In preparation for changes in pavement design methodologies and to begin to assess the effectiveness of the weigh-in-motion (WIM) system in Utah, the Utah Department of Transportation (UDOT) contracted with a Brigham Young University (BYU) research team to conduct an evaluation of their commercial motor vehicle (CMV) data collection system statewide. The objective of this research was to evaluate the CMV data collection program in the state of Utah and to make limited recommendations for potential improvements and changes that will aid in more detailed and accurate CMV data collection across the state. To accomplish the research objectives, several tasks were conducted, including: 1) a review of literature to establish the state-of-the-practice for CMV monitoring, 2) collection of WIM data for the state of Utah, 3) analysis of the collected WIM data, 4) development of a calibration methodology for use in the state, and 5) presentation of recommendations and conclusions based on the research. The analysis of collected WIM data indicated that the CMV data collection system in the state of Utah currently produces data consistent with expectations with a few exceptions. Recommendations for improvements to the CMV data collection system come in the form of a proposed calibration methodology that is in line with current standards and the practices in other states. The proposed calibration methodology includes calibration, verification, and a quality assurance programs.

Driver distraction has become a major concern for the U.S. Department of Transportation (US DOT).  Performance decrements are typically the result of driver distraction because attentional resources are limited, which are limited; fatigue and drowsiness limit attentional resources further.  The purpose of the current research is to gain an understanding of the relationship between mobile device use (MDU), fatigue, through driving time and time on duty, and drowsiness, through time of day and amount of sleep, for commercial motor vehicle drivers.  A re-analysis of naturalistic driving data was used to obtain information about the factors, MDU, safety-critical events (SCE), and normal driving epochs.  Odds ratios were used to calculate SCE risk for 6 mobile device use subtasks and each of the factors, which were divided into smaller bins of hours for more specific information.  A generalized linear mixed model and chi-square test were used to assess MDU for each factor and the associated bins.  Results indicated visually demanding subtasks were associated with an increase in SCE risk, but conversation on a hands-free cell phone decreased SCE risk.  There was an increase in SCE risk for visual manual subtasks for all bins in which analyses were possible.  Drivers had a higher proportion of MDU in the early morning (circadian low period) than all other times of day that were analyzed.  These results will be used to create recommended training and evaluate policy and technology and will help explain the relationship between MDU, fatigue, and drowsiness. / Master of Science

In 2004, 5,190 people were killed due to a traffic accident involving a commercial motor vehicle (CMV), up from 4,793 people killed in 2001 (Traffic Safety Facts, 2004; Traffic Safety Facts, 2001). Driver drowsiness is an important issue to consider when discussing CMVs. According to the FMCSA, over 750 people are killed and 20,000 people are injured each year due to drowsy CMV drivers (as cited in Advocates for Highway and Auto Safety, 2001). Driver drowsiness is an important issue for CMV drivers for several reasons, including long work shifts, irregular schedules and driving long hours on interstates and highways with no scenic interruptions to help keep the driver alert. Because of these and other factors, including the high mileage exposure that CMV drivers face, drowsiness is an important issue in a CMV driver's occupation. There were two main goals to this research: 1) gain a better understanding of the time-related occurrences of drowsy-related critical incidents (i.e., crashes, near-crashes and crash-relevant conflicts), and 2) obtain drivers' opinions of the 2004 Revised Hours-of-Service regulations. To do this, recent data were used from a Field Operational Test conducted by the Virginia Tech Transportation Institute in which 103 participants drove in an instrumented heavy vehicle for up to 16 weeks; video data, and sensor data were collected from each participant. In addition, actigraph data was collected from 96 of the 103 participants. Each vehicle was instrumented with four video cameras to capture images of the drivers face, the forward roadway, and the adjacent lanes on each side of the truck. In addition, multiple sensors were installed in the vehicle in order to collect data such as the driver's speed, braking patterns and steering wheel movement. These data were combined to provide a complete picture of each driver's environment and behavior while they drove their normal routes. Data analysts reviewed the data for critical incidents (crashes, near-crashes, and crash-relevant conflicts) and determined a drowsiness level for each incident; these downiness levels were compared to drowsiness levels of baseline incidents (i.e., normal driving periods). The results show that drivers were more likely to have a drowsy-related critical incident between 2:00 pm and 2:59 pm. In addition to the video and sensor data, each driver was asked to fill out a subjective questionnaire regarding the revised HOS regulations. Drivers preferred the revised HOS regulations over the old HOS regulations and the number one item that was preferred in the revised HOS regulations is the 34-hour restart which allows drivers to restart their work week by taking off 34 consecutive hours. / Master of Science

The Federal Highway Administration (FHWA) currently requires that all persons seeking a commercial driver's license for interstate commerce possess a certain minimal level of hearing. After an extensive literature review on topics related to hearing and driving, a human factors engineering approach was used to evaluate the appropriateness of this hearing requirement, the methods currently specified to test drivers' hearing, and the appropriate hearing levels required. Task analysis, audiometry, dosimetry, in-cab noise measurements, and analytical prediction of both speech intelligibility and masked thresholds were all used in performing the evaluation. One of the methods currently used to test truck driver hearing, the forced-whisper test, was also evaluated in a laboratory experiment in order to compare its effectiveness to that of standard pure-tone audiometry. Results indicated that there are truck driving tasks which require the use of hearing, that truck drivers may be suffering permanent hearing loss as a result of driving, that team drivers may be approaching a 100% OSHA noise dose over 24 hours, and that truck-cab noise severely compromises the intelligibility of live and CB speech, as well as the audibility of most internal and external warning signals. The forced whisper experiment demonstrated that there is significant variability in the sound pressure level of whispers produced using this technique (in the words, word types, and trials main effects). The test was found to be repeatable for a group of listeners with good hearing, but was found to have only a weak relationship to the results of pure-tone audiometry for a group of 21 subjects with hearing levels ranging from good to very poor. Several truck cab and warning signal design changes, as well as regulatory changes, were recommended based on the overall results of this evaluation. / Ph. D.

The collection and interpretation of data is a critical component of traffic and transportation engineering used to establish baseline performance measures and to forecast future conditions. One important source of traffic data is commercial motor vehicle (CMV) weight and classification data used as input to critical tasks in transportation design, operations, and planning. The evolution of Intelligent Transportation System (ITS) technologies has been providing transportation engineers and planners with an increased availability of CMV data. The primary sources of these data are automatic vehicle classification (AVC) and weigh-in-motion (WIM). Microscopic traffic simulation models have been used extensively to model the dynamic and stochastic nature of transportation systems including vehicle composition. One aspect of effective microscopic traffic simulation models that has received increased attention in recent years is the calibration of these models, which has traditionally been concerned with identifying the "best" parameter set from a range of acceptable values. Recent research has begun the process of automating the calibration process in an effort to accurately reflect the components of the transportation system being analyzed. The objective of this research is to develop a methodology in which the effects of CMVs can be included in the calibration of microscopic traffic simulation models. The research examines the ITS data available on weight and operating characteristics of CMVs and incorporates this data in the calibration of microscopic traffic simulation models. The research develops a methodology to model CMVs using microscopic traffic simulation models and then utilizes the output of these models to generate the data necessary to quantify the impacts of CMVs on infrastructure, travel time, and emissions. The research uses advanced statistical tools including principal component analysis (PCA) and recursive partitioning to identify relationships between data collection sites (i.e., WIM, AVC) such that the data collected at WIM sites can be utilized to estimate weight and length distributions at AVC sites. The research also examines methodologies to include the distribution or measures of central tendency and dispersion (i.e., mean, variance) into the calibration process. The approach is applied using the CORSIM model and calibrated utilizing an automated genetic algorithm methodology.

The collection and interpretation of data is a critical component of traffic and transportation engineering used to establish baseline performance measures and to forecast future conditions. One important source of traffic data is commercial motor vehicle (CMV) weight and classification data used as input to critical tasks in transportation design, operations, and planning. The evolution of Intelligent Transportation System (ITS) technologies has been providing transportation engineers and planners with an increased availability of CMV data. The primary sources of these data are automatic vehicle classification (AVC) and weigh-in-motion (WIM). Microscopic traffic simulation models have been used extensively to model the dynamic and stochastic nature of transportation systems including vehicle composition. One aspect of effective microscopic traffic simulation models that has received increased attention in recent years is the calibration of these models, which has traditionally been concerned with identifying the "best" parameter set from a range of acceptable values. Recent research has begun the process of automating the calibration process in an effort to accurately reflect the components of the transportation system being analyzed. The objective of this research is to develop a methodology in which the effects of CMVs can be included in the calibration of microscopic traffic simulation models. The research examines the ITS data available on weight and operating characteristics of CMVs and incorporates this data in the calibration of microscopic traffic simulation models. The research develops a methodology to model CMVs using microscopic traffic simulation models and then utilizes the output of these models to generate the data necessary to quantify the impacts of CMVs on infrastructure, travel time, and emissions. The research uses advanced statistical tools including principal component analysis (PCA) and recursive partitioning to identify relationships between data collection sites (i.e., WIM, AVC) such that the data collected at WIM sites can be utilized to estimate weight and length distributions at AVC sites. The research also examines methodologies to include the distribution or measures of central tendency and dispersion (i.e., mean, variance) into the calibration process. The approach is applied using the CORSIM model and calibrated utilizing an automated genetic algorithm methodology.