worked less than 65 hours per work week also changed their schedules after the rule went into effect, a result not anticipated by FMCSA.
The ability of FMCSA and others to assess the effects of rules, such as the 2011 HOS rule, is impacted by the limited availability of representative driver schedule data. No organization collects or maintains a centralized database with such data that can be generalized to the motor carrier industry as a whole. Collecting schedule data has historically been difficult, but a recent statutory change that requires carriers to electronically record and store these data provides a potential data source for the future. However, before these data can be used for research purposes several challenges would have to be addressed. First, there are statutory limits on the use of these data for purposes other than enforcing motor carrier safety regulations. Additionally, privacy and cost concerns must be resolved before these data could be made available for analysis. According to FMCSA officials, they do not plan to study how to use these data in a way that will address privacy and cost concerns, in part, because of the statutory limits. Given the potential value of these data to future regulatory analysis, it may be important to provide Congress with information on how these data can be extracted, stored, and analyzed while addressing any privacy and cost concerns.
section_title: What GAO Recommends
paragraphs: GAO recommends that FMCSA adopt guidance outlining agency research standards. FMCSA agreed with GAO's recommendation. GAO also suggests that Congress consider directing DOT to study and report on how electronically collected driver schedule data can be extracted, stored, and analyzed in a way that addresses cost and privacy concerns.

section_title: Letter
paragraphs: The United States economy depends on commercial motor vehicles (e.g.,  large trucks) to move goods across the country. Commercial motor  vehicles were responsible for shipping approximately 13-billion tons of  freight in the United States in 2012—a figure that is estimated to increase  over forty percent by 2040. However, the large number of commercial  motor vehicles on the road poses safety risks. In 2013, there were over  2,500 fatal crashes that involved large trucks.
The primary mission of the United States Department of Transportation’s  (DOT) Federal Motor Carrier Safety Administration (FMCSA) is to reduce  crashes, injuries, and fatalities involving large trucks and buses. To  achieve this mission, FMCSA issues regulations to address safety  concerns, including rules governing the number of hours that property- carrying commercial motor vehicles can work and drive per day and  week. other modes of transportation, such as aviation and rail, and are designed  to ensure that those operating commercial vehicles do not become  fatigued and, as a result, crash.
Federal law gives the Secretary of Transportation the authority to issue hours of service  regulations “when needed to promote safety of operation.” 49 U.S.C. § 31502. Similar,  concurrent authority is provided by 49 U.S.C. § 31136(a).
In 2003, FMCSA published several changes to the HOS rule for drivers of  property-carrying commercial motor vehicles. For example, FMCSA  increased the daily driving limit from 10 to 11 hours, but also extended the  amount of time drivers must spend off-duty following time spent driving or  working from 8 to 10 hours. The 2003 final rule led to a decade of legal  challenges from organizations representing motor carriers, which believed  the HOS rule would negatively impact economic productivity, and from  safety advocacy organizations, which believed that the rule did not  sufficiently protect safety and health. In an effort to address these  concerns, FMCSA revised the HOS rule three more times between 2005  and 2011, publishing a final rule on December 27, 2011. FMCSA began  to enforce the new provisions on July 1, 2013 and in August 2013, the  United States Court of Appeals for the D.C. Circuit upheld all but one  relatively minor provision of the rule.
The 2011 final rule made several key changes to the 2003 rule, including  altering the “restart” provision that allows commercial drivers to reset or  begin at zero the on-duty hours counted against maximum work week  requirements (up to 70 hours over 8 days) by taking 34 hours off-duty. Specifically, the new rule added the following provisions:
The two-night provision: Drivers choosing to use the restart  provision must include two-nighttime rest periods from 1 a.m. to 5  a.m.
The 168-hour limit: Drivers can only use the restart provision once  per week (every 168 hours).
30-minute rest break: Drivers are required to take a 30-minute, off- duty break at some point during the first eight hours of work, i.e., a  work shift, to continue to operate a commercial vehicle.
FMCSA has been required to conduct two studies on the 2011 HOS rule.  Specifically, in July 2012 FMCSA was required by law to complete a field  study examining the impact of the two-night provision of the HOS rule on  commercial drivers in a naturalistic environment. In December 2014,  enforcement of two of the new provisions was suspended by law—the  168-hour limit and the two-night provision—and FMCSA was required to  complete an additional naturalistic study examining driver work schedules  and fatigue levels before and after the 2011 final rule went into effect.,Given the high level of interest in the 2011 HOS rule and its potential  impacts, you asked us to examine research that evaluated the rule and  assess the rule’s effects. This report examines (1) the strengths and  limitations of FMCSA’s field study that examined whether the two-night  provision reduces commercial driver fatigue and (2) the key assumptions  FMCSA used, at the time the 2011 HOS rule was promulgated, in  estimating its effects and what is known about the economic, safety, and  health effects of the rule.
FMCSA was also required to assess safety critical events and driver health outcomes,  among other things. See the Consolidated and Further Continuing Appropriations Act,  2015, Pub. L. No. 113-235, div. K, tit. I, § 133(a) 128 Stat. 2130, 2711 (2014). determined that these data were reliable for the purposes of assessing  the field study, including the methods used. We also conducted a  literature review of scholarly peer reviewed materials, government  reports, and conference papers that discussed the validity and reliability  of the fatigue measures used in the field study, as well as assessed the  relationships between these measures and on-the-road safety outcomes  (e.g., crashes).
To identify key assumptions FMCSA used to estimate the effects of the  2011 HOS rule, we first identified those assumptions that likely had an  important effect on estimated costs and benefits. We then categorized  these assumptions into five areas: which drivers were affected, how  drivers were affected, economic costs, safety benefits, and health  benefits. FMCSA officials confirmed that our characterization of key  assumptions was accurate. To evaluate what is known about the  economic, safety, and health effects of the 2011 HOS rule, we conducted  a comprehensive search for existing databases with information on motor  carrier operations and asked officials from FMCSA and other  stakeholders for recommendations of databases we could use to analyze  the effects of the rule. Through this process we identified three relevant  data sources suitable for analysis: (1) driver logbook data from the  American Transportation Research Institute that covers the years 2012,  2013, and 2014 and includes information on over 15,000 drivers from 16  for-hire carriers; (2) vehicle count data from the Federal Highway  Administration that covers the years 2012 and 2013 and includes data on  non-commodity-carrying (including passenger) vehicles, and commodity- carrying (i.e., commercial) vehicles from select locations in 14 states; and  (3) FMCSA data on all reported crashes involving commercial motor  vehicles in the United States that covers the years 2008 through 2014.  We reviewed the reliability of these data by examining documentation,  talking with knowledgeable officials, and checking the data for  completeness and reasonableness and determined that the data were  sufficiently reliable for the specific purposes of our data analysis. We  analyzed data from each of these sources to identify any possible  changes in motor carrier behavior and safety outcomes as a result of the  HOS rule. While these were the best data available, the results of the  analysis of driver logbook data cannot be generalized to the entire motor  carrier industry. In addition, we used a biomathematical fatigue model that  estimates human alertness based on working schedules to determine  possible fatigue levels for drivers before and after the 2011 HOS rule  went into effect in July 2013. We also reviewed provisions in the Moving  Ahead for Progress in the 21st Century Act (MAP-21) requiring motor  carriers to electronically record drivers’ schedules and compared  FMCSA’s lack of such schedule data to data collection practices of other  DOT agencies that regulate hours of service.
We also spoke with FMCSA officials, the authors of the field study, and  other selected experts and stakeholders, including fatigue experts,  organizations representing motor carriers, safety advocacy organizations,  motor carrier companies, commercial vehicle drivers, and customers of  the motor carrier industry (e.g., companies that ship freight). We selected  fatigue experts based on academic articles they published related to the  fatigue measures used in the field study, as well as our prior work on  fatigue science. We selected organizations representing motor carriers,  safety advocacy organizations, and shipping companies to interview  based on our prior work on commercial motor carrier safety and  recommendations from various stakeholders. We selected motor carrier  companies to speak with based on several criteria, including how they  operated (i.e., the distance they traveled), their fleet size, and the cargo  they transported. Motor carrier companies we spoke with also put us in  contact with drivers whom we interviewed to understand how the 2011  rule had impacted them. The views and information collected from  interviews of these stakeholders are not generalizable to other motor  carrier stakeholders. We also conducted several literature reviews of  academic peer reviewed materials, government reports, and conference  papers that studied the potential impacts of the HOS rule. Appendix I  contains a more detailed explanation of our scope and methodology.  Appendix II contains details about our work to test the sensitivity of the  results of FMCSA’s field study to various alternative methodological  choices. Appendix III contains more detail on the process we used to  identify FMCSA’s key assumptions. Appendix IV contains details on our  analysis of driver schedule data. Appendix V contains details on our use  of a biomathematical fatigue model. Appendix VI contains details on our  analysis of vehicle count data. Appendix VII contains details on our  analysis of crash data.
We conducted this performance audit from June 2014 to July 2015 in  accordance with generally accepted government auditing standards.  Those standards require that we plan and perform the audit to obtain  sufficient, appropriate evidence to provide a reasonable basis for our  findings and conclusions based on our audit objectives. We believe that  the evidence obtained provides a reasonable basis for our findings and  conclusions based on the audit objectives.
subsections: section_title: Background
paragraphs: The commercial motor-carrier industry is a vital part of the U.S. economy.  As of December 2013, FMCSA estimates that there were more than  539,000 active motor carriers and 5.6 million commercial drivers  operating in the United States that transport freight and move people,  numbers that fluctuate over time due to the tens of thousands of carriers  that enter and leave the market annually. Truck carriers are responsible  for transporting approximately 13 billion tons of cargo annually,  representing 67 percent of all cargo weight that was shipped in 2012.
The United States’ commercial motor-carrier industry represents a range  of businesses, including private and for-hire freight transportation, passenger carriers, and specialized transporters of hazardous materials.  These carriers can be small and involve a single vehicle that is owned  and operated by a single individual, or large corporations that own tens of  thousands of vehicles. Similarly, commercial motor-vehicle operations  differ greatly, including whether a carrier operates locally, regionally, or  across the country (i.e., “over-the-road”), as well as the time of day when  a driver operates. In addition, some carriers move goods as part of a  single shipment to one destination (called “truckload” operations), while  other carriers make numerous deliveries to many different destinations  (called “less-than-truckload” operations).
Private carriers run an internal trucking operation to support a primary business in  another industry, such as a retail store chain, while for-hire carriers sell their trucking  services on the open market. vehicle. FMCSA has reported that the most commonly cited driver-related  factor in a fatal truck crash in 2012 was speeding. That same year,  driver impairment, including by fatigue, was the fourth most commonly  cited factor.
Research indicates fatigue can lead to a state of diminished capacity,  which can have ramifications for humans such as having more difficulty  maintaining attention, becoming less communicative, and having reduced  situational awareness. People are then at greater risk of committing  errors in their work, which can ultimately lead to more crashes. Managing  fatigue is particularly critical for tasks that require constant attention, such  as driving a commercial vehicle.
FMCSA is responsible for overseeing the large and diverse commercial  motor-vehicle industry. To do so, FMCSA establishes safety standards for  interstate motor carriers as well as intrastate hazardous-material carriers  operating in the United States. To enforce compliance with these  standards, FMCSA partners with state agencies to perform roadside  inspections of vehicles and investigations of carriers. In fiscal year 2014,  FMCSA had a budget of approximately $572 million and almost 1,100  FMCSA staff members located at headquarters, four regional service  centers, and 52 division offices.
Drivers of commercial motor vehicles in the United States have been  subject to driving and working hour restrictions for almost 80 years. In 1937, the entity that previously carried out certain of FMCSA’s current  functions—the Interstate Commerce Commission—adopted the first HOS  rule. While the specific requirements in several of the provisions of that  rule have changed over time, there are three general requirements that  are still in place today:
Daily off-duty period: Drivers must be off-duty—not working or  driving—a minimum number of hours per day.
Daily driving limit: Drivers can only drive a maximum number of  hours per day.
60/70 hour on-duty limit: Drivers are restricted from driving when  they reach a total of 60 or 70 hours of on-duty time over a rolling 7-  or 8-day period, respectively.
Because the 60/70 hour on-duty limit is a rolling period, drivers must  calculate these hours by looking back over the prior 7- or 8-day period.  For example, according to the schedule below, the driver has  accumulated 68 hours in the 8-day rolling period from Thursday to  Thursday, and 72 hours of on-duty time from Friday to Friday (see fig. 1).  In this example, the driver worked under 70 hours (68 hours total) and is  in compliance with the 60/70 hour on-duty limit from Thursday to  Thursday, but not from Friday to Friday when he worked more than 70  hours (72 hours total) over an 8 day period.
In 2003, FMCSA altered the HOS rule for drivers transporting freight in  several significant ways:
Created a 14-hour “driving window” that restricts driving beyond the  14th consecutive hour after a driver comes on-duty.
Increased the daily driving limit (from 10 to 11 hours).
Increased the off-duty period (from 8 to 10 hours).
Collectively these provisions allow drivers to operate a commercial motor  vehicle for 11 hours a day and be on-duty or work up to 14 hours per day,  but also require drivers not to work or drive for at least 10 hours per day.
FMCSA retained the 60/70 hour on duty limit, but allowed drivers  transporting freight to “restart,” or begin at zero, their on-duty hours  following an off-duty period of 34 hours or longer. Drivers are not required  to take this 34-hour break, but may choose to do so to increase their  scheduling flexibility, among other reasons. The provision, according to  FMCSA, was adopted because it would provide drivers experiencing  sleep loss or significant sleep debt with an opportunity to recover and,  therefore, prevent a significant number of fatigue-related crashes.
To understand the restart provision, consider the example depicted below  in which a driver does not have any on-duty hours for Saturday and  Sunday (48 hours). Prior to the restart provision, this driver would have  reported working 72 hours from Friday through Friday and would be out of  compliance with the HOS rule. Under the restart provision, the driver may  now reset his/her total accumulated hours to zero on Monday because  the driver was not driving or working over the weekend, a period greater  than 34 hours. Therefore, while the driver was not in compliance with the  60/70 hour on-duty limit under the prior rule, he/she would be in  compliance using the new restart provision, having accumulated only 60  hours for the 8 day period from Friday to Friday. (See figure 2).
As previously discussed, in 2011 FMCSA made several significant  changes to the HOS rule, including the restart provision. Specifically, the  rule added the following provisions:
Two-night provision: For the 34-hour off-duty period to count as a  restart, it must include two nighttime periods from 1 a.m. to 5 a.m.
168-hour limit: Drivers can only take a 34-hour restart every 168  hours (once per week).
30-minute rest break: Drivers must take a 30-minute, off-duty break  during the first 8 hours of their work day in order to continue to drive.
According to FMCSA, the 2011 changes were made for several reasons.  Specifically, the two-night requirement was intended to allow drivers more  nighttime rest, which is more restorative than daytime rest because it  better accords with the body’s circadian rhythms. The 168-hour limit was  put into place to reduce fatigue by limiting the ability of drivers to work  close to the maximum number of on-duty hours permitted on a continuing  basis. For example, prior to the 2011 rule changes drivers who used a  restart could work more than 80 hours in an average week. By limiting  use of the restart to once every 168 hours, drivers could work no more  than 70 hours in an average week. Lastly, the rest break requirement was  designed to alleviate fatigue and fatigue-related performance  degradation.
FMCSA asserted that, while the 2011 rule would result in some economic  costs, the changes would affect only a small percentage of drivers who  regularly work long hours and that the safety and health benefits would  outweigh any loss in productivity. Several organizations that represent  motor carriers, however, have stated that the rule negatively impacted a  larger portion of the trucking industry than was anticipated by FMCSA and  that the rule had several unintended consequences, such as forcing a  large amount of commercial vehicle traffic onto the roads during  congested periods of the day.
In July 2012, FMCSA was required by federal law to conduct a study  examining the efficacy of the restart rule, which was to be completed by  March of 2013. The study was to include statistically valid analysis and  data on drivers in real world conditions affected by the maximum driving- time requirements. FMCSA was required to develop a methodology  consistent with prior research examining the two-night provision that was  conducted in a laboratory. Based on these requirements, FMCSA  completed a field study (i.e., looking at drivers in real world conditions)  that focused on the two-night provision, but did not examine the other two  new HOS provisions: the 168-hour limit and the 30-minute rest break. To  complete the field study, FMCSA contracted with Washington State  University and Pulsar Informatics—a technology development and  research company.amounts of data from 106 commercial vehicle drivers during two duty  cycles that included two different restarts of at least 34 hours or longer.  Data collected included information on sleeping and activity patterns,  caffeine use, and three fatigue measures:   Researchers from these institutions collected large  the psychomotor vigilance test (PVT), which was the primary outcome  measure of the study and measured a driver’s ability to react to visual  stimuli, e.g., flashing lights, on a smart phone within a specific amount  of time;  the Karolinska Sleepiness Scale (KSS) that includes a subjective self- assessment of how tired a driver was at a specific point in time; and  lane deviation, which measured lateral vehicle movements within a  driving lane.
Researchers used these data to compare fatigue levels for drivers taking  a restart that included one-nighttime period versus drivers taking a restart  that included two-or-more nighttime periods. Based on this evidence, the  study concluded that a restart with two-nighttime breaks helps to mitigate  fatigue and provides support of the efficacy of the new restart rule. The  final report was submitted to FMCSA in September 2013 and issued  publicly in January 2014.
In December 2014—approximately 18 months after the HOS rule went  into effect—congressional action resulted in the suspension of two  provisions: the 168-hour limit and the two-night provision. These  provisions are suspended until September 30, 2015, or FMCSA  completes a new, naturalistic study—currently underway—examining the  operational, safety, health, and fatigue impacts of the restart provisions in  the HOS rule, whichever comes later. While similar in many respects to  the prior field study, the new study is required to include more robust data  and examine several issues not discussed as part of the prior field study.  For example, both studies collected or will collect PVT data to assess  driver fatigue. However, the new study will collect data over a significantly  longer time period (approximately two weeks of data for each driver for  the prior field study versus 5 months of data for the new study) and will  examine crash and driver health data that were not considered as part of  the field study. FMCSA is required to provide a copy of the new study to  Congress by December 2015.
section_title: The Field Study Followed Several Accepted Research Standards, but Did Not Report Its Limitations or Make Conclusions That Were Fully Linked to the Results
paragraphs: Based on our prior work evaluating research programs, our internal  expertise in research design, and established guidelines and reports for  conducting research and program evaluations, we identified six generally  accepted research standards that are critical for designing, analyzing, and  reporting the results of scientific research. FMCSA’s 2014 field study  followed three of these standards, including using measures that are valid  and reliable, employing quality controls, and reporting results that are  supported by data (see table 1). The field study partially met the  standards of using a methodology that supports the study’s objectives  and reporting conclusions that are linked to the results. The field study did  not meet the standard of reporting methodological limitations and their  impacts.
The primary fatigue measure used in the field study is valid and reliable  and supported by two secondary measures: According to our literature  review and interviews with fatigue experts, the primary fatigue measure  used in the field study—the PVT—is a reliable and valid measure of  fatigue and is widely used in fatigue science research. While we  identified limitations on the reliability and validity of the secondary  measures used in the field study—the KSS and lane deviation—we also   We also discussed their  found they are widely used in fatigue science.use with FMCSA officials and determined that the KSS and lane deviation  measures were used to support the primary findings from the PVT.  Specifically, FMCSA officials stated, and we agree, that using multiple  measures with different strengths and weaknesses can help to reinforce  findings. In the case of the field study, each of these three measures  independently supported the finding that drivers taking a one-night restart  were more fatigued than those taking a two-or-more night restart.
The field study included controls to identify data inconsistencies and  errors: Specifically, the field study involved a visual data check that  compared data for each driver across nine variables, including activity— as recorded by a wrist monitor, test results on the PVT and KSS, and  GPS location. Researchers used this data check to identify any obvious  errors and possible contradictions in the data, such as instances when  the data might indicate a driver was coded as driving and sleeping  simultaneously. Researchers also debriefed the study participants at the  end of the study to discuss inconsistencies, mistakes, or missed entries  as well as any outliers on the fatigue measures. This approach allowed  researchers to account for incomplete or obviously biased data. Based on  our review of the data and statistical coding, we believe that these quality  controls were sufficient to ensure the data used in the field study are  reliable.
The results and analysis reported in the study are supported by the data:  To assess whether the results and analysis were supported by the data,  we replicated several aspects of the study. First, we acquired the raw  data and the computer programs used in the field study to process the  data and run statistical analyses. As a check on the reliability of the  computer programs, we applied the programs to the data for one driver  and verified that the results were consistent with numbers reported in the  field study. Because the same computer program was applied to all  drivers by the authors of the field study, this check provided assurance  that the analysis for all drivers was reliable. Second, using data files  provided by the authors, we replicated demographic information reported  in the study, including average age, years of experience, and sex of the  driver, among others. Similarly, using data files provided by the authors,  we replicated the main study results. For example, our replication showed  that the study accurately reported the number of reaction time errors or  “lapses” on a PVT test for drivers following a one-night restart and a two- or-more night restart.
The methodology used in the field study partially supports the study’s  objectives: The field study examined drivers in a naturalistic environment,  driving their normal routes, and collected fatigue related data in a way  that was consistent with the study’s objectives. The study collected data  on sleep patterns, fatigue, and driving behavior using wrist monitors,  smartphones, and sensors attached to truck equipment. These methods  did not require drivers to interact repeatedly with researchers or change  their driving behaviors in response to study requirements, which  otherwise might have biased measurements or prevented the results from  applying to real world conditions. In addition, we determined the statistical  method used to evaluate the study’s findings—mixed-effects regression  modeling—was appropriate. Mixed effects modeling is an accepted  method of analyzing longitudinal data, which, in this study, involved the  same drivers taking the PVT at various times of day over several weeks.  In addition, the models reasonably allowed for the possibility that the  rule’s effects could vary according to the time of day and period of  observation.
However, we did find one area in which the methodology used did not  support the study’s objectives. Specifically, the purpose of the study was  to examine the efficacy of the 2011 HOS rule, which required drivers  choosing to take a restart include two-nighttime periods (from 1 a.m. to 5  a.m.). This rule was expected to reduce fatigue for drivers that previously  took restarts with one-nighttime period as part of their restart. Therefore,  the study should have been designed to compare the ability of drivers to  perform fatigue tests, e.g., the PVT, after a one-night or a two-night  restart. However, the field study actually compared drivers with one-night  restart breaks to those with two or more nights of rest. One concern with  including drivers taking more than two nights rest is the possibility of  biasing the results. For example, drivers with more rest could be less  fatigued and have fewer lapses on a PVT test. Thus, including such  drivers does not directly support the objective of the study to determine  the impact of the changes to the restart provision from a one-night to a  two-night restart. According to FMCSA officials, the decision to include  drivers taking more-than-two night restarts was a judgment call made by  researchers, and was likely due to the timeframe mandated by law for  completing the study.
Following the study’s issuance FMCSA officials stated that they  conducted additional analysis and determined that the inclusion of drivers  with a two-or-more night restart did not influence the results. We  conducted a similar analysis and also found that the inclusion of drivers  with two-or-more night restarts did not bias the results. Specifically, we  found that the number of on-duty, PVT lapses for drivers who took a two- night restart was virtually the same as those taking a two-or-more night   While we did not identify any bias, the decision to include drivers  restart.with more than two-night restarts was not appropriate given the objectives  of the study.
The results of the field study support the report’s conclusions about  fatigue, but conclusions about crash risk may be overstated: The study  found a statistically significant difference across several measures  between drivers with a one-night restart and those with a two-or-more  night restart. Specifically, drivers with a one-night restart had more lapses  on the PVT, rated themselves as more sleepy on the KSS, and had  greater lane deviation than those with a two-or more night restart.
FMCSA used these results to support two points in the conclusions  section of the study: (1) the two-night provision mitigates fatigue, and (2)  the two-night provision supports the efficacy of the restart rule and has  implications for real-world driving performance, road safety, and crash  risk. As discussed above, our review of relevant research and discussions  with fatigue experts shows that the primary measure used in the field  study—the PVT—is a reliable and valid measure of fatigue and is  supported by the KSS and lane deviation, which are widely used in  fatigue science. Therefore, the evidence supports FMCSA’s conclusion in  the study that the two-night provision mitigates fatigue.
Lane deviation, as defined in the field study, could mean lateral movement within a lane  and did not imply crossing a line into another lane. test—an average difference of 0.3 lapses. In addition, FMCSA found a  larger difference in the number of lapses between drivers taking a one- night versus a two-or-more night restart when operating at night—an  average difference of 0.8 lapses per PVT test. FMCSA used these  results to support its conclusions that this number of additional lapses,  i.e., 0.8 more lapses, has “implications for real-world driving performance,  road safety threat detection, evasive maneuvering and braking response  speed and crash risk.” Further, FMCSA also provided a hypothetical  example of the distance a truck could travel during a single lapse, noted  that such inattention may mean the driver is not seeing critical  information—such as road signs, traffic signals or other vehicles—and  concluded that “an increase in lapses of attention increases crash risk.”
The conclusion of the report—which implies a direct relationship between  the study’s results and crashes—may overstate the findings and  erroneously imply that this study provides evidence of reduced crash risk.  As we discuss later in this report, while there is evidence that fatigue and  crashes are generally related, we found little research that attempted to  quantify this relationship using any of the fatigue measures in the field  study. In particular, through our review of literature and conversations  with fatigue experts, it was uncertain the extent to which, if any, a  difference of 0.3 or 0.8 lapses on the PVT might be related to on-road  safety outcomes.directly link results to crash risk due to challenges associated with   According to FMCSA officials, its study does not  performing such a study. Instead, FMCSA used lane deviation as a proxy  measure for crashes and driving performance. In addition, FMCSA  officials stated that, since fatigue is connected to crash risk, increases in  fatigue, such as those shown in the field study, are associated with crash  risk. While we agree that evidence generally supports that fatigue and  crash risk are related, we are uncertain how fatigue differences of the size  reported in the field study would be associated with crash risk. Thus, the  safety implications and policy importance of the study’s estimated effects  on fatigue may be overstated.
The field study did not report all of its methodological limitations, including  how these limitations might have affected the results: As with any  research effort, FMCSA made methodological choices to achieve the  goals of the study under the requirements outlined in MAP-21. For  example, according to FMCSA, many of the decisions on recruiting  drivers to participate were dictated by the short time frame under which  FMCSA was required to complete the study—approximately 9 months— and the availability of resources. After considering several of these  methodological choices that we reviewed, such as the sample size used,  they appear to be reasonable given these constraints. However, by not  reporting how certain decisions may have limited the study and impacted  the results, FMCSA did not follow accepted research standards, as  discussed below:
The field study did not describe its driver recruitment process.
According to FMCSA officials, participants in the study were recruited  from three carriers that performed several different types of operations  (i.e., local, regional, and over-the-road). One of these motor carriers  was selected because of a prior relationship it had with FMCSA and  the other two carriers were chosen after being identified as relatively  safety conscious. A third-party data provider used logbook data from  these carriers to identify drivers that met several criteria—including  working near the maximum allowable weekly hours—which FMCSA  used to identify specific operating locations that were likely to employ  drivers meeting the study’s requirements. By not including information  on the recruiting process in the field study, FMCSA’s study prevents  readers from assessing potential bias in the collection of data and  invites skepticism of the recruitment process. For example, FMCSA  did not disclose that it recruited a substantial proportion of its sample  of drivers with a one-night restart from a single, large U.S. city,  according to one of the carriers selected to participate in the field  study. Because these drivers likely operate in a similar environment,  such a sample raises questions about whether these drivers’  measured fatigue levels after one-night restarts might have been  related to other factors with that location. Such other factors might  include characteristics of the local traffic or trucking operations.  According to FMCSA officials, they recruited drivers in this manner  because they needed to complete the work in a relatively short time  frame—approximately 9 months—and had difficultly identifying drivers  likely to take a one-night restart. While this method of recruiting  drivers does not necessarily bias the study’s results, it has the  potential to do so and should have been reported by FMCSA.
The field study did not report the implications of its sample size. The  field study collected data on a sample of drivers that produced  statistically significant results. However, the sample size was not  sufficient to address questions that have been raised about how the  restart provision affects the diverse commercial-trucking industry.  Several stakeholders we spoke with said that the restart provision  likely has different effects across industry segments, including local,  regional, and over-the-road drivers. These stakeholders said that the  restart provision may more successfully reduce fatigue for over-the- road drivers who are away from home for weeks at a time than for  drivers who operate locally and sleep in their own beds every night.
We analyzed the study’s data to assess the implications of its sample  size. We found that, while the field study included a diverse population  of drivers from industry segments, its sample did not include enough  drivers from each of these segments to estimate differences in PVT at  all or to identify statistically significant differences.assessed the sample size that might have been required to estimate  significant differences across industry segments. We found that the  field study’s sample size was insufficient to estimate significant  differences in the primary fatigue measure—the PVT—for each of   In addition, we  several segments of the industry.decisions on the sample size for the field study were made to  accommodate the deadline for when it must be completed. Given the  importance of understanding how the results of the field study might  have varied by segment, FMCSA should have stated how the study’s  sample size limited the generality of its findings and conclusions.
According to FMCSA officials,
The study did not report how a critical analytical decision might impact  the results. As was discussed above, the field study compared drivers  with one- versus two-or-more night restart breaks. According to  accepted research standards, FMCSA should have reported why  drivers taking a more than two-night restart were included in the  study, even though the rule should have been designed to compare  ability of drivers to perform fatigue tests, e.g., the PVT, after a one- night or a two-night restart. Furthermore, FMCSA should have  reported the potential for this decision to bias the results and, in  particular, to exaggerate the difference in fatigue between the two  groups of drivers.
According to FMCSA officials, researchers followed several standards  when developing and conducting the field study, including Office of  Management and Budget’s (OMB) guidelines on the use of independent  peer review panels and the DOT Scientific Integrity Policy. The OMB  guidelines FMCSA used require agencies to use a peer review process  for important scientific information to enhance its quality and credibility.  FMCSA officials reported using the guidelines to evaluate the research  design prior to implementation as well as to assess the report findings. In  addition, DOT’s Scientific Integrity Policy lays out nine elements that  primarily detail the importance of communication, transparency, and  integrity when using scientific information for decision making. While  these standards address several important considerations, including  enhancing credibility and transparency, they do not provide specific  guidance on how to determine appropriate methods for designing,  analyzing, and reporting the results of scientific research. Without such  guidance, FMCSA research may not include critical elements, as  occurred in the reporting of the field study. Without these elements,  FMCSA leaves itself vulnerable to criticism over the integrity of its  research—an important consideration, given the heightened profile of  HOS regulations.
section_title: FMCSA Anticipated Several Effects of the 2011 Hours of Service Rule, and While Available Data Provide Some Insight, Data Limitations Hinder the Ability to Fully Assess the Rule’s Effects
paragraphs: FMCSA used several key assumptions to estimate the costs and benefits  of the 2011 HOS rule. For example, FMCSA assumed that the rule  would result in schedule adjustments for approximately 15 percent of  drivers who work more than 65 hours per week on average. These  assumed schedule adjustments drove FMCSA’s estimates of the  economic costs and the health and safety benefits of the 2011 HOS rule.  Our analysis of available data provides some insights into the potential  effects of the 2011 HOS rule. For example, we found that about 12  percent of drivers in our dataset worked an average of over 65 hours per  8 day work week before the rule went into effect. The lack of nationally  representative driver schedule data limits the ability of researchers to fully  evaluate the rule’s impact. Although soon all carriers will be required to  collect schedule data electronically and these data could be useful for  evaluating the effects of this rule and in formulating future rules, current  legal restrictions limit how these data can be used.
subsections: section_title: FMCSA Used Several Key Assumptions to Estimate the Potential Effects of the Rule
paragraphs: As part of its rule-making process, FMCSA developed assumptions about  the motor carrier industry and its operations to estimate the economic  costs and the safety and health benefits of the 2011 HOS rule. For  example, FMCSA assumed that drivers working more than 65 hours per  week would incur the majority of schedule changes as a result of the  rule. FMCSA estimated that such drivers made up approximately 15  percent of the driver workforce. FMCSA then made assumptions about  how drivers would likely be affected by the rule to estimate a weekly per  driver work time reduction of 0.05 hours (3 minutes) for drivers working an  average of 60 hours per week; .38 hours (23 minutes) for drivers working  an average of 70 hours per week; and 8.7 hours for drivers working an  average of 80 hours per week. To estimate the economic costs of the  rule, FMCSA calculated the cost of this lost work time and estimated $430  million in annual costs. Further, FMCSA assumed that reduced work  time would increase a driver’s opportunity to sleep leading to safety and  health benefits. FMCSA estimated the rule would save between 10 and  26 lives per year and result in $150 to $390 million in annual safety  benefits. FMCSA also estimated between $70 million and $850 million in  health benefits. (See figure 3).
section_title: Analysis of Available Data Provides Some Insight into the Effects of the Rule
paragraphs: Analyzing available data can help provide some insight into the rule’s  potential effects and the extent to which they aligned with FMCSA’s  assumptions and estimates. Specifically, we conducted a  comprehensive search for available data that might provide insight into  the rule’s effects in the five main areas in which FMCSA made  assumptions: (1) which drivers were affected, (2) how drivers were  affected, (3) economic costs, (4) safety benefits, and (5) health benefits.  The results from our analysis of schedule data from 16 for-hire carriers is  consistent with FMCSA’s assumption that drivers working more than 65  hours per work week would be more likely to reduce their work hours. We  also found support for FMCSA’s assumption that certain schedule  changes could reduce driver fatigue and while total crashes, and crashes  with injuries did not appear to change, crashes involving fatalities may  have declined after the rule went into effect. However, we were unable to  assess, due to a lack of data, FMCSA’s assumptions on economic and  health effects. All of the data and analyses we used had limitations,  including the extent to which the data are representative of the motor  carrier industry and the extent to which we can attribute observed  differences to the rule change. In addition, we could not fully analyze the  possible effects of the 2011 HOS rule due to the relatively short time  frame during which the rule was in place—approximately 18 months. (See  table 2).
Which drivers were affected: Our analysis of 2012 to 2014 driver  schedule data from 16 for-hire motor carriers is consistent with FMCSA’s  expectation that the percentage of drivers working the longest hours— over 65 hours per 8-day work week—decreased after the rule went into  effect on July 1, 2013. Specifically, we found that the percentage of  drivers in our dataset working over 65 hours per 8-day work week  decreased from 12 percent immediately before the rule went into effect to  6 percent afterwards. Our analysis also found that the percentage of  drivers working between 55 and 65 hours per 8-day work week  decreased from 33 percent to 29 percent. In contrast, the percent of  drivers working 35 to 55 hours per 8-day work week increased from 48  percent to 57 percent after the rule went into effect. (See figure 4).  However, the driver schedule data used in our analysis are not  representative of the entire motor carrier industry and do not include any  information on drivers who work in some segments of the trucking  industry, such as private motor carriers. Therefore, our findings cannot be  generalized to the motor carrier industry as a whole.
In addition, our interviews with representatives from 20 motor carriers  suggest that a wide variety of motor carrier operations were potentially  affected by the HOS rule. Specifically, 13 motor carriers we spoke with,  including 10 for-hire carriers and three private carriers, stated they were  affected by the rule. These carriers described themselves as truckload  carriers, less-than-truckload carriers or a combination of both and  employed drivers working both more than and less than 65 hours per  week. In addition, seven motor carriers reported they were not affected by  the HOS rule, including three for-hire carriers, three private carriers, and  one carrier that used a truck on a part-time basis and did not fall into  either category.
How drivers were affected: Our analysis of schedule data from 16 for-hire  carriers supports the assumption that some drivers may be working fewer  hours and using restarts less often. Specifically, as shown above in figure  4, these schedule data show that fewer drivers were working more than  55 hours per 8-day work week after the rule went into effect in July 2013.
We also estimated that drivers, on average, worked approximately 1 to 2  fewer hours per work week after the rule went into effect. In addition, the  number of restarts drivers took per calendar week (168 hours) generally  declined after the rule went into effect, including for drivers working more  than 65 hours per 8-day work week (see fig. 5). We also found that  drivers took approximately 6 percent fewer restarts per week, on average,  after the rule went into effect.
We also found evidence supporting a shift in traffic volume from night  time hours to other parts of the day following the effective date of the  HOS rule. These shifts cannot be directly attributed to the rule change  and the data we analyzed from the Federal Highway Administration were  limited to 14 states. Our analysis showed that commodity carrying, i.e.,  commercial, motor vehicle traffic volume increased by 2.6 percent  between November and December 2012 and November and December  2013. However, as figure 6 shows, this increase was not consistent  across the time of day. Specifically, commodity carrying motor vehicle  traffic mostly increased during daytime hours (6 a.m. to 6 p.m.) and  decreased during evening and nighttime hours (6 p.m. to 6 a.m.), relative  to the increase in overall commodity carrying vehicle traffic. The largest  differences from the growth in traffic volume occurred in the middle of the  night (11 p.m. to 4 a.m.), when volume declined or remained flat despite  an increase in commodity carrying vehicle traffic overall and in the mid- morning hours (7 a.m. to 10 a.m.) when volume grew faster than the  overall growth rate.
Consistent with our data analysis, representatives from 13 of the 20 motor  carriers we interviewed reported a range of operational effects or changes  they attributed partially or entirely to the 2011 HOS rule. The other seven  motor carrier representatives did not report any operational effects due to  the rule. For example, motor carriers reported a decrease in driving hours  per driver (10 carriers), less frequent restart use (10 carriers), more  driving during periods of traffic congestion (8 carriers), hiring more drivers  (6 carriers), and an overall decrease in operational flexibility (10 carriers).  Several of these operational changes, such as a decrease in driving  hours per driver and subsequent hiring of more drivers to make up for  reduced driver productivity, could lead to more commercial vehicles on  the road.
Economic Costs: We interviewed motor carriers and industry  stakeholders about the economic impacts of the 2011 HOS rule but did  not find a data source that would allow us to reliably analyze these  impacts for the industry. Representatives of 11 out of the 20 motor  carriers we interviewed mentioned negative economic impacts due to the  2011 HOS rule. Ten motor carriers reported a productivity loss, six  reported a decrease in delivery timeliness, six reported less profitability,  and four reported an increase in driver turnover. The other nine motor  carriers we interviewed reported no negative economic effects. In  addition, four of the eight drivers we spoke with reported a decrease in  their income due to the 2011 HOS rule. These drivers reported that  reduced work time—fewer or shorter work shifts—was the reason for their  loss in income. The four drivers that reported no decrease in pay either  did not have a schedule change or were working more hours per week to  compensate for schedule changes due to the rule. Other industry  stakeholders we interviewed, including 12 out of 13 associations  representing segments of the motor carrier industry, shippers, and  warehouse operators, also reported negative economic impacts on their  respective industries due to the 2011 HOS rule, such as reduced  productivity. For example, some industry stakeholders told us their  members must hire more drivers to do the same amount of work and  keep more inventory in stock to mitigate delays and delivery uncertainty.
Safety Benefits: According to academic literature we reviewed, fatigue  can negatively impact the ability to perform tasks. Fatigue, as a result of  being awake for long periods of time or insufficient sleep, can have a  negative impact on driver performance and can lead to safety critical  events, including crashes. However, the literature is less clear on which  HOS interventions (e.g., rest breaks and limiting driving and on-duty time)  best minimize fatigue and reduce crash risk. For example, research has  found that crash risk is higher when drivers start their shifts or after taking  an extended break; but research also shows that drivers who do not take  breaks have a higher crash risk than drivers who do take breaks.
Our analysis suggests that drivers’ operating after the HOS rule went into  effect on July 1, 2013, could have a lower risk of fatigue.  To assess the  extent to which the 2011 HOS rule might affect driver fatigue, we  conducted two separate analyses using a biomathematical fatigue  model. First, we used the model to compare hypothetical driver  schedules that complied with two of the 2011 HOS rule provisions—the  168-hour limit and the two-night provision—to similar schedules that did  not comply with the rule but were in compliance with the previous HOS  rule.the rule went into effect would result in lower fatigue scores and,   We found that some schedule changes that would be required after  therefore, a lower risk of driver fatigue.schedules we modelled that had to change after July 1, 2013, in order to  comply with the rule were:
Maximum Allowed-Hours Day and Night schedules: 14-hour shifts  over 5 consecutive days or nights. Drivers following a daytime  schedule of 14-hour shifts would have to modify their schedules to  comply with the 168-hour limit. Specifically, they could no longer take  a restart with less than 168 hours (7 days) between when they began  their last restart period and the beginning of the next restart period. To  comply with the 168-hour limit, drivers following this daytime schedule  must take 2 days off instead of one day off between work cycles. For  night drivers working 14-hour shifts, working 5 consecutive days with  one day off for a restart would not comply with either the 168-hour  limit or the two-night provision. These drivers must also take 2 days  off instead of one day off between work cycles. The longer restart  period (2 days versus one day) taken by these hypothetical day and  nighttime drivers working the maximum hours allowed each day  results in lower fatigue scores and, therefore, lower risk of driver  fatigue over the five day work cycle.
Sixty- and 70-Hour Night Schedules: 10- to 12-hour shifts over six  consecutive nights. Drivers working through the night but working less  than the maximum allowed 14 hours each day would also have to  change their schedules after July 1, 2013. Specifically, these drivers  would have to take 2 days off between work cycles in order to comply  with the two-night provision. Again, the longer restart period (2 days  versus one day) results in the hypothetical driver having lower fatigue  scores and, therefore, a lower risk of fatigue over the 6-day work  cycle.
Our second analysis using the biomathematical fatigue model estimated  possible fatigue effects of the 2011 HOS rule based on information  provided by motor carriers, industry stakeholders, and drivers we  interviewed. We found that some of the real world schedule changes  described by motor carriers resulted in a lower risk of driver fatigue, while  other schedule changes showed a higher risk of driver fatigue. For  example, a motor carrier told us that, to comply with the two-night  provision, several of its nighttime drivers switched from a 6-day schedule  to a 5-day schedule. Our analysis of this schedule change shows that  drivers under this schedule have a lower risk of fatigue after the rule went  into effect. In contrast, a different motor carrier told us that, to comply with  the 168-hour limit, several of its nighttime drivers switched from a 4-day  schedule with 3 days off to an alternating schedule of 5 days working and  2 days off and then 5 days working and 3 days off. Our analysis of this  schedule change shows that these drivers have a higher risk of fatigue on  certain days in their schedule after the rule went into effect. This may be  due to more work time and less off-duty time for drivers following the  schedule that complies with the 2011 HOS rule.
We also collected data on crashes involving commercial trucks from 2008  to 2014. As shown in figure 7, the monthly incidence of truck crashes and  crashes involving fatalities varies considerably over time. To assess  whether the rule had any discernable effect on crashes, we conducted  two types of analyses. We examined (1) whether the rule affected the  number of monthly crashes and (2) whether the rule resulted in any shift  in the time of day in which crashes occurred.
Trend in crashes over time: Many factors influence the number of crashes  over time. For example, as the economy grows, typically there will be  more trucks on the highways moving freight and this would be expected  to be correlated with a higher incidence of crashes, all other things being  equal. We used a statistical model to examine how the rule’s  implementation may have affected the number of monthly crashes,  crashes with injuries, and fatal crashes, while controlling for other factors  that affect the incidence of crashes, such as a proxy for trucking volume  and seasonal variation. Our analysis showed a strong correlation  between our measure of trucking volume and crashes—crashes occurred  with more frequency when the industry was moving more freight—which  is consistent with other previous work.found a positive statistical association between the implementation of the  rule and the number of all crashes, suggesting that crashes rose—holding  other factors constant—after the rule when into effect. However, our  discussions with industry participants indicated that the winter of 2013 to  2014 was unusually harsh and made operations very difficult for the  industry. These weather conditions may have been a contributing factor,  irrespective of the rule change, to a short-term rise in crashes in  December 2013 through February 2014. When we used statistical  methods to control for these months in a second iteration of our analysis,  we no longer found a statistically significant increase in the number of  crashes after implementation of the rule. That is, the incidence of crashes  would appear to not have changed due to the rule in this scenario. Our  analysis of crashes with injuries and crashes with fatalities was similarly  structured—we included variables to account for truck volume, seasonal  variation, and in a second iteration of the model, the unusual 3 months of  weather during the winter of 2013 to 2014. We found that the  implementation of the rule was not associated with any change in the  incidence of crashes with injuries, whether or not we statistically  controlled for the winter of 2013 to 2014. However, our analysis does  indicate that crashes involving fatalities may have become less frequent  after the implementation of the HOS rule.
In addition, our initial analysis  Time of day shifts in crashes: Some stakeholders told us that the two- night provision of the HOS rule would potentially result in an increase in  crashes involving commercial trucks during morning rush hours (i.e., 5  a.m. to 9 a.m.). They believed that some truckers who might have  previously driven during the middle-of-the-night hours would be shifted to  the early and mid-morning timeframe due to provisions of the new HOS  rule. To test this possibility, we developed statistical models to examine  whether, conditional on the number of crashes that occurred, there was a  change in the likelihood of truck crashes occurring between 5 a.m. and 9  a.m. after the HOS rule went into effect. Our analysis found no statistically significant change in this likelihood associated with the  implementation of the rule.
An important caveat to our statistical analyses of crash data is that there  are only 15 months of available data following the implementation of the  rule. These limited data, which have non-trivial variation month to month,  may not suffice to discern trends in the incidence of crashes. For  example, as we explained above, our post-rule time frame covers only  one winter—a time when crashes tend to rise—and we were told that this  was an unusually difficult winter for the industry. These data limitations  affect the ability to draw empirical inferences on the effects on crashes of  the rule’s implementation.
The 20 motor carriers we interviewed said they believed the rule had no  noticeable effect or a negative impact on safety. Of the eight drivers we  interviewed, one said the rule had no effect on safety, and seven said  they had seen a negative impact on safety. For example, drivers  mentioned that the time pressures they stated were associated with the  2011 HOS rule—particularly the 30 minute rest break—had resulted in  more speeding. Drivers said that speeding occurs as drivers try to make  deliveries or mileage goals before taking a 30-minute rest break. Safety  groups we interviewed generally supported the 2011 HOS rule and  thought it could improve safety for certain segments of the industry, but  had concerns about ensuring compliance with the rule.
Health Benefits: Academic literature we reviewed indicates that  insufficient amounts of sleep have negative health impacts. These effects  include obesity, diabetes, hypertension, and cardiovascular disease.  However, we did not find a data source that would allow us to assess  health impacts of the rule. According to FMCSA officials and other  stakeholders we spoke with, it could take years to identify health impacts  following an intervention, such as the HOS rule. In addition,  representatives from 16 of the 20 motor carriers we interviewed said they  believed the 2011 HOS rule had no noticeable health effects. The other  four motor carriers said they either noticed negative health effects— due  to increased stress or fatigue as drivers tried to make up for lost income,  among other reasons—or had no way to measure health impacts. In  addition, of the eight drivers we interviewed, six said their health was  negatively impacted by the 2011 HOS rule, and two said there has been  no impact. Those reporting a negative health impact cited, for example,  being more stressed as they adjust to new schedules or try to make up  for lost income. Safety groups we spoke with generally thought the 2011  HOS rule could improve driver health, but that health impacts would be  difficult to attribute to the rule change.
section_title: Lack of Representative Driver Schedule Data Limits Analysis of the Effects of the HOS Rule
paragraphs: The ability of FMCSA and others to assess the effects of the 2011 HOS  rule is impacted by the limited availability and representativeness of driver  schedule data (i.e., records of drivers’ work hours). No organization,  including FMCSA, collects or maintains a centralized database with  representative driver schedule data that can be generalized to the entire  motor-carrier industry. For example, while we obtained and analyzed  drivers’ schedule data from a private research organization that includes  over 15,000 usable driver records from 16 for-hire trucking companies,  findings from that dataset cannot be generalized to the estimated 539,000  active motor carriers and 5.6 million drivers who operate in the United  States. As a result it is not possible to determine the extent to which  different types of commercial motor carriers, such as private carriers or  drivers who own and operate their own vehicle, were affected by the rule  because they are not included in the dataset. Similarly, FMCSA does not  collect or use representative driver schedule data. Instead it uses  roadside inspection, compliance review, and safety audit data as well as  special studies to analyze motor carrier and drivers’ safety behaviors.
Collecting representative schedule data is well recognized as a valuable  analytical resource. Other DOT agencies that regulate the hours of  service for transportation operators have demonstrated the importance of  using representative schedule data. For example, the Federal Railroad  Administration has collected logbook data from a representative sample  of rail employees to examine fatigue and safety concerns since 2001. The  Federal Aviation Administration also has access to electronic flight data  on aircraft operations that is used to identify and analyze national trends,  target agency resources, and identify and reduce or eliminate safety risks.  In addition, FMCSA itself has cited the need to evaluate the actual  impacts of its regulations, including the 2011 HOS rule, in order to meet  executive requirements on regulatory review. We have also found that  sufficient and appropriate data are critical for rulemaking, decision  making, and assessing the effects of regulations and programs. As we  have previously found, using data is especially critical for FMCSA, which  has limited resources to oversee a large and diverse industry.
Collecting these types of data has historically been difficult, but a recent  statutory change to how driver schedule data will be collected provides a  potential opportunity to do so. Many carriers use paper logbooks, a  practice that makes collecting and analyzing schedule data  administratively unmanageable. MAP-21 required FMCSA to develop a  rule that will require motor carriers to stop using paper logbooks and  instead, install electronic devices on individual vehicles to record and  store drivers’ schedule data. This shift to electronic data will provide a  potential source of representative data that could more easily be  accessed and analyzed. However, FMCSA officials said that they do not  currently plan to collect and use such data for research purposes  because MAP-21 limits their use to the enforcement of laws.
As we have shown, representative data on the motor carrier industry— specifically the number of drivers nationwide, the amount they drive, and  how they use the restart provision—would provide critical information to  FMCSA about the operations of the industry it oversees. Access to this  type of data would also support data-driven rulemaking efforts and  regulations. According to FMCSA officials, access to driver schedule data  would enhance program evaluation, rulemaking, and the analysis of  existing rules, including the HOS rule. We recognize, however, that there  are concerns to collecting, storing, and analyzing these data, for example:
Privacy: MAP-21 put protections in place to ensure driver privacy,  including provisions to limit the harassment of drivers, protect  personally identifiable information, and preserving the confidentiality  of any personal data. Additionally, as we have previously found,  collecting motor vehicle data raises concerns about sharing data with  third parties, tracking drivers’ movements, and vehicles’ speed and  location.
Cost: FMCSA officials expressed concerns about the costs to the  agency and industry to collect, store, and analyze representative  schedule data. For example, FMCSA officials believe that obtaining,  standardizing, and depersonalizing data for millions of drivers would  pose substantial costs on the agency, motor carriers, and drivers.
There may be ways of mitigating these privacy and cost concerns, but  FMCSA has not examined the costs and benefits of collecting electronic  driver-schedule data on a large-scale and currently does not plan to do  so. For example, to address privacy concerns, information can be “de- identified” for the purposes of analyzing data. Specifically, the Federal  Aviation Administration uses de-identified electronic flight data to identify  and reduce or eliminate safety risks. In addition, our analysis of the  effects of the HOS rule used schedule data from several thousand  drivers, which we analyzed without any personally identifiable driver or  motor carrier information. Cost concerns could potentially be addressed  by collecting a nationally representative sample of the data—rather than  from the entire universe of drivers. Sampling is a commonly used social  science practice that allows researchers to apply conclusions drawn from  a subset of a population to the entire population. Although FMCSA  officials told us that they have no plans to examine the possibility of  collecting, storing, and analyzing electronic driver schedule data because  of the MAP-21 provision, they thought privacy concerns, for example,  could be addressed through stripping raw data of drivers’ personally  identifiable information before being shared with the agency or others.  Until FMCSA evaluates the potential for collecting, storing, and analyzing  data; the potential benefits of using the data; and privacy and cost  concerns as well as the potential for FMCSA to mitigate them, the  agency’s ability to use electronic schedule data in the future will remain  unknown.
section_title: Conclusions
paragraphs: The HOS rule has now been debated for over a decade with supporters  and critics arguing over the extent to which it improves safety and health  outcomes and negatively impacts the economy. FMCSA has attempted to  quantify these effects through research efforts, but many continue to  question the results. This is why it is critical for FMCSA to evaluate  potential effects of rules, such as the HOS rule that has a heightened  public profile, using established research standards and representative  schedule data. FMCSA’s field study demonstrated that the agency did a  reasonable job in designing the study and analyzing data. However,  because the agency did not use guidance outlining specific standards for  conducting and reporting the results of scientific research, the field study  fell short in reporting several limitations and did not fully link the results to  its overall conclusions. These shortcomings leave the agency open to  criticism over the integrity of the study and invite skepticism about the  results.
Similarly, by not having access to and analyzing representative driver  schedule data, FMCSA may continue to face challenges to the credibility  of its rules. The lack of representative schedule data required FMCSA to  make a number of assumptions about how the motor carrier industry  would be affected by the HOS rule. Due to current data limitations, it is  not possible to fully evaluate the rule’s impact. For example, our analysis  suggests that the rule may have affected a larger population of drivers  than FMCSA anticipated, but without representative data there is no way  to be certain this is universally the case. Understanding the population of  drivers affected by the HOS rule is critical for determining the associated  economic costs and safety and health benefits. While electronically  collected, representative schedule data that will soon be available to  FMCSA would allow it to assess the impact of its rules, MAP-21 places  limitations on the use of this data for purposes other than enforcing laws.
Further, collecting, storing, and analyzing the data would require  mitigating privacy and cost concerns. Given the potential value of such  data for evaluating the impact of future regulatory rules, Congress may  benefit from information on how the electronic data to be collected in  response to the MAP-21 requirements could be extracted, stored, and  analyzed and how privacy and cost concerns associated with the use of  these data could be addressed. Such a study could help Congress  determine whether limitations on the use of electronic schedule data for  purposes other than enforcement could be amended in the future.
section_title: Matter for Congressional Consideration
paragraphs: Congress may wish to consider directing DOT to study and provide a  report to Congress identifying approaches for extracting, storing, and  analyzing electronically collected motor carrier drivers’ schedule data,  including the potential benefits, privacy, and cost concerns, and options  for how such concerns could be mitigated.
section_title: Recommendations for Executive Action
paragraphs: To help ensure that FMCSA’s future studies follow generally accepted  research standards, the Secretary of Transportation should direct the  FMCSA administrator to adopt guidance outlining research standards for  designing, analyzing, and reporting the results of scientific research.
section_title: Agency Comments and Our Evaluation
paragraphs: We provided a draft of this report to DOT for review and comment.  DOT  provided written comments, which are reprinted in appendix VIII. In its  written comments, DOT stated that FMCSA agreed with our  recommendation, although DOT also stated that FMCSA adhered to  standard principles and practices of scientific research in conducting its  January 2014 HOS study. As stated in this report, we identified generally  accepted research standards that DOT followed; however, we also  identified standards that it did not fully follow, namely, using a  methodology that supports the study’s objectives, reporting conclusions  that are linked to the study’s results, and reporting methodological  limitations. We believe that FMCSA would strengthen its future research  and enhance the integrity of future studies by adopting guidance outlining  research standards for agency employees to follow.
DOT also stated in its written comments that our report recognized  several achievements associated with its 2011 HOS rule, specifically a  decrease in the frequency of drivers using long work schedules, a lower  risk of driver fatigue, and a reduction in the number of commercial vehicle  crashes involving fatalities. As we state in this report, however, these  findings cannot be directly attributed to the rule.
In addition to the written comments, DOT also provided technical  comments, which we incorporated as appropriate.
We are sending copies of this report to the Administrator of FMCSA, the  Secretary of the Department of Transportation, and interested  congressional requesters. In addition, the report will be available at no  charge on the GAO website at http://www.gao.gov.
If you or your staff has any questions about this report, please contact me  at (202) 512-2834 or flemings@gao.gov. Contact points for our Offices of  Congressional Relations and Public Affairs may be found on the last page  of this report. GAO staff who made major contributions to this report are  listed in appendix IX.
section_title: Appendix I: Objectives, Scope, and Methodology
paragraphs: Our objectives were to examine (1) the strengths and limitations of the  Federal Motor Carrier Safety Administration’s (FMCSA) field study that  examined whether the two-night provision reduces commercial driver  fatigue, and (2) the key assumptions FMCSA used, at the time the 2011  hours of service (HOS) rule was promulgated, in estimating its effects and  what is known about the economic, safety, and health effects of the rule.
subsections: section_title: Methodologies for Evaluating the Field Study
paragraphs: To assess the strengths and limitations of the field study, we drew on  established guidelines and reports for assessing research and analysis,  our reports on evaluating research programs, and our internal expertise in  research design. We identified key standards used by professional  associations, academics, and our prior work, with an emphasis on  standards applicable to the design and methods used in the field study.  (See table 3). Specifically, we identified six standards relating to (1)  choosing evaluation measures that are valid and reliable; (2) using quality  controls to identify data inconsistencies or errors; (3) reporting results and  analysis that are supported by the data; (4) using a methodology,  including statistical techniques, that support the objectives of the study;  (5) reporting conclusions that are linked to the results; and (6) reporting  all methodological limitations, including their potential impact on the  results. We compared these standards to information included in the field  study, statements made by FMCSA officials and researchers contracted  by FMCSA to complete the study, and our analysis of study data.
To assess the reliability and accuracy of data and results reported in the  field study, we collected and reviewed data provided by the primary  researchers. These data, collected between January and July 2013,  included between 50 and 60 files for each of the 106 study participants,  which the authors of the study used to build summary-level datasets for  each driver. We also acquired the computer programs used by the field  study’s authors to process the data and run statistical analyses. As a  check on the reliability of both the computer programs and the data we  received, we applied the programs to the data for one driver and verified  that the results were consistent with numbers reported in the field study.In addition, we replicated results for 15 driver demographic variables,  including age ranges, gender, experience, and type of operation, among  others. For example, we confirmed that 100 men and 6 women  participated, as the study reported. We also talked with the primary  authors of the study about the steps they took to ensure the  completeness and accuracy of the data, and checked the data for  completeness and reasonableness. We determined that the data were  sufficiently reliable for the purpose of our data analysis; in particular, we  determined that we could, with sufficient reliability, conduct additional  analysis of the data and align our results with those reported in the field  study.
We assessed the sensitivity of the study’s results to various alternative  methodological choices. For example, we tested whether the results  varied depending on whether the study included drivers having more than  two nights of rest, as suggested by several stakeholders we interviewed.
More discussion on this theory is included in the body of our report. those reported in the field study.our sensitivity testing and results, see appendix II.
Databases consulted for these searches include Ei Compendex®, Embase®, EMCare®,  MEDLINE®, NTIS: National Technical Information Service, PsycINFO, and Transport  Research International Documentation, among others. selected these experts based on their numbers of published articles  related to the fatigue measures used in the field study, as well as our  prior work on rail’s HOS regulations, work that included opinions from  several fatigue-science experts.
To identify the data collection and analytical methods used in the field  study we spoke with officials from FMCSA and individuals contracted to  complete the study, including the primary researchers of the study. These  officials provided information on the statistical analysis used in the field  study, driver recruitment, and data reliability assessments. To better  understand the recruitment process and data collection methods, we also  interviewed officials from all three motor carriers that participated in the  study. Finally, we spoke with numerous industry and safety stakeholders  to get their views on the strengths and limitations of the field study. For a  list of these stakeholders, see table 6 below.
section_title: Methodologies for Evaluating the Anticipated and Actual Effects of the Hours of Service Rule
paragraphs: To identify key assumptions FMCSA used to estimate the effects of the  2011 HOS rule, a GAO economist, not involved in other aspects of our  work and with substantial experience in cost-benefit analyses, reviewed  the Regulatory Impact Analysis for the 2011 HOS rule. This expert  identified an initial list of assumptions. We further categorized these  assumptions into five areas: which drivers were affected, how drivers  were affected, economic costs, safety benefits, and health benefits.  FMCSA officials confirmed that our characterization of key assumptions  was accurate. For more information on this process and the full list of  FMCSA assumptions, see appendix III.
To identify what is known about the possible economic, safety, and health  effects of the 2011 HOS rule, we conducted a comprehensive search for  existing databases with information on motor carrier operations. We  conducted web searches of academic journals, as well as government  and other stakeholder websites. We also used search engines and asked  officials from FMCSA and other stakeholders for recommendations on  databases we could use to analyze the effects of the rule. We categorized  the databases we found by the type of data collected, source, whether  that data was publicly or commercially available, and potential costs.  Based on this information we identified three different, relevant data  sources, which were used for a variety of purposes (see table 5). To  assess the reliability of these databases, we reviewed documentation on  data collection efforts and quality assurance processes, talked with  knowledgeable officials about these data, and checked the data for  completeness and reasonableness. We determined that the data were  sufficiently reliable for the purpose of our data analysis, though we also  identified limitations in each dataset. More information on our analysis of  American Transportation Research Institute (ATRI) logbook data, Federal  Highway Administration (FHWA) vehicle count data, and FMCSA crash  data can be found in appendixes IV, VI, and VII, respectively.
We spoke with multiple stakeholders, including organizations  representing motor carriers, safety advocacy organizations, motor carrier  companies, commercial vehicle drivers, and customers of the motor  carrier industry (e.g., companies that ship freight) to understand how the  2011 HOS rule affected them and their members (see table 6). We  selected stakeholders and shipping organizations to interview based on  our prior work on commercial motor-carrier safety and recommendations  from these stakeholders. We used FMCSA’s Motor Carrier Information  Management System to select active, U.S.-based motor carrier  companies subject to the hours of service rule. Our selection was based  on three primary criteria: (1) operation type (for-hire versus private), (2)  fleet size, and (3) cargo type (interstate versus intrastate hazardous  material). Using these criteria we created six selection groups to identify  target populations that included for-hire and private carriers and had a  range of fleet sizes. We also created a seventh group that included only  hazardous material carriers. We randomly selected 150 carrier  companies from each of these groups for a total of 1,050 carrier  companies across all seven groups. We then selected 3 to 5 carrier  companies from each group using four additional selection criteria: (1)  driver radius in miles (to identify carriers that drove a wide range of  distances); (2) FMCSA region (for the purposes of ensuring geographic  diversity); (3) HOS violations in the past 2 years (to identify carriers that  might have experience with these violations); and (4) years in operation  (to identify carriers with a range of experience in the industry). Motor  carrier companies we spoke with also put us in contact with eight drivers  whom we interviewed to understand how the 2011 HOS rule had  impacted them. The information collected from these interviews with  representatives of motor carriers and drivers is not generalizable to all  motor carrier companies or drivers.
To summarize information from our interviews with motor carriers, we  systematically analyzed information they provided on how the 2011 HOS  rule had impacted them. To do so, one analyst coded interview  responses. A second analyst randomly selected three interviews to code.  The results from the two analysts were compared to ensure that the  coding method was consistently applied, which confirmed that the coding  done by the first analyst was reliable.
To identify possible fatigue effects on driver safety and health as well as  the effectiveness of HOS rules in mitigating fatigue we conducted a  literature review. Our literature review was also used to provide additional  information on the biomathematical fatigue model we selected to analyze  simulated and example driver schedules (see below). We identified  specific articles for our literature review through searches conducted by a  GAO librarian across bibliographic databases containing peer-reviewed  materials, government reports, and conference papers.list of results by reviewing abstracts and focusing on articles published  since 2008. This process identified 54 articles, most of which we used for  background purposes or to provide additional context. We also identified  several articles related to the effectiveness of HOS rules that were used   We honed the  to support our findings and required additional review. To select these  articles we had two analysts code 10 of the 54 articles as either relating to  effectiveness of HOS rules, relationship between fatigue and driver  performance and safety, and/or relationship between fatigue and driver  health. We compared the coding results and determined that the  remaining coding could be reliably completed by a single analyst. Using  this method we identified 15 articles related to the effectiveness of HOS  rules. The methodologies and findings of these articles were reviewed by  GAO’s technical experts (research methodologists and an economist) to  ensure that the findings were well supported.
To examine the potential effects of the 2011 HOS rule on driver fatigue,  we used a biomathematical fatigue model of human alertness response to  work and rest patterns. We obtained the Fatigue Audit InterDyne™  (FAID) model from InterDynamics Pty Ltd as the result of a competitive  procurement. Our analysis using the FAID model involved two parts:  1.  We developed schedules with different work periods (day and night)  and different shift lengths (e.g. 14-hour shifts to simulate the  maximum allowed daily on-duty hours).We then created different  scenarios based on whether a schedule would need to change to  comply with the two restart provisions in the 2011 HOS rule. For  example, we used the FAID model to compare driver fatigue levels for  a driver working 14-hour night shifts 5 days a week with one-night off  (not in compliance with the two restart provisions in the 2011 HOS  rule) to a driver working the same 14-hour night shifts 5 days a week  with two-nights off (in compliance with the two restart provisions in the  2011 HOS rule).  2.  We analyzed real world schedules described to us by motor carrier  operators and drivers we interviewed regarding how they had  changed schedules due to the new rule. For example, we used the  FAID model to analyze fatigue levels of drivers for a motor carrier that  told us their drivers had changed from six shifts per week (before the  2011 HOS rule went into effect) to five shifts per week (after the 2011  HOS rule went into effect) in order to be compliant with aspects of the  rule.
For a technical discussion of the scope, methodology, and additional  results using the FAID model, see appendix V.
section_title: Appendix II: Analysis of Data Analysis and Collection in FMCSA’s Field Study
paragraphs: As part of our review of the strengths and limitations of FMCSA’s field  study that examined the two-nighttime provision of the 2011 hours of  service (HOS) rule, we conducted two statistical analyses, as described  below. First, we conducted sensitivity tests to assess how certain  methodological decisions in the field study potentially affected its results.  Second, we conducted a power analysis to assess the adequacy of the  study’s sample size for estimating how the rule affected fatigue within  each of several industry segments.
subsections: section_title: Sensitivity Tests
paragraphs: During the course of our review, we asked stakeholders we interviewed to  describe their general impressions of the data and statistical methods  used to evaluate the rule. Stakeholders specifically questioned two  methodological decisions:  1.  The study analyzed data for drivers who had taken more than a two- night restart. Some stakeholders expressed concern that this decision  did not conform with the purpose of the study to analyze the impact of  changing the restart provision from requiring one-night of rest to  exactly two nights.  2.  The study collected data from what stakeholders characterized as a  small sample of drivers, a sample that included local and regional  drivers. Some stakeholders said that the study’s sample size of 106  drivers was not large enough to provide enough data to draw sound  conclusions or be representative of the trucking industry. In addition,  several stakeholders we spoke with said that the restart provision  likely has different effects across industry segments, including local,  regional, and long-haul or over-the-road drivers.
To test the validity of these concerns, we acquired the source data and  computer code used in the field study from the authors. We conducted  sensitivity tests of these data that assessed whether the study’s results  might have been different if the researchers had made different  methodological choices. Our tests examined whether the results of the  psychomotor vigilance tests (PVT) and the Karolinska Sleepiness Scale  (KSS) would have been different had the study: 1) excluded drivers taking  a two-or-more night restart and 2) allowed the outcomes to vary by the  drivers’ industry segments, i.e., local, regional, or over-the-road.
𝑌𝑖𝑗 ~ N(𝐱𝐢𝐣𝛃 + 𝜇𝑗,𝜎𝑌2)  𝜇𝑗 ~ N(𝜇,𝜎𝜇2) xij   =  Condition   =  =  Timingij   Periodij   =  {I(Condition x Timingij x Periodij)}   {1 night restart, 2+ night restart}   {12:00 – 4:00 AM, 4:00 – 8:00 AM, … , 8:00 – 11:59 PM}   {1st duty period, restart period, 2nd duty period}  given by 𝑁 = ∑𝑁𝑗  Yij denoted the number of PVT lapses or the KSS score for driver j = 1, 2,  … , J on measurement occasion i = 1, 2, … , N, with the total sample size  . xij was a vector of indicator variables for each level  of the Cartesian product of Condition, Timingij, and Periodij, similar to a  design vector in an experimental study. Condition measured the number  of nights in the restart period between the two duty periods in the study.  Timingij and Periodij measured the time of day and stage of the study,  respectively, when the PVT or KSS outcomes were measured.column vector of fixed coefficients. Since xij did not include an intercept   𝛃 was a  and all covariates were categorical, 𝛃 denotes the cell means for each  subgroup in xij.
We increased the granularity of Condition in order to estimate the effect  of interest, the contrast between mean fatigue after one-night versus two- night night restarts, by adding a separate category for three-or-more night  restarts. The study estimated this overall contrast by taking equally  weighted linear combinations of the estimated effects across  subpopulations defined by Timingij x Periodij, using the “least-squares  means” method implemented in SAS.order to minimize methodological changes other than those we evaluated.
We used the same approach, in  Our test found that including drivers who took more than a two-night  restart in the treatment group did not affect the results, as shown by table  7. On-duty drivers taking only a two-night restart had 0.32 lapses more  lapses per PVT bout, on average, than drivers taking a one-night restart.  This estimate is statistically indistinguishable from the 0.33 lapses  reported by the field study, which included drivers taking more than a two- night restart in the effect estimate. The results from the study and our  tests are both statistically distinguishable from zero at the 0.10 level of  confidence. Similarly, the KSS results reported in the field study for on- duty drivers are statistically indistinguishable to those we found when  removing drivers taking more than two-night restarts from the treatment  group.
Our second sensitivity test estimated the difference in mean PVT and  KSS outcomes between drivers with one-night restarts and two-or-more  night restarts, separately by industry segments defined by local versus  regional and over-the road operations. We attempted to estimate  separate effects for the regional segment alone, but the study data did not  include any regional drivers who took one-night restarts. As a result, we  combined regional and over-the-road drivers, who may behave in similar  ways such as driving longer distances and sleeping away from home with  some regularity. Finally, we omitted effects for Periodij to simplify the  model, since the estimated treatment effects did not vary when this  variable was included or excluded. Thus, we estimated two alternatives:   =  xij  Condition   =  Timingij   =  Operation   =   {I(Condition x Timingij x Operation)}   {1 night restart, 2 night restart, 3+ nights restart}   {12:00 – 4:00 AM, 4:00 – 8:00 AM, … , 8:00 – 11:59 PM }   {Local, Regional or Over-the-Road}  xij  =  Condition   =  Timingij   =  Operation   =  {I(Condition x Timingij x Operation)}   {1 night restart, 2+ nights restart}   {12:00 – 4:00 AM, 4:00 – 8:00 AM, … , 8:00 – 11:59 PM }   {Local, Regional or Over-the-Road}  Our test found that the field study did not sample enough drivers from  each of these segments to estimate effects for each subgroup that were  statistically distinguishable from zero (significant). We found that regional  and over-the-road drivers had a significant difference of 0.42 PVT lapses  and an insignificant difference of 0.13 KSS scores, on average, between  drivers with a one-night and a two-or-more night restart (see table 8). In  contrast, local drivers had insignificant differences of -0.15 PVT lapses  and 0.26 KSS scores, on average. We found similar effects when  comparing drivers with one-night versus two-night restarts. In sum, the  variation in the results suggests that the 2011 HOS rule may have had  variable effects on different segments of the industry. However, the  relatively wide confidence intervals of these estimates, in part due to  limited sample sizes within each industry segment, makes the exact  degree of variation uncertain.
section_title: Power Analysis
paragraphs: To assess stakeholder concerns about the adequacy of the field study’s  sample, we conducted a statistical power analysis to assess how many  drivers and PVT measurements might be needed to estimate the HOS  rule’s effects for each of several segments of the industry. In general, a  power analysis estimates the probability that a study will conclude that an  effect exists when, in fact, the effect does exist in the population of  interest. In our application, we estimated the probability of identifying  differences in mean PVT results for drivers taking a one-night versus two- night restart, separately for subpopulations of drivers operating in the  local and regional or over-the-road industry segments and at various  times of day.
We found that the field study’s sample size was insufficient to estimate  statistically significant differences in the primary fatigue measure—the  PVT—for each of these industry segments and times. By collecting data  from approximately 100 drivers, each taking 25 PVTs, the study would  have had an approximately 5 percent or lower chance of identifying  statistically significant differences as small as 0.3 PVT lapses for each  subpopulation. This effect size is similar to what the study found across  all time periods, using a sample of a similar size. In contrast, collecting  data from approximately 4,000 to 6,000 drivers, each taking 200 PVTs,  would have increased the chance of identifying significant differences for  each subpopulation separately to approximately 40 to 60 percent. For an  effect of 1 PVT lapse—slightly larger than what the study found for  overnight observation periods—sample sizes of 800 drivers and 200  PVTs would have achieved approximately 60 to 80 percent power. These  results illustrate the range of sample sizes that FMCSA would need to  consider, subject to available resources for driver recruitment and study  administration, in order to confidently detect effects of this size for each of  several industry subpopulations.
Below, we discuss the methods we used to conduct the power analysis  and its results in more detail.
subsections: section_title: HOS Study Analytical Methods and Data
paragraphs: The HOS study evaluated the effects of the rule on three measures of  driver fatigue: PVT, KSS, and lane deviation. For the purpose of our  power analysis, we focused on scores on the PVT due to the extensive  scientific research that has validated this measure of fatigue. PVT scores  consist of the number of lapses in attention a study participant  experiences across a battery of tests. The study administered the PVT  test at various times during the participation period, which spanned one- duty period, a restart period, and a second-duty period. Our power  analysis focuses on the duty periods during which drivers would need to  be alert in order to safely operate their commercial motor vehicle.
Tables 9, 10, and 11 provide descriptive statistics about the study’s data  collected during the duty periods from the 106 participating drivers. The  number of PVTs varied across drivers from 8 to 53, with a median of 24.5  and a mean of 25.0. The study collected a total of 2,653 non-missing  observations during the duty periods at the level of driver-tests, after  excluding observations during the restart period. Across all observations,  the mean number of PVT lapses was 1.8. PVT lapses had a skewed  distribution, with a 90th quantile of 5, a 95th quantile of 8, and a 99th  quantile of 18. (See table 9.) The study administered several hundred  PVTs to drivers taking one, two, or three-or-more night restarts across  various types of operations (e.g., local, regional, and over-the-road). (See  table 10.) The study made fewer observations across subpopulations  defined by restart nights and time period of observation, however, ranging  from 44 to 274 tests per subpopulation (see table 11). This suggests that  the three-way cross-classification of restart nights, operation type, and  time period may produce limited sample sizes for each subpopulation.
As we discuss above, the authors of the field study analyzed these data  using a hierarchical linear model, with normally distributed random effects  at the level of drivers but fixed effects otherwise. To analyze power, we  used models similar to those we used above to estimate separate effects  across industry segments.
We used two separate methods to estimate power. These methods  estimated power to detect effects for each of several subpopulations of  drivers and for the overall population, respectively. Using the first method,  we made distributional assumptions about the data generation process,  informed by the field study’s results, and used Monte Carlo simulation  methods and the actual model of the data to be estimated (from above).  Using the second method, we made similar distributional assumptions,  but we applied power equations for simplified versions of this model  available in the statistical literature.
section_title: Power Analysis for Multiple Subpopulations
paragraphs: Our analysis estimates power for estimating separate effects for each of  12 subpopulations defined by two industry segments (local versus  regional/over-the-road) and six equally spaced observation periods (e.g.,  12:00 AM to 4:00 AM).
𝑌�𝑖𝑗(𝑚)  ~ 𝑁(𝐱�𝐢𝐣(𝐦)𝛃� + 𝜇�𝑗(𝑚),𝜎�𝑌2)  𝐱�𝐢𝐣(𝐦) ~ 𝑀𝑢𝑙𝑡𝑖𝑛𝑜𝑚𝑖𝑎𝑙(𝛉�)  𝜇�𝑗(𝑚) ~ 𝑁(𝜇̂,𝜎�𝜇2)
For the mth simulated dataset, the estimates of interest are 𝛅(𝑚)T = [𝛽̂21(𝑚)− 𝛽̂11(𝑚),… ,𝛽̂2𝐾(𝑚) – 𝛽̂1𝐾(𝑚)], which, under the model, have  entries equal to the difference in mean outcomes between drivers in  subpopulation k having two versus one-night restarts. We use the set of  M simulated values of δ to test composite hypotheses about effects for all  subgroups, as well as multiple simple hypotheses about the effects for  each subgroup.
section_title: Composite Hypotheses
paragraphs: This implies that RTβ = δ = 0 under HLet VR = Var(RTβ) = RTVβR, where Vβ is the covariance matrix of β. For  each simulated dataset, we test these hypotheses using the multivariate  Wald test statistic, = 𝛃T𝐑𝐕𝐑−𝟏𝐑T𝛃 ~ 𝜒𝐾2 , with each simulation’s value  equal to 𝐻(𝑚)= 𝛃�(𝑚)T 𝐑𝐕�𝐑(𝑚)−1 𝐑T𝛃�(𝑚). We reject H for simulation m if , the critical value of the chi-squared distribution with K  𝐻(𝑚)>𝜒𝐾,.952degrees of freedom, assuming α = .05.
Since we assume that H) = E[I(Reject H  in simulation m. We estimate power as �1−𝛽̂�= 1𝑀∑ I(𝐻(𝑚)>𝜒𝐾,.952 where I() is an indicator function ranging on {0,1}.
section_title: Multiple Simple Hypotheses
paragraphs: that the inner product is the joint probability of rejecting all hypotheses.
This is follows from the contrast weights in 𝐑𝐓, whose orthogonality  implies independent comparisons.
Sample and effect sizes similar to those in the field study provided low  amounts of power to detect effects of -0.3 or -1 PVT lapses, as shown in  table 12. The probability of detecting effects of this size for each of the 12  driver subpopulations was approximately 5 percent or less when sampling  100 drivers and administering 25 PVTs, using either a joint hypothesis  test or multiple simple tests across subpopulations. Power increased to  0.11 when detecting an effect equal to -1 lapse using a joint test, but it  remained approximately zero using multiple simple tests. These results  confirm that the field study could not reliably estimate separate effects by  type of driver and time of day.
Our analysis found that increasing the field study’s sample size by several  hundred additional drivers and PVTs would be necessary to achieve  conventional levels of power, depending on the true effect size to be  detected. Using a joint hypothesis test or multiple simple tests when the  effect equaled -0.3 lapses, we found that about 6,000 drivers and 200  PVTs would be required to achieve power of 0.6 using a joint hypothesis  test and 0.37 using multiple simple tests. To detect an effect of -1 lapse  with power of approximately 0.6 to 0.8, samples of more than 800 drivers  and 200 PVTs would be necessary. Since the field study reports effects in  this range, the appropriate sample size would appear to fall between  these bounds, subject to available resources for driver recruitment and  study administration.
Power generally increased more strongly with increases in the number of  drivers, rather than the number of PVT tests. This result is consistent with  the statistical literature on hierarchical linear models in which the marginal   The moderately high  effects of interest vary only across clusters.intraclass correlation of 0.68 in the study’s PVT data within drivers  increases the required driver sample size to achieve a given level of  power, all else being equal. However, the number of PVTs increases  power more strongly when using multiple simple tests, because more  PVTs increases the number of observations across time periods and  therefore increases power for each test.
section_title: Power Analysis for the Overall Population
paragraphs: To provide an alternative power estimate for the overall population of  drivers, using less complex methods, we analyzed power for a simplified  version of the model using results from the literature on hierarchical linear  models.
For this analysis, we calculated power for estimating one treatment effect  𝛿 as defined above, without assuming that the effect varied across  subpopulations. This reduced the model to xij = {I(1 night restart), I(2+  night restart)}. We further assumed that the sample had the same  (balanced) number of observations for each of the two treatment  conditions. This simplified the model so that variance estimation,  hypothesis testing, and power calculation became tractable using the  closed-form methods developed by the literature on hierarchical linear  models (largely by avoiding many covariates). The more realistic Monte  Carlo analysis summarized above compensated for these simplifications,  since using two different methods checked the robustness of results from  either one.
H0: 𝛽2 − 𝛽1 = 0  H1: 𝛽2 − 𝛽1≠0  E(𝑀𝑆𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡)=𝑁𝜎𝜇2+𝜎𝑌2+𝑁𝐽𝛿24   𝐹(1,𝐽−2,𝜆)=𝐸(𝑀𝑆𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛) similar to the Monte Carlo analysis above, and using various values for N  We estimate F() using estimates of 𝜎𝜇2 and 𝜎𝑌2 from the prior study’s data,  and J. We estimate power as (1−𝛽̂𝑘) = Pr(𝐹� > 𝐹.95) , where 𝐹.95 is the  noncentrality parameter 𝜆= 𝜆̂, assuming α = .05. critical value of the F distribution with (1, J-2) degrees of freedom and  Our analysis found that the field study had more power to detect overall  effects than effects across multiple subgroups (see table 13). The field  study’s approximate sample size of 100 drivers and an average of 25  PVTs had a 0.08 probability of detecting effects of -0.3 PVT lapses and a  0.42 probability of detecting effects of -1 PVT lapses for the overall  population of drivers. Given these results, the study had low power to  detect the overall effect of -0.33 lapses that it reported across all time  periods, suggesting that the result may have been statistically unusual.  Our analysis found that increasing the sample size to approximately  2,000 to 4,000 drivers and 200 PVTs would have been required to detect  an effect of -0.3 PVT lapses with conventionally adequate levels of power.  However, samples as small as 250 drivers and 25 PVTs would be  sufficient to detect effects of -1 PVT lapse.
section_title: Appendix III: GAO Identified Assumptions in the Hours of Service Rule’s Regulatory Impact Analysis
paragraphs: As part of our review of the effects of the 2011 hours of service (HOS)  rule, we were asked to identify the assumptions used by the Federal  Motor Carrier Safety Administration (FMCSA) to estimate the costs and  benefits of the rule. To do so, a GAO economist identified the key  assumptions in the regulatory impact analysis (RIA) used by FMCSA to  detail the costs and benefits of the 2011 HOS final rule. This expert  developed an initial list of key assumptions after reading the RIA. We then  categorized these assumptions into five groups: which drivers were  affected, how drivers were affected, economic costs, safety benefits, and  health benefits. We also had a sixth category for assumptions that did not  clearly fit into one of the aforementioned categories. FMCSA officials  reviewed our list of assumptions, offered some corrections, and ultimately  agreed that the list of assumptions accurately represented the key  assumptions used in the RIA.
Below is a list of the assumptions we identified and FMCSA officials  confirmed. These assumptions cover which drivers would be affected by  the HOS rule and how they would be affected. The assumptions below  also detail how operational changes (e.g. driver schedule changes) result  in the economic costs and the safety and health benefits of the HOS rule  that went into effect July 1, 2013. (See table 14).
section_title: Appendix IV: Analysis of Driver Schedule Data
paragraphs: To identify possible effects of the 2011 hours of service (HOS) rule,  including which drivers were affected and how those drivers were  affected, we purchased 3 years of drivers’ schedule data from the  American Transportation Research Institute (ATRI). The data contain  information on daily on-duty hours for drivers that worked for 16 for-hire  motor carriers from January 1, 2012, through December 31, 2014.  Specifically, the dataset includes a driver identification number, the  number of recorded on-duty hours for each driver for each day in the  dataset, and the length of each restart the driver took. The 2012 dataset  contains 57,096 unique driver records; the 2013 dataset contains 60,196  unique driver records, and the 2014 dataset contains 63,957 unique  driver records. The data we used for our analysis of drivers’ schedule  data are not representative of the motor carrier industry, and therefore,  our findings based on these data cannot be generalized to all carriers in  that population.
subsections: section_title: Filtering the Data
paragraphs: The data we purchased was not filtered or processed by ATRI. We  applied four data-quality filters to remove records that contained likely  errors and to exclude drivers with minimal on-duty time that could  potentially bias our results, specifically:
We removed driver records with five or more null values in the on- duty-hours cells. This filter removed 40,622 records from the 2012  dataset, 42,496 records from the 2013 dataset, and 45,564 records  from the 2014 dataset.
We removed driver records with one or more instance of 18 or more  hours of on-duty recorded in a single day. This filter removed 1,126  records from the 2012 dataset, 1,735 records from the 2013 dataset,  and 1,520 records from the 2014 dataset.
We removed driver records with less than 20 hours of total on-duty  time logged during the year. This filter removed 32 records from the  2012 dataset, 34 records from the 2013 dataset, and 9 records from  the 2014 dataset.
We removed driver records that did not have a 0 value in the on-duty  hours column on the day before a recorded restart with a value of 48  or greater. This filter removed 8 records from the 2012 dataset, 13  records from the 2013 dataset, and 61 records from the 2014 dataset.
After applying these filters, there were 15,308 unique driver records in the  2012 dataset, 15,918 unique driver records in the 2013 dataset, and  16,803 unique driver records in the 2014 dataset.
section_title: Defining and Calculating a Driver’s Work Week
paragraphs: To compare our driver data to the Federal Motor Carrier Safety  Administration’s (FMCSA) estimates of drivers’ weekly hours as well as to  provide a meaningful denominator for hours worked (e.g. on-duty hours  per work week), we defined and calculated a driver’s work week. As  described below, we considered several specifications in our analysis,  including how many days to include in a work week, how to calculate a  work week, whether to use the driver or the work week as the unit of  analysis that is compared over time, and how to categorize weekly on- duty time.
subsections: section_title: Number of Days in a Work Week
paragraphs: As described in the background section, drivers may follow either a 70- hour over 8 days on-duty limit or a 60-hour over 7 days on-duty limit.  According to ATRI, the majority of the drivers in the dataset we purchased  likely follow the 70-hour on-duty limit. We calculated a work week using  both 7 and 8 days to ensure we covered the range of possible work  weeks. However, given that the majority of drivers in the dataset likely  followed the 70-hour/8-day work week, we included these results in the  body of this report. The number of days used to calculate a driver’s work  week has an effect on the total and average hours worked.
section_title: Calculation of a Work Week
paragraphs: We used a fixed 7 day and fixed 8 day work week in the analysis  presented below and in our report. Before making this decision, we  considered two ways of calculating a driver’s work week: 1) a fixed work  week and 2) a rolling work week. For drivers following the HOS rule, a  work week is not based on a set week (e.g. Monday through Friday).  Rather, drivers calculate their 60- or 70-hour on-duty limits over a rolling  period of 7 or 8 consecutive days. To take this into account, we calculated  a fixed work week and a rolling work week to approximate the weekly on- duty hours of drivers in our dataset. The fixed work week has 7 or 8  consecutive days that do not overlap. For example in a 90 day period,  there are 12 7-day weeks and 11 8-day weeks. The rolling work week  also has 7 or 8 consecutive days, but each subsequent week shifts the  start of the week by only one day. Each rolling week overlaps with the  previous week. We tested these two calculation methods on a subset of  data (January 1 to March 31, 2013, and 2014), and we found that the  method we used to calculate a work week did not change the results of  our analysis.
section_title: Weekly On-Duty Categories
paragraphs: To examine the assumptions and estimates FMCSA made in its  regulatory impact analysis (RIA) for the 2011 rule, we categorized our  results using weekly on-duty hour categories similar to those included in  that document.following parameters:   FMCSA defined four categories of drivers with the  “Moderate”—average weekly work time of 45 hours, includes weekly  averages between 20 to 55 hours  “High”—average weekly work time of 60 hours, includes weekly  averages greater than 55 to 65 hours  “Very High”—average weekly work time of 70 hours, includes weekly  averages greater than 65 to 75 hours  “Extreme”—average weekly work time of 80 hours, includes weekly  averages greater than 75 hours  In our analysis, we split the Moderate category into two groups: those  working less than 35 hours per week and those working more than 35  hours per week. We did this split to determine whether the behavior  changes made by those at the low end of the Moderate category differed  from those at the high end of the Moderate category. As a result, our  analysis uses five weekly on-duty categories.
section_title: Unit of Analysis
paragraphs: To analyze the data, we considered two different units of analysis: a  driver and an individual work week. Specifically, for the time periods we  examined, we determined the number of drivers whose average work  weeks fell into each of the on-duty hour categories described above. For  example, a driver that averaged 57 on-duty hours per 8-day work week  would be placed in the greater than 55- to 65-hour on-duty category. We  also analyzed the data by the number of work weeks that fell into each of  the on-duty categories. For example, we identified 342,747 out of  959,529 total weeks with on-duty hours between 35 and 55 from January  1, 2012, to June 15, 2013 (see table 24 below).
section_title: Restart Length
paragraphs: To analyze drivers’ use of restarts within our dataset, we followed the  practice of both FMCSA and ATRI of excluding restarts longer than 72  hours (3 days). The rationale for this practice is that off-duty periods  greater than 72 hours do not represent a normal or operational restart  period by a commercial motor vehicle driver. While we provide descriptive  information on all restarts in our dataset (see table below), we exclude  restarts greater than 72.99 hours from our analysis of restart use by driver  or driver groups (see tables 20, 21, and 25 below).
section_title: Data Analysis
paragraphs: We analyzed driver schedule data in two different ways. In the first  section, we describe our analysis of the data by dividing the 3 years of  data into: 1) seasonal datasets by creating 12 individual datasets  composed of 75- to 90-day seasons and 2) before and after the rule  datasets by creating two datasets of approximately 18 months before and  after July 1, 2013. In the second section, we describe our statistical  analysis of drivers’ schedule data using a subset of the full dataset that  covered the entire time frame—2012 to 2014—but were limited to drivers  for whom we had data in all 3 years.
subsections: section_title: Analysis of the Dataset over Different Time Periods
paragraphs: We divided each year of data into four seasons: winter, spring, summer,  and fall. The spring, summer, and fall seasons were truncated to account  for (1) the effective date of the 2011 HOS rule on July 1, 2013, and (2) the  suspension of two of the restart provisions in the 2011 HOS rule.
Specifically, data from June 16 to June 30 were removed to ensure that  behavior changes in anticipation of the rule’s going into effect did not bias  the dataset. Similarly, data from July 1 to July 14 were removed to  account for drivers’ getting used to and understanding the rule. We also  omitted data from December 16 through December 31 to account for the  suspension of several restart provisions on December 16, 2014. To  ensure we were comparing the same time periods in each season, all of  these changes were applied across each year of data. (See table 15.)
Dividing the data into seasons allowed us to isolate changes potentially  due to seasonal variation and to show how drivers’ behavior—averaged  over a relatively short period of time (75 to 90 days)—changed. For  example, several motor carriers and drivers told us that bad weather in  January and February 2014 disrupted operations for many motor carriers.  By separating the datasets into seasons, we were able to ensure that  changes we observed from one period to another were consistent across  all seasons and were not only due to seasonal variation.
We found that the percentage of drivers in our dataset working more than  55 hours per work week decreased after the 2011 HOS rule went into  effect—between spring 2013 and summer 2013. In addition, our analysis  suggests that the percentage of drivers working 55 or fewer hours per  work week increased after the rule went into effect. Tables 16 through 22  show the results of our seasonal analysis of drivers’ schedule data using  an 8-day work week and a 7-day work week.
Consistent with our results using drivers as the unit of analysis, the  overall percentage of weeks from our dataset in the more-than-55-hours- per-week categories decreased after the 2011 HOS rule went into effect.  The overall percentage of weeks in the 55-or-fewer-hours categories  increased after the rule went into effect. In both tables 18 and 19, the  percentage of weeks with total on-duty hours greater than 65 hours per  work week is higher than those found in tables 16 and 17. This finding  suggests that while a driver may have an intense work schedule over one  week, on average, drivers tend to have less intense work schedules over  longer periods of time. Tables 18 and 19 show the total number of weeks  that fell into our on-duty categories for drivers in our dataset using an 8-  and 7-day work week.
We found that restarts per driver remained relatively constant before and  after the 2011 HOS rule went into effect for drivers in our dataset working  between 35 to 75 hours per 8-day work week. We also found an increase  of approximately one restart per driver for those working more than 75  hours per 8-day work week and a reduction of approximately one restart  per driver for drivers working less than 35 hours per 8-day work week.  Table 20 compares the restart use per driver across the on-duty hour  categories.
We also analyzed restart use per driver per calendar week to understand  how often drivers in our dataset use a restart. With this specification, we  found that restart use after the 2011 HOS rule went into effect appears to  vary by drivers’ average weekly on-duty hours (see table 21). For drivers  working on average more than 55 hours to 75 hours per 8-day work  week, restart use per calendar week remained relatively constant before  and after the 2011 HOS rule went into effect. For drivers working more  than 75 hours per 8-day work week, restart use per calendar week  appears to increase by almost 1. However, before the rule there were 69  drivers in this category and after the rule there were 15 drivers, therefore  the change observed in Table 20 is based on a relatively small number of  drivers. For drivers working 55 hours or less per 8-day work week, restart  use per calendar week appears to decrease after the 2011 HOS rule went  into effect.
We analyzed the length of restart periods to understand how many off- duty hours drivers in our dataset took. We found that before the 2011  HOS rule went into effect, about 8 percent of the restarts taken by drivers  in our dataset were between 34 and 37 hours (see table 22). As a  percentage of all restarts taken in each season, restarts with lengths of 34  to 37 hours decreased after the rule went into effect. The percentage of  restarts with a length between 45 to 72.99 hours increased after the rule  went into effect. As our dataset did not contain the start and end times of  a driver’s schedule or restarts, we are not able to analyze the extent to  which there was a change in restarts with one- or two-night periods. For  example, a driver working during daytime hours could take a 34 hour off- duty period that contains two nighttime periods (1 a.m. to 5 a.m.). In  contrast, a driver working during nighttime hours may not have two  nighttime periods (1 a.m. to 5 a.m.) when taking a 34-hour off-duty period.  Therefore, we cannot determine the change in actual nights taken off-duty  by looking at the length of a restart period on its own.
Analysis of Data before and after the HOS Rule  We also analyzed the data by dividing driver records into two periods: (1)  before the rule went into effect—January 1, 2012, to June 15, 2013—and  (2) after the rule went into effect—July 15, 2013, to December 15, 2014.  As with the analysis by seasonal datasets, we applied filters to each year  of data. To avoid double-counting drivers who appear across multiple  years of data, we used the driver identification number to connect driver  records with data in multiple years. The weekly on-duty hour average is  calculated using the weeks that we have data for a driver. If a driver is  only present in the 2012 data set, then that driver’s average weekly on- duty hours are calculated based on only 2012 data. If a driver is present  in both the 2012 and 2013 datasets, then that driver’s average weekly on- duty hours are calculated using 2012 and 2013 (up to June 15, 2013) on- duty hours. The calculation for the post-rule dataset used the same  method.
We compared the number and percentage of drivers in our dataset by  average weekly on-duty hours before the rule went into effect and after  the rule went into effect. As with the seasonal analysis, the percentage of  drivers in our dataset working more than 55 hours per work week  decreased, and the percentage of drivers in our dataset working 55 hours  or less per 8-day work week increased. While this analysis shows similar  trends in the data, there are differences in the number and percentage of  drivers in each weekly on-duty hour group. Specifically, in the seasonal  analysis, before the rule went into effect (winter 2012 through spring  2013) the percentage of drivers in our dataset working more than 65  hours per 8-day work week ranges from about 9 to close to 12 percent.  As shown in table 23, approximately 8 percent of drivers in our dataset  before the rule went into effect are categorized as working more than 65  hours per 8-day work week on average. This difference is likely due to the  time frame over which the average weekly on-duty hours are calculated.
We also compared the number and percent of weeks before and after the  rule went into effect categorized by total weekly on-duty hours (see table  24). Consistent with the seasonal analysis, the percent of 8-day work  weeks with total on-duty hours of more than 65 also decreased.
We also analyzed the length of restarts used by drivers’ average weekly  on-duty time. We found that before the rule went into effect the  percentage of 34- to 36.99-hour restarts taken by drivers in our dataset in  each on-duty time category ranges from about 6 to 11-percent (see table  25). After the HOS rule went into effect, the relative distribution of restarts  by length taken in each weekly on-duty category changed. Specifically,  the percentage of restarts lasting 34- to 36.99- hours decreased for all  driver categories. The percentage of 34- to 36.99-hour restarts taken by  drivers in our dataset working the longest hours decreased from close to  11 percent to about 9 percent. The percentage of 34- to 36.99-hour  restarts taken by the other driver groups saw larger decreases. As with  our seasonal analysis of restart length, our results do not allow us to state  that the number of night time periods in a restart period changed after the  2011 HOS rule went into effect.
Statistical Analysis of Driver Schedule Data  We also analyzed whether there were differences in how the same set of  drivers behaved before and after the 2011 HOS rule went into effect. To  do this analysis, we developed several statistical regression models of  hours worked and restart use, in order to further account for seasonal  variation and differences across drivers who do not change over time,  such as carrier. Using these models, we estimated whether the  differences in hours worked and restart use before and after the 2011  HOS rule went into effect were statistically distinguishable from zero. We  applied these models to a sample of 6,934 drivers who met our filters for  the entire period between January 1, 2012, and December 31, 2014.
We estimated two sets of models, which made different assumptions  about the distribution of the outcomes and the structure of the correlation  of those outcomes within drivers over time. Both sets of models included  season or month fixed effects and predicted hours worked per week;  working 35 to 54, 55 to 64, or 65 or more hours per 8-day week; or the  number of restarts per week.
First, we estimated linear models with driver fixed effects (implemented  through a within-transformation) and standard errors that were robust to  arbitrary forms of heteroskedasticity and driver-level autocorrelation.  These adjustments corrected for heteroskedasticity caused by using  linear models to analyze binary outcomes and for autocorrelation caused  by analyzing repeated outcomes within drivers over time.
Second, we estimated generalized estimating equations (GEE) models,  with outcomes within drivers having exchangeable or AR1 covariance  structures. These covariance models were appropriate for the 3-year  period, when the autocorrelation of driving schedules should be relatively  consistent across days (exchangeable) or decline linearly (AR1). The  models assumed the outcomes to be distributed normally, binomially, or  negative binomially, depending on the scale, and used canonical link  functions.
We summarize the results of our analysis as ranges across models in the  estimated contrast between time periods before and after the policy  change. Most of our findings were robust to these plausible model  assumptions.
section_title: Results: Hours Worked per 8-Day Week
paragraphs: Drivers in our analysis sample worked approximately 1.1 to 2.5 fewer  hours per 8-day week, on average, after the HOS rule was implemented  than before, depending on model assumptions. Expressed as a  proportion, the differences ranged from approximately 2.0 to 4.8 percent  fewer hours. These differences were statistically distinguishable from zero  at the 0.001 level.
Drivers in our analysis sample were approximately 24 to 29 percent less  likely to work 65 hours or more per 8-day work week after the HOS rule   Similarly, drivers  was implemented, depending on model assumptions.were approximately 12 percent more likely to work 35 to 54 hours per 8- day work week. All of these differences were statistically distinguishable  from zero at the 0.001 level. The likelihood of working 55 to 64 hours per  8-day work week after the rule went into effect changed by less than 2  percent but the statistical significance and direction of the change varied  according to model assumptions.
section_title: Results: Number/Rate of Restarts
paragraphs: Drivers took approximately 6.1 to 6.5 percent fewer restarts per 8-day  week, on average, after the HOS rule was implemented than before,  depending on model assumptions and adjusting for variation in driving  intensity (as a measure of exposure). These differences for the overall  analysis population of drivers were statistically distinguishable from zero  at the 0.001 level.
section_title: Appendix V: Biomathematical Models of Fatigue
paragraphs: GAO was asked to assess the safety impacts of the 2011 hours of service  (HOS) rule, which are predicated on reducing driver fatigue. To assess  whether the rule change could result in less fatigued drivers and  potentially fewer fatigue-related crashes, we used a biomathematical  fatigue model—the Fatigue Audit InterDyne™ (FAID) model—to assess  the risk of driver fatigue for schedules that comply with the 2011 HOS rule  and similar schedules that do not.
The FAID model provides a fatigue score based on the start and end time  of a work shift. The higher the FAID score, the higher the risk of fatigue. A  standard work week of 40 hours, Monday to Friday, 9 a.m. to 5 p.m.  results in a peak FAID score of 41. In contrast, a 40-hour work week from  11 p.m. to 7 a.m. results in a peak FAID score of 97. Research indicates  that scores above 80 are comparable to the fatigue-related impairment  found in individuals with a blood alcohol level of over 0.05 percent, above  the legal limit in many countries. those based on hypothetical driver schedules and those based on  interviews with motor carriers, differ significantly from a typical 9 a.m. to 5  p.m. 40-hour work week.
The examples described below, both  The schedules we modelled below often involve working long hours—60  or more hours per week—and working overnight. As a result, many of the  peak fatigue scores shown below are well above the range considered  safe. The purpose of our analysis is to show the relative difference in  peak fatigue scores for schedules in operation after the rule went into  effect and those in operation before the HOS rule went into effect.
A. Fletcher, N. Lamond, C. van den Heuvel, and D. Dawson, “Predication of performance  during sleep deprivation and alcohol intoxication by a quantitative model of work-related  fatigue,” Sleep Research Online, 5(2) (2003). The study found that a fatigue score of 80 is  comparable to the impairment that would be observed in an individual with a blood alcohol  concentration of 0.09 percent or greater.
Two-night provision (two 1 a.m. to 5 a.m. periods in a restart)
168-hour limit (limits restarts to once every 168 hours)
We also created scenarios based on interviews with motor carriers and  drivers. These interview-based scenarios include the schedules  interviewees reported using before the 2011 HOS rule went into effect  and schedules interviewees said they started using in order to comply  with the 2011 HOS rule. These schedules demonstrate real world  adaptations to the 2011 HOS rule as described to us. Our analysis is not  generalizable to the entire industry, but rather is intended to illustrate how  HOS schedule changes can affect the risk of driver fatigue.
Of the schedules we simulated, some did not require a change after the  rule went into effect. For those that did require a schedule change, figures  8 through 11 below show the daily peak fatigue score when complying  with the two-night provision and 168-hour limit in the 2011 HOS rule and  the daily peak fatigue score for similar schedules that do not comply with  one or both of those provisions in the 2011 HOS rule.
subsections: section_title: Simulated Schedules for Drivers Working Maximum Allowed Hours during Daytime Hours
paragraphs: Drivers can work up to 14 hours per day until reaching 70 hours—at  which point they must either take a restart of at least 34 hours or remain  off-duty until their consecutive 8-day sum of hours is less than 70.  Therefore, we modelled a maximum daytime schedule with the following  parameters:
14-hour shifts on-duty from 6 a.m. to 8 p.m. over five consecutive  Under the previous HOS rule, a driver following this schedule would only  need to take 34 hours off-duty to have a valid restart. However, after the  rule went into effect and to comply with the 168-hour limit, a driver with  this schedule must take 2 days off between work cycles. For example, the  driver can work 14 hours between 6 a.m. and 8 p.m. on Monday through  Friday, but must be off-duty Saturday and Sunday. This results in 58  hours off-duty. This schedule change—2 days off between work cycles  instead of one day off—effectively lowers the average number of hours a  driver can work. In terms of work time per 7-day period, the schedule that  complies with the 168-hour limit averages 72 hours and the schedule that  does not comply averages 82 hours. As shown in figure 8, the peak daily  fatigue scores for the schedule that complies with the 2011 HOS rule (the  schedule with 2 days off) are lower than the peak daily fatigue scores for  the schedule that does not comply with the 2011 HOS rule. The peak  daily fatigue scores for both of these schedules are high and above the  levels generally considered safe on several days that are modelled.
section_title: Simulated Schedules for Drivers Working Maximum Allowed Hours during Nighttime Hours
paragraphs: We also modelled a driver working the maximum allowed hours but  during nighttime hours. The hypothetical schedule had the following  parameters:
14-hour shifts on-duty from 8 p.m. to 10 a.m. over 5 consecutive days  Under the previous HOS rule, the driver only had to take off 34 hours  between the end of the work shift at 10 a.m. on Saturday and the start of  the next work shift at 8 p.m. on Sunday, but this schedule violates both  the two-night provision and the 168-hour limit because the off-duty period  does not contain two periods from 1 a.m. to 5 a.m. and begins less than  168 hours after the prior restart period began. Thus, to comply with both  the two-night provision and the168-hour limit in the 2011 HOS rule, a  driver with this schedule must take 2 days off between work cycles. For  example, the driver can work a 14-hour shift between 8 p.m. and 10 a.m.  on Monday through Friday nights, but must be off-duty Saturday and  Sunday nights to comply with the 2011 HOS rule. This results in an off- duty period of 58 hours. As shown in figure 9, the peak daily fatigue  scores for the schedule used after the rule went into effect (the schedule  with two days off) are lower than the peak daily fatigue scores for the  schedule in use before the rule went into effect. The peak daily fatigue  scores for both of these schedules are high, above the levels generally  considered safe.
section_title: Simulated Schedules for Drivers Working 60- to 70- Hour Shifts during Nighttime Hours
paragraphs: Of the schedules we simulated, two additional hypothetical schedules  needed to change to comply with the 2011 HOS rule. These hypothetical  schedules had the following parameters:
10- to 12-hour on-duty shifts between 10 p.m. to 10 a.m. over 6  consecutive days (70 hours over a 6-day work cycle)
10-hour on-duty shifts from 12 a.m. to 10 a.m. over 6 consecutive  days (60 hours over a 6-day work cycle)
To comply with both the two-night provision and the168-hour limit in the  2011 HOS rule, drivers with these schedules must take 2 days off  between work cycles instead of one day off as allowed under the prior  HOS rule. For example, a driver can work a 10-hour shift between 12  a.m. and 10 a.m. on six consecutive nights but must be off-duty the next  two nights. These two consecutive nights off allow this schedule to  comply with the two-night provision, which requires two 1 a.m. to 5 a.m.  periods in an off-duty period to qualify as a restart. As a result, a driver  working 10-hour shifts on six consecutive nights (60 hours in a work  cycle) must take an off-duty period of 52 hours. A driver working 10-to-12- hour shifts on six consecutive nights (70 hours in a work cycle) must take  an off-duty period of 62 hours. As shown in figures 10 and 11, the peak  daily fatigue scores for the schedules in use after the rule went into effect  (the schedules with 2 days off) are lower than the peak daily fatigue  scores for the schedules in use before the rule went into effect. The peak  daily fatigue scores for the schedules shown in figures 10 and 11 are  high, above the levels generally considered safe.
We also used the FAID model to estimate the risk of driver fatigue based  on schedule changes described in our interviews with an industry  stakeholder, motor carriers, and drivers. Figures 12 through 15 below  show the daily peak-fatigue score for the schedules interviewees adopted  in order to comply with the 2011 HOS rule and the daily peak-fatigue  score for the schedules they used before the 2011 HOS rule went into  effect.
section_title: Simulating Schedule Changes due to the Two- Night Provision Reported by Drivers
paragraphs: Representatives of a motor carrier and an industry association we spoke  with reported changing the schedules of drivers working over-night or in  the early morning hours to ensure that drivers could continue to take the  restart and comply with the two-night provision in the 2011 HOS rule. In  the first example, drivers worked six consecutive10-hour shifts per week  from 12:30 a.m. to 10:30 a.m. before the rule went into effect. Over 7  days, their schedules averaged 61 on-duty hours. These drivers generally  began and ended their shifts at the same time each day. After the rule  went into effect, the motor carrier reduced the number of shifts per driver  from six consecutive shifts per week to five consecutive shifts per week.  This change was made to ensure drivers would have two 1 a.m. to 5 a.m.  periods during their off-duty period and could still count the off-duty time  as a restart. In the second example, drivers worked 10-hour shifts from  3:00 a.m. to 1:00 p.m. over six consecutive days. Similar to the first  example, drivers working this schedule averaged 61 hours on-duty over 7  days. To ensure drivers could take a restart that complied with the two- night provision, the motor carrier reduced the total number of shifts per  driver from six consecutive days to five consecutive days. As shown in  figures 12 and 13, the schedules that comply with the 2011 HOS rule  have lower peak-fatigue scores for each day a driver works this schedule.  The lower peak-fatigue scores suggest a lower risk of fatigue for a driver  working according to the schedule complying with the 2011 HOS rule  than the driver working according to the schedule that does not comply  with the rule. The peak daily-fatigue scores for the schedules shown in  figures 12 and 13 are high, above the levels generally considered safe on  many of the days modelled.
section_title: Simulating Schedule Changes due to the 168- Hour Limit Reported by Drivers
paragraphs: Representatives of other motor carriers we spoke with described  schedule changes they made to ensure off-duty time complied with the  168-hour limit. For example, a representative of one motor carrier told us  that a driver would typically work a long-haul route over 10 days. The  driver would work between 10- and 14-hours per day for 5 days, reach  the drop-off destination and go off-duty for 34 hours (a restart). Then the  driver would drive home. Once home, the driver would take 3 days off  which would count as a restart. To comply with the 168-hour limit and  ensure the off-duty period would count as a restart, this schedule would  have to change to ensure the off-duty periods began at least 7 days apart  (168 hours). To do this, the driver could work 10- to 12-hour days for 6  days, take a 34-hour break on day 7, then drive home and take a shorter  break, 2 days in this example. As shown in figure 14, complying with the  168-hour limit results in generally lower peak fatigue scores and therefore  a lower risk of driver fatigue.
In the second example, representatives of a motor carrier told us about  short-haul drivers working overnight shifts whose schedules were  changed to comply with the 168-hour limit. Before the rule went into  effect, drivers could choose between two possible schedules. The first  schedule (schedule A) had drivers working 12-hour shifts for 5  consecutive days, in our simulation from 8 p.m. to 8 a.m. Then drivers  took the next 2 days off-duty and counted this time as a restart. The  second schedule option (schedule B) had drivers working 13-hour shifts  for 4 consecutive days, in our simulation from 8 p.m. to 9 a.m. These  drivers then took the next 3 days off-duty and counted this time as a  restart. Due to the 168-hour limit, the motor carrier switched drivers to the  following schedule (schedule C): 12-hour shifts for 5 consecutive days  with 2 days off followed by 12-hour shifts for 5 consecutive days with 3  days off. We compared the two schedules offered before the rule went  into effect to the schedule offered after the rule was effective. As shown in  figure 15, we found that schedule C, which complied with the 2011 HOS  rule, had similar peak fatigue scores to Schedule A with the 5 days on- duty with 2 days off-duty schedule. Schedule C, the 2011 HOS compliant  schedule, had slightly higher peak-fatigue scores than Schedule B, the 4  days on duty with 3-days off-duty schedule. This suggests that the  schedule changes made by this carrier to comply with the 168-hour limit  did not consistently lower the risk of fatigue faced by drivers working this  schedule.
section_title: Appendix VI: GAO Analysis of Vehicle Count Data
paragraphs: As was discussed in the body of our report, on July 1, 2013, the Federal  Motor Carrier Safety Administration (FMCSA) began to enforce the 2011  hours of service (HOS) rule that made several key changes to the number  of hours commercial drivers can work and drive per day and week.  Specifically, the new rule altered when and how commercial drivers are  permitted to “restart” the maximum number of hours they can work over a  7- or 8-day period, and required drivers to take a 30-minute, off-duty  break during shifts that last more than 8 hours. FMCSA understood that  when it designed this rule, it would result in changes to drivers’  schedules. Specifically, FMCSA believed the rule would reduce driving  time for drivers working 65 or more hours per week, but assumed that  these hours would be shifted to other drivers or to other workdays rather  than being eliminated altogether. While FMCSA asserted that total driving  time for some individual drivers was likely to drop slightly due to the HOS  rule, no attempt was made to quantify the effects of the rule on  congestion. Stakeholders we spoke with were concerned that FMCSA did  not adequately assess how the new HOS rule would impact traffic  patterns, especially whether the rule would result in increased traffic  congestion during morning hours, i.e., between 5 a.m. and 9 a.m.
To evaluate changes in commercial vehicle traffic before and after the  2011 HOS rule went into effect on July 1, 2013, we collected and  analyzed data from the Federal Highway Administration’s (FHWA) Weigh- in-Motion (WIM) and Classification database. This database includes  information on non-commodity-carrying vehicles, including passenger  vehicles, and commodity-carrying (i.e., commercial) motor vehicles  through data sensors that are installed in roadways. These sensors allow  states and FHWA to collect data on time and date, lane, speed, vehicle  classification, and vehicle length, among other variables.
Our analysis of this data was limited to two time periods before and after  the rule went into effect. Specifically, we chose to evaluate data from  November through December 2012 and November through December  2013 for the following reasons:
We wanted to allow sufficient time for carriers to adapt to the new  rule, including preparing for the change prior to July 1, 2013 and  adapting to the change after July 1, 2013.
We wanted to control for any potential seasonal effects within the  commercial carrier industry by including the same months prior to and  after the HOS rule went into effect.
We wanted to control for unusual events that could have impacted the  commercial motor vehicle industry. For example, we excluded the  time period from January and February 2014, which according to  motor carriers and drivers we spoke with, had a large number of  winter storms that severely impacted the industry. We also excluded  the end of October 2012 to avoid capturing potential impacts on the  industry from Superstorm Sandy.
We worked with FHWA officials to provide us with WIM data that fell into  these timeframes. In order to ensure we were comparing data from  similar locations between the two time periods we requested data only  from stations that reported data for all days and hours between November  through December 2012 and November through December 2013.  Because not all stations reported data for every day or hour during these  timeframes, our analysis was restricted to 324 stations from 14 states out  of 987 total stations across the country (approximately 33 percent). (See  figure 16).
We also asked FHWA officials to filter the data for us into variables most  useful for our analysis. For example, although WIM data include  information on direction of travel and specific lane of travel, we were  primarily interested in total vehicle counts at each station, to capture  traffic volume. As a result we requested vehicle count totals for each  station by month, day, and hour. In addition, FHWA grouped the data into  two distinct categories: (1) non-commodity-carrying vehicles, and (2)  commodity-carrying vehicles.
We analyzed these data by combining individual station data and sorting  them by hour of day before and after the HOS rule went into effect. We  did this analysis for all days of the week, weekdays, and weekends to  account for any change in traffic patterns caused by time of week. We  also compared traffic patterns for non-commodity-carrying vehicles and  commodity-carrying vehicles to determine whether one of those  populations was behaving differently than the other and account for  possible factors that might be influencing both groups, such as the  economy.
section_title: Appendix VII: GAO Analysis of Crash Data
paragraphs: The Federal Motor Carrier Safety Administration (FMCSA) promulgated  the 2011 hours of service (HOS) rule on the basis that it would improve  safety due to a reduction in fatigue-related crashes. At the same time,  however, some industry stakeholders, motor carriers, and drivers that we  spoke with believe that the HOS rule has had some unintended effects  that may actually lead to a decrease in safety, particularly because they  anticipate increased congestion during certain early morning hours when  roads tend to be congested. To analyze the safety effects of the 2011  HOS rule, we undertook two separate analyses to (1) assess whether  there has been any apparent effect of the 2011 HOS rule on the numbers  of motor carrier crashes and (2) whether the rule change appeared to  affect the relative number of crashes that occurred after the rule’s  implementation between 5 a.m. and 9 a.m. We used monthly crash data  from the Motor Carrier Management Information System (MCMIS) from  2008 through 2014, supplied by FMCSA, to address the first question,  and individual crash data from the same source to address the second.
subsections: section_title: Trends in the Numbers of Crashes
paragraphs: Table 26 shows information for each month from January 2008 to  September 2014, on the numbers of total crashes involving motor  carriers, as well as the number of crashes with injuries and the numbers  of fatal crashes. As can be seen, the numbers of crashes show a good  deal of variation over time, but there appears little by way of any  patterned variation, or trend. For example, the monthly numbers of total  crashes averaged 7,042, but ranged from a low of 5,593 in February 2009  to a high of 9,887 in January 2014. The monthly numbers of crashes with  injuries and fatal crashes also showed a good deal of variation.
Many potential factors can influence the number of crashes over time. For  example, as the economy grows, it is reasonable to expect more goods  and services are traded, resulting in more freight needing to be  transported. Given the size of roads is unlikely to change much in the  near term, more traffic may result in more congestion, and more  congestion may result in more accidents. To assess whether the  implementation of the 2011 HOS rule had an effect on crashes we  developed two ordinary least squares regression models that controlled  for trucking volume and seasonal variation, and in one case also  controlled for the unusual winter weather in December 2013 to February  2014. (See table 27.)
Model 1, fit separately for each of the three categories of crashes,  regressed the numbers of crashes on 1) three quarterly dummy variables  to assess for seasonal differences in the numbers of crashes across the  four quarters of the year, 2) a dummy variable (denoted “Rule Period”) to  contrast the numbers of crashes in the months following the rule change  with the numbers of crashes in the months preceding it, and 3) a linear  covariate measuring “trucking gross output,” which is a quarterly measure  used as a proxy for the numbers of motor carriers on the road and at risk  of crashing. The trucking gross output variable had a significant and  positive relationship with the monthly numbers of all three groups of  crashes (all crashes, injury crashes, and fatal crashes), and seasonal  differences, as measured by quarter, were generally significant for total  crashes and fatal crashes, but not for crashes with injuries. In model 1,  the apparent effect of the rule change on crashes was significant and  positive for total crashes, insignificant for crashes with injuries, and  significant and negative for fatal crashes. However, our discussions with  industry participants indicated that the winter from December 2013  through February 2014 was unusually harsh and made operations very  difficult for the industry. These weather conditions may have been a  contributing factor, irrespective of the rule change, to a short term rise in  crashes during this time period. Because of this issue, we also tested an  alternative iteration of this model: Model 2.
Model 2 estimates all these same effects as the first model for each of the  three groups of crashes but includes an additional dummy variable to  account for any independent effect on the numbers of crashes due to the  unusually disruptive winter weather from December 2013 to February  2014. Adding this dummy variable had no effect on our estimate of the  effect of the rule change on crashes with injuries, which remained  insignificant, or in fatal injuries, which remained significant and negative.  It did, however, reduce the size of the estimated impact of the rule  change for total crashes and rendered the effect of the rule change on  total crashes insignificant.
section_title: Crashes Occurring between 5 a.m. and 9 a.m.
paragraphs: As noted above, we were told by stakeholders that the two-nighttime  provision of the HOS rule would potentially result in an increased traffic  involving large trucks between 5:00 a.m. and 9 a.m. Presumably,  because of this provision, drivers taking a restart would have to wait until  at least 5:00 a.m. to begin their day when roads are more congested.  Using individual crash data from MCMIS, we also investigated whether,  among all crashes, there was any effect of the rule change on the  likelihood of crashes occurring between 5 a.m. and 9 a.m. versus any  other time of the day. For each of the three sets of crashes, we first  examined simple two-way cross-classifications (shown in table 28) and fit  a simple bivariate logistic regression model that regressed the logarithm  of the odds on crashes occurring between 5 a.m. and 9 a.m. on a dummy  variable denoted as “Rule Period” that contrasted crashes that occurred  before and after the rule change. Table 28 provides information on the  numbers and percentages of total crashes, crashes with injuries, and fatal  crashes that occurred between 5 a.m. and 9 a.m., and at all other times of  the day, before and after the rule change. As can be seen, between 19  percent and 20 percent of crashes occurred between 5 a.m. and 9 a.m.,  both before and after the rule change. The value of the likelihood-ratio  chi-square, shown at the base of each table, indicates that there was no  statistically significant difference in the occurrence of crashes in the 5  a.m. to 9 a.m. time.
Table 28. Numbers of Total Crashes, Crashes with Injuries, and Fatal Crashes That Occurred between 5 a.m. and 9 a.m. and at  Other Times, before and after the Rule Change  Time of crash (total crashes)
Likelihood ratio chi-square = 0.014 with 1df, P - 0.91  Time of crash (crashes with injuries)
Likelihood ratio chi-square = 0.637 with 1df, P - 0.43  Time of crash (fatal crashes)
Also shown in table 28 is an alternative method of estimating the  likelihood of crashes occurring between 5 a.m. and 9 a.m. by calculating  the differences in the likelihoods of crashes in the 5 a.m. to 9 a.m. time  frame and calculating odds and odds ratios, which are shown in the last  two columns of table 28. The odds on crashes occurring between 5 a.m.  and 9 a.m. are calculated by taking the number (or percentage) of  crashes that occurred in that interval and dividing it by the number (or  percentage) of crashes that occurred at other times. With respect to total  crashes, for example, the odds on crashes occurring between 5 a.m. and  9 a.m. before the rule change were 90,769 ÷ 364,866 = .2488, and the  odds of crashes occurring between 5 a.m. and 9 a.m. after the rule  change were 22,880 ÷ 91,882 = .2490. While somewhat different and less  traditional than percentages, the odds have a fairly direct and simple  interpretation—in this case they imply that in both periods there were  roughly 25 crashes in that interval for every 100 that occurred at all other  times of the day. The odds of crashes occurring between 5 a.m. and 9  a.m. are very similar for crashes with injuries and fatal crashes. The odds  ratios in the final column are calculated by taking the odds of crashes  occurring in the designated time frame after the rule change and dividing  by the odds of crashes at that time before the rule change. For total  crashes, for example, the odds ratio is calculated as follows: (.2490 ÷  0.2488) = 1.001. This ratio is only slightly different than 1, indicating that  the likelihood of crashes occurring between 5 a.m. and 9 a.m. was nearly  identical before and after the rule. The odds ratios for crashes with  injuries and fatal crashes produce similar conclusions.
One advantage of odds ratios is that unlike percentage differences, they  can be adjusted using multivariate models (logistic regression models) so  that they reflect the net effect of the rule change, after adjusting for other  characteristics that might have differed, in this case, before and after the  rule change. Table 29 shows that results of fitting bivariate (or  unadjusted) logistic regression models (in the first row), and the  multivariate regression models (in the remaining rows) to estimate the  effect of the rule change on the likelihood of crashes occurring between 5  a.m. and 9 a.m. for total crashes, crashes with injuries, and fatal crashes.  The bivariate, or unadjusted models, reproduce the same odds ratios we  derived from the observed data in the two-way tables in table 28. We then  fit multivariate models that regressed those same odds on the rule period  variable while simultaneously controlling for a number of potentially  confounding factors, including road, weather, and lighting conditions,  trucking gross volume, and whether the crash occurred during a weekday  or on the weekend. The coefficients for the multivariate model shown in  table 29 are exponentiated odds ratios, which indicate the size and  significance of the differences in the odds on crashes occurring between  5 a.m. and 9 a.m. as opposed to some other time of the day, both as a  result of the rule change and as a result of the factors included in the  model. The multivariate models reveal that a number of the control  variables affected the likelihood of crashes occurring from 5 a.m. to 9  a.m., including different road, weather, and light conditions. Crashes were  more likely to occur between 5 a.m. and 9 a.m., for example, when roads  were icy rather than dry (by factors ranging from 2.2 for total crashes to  3.6 for fatal crashes), when conditions were foggy rather than clear (by  factors ranging from 4.4 for total crashes to 5.0 for crashes with injuries  and fatal crashes), and during dawn rather than daylight hours (by factors  exceeding 40 in all cases). However, even with these factors controlled,  the rule change appeared to have no significant effect on the likelihood of  total crashes occurring between 5 a.m. and 9 a.m. Odds ratios in the  multivariate models estimating the differences in the likelihood of crashes  occurring before and after the rule change were very nearly 1.0, and  ranged from 0.999 for total crashes to 1.025 for fatal crashes.
As previously discussed, there are many potential factors that could  influence the number of crashes in any given month, including weather  and road conditions at the time of a crash. Our analyses take into account  some of these factors by controlling or adjusting for seasonal differences  and freight volume, but there are undoubtedly other potentially  confounding factors, and we would have liked to have had a more direct  measure of the numbers of motor carriers on the road in each month and  at risk of crashing. Moreover, our dataset only included 15 data points, or  months, since the rule went into effect, and neither the significant or  insignificant changes we find in this short term are guaranteed in the  longer term. Without additional data over a longer period of time, we are  unable to robustly determine whether the HOS rule had an impact on  crashes.
section_title: Appendix VIII: Comments from the Department of Transportation
section_title: Appendix IX: GAO Contact and Staff Acknowledgments
subsections: section_title: GAO Contact
section_title: Staff Acknowledgments
paragraphs: In addition to the individual named above, H. Brandon Haller, Assistant  Director; Amy Abramowitz; Sarah Arnett; Russell Burnett; Matthew Cook;  Melinda Cordero; Leia Dickerson; Colin Fallon; David Hooper; Hannah  Laufe; Ethan Levy; Grant Mallie; Joshua Ormond; Anna Maria Ortiz; Jerry  Sandau; Doug Sloane; and Jeff Tessin made key contributions to this  report.