Commercial Motor Vehicle Driver Fatigue, Long-Term Health, and Highway Safety Research Needs (2016) / Chapter Skim
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5 Data Sources
5 Data Sources
Pages 61-86
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From page 61... ...
The purpose of this chapter is to provide an understanding of the wide range of data sources that are potentially available to researchers for identifying factors that can reduce risk and enhance safety in the transportation network. For each source, the strengths and limitations of the data are considered, especially with respect to answering key research questions about fatigue among commercial motor vehicle (CMV)
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From page 62... ...
PUBLICLY AVAILABLE COMMERCIAL MOTOR VEHICLE CRASH DATABASES Databases containing information on CMV crashes are maintained primarily by the National Highway Traffic Safety Administration (NHTSA) and the Federal Motor Carrier Safety Administration (FMCSA)
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From page 63... ...
. GES is a general-purpose crash data file of motor vehicle crashes in the United States.
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From page 64... ...
The MCMIS crash file, carrier file (registration data on all qualifying motor carriers) , and inspection file (data from all inspections of motor carrier vehicles and drivers)
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From page 65... ...
The coverage of crash severity in FARS/TIFA, GES, and the MCMIS is summarized in Table 5-1. Limitations of CMV Crash Databases The data in the FARS, TIFA, GES, and MCMIS crash databases all begin with police crash reports.
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From page 66... ...
The researchers conducted in-depth investigation for each of the crashes included in the study, encompassing scene diagrams; photographs; and extensive information on the vehicles, environment, and drivers. The crash data collection was structured around precrash maneuvers, the critical event itself, the critical reason for the critical event, and associated factors.
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From page 67... ...
Even though the data are more than 10 years old, the LTCCS is still the most comprehensive and detailed truck crash investigation data set available. (For more information on this study, see National Highway Traffic Safety Administration and Federal Motor Carrier Safety Administration, 2006a, 2006b, 2012.)
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From page 68... ...
Crash data are collected to monitor overall levels of safety, to enforce the law, and to allocate public resources for reducing the toll of motor vehicle crashes. Similarly, the limited exposure data available, such as VMT, are collected to monitor overall trends in highway usage and to allocate highway funds to the states.
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From page 69... ...
The final data set provides an "instant replay" of the entire driving trip, including any incidents, allowing researchers to focus on event factors including driver behavior and crash precursors. NDS have been conducted with light vehicles (e.g., the 100-car study [see Dingus et al., 2006]
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From page 70... ...
focused on a time frame of 6 seconds. By contrast, more recent analyses of light vehicle data focused on assessing driver behavior prior to an event trigger have reviewed video and other driving data 12 seconds prior to the event (Victor et al., 2014)
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From page 71... ...
For example, many NDS studies analyze incidents often referred to as "safety-critical events" (SCEs) , which may include near-crashes and other driver errors (e.g., unintended lane deviations)
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From page 72... ...
An important benefit of NDS data collection is that data exists for travel times when crashes or SCEs did not happen.8 Since data are available for both crashes and SCEs, one can match a situation in which a crash or SCE occurred with an analogous situation in which one did not occur, and then examine the frequency of various possible causal factors to see whether it differs between the two situations. The data contain precrash and pre-SCE information on driver behaviors including the presence of fatigue or distraction and interactions with pas sengers, devices, and the vehicle.
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From page 73... ...
Again, this limitation is not unique to NDS; surveys and studies based on paper logs, for example, are restricted to certain segments of the CMV population. Achieving a study sample that is representa tive of the population is hampered by the fact that estimates of characteristics of all CMV drivers are not available.
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From page 74... ...
. Driving Simulator Studies Driving simulators are useful for research purposes as they allow a researcher to observe driver behavior under certain conditions while controlling for others.
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From page 75... ...
PROPRIETARY DATA Proprietary data include data collected by the American Transportation Research Institute (ATRI) and by large truck carriers.
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From page 76... ...
Also, crash data can be linked to personnel/work records, as well as to equipment manifests. Some carriers have relatively sophisticated data collection programs with respect to loss events, similar in construction to public crash files.
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From page 77... ...
Inspection Reports A state inspection system nationwide conducts more than 3 million roadside inspections of commercial motor vehicles annually to ensure
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From page 78... ...
Drivers also are checked for visible signs of fatigue. If the vehicle and/or the driver is in violation of FMCSA regulations, the vehicle and/or driver may be placed "out of service." An example of a vehicle violation is "oil and/or grease leak," while an example of a driver violation is "failing to use seat belt." There were 3,497,937 roadside inspections in 2013 (Federal Motor Carrier Safety Administration, 2014, Table 2-5)
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From page 79... ...
As the survey was conducted at truck stops, the truck driver population that was interviewed for the survey comprised only long-haul drivers, and excluded drivers who deliver goods locally. It is difficult to know whether the survey was strongly unrepresentative of all drivers given that there are no baseline health data on all truck drivers in the United States.
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From page 80... ...
are important predictors of crash risk and driver fatigue. An ATRI report on the safety impacts of HOS regulations, which consists of analysis based on TIFA data, states that 80 percent of fatal truck collisions in 2007 occurred within the first 8 hours of driving (American Transportation Research Institute, 2010, Figure 2)
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From page 81... ...
The lack of such exposure data makes it difficult to calculate crash rates by hours driven. • Diversity of route and load: Loads range from various types of freight, to liquids, to agricultural products (e.g., livestock, produce)
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From page 82... ...
investigated sleep obtained by long-haul truck drivers in Australia and found minor differ ences in the quality of sleep obtained in a sleeper berth versus at home. 10 This report does not specifically consider off-road operations.
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From page 83... ...
In 2010, FMCSA published a final rule on mandatory installation of ELDs on commercial motor vehicles manufactured after June 4, 2012. In August 2013, the Seventh Circuit Court rendered judgment that the agency could not proceed with the rule as it failed to consider driver harassment.
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From page 84... ...
As the data are collected in real time, the dispatcher can warn the driver of potential problems. Vehicle data potentially available for monitoring unsafe driving include hardbraking events, sudden accelerations, and speeding.
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From page 85... ...
vehicle characteristics, reported crash events; lack road conditions, and exposure data weather conditions Difficulty of identifying Generate aggregate crash driver fatigue from crash statistics reports since the data are collected by nonresearchers Naturalistic Driving Assess driver and vehicle Crashes are a relatively Studies performance under rare event; aspects of actual road conditions data reduction are done Provide exposure data manually Driving Simulators Replicate experimental Enable assessment of relative road conditions, which validity but not absolute enables testing various validity scenarios Can be used to quantify performance profile of drivers who suffer from various medical conditions Electronic On-Board Identify unsafe driving Different set of technologies Recorders, Electronic practices and at-risk oriented toward different Logging Devices, On- drivers factors related to safety Board Safety Systems Real-Time GPS Data Provide exposure data Potentially proprietary SOURCE: Adapted from Rizzo (2011, Table 2)
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From page 86... ...
The challenges entailed in doing so include the following: (1) some data sources are proprietary and would require collaboration of the data holders; (2)
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