Master of Science in Engineering, University of Akron, 2021, Civil Engineering

Traffic safety is a continued concern among numerous agencies in the United States. Traffic safety agencies at the national and state level have made it their priority to decrease the number of traffic related fatalities and injuries each year. In order to see a decrease in the number of traffic related fatalities and injuries each year, grant money is applied to programs that are developed around traffic education, enforcement, engineering, and emergency medical services. These traffic safety agencies coordinate with one another to identify the key traffic related problem areas for which to apply the grant money. After reviewing the applicable criteria for developing the Highway Safety Plan, it was determined that the traffic safety problem identification procedure used to allocated traffic safety resources could be enhanced. Multiple methods were reviewed to analyze the accuracy and efficiency of traffic safety problem identification. These methods were applied to a case study to test the transferability of these problem identification methods. The research team found that the newly developed problem identification methodology may be used at a state level as well as a county level to identify traffic safety problems and problem areas for which resources should be allocated to improve traffic safety.

Committee: William Schneider Dr. (Advisor); David Roke Dr. (Committee Member); Teresa Cutright Dr. (Committee Member) Subjects: Civil Engineering; Engineering

Doctor of Philosophy, The Ohio State University, 2024, Nursing

Introduction: Walking is a healthy activity with numerous benefits, but pedestrian fatalities in the United States have risen alarmingly. From 2010 to 2020, pedestrian fatalities due to motor vehicle crashes increased by approximately 54%, reaching 6,607 deaths in 2020, accounting for nearly 18% of all road traffic fatalities. Concurrently, the U.S. population is aging, with one in five Americans projected to be aged 65 or older by 2030. These trends underscore the importance of understanding pedestrian fatalities across different age groups, particularly among older adults. However, few studies have explored age-related crash outcomes among pedestrian fatalities in both rural and urban contexts. Therefore, this research aims to identify the associations between age groups of fatally injured pedestrians and crash characteristics in rural and urban areas, examine factors influencing the place of death among fatally injured pedestrians, and analyze factors affecting the survival times of fatally injured pedestrians. Methods: This research used a cross-sectional design with data from the Fatality Analysis Reporting System (FARS) for the year 2021. Statistical analysis included descriptive statistics, chi-square tests, and log-rank tests. Multivariable logistic regression was performed to determine the predictors of binary outcomes, and Cox proportional hazards regression was used to determine the predictors of survival times. Results: Among fatally injured pedestrians, urban older adults had lower odds of being hit by a passenger car compared to urban younger adults. Urban middle-aged adults, urban older adults, and rural older adults, were less likely to be hit by vehicles traveling at 35 mph or over 35 mph, in the areas without crosswalks or sidewalks, in dark conditions, during weekends, and at non-intersections compared to their younger counterparts. Older adults had lower odds of dying at the scene/en-route compared to younger adults. The odds of dying at th (open full item for complete abstract)

Committee: Ethan Morgan (Advisor); Brittany Punches (Committee Member); John Bolte (Committee Member); Jodi McDaniel (Committee Member) Subjects: Nursing

Doctor of Philosophy, The Ohio State University, 2024, Industrial and Systems Engineering

Sociotechnical systems in safety-critical domains are distributed and contain interdependencies between the different elements, including human and automated roles that need to coordinate and synchronize their activities with dynamic events in the environment. The advancement of technology and the introduction of machines capable of acting at a higher level of autonomy has increased the complexity of such Distributed Work Systems (DWSs). An envisioned DWS is described by a set of static paper-based documents and will be deployed in the next few years. The short-range low-altitude air mobility system is one very good example of an envisioned DWS. Interactions between human and automated roles and their environment are dynamic, evolve, and change over time, causing emergent effects like taskload peaks and coordination breakdowns. A well-designed DWS will be able to keep pace with the work environment dynamics (like the dynamics of aircraft governed by laws of flight in a short-range low-altitude air mobility system) and succeed in responding to the disturbance. This creates the need to understand the dynamics of envisioned DWS, such as how a DWS performs in high-paced situations like anomaly response. Assessing the feasibility and robustness of an envisioned DWS comes with challenges: the physical system does not yet exist, its design and operations are often underspecified, and multiple versions may exist within a designer community about what future operations will look like. Therefore, as a part of this dissertation, an exploratory early-stage computational modeling and simulation technique is described and demonstrated to evaluate an envisioned DWS. Using functional modeling and computational simulation capabilities, the dissertation shows a technique that can help evaluate envisioned DWS by discovering things that are not uncovered by traditional normative simulations. The primary advantage of the technique is the ability to evaluate the dynamics of work in (open full item for complete abstract)

Committee: Martijn Ijtsma (Advisor); Michael Rayo (Committee Member); David Woods (Committee Member) Subjects: Industrial Engineering; Systems Design

Master of Science, The Ohio State University, 1969, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1967, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science (MS), Ohio University, 2024, Civil Engineering (Engineering and Technology)

This study focuses on the efficacy of Driveway Assistance Devices (DADs) compared to Traditional Traffic Signal Devices (TTSDs) in rural two-lane road work zones, highlighting the critical role of work zones in highway construction and maintenance projects. Utilizing PTV Vissim software, the research evaluates the impact of various work zone lengths on traffic parameters such as travel time, delay time, and queue length. The findings underscore that the length of work zones significantly influences traffic dynamics, with longer zones resulting in greater delays, queue lengths and travel times. The study also reveals that while DADs generally manage queue delays more effectively than TTSDs for most work zone lengths, both types face challenges in efficiently managing traffic in zones exceeding 3000 feet, indicating a need for further research into extended work zone challenges. The cost analysis part of the study demonstrates the financial advantages of using DADs, particularly in extended work zones for construction trucks and road users. A work zone length of 2000 feet is identified as optimal in balancing delay cost reduction and construction efficiency. The study also addresses the limitations of TTSDs in adaptability and safety, leading to an increased interest in innovative solutions like DADs that offer improved synchronization in work zones. Overall, the research aims to fill the gap in understanding the benefit-cost implications of different traffic control devices, advocating for the potential inclusion of DADs in the Manual on Uniform Traffic Control Devices (MUTCD). The insights gained are anticipated to significantly influence future traffic management in road construction and maintenance projects, emphasizing the need for more efficient and safer work zones for enhanced transportation systems and community well-being.

Committee: Deborah McAvoy (Advisor); Mario Grijalva (Committee Member); Daniel Che (Committee Member); Bhaven Naik (Committee Member) Subjects: Civil Engineering; Transportation

Master of Science (MS), Ohio University, 2024, Civil Engineering (Engineering and Technology)

In the field of transportation engineering, there has been a shift towards implementing Connected Vehicle (CV) Technology as a means of improving transportation systems. This approach is becoming increasingly important due to limited space, high delay on roadways, and significant crashes. The CV technology is expected to be the most effective solution for making transportation systems more functional and safer, as it enhances drivers' decision-making abilities and helps to control traffic flow. To assess the impacts of CV technology, simulations and closed-course testing have been conducted. In addition, some pilot studies have been carried out in urban settings, where the goal is to achieve "zero deaths." However, a comprehensive understanding of the applications of Connected Vehicles in rural settings is necessary, as driver behavior can be unpredictable and location-dependent. This research aims to evaluate the performance of four CV applications in a rural environment: Red Light Violation Warning (RLVW), Pedestrian in Signalized Crosswalk (PEDINXWALK/ PEDPSM), Curve Speed Compliance (CSPDCOMP), and Speed Compliance in work zones (SPDCOMPWZ). There were no work zones in the study area hence analysis on SPDCOMPWZ was not included in this study. Though the research had four CV applications but each driver had only three applications installed in their vehicle. Hence the study obtained 4 different groups for all 3-paired CV applications. The study analyzed the impact of these applications on drivers' behavior and their reactions to evaluate the performance of CV applications. The analysis focused on drivers' speeds since speed happens to be one of the primary traffic parameters that can provide in-depth information on driver's behavior on the road. The driver's speed is analyzed once they receive a warning or trigger prior to a potential violation of these specific traffic rules; 1.Running red-light, 2. Conflict/ potential crash between vehicle and pedestri (open full item for complete abstract)

Committee: Bhaven Naik Ph.D., P. E., PTOE (Advisor); Felipe Aros-Vera Ph.D. (Committee Member); Deborah McAvoy Ph.D., P.E. (Committee Member); Gaurav Sinha Ph.D. (Committee Member) Subjects: Artificial Intelligence; Automotive Engineering; Civil Engineering; Engineering; Technology; Transportation; Transportation Planning

MS, University of Cincinnati, 2023, Engineering and Applied Science: Civil Engineering

The concepts of “smart mobility” and “vehicle automation” have recently emerged with the aim of eliminating traffic crashes caused by driver error such as distraction, recklessness, speeding, alcohol/drugs-impaired driving, and fatigue, by minimizing or completely removing the human from the driving task. However, the uncertainty about the safety benefits of fully automated vehicles (AVs), especially in the short term, as well as the long-expected transition period before they become widespread on roadways makes it crucial to investigate their potential impact on traffic performance. Therefore the main objective of this research was to use a microsimulation-based method to assess the potential delay and safety impacts of AVs in a mixed autonomy setting where both human-driven and fully automated vehicles operate in the same traffic stream, at a busy signalized intersection with permitted left turns and right turns on red. PTV VISSIM was used to develop the simulation environment comprising of 13 simulation cases, with 4 traffic composition scenarios and 5 different AV market penetration rates (0%, 25%, 50%, 75%, and 100%). The Surrogate Safety Assessment Model (SSAM) was employed to conduct a thorough safety analysis of the diverse scenarios, through the utilization of PET and TTC as surrogate safety measures. 3 TTC approaches were then considered by varying the TTC threshold values to determine the number potential traffic conflicts. The results indicate that at 25% AV MPR, the average intersection delay could be reduced by 25% and as AVs reach 100% MPR, this reduction could go up to 60%. In terms of traffic safety, the total number of conflicts generated decreased as AV MPR increased, with potential safety benefits ranging from 11% to 81% as the AV percentage increased from 25% to 100%. The advanced capabilities of AVs were clearly highlighted in the analysis of rear-end conflicts, where incidents involving human-driven vehicles colliding with autonomous vehic (open full item for complete abstract)

Committee: John Ash Ph.D. (Committee Chair); Zhixia Li Ph.D. (Committee Member); Mohamed Ahmed Ph.D. (Committee Member) Subjects: Civil Engineering

PhD, University of Cincinnati, 2021, Engineering and Applied Science: Civil Engineering

Connected and automated vehicles (CAVs) have the potential to improve the capacity, safety, and environmental sustainability of the transportation system. But it is still unclear what a CAV future will mean for both the transportation facilities and human driven vehicles. In the meantime, transportation agencies must make investment and policy decisions to address the future needs of the transportation system, and researchers must develop a systematic evaluation method to figure out what kind of impact CAVs could bring to the system. Researchers need to figure out what is the impact of CAVs on human driven vehicles and come up with proper transportation system management methods. This research provides much-needed guidance for agencies about planning-level capacities in a CAV future and quantify Highway Capacity Manual (HCM) capacities as a function of CAV penetration rates and vehicle behavior of car following, lane change, and merge, and a set of evaluation methods to evaluate the safety, environmental, and oscillation impact of CAVs on the traffic. Due to numerous uncertainties on CAV implementation policies and mixed traffic impacts on the network performance, the study considers a large number of scenarios which include variations in parameters such as CAV gap settings, roadway geometric characteristics, and roadway traffic characteristics. Capacity of each traffic facility is calculated based on well-established and widely used method based on microscopic traffic simulations. Then capacity adjustment factors are developed for CAVs. Results show that the existence of CAVs in the traffic stream significantly enhanced the roadway capacity, not only on the basic freeway but also on the merge and weaving segments, as their market penetration rates increase. The human driver behavior of baseline traffic also impacts the capacity benefits, particularly at lower market penetration rates of CAVs. Other than the overall network performance estimation, the impact of C (open full item for complete abstract)

Committee: Jiaqi Ma Ph.D. (Committee Chair); John Ash Ph.D. (Committee Member); Na Chen Ph.D. (Committee Member); Hazem Elzarka Ph.D. (Committee Member) Subjects: Transportation

Master of Arts, Miami University, 2021, Economics

Over the last decade, Uber has quickly become a leader in the transportation industry. With over 1 million drivers in the US as of 2021, it is worth exploring exactly what kind of impacts Uber has had on the general public. This paper serves to identify the association between the presence of Uber service and automotive fatalities by exploiting the differences in timing of Uber's entry into a county market. Ultimately, this study finds no discernible association between Uber entering a market and the total traffic fatalities. These results are consistent through a variety of high-risk sub-groups including alcohol-related fatalities, weekend fatalities, and a variety of age demographics. When expanded to incorporate lagged effects, there is still no significant association, but it does appear that there is potential for delayed effects over an extended time-period.

Committee: Austin Smith Ph.D. (Advisor); Peter Nencka Ph.D. (Committee Member); Gregory Niemesh Ph.D. (Committee Member) Subjects: Economics

Master of Science (M.S.), University of Dayton, 2020, Civil Engineering

The impacts of raising speed limits on traffic safety is an area that has generated much research, although a strong consensus has not emerged on the relationship between speed and safety. Responding to the nationwide ongoing trend of raising speed limits, the Ohio legislature implemented the 70-mi/h speed limit on 570 miles of rural freeways in Ohio on July 1, 2013 and an additional 398 miles of rural freeways starting on September 29, 2013. The primary goal of the research detailed in this study was to investigate the safety impacts of this new speed limit using available crash, roadway, and traffic characteristics data. Statewide crash data from January 1, 2010 to December 31, 2015 were obtained from the Highway Safety Information System (HSIS). The study utilizes the Empirical Bayes (EB) before-after study in the evaluation of the safety effectiveness of the raised speed limit. The intent of the before-after study is to estimate the actual performance (in terms of crash frequency and severity levels) following the speed limit increase and what the safety performance would have been if the increase in speed limit had not been applied. Safety performance functions (SPFs) were developed for both the total crashes and the fatal and injury (FI) crashes combined using the negative binomial regression and the SPFs were used to predict the average crash frequency of each of the segments in the observed period. The EB analysis showed that total crashes went down by 24.6 percent and the injury and fatal crashes combined went down by 8.8 percent for the two years after the speed limit was changed. Therefore, caution should be taken in drawing conclusion from this study because the after period did not meet the minimum of three years recommended by the HSM since the data available for the after period were only for two years. It is recommended a further study be conducted after additional data for the after period will be available.

Committee: Eustace Deogratias (Advisor); Peter Hovey (Committee Member); Philip Appiah-Kubi (Committee Member) Subjects: Civil Engineering; Transportation; Transportation Planning

Master of Science (M.S.), University of Dayton, 2020, Civil Engineering

The Highway Safety Manual (HSM), which is the guidance document for state departments of transportation (DOTs), was published in 2010, and one of its sections, called Part C of HSM, it involves the development of crash prediction methods. The goal of the predictive method is to develop and calibrate safety performance functions (SPFs). SPFs are mostly regression models that associate the expected number of crashes quantitatively with traffic exposure and geometric characteristics of the road. However, HSM's default prediction models are not suitable for all states or jurisdictions because each state and jurisdiction have different characteristics, such as terrain, driver behaviors, weather conditions, etc. Hence, the principal objective of this study is to develop a prediction method for producing Ohio-specific SPF models to use for rural two-lane highways in the state of Ohio. This study aims to create jurisdiction-specific SPFs for two-lane rural highway segments as the first study for this type of roadway facility in the state of Ohio. Highway geometric data for almost 40,067 segments that have 21,666.03 miles and 79,481 total crashes that occurred for 4 consecutive years (2012-2015) were obtained from the Highway Safety Information System (HSIS) to create these new models using negative binomial regression and the pruned forward selection method by adding the interaction terms via JMP Pro software. The most critical variables used for analyzing and creating the best models for the state of Ohio are average annual daily traffic (AADT), segment length, lane width, shoulder width, posted speed limit, presence of curves and grades, which were proven to be statistically significant in developing SPFs. Besides, the standard goodness-of-fit parameters were chosen to evaluate the regression models was AIC. Two models were created for rural two-lane, two-way road segments in the state of Ohio, which can be used to predict all crash types and fatal and injury crashes.

Committee: Eustace Deogratias Associate Professor (Advisor); Appiah-Kubi Philip Assistant Professor (Committee Member); Hovey Peter W. Associate Professor (Committee Member) Subjects: Civil Engineering; Transportation

Master of Science (M.S.), University of Dayton, 2019, Civil Engineering

Large trucks play a key role in the overall safety of the highway transportation system. Previous studies have shown that in Ohio large trucks are over-represented in crashes that lead to serious and fatal injuries. A previous study that analyzed factors affecting truck-related crashes in Ohio found that posted speed limit and speed-related factors were among the significant factors impacting crash severity of truck-related crashes. Several studies have shown that increasing speed limits on roadways has a significant impact both on safety and operating speeds. On July 1, 2013, the Ohio's legislature raised the speed limits on rural freeways from 65 mph to 70 mph for passenger vehicles, buses and trucks and to date the safety impact of this speed limit raise has not been evaluated. The current study investigated the impact of raising the speed limit on crash severity specifically with interest in large trucks and buses on rural freeways in Ohio. Statewide crash data from January 1, 2010 to December 31, 2018 were obtained from the Ohio Department of Public Safety (ODPS). Given that the numbers of rural freeway segments located all over the state and traffic volumes for each segment over the study period are not easily obtainable; therefore, the use of standard observational before/after study Empirical Bayes (EB) method was not feasible for the current study. Because of the model requirement for stationarity on a response series, this study utilized the Autoregressive Integrated Moving Average (ARIMA) time series intervention analysis method using monthly and seasonal crash data. A time series statistical method takes care of differences in crashes occurring in different years and recognizes trends in different periods of times. Time series analysis is a statistical technique that deals with time series data or trend analysis. Time series data means that data is in a series of time periods or intervals. Results of the current study show that the increase of speed in t (open full item for complete abstract)

Committee: Deogratias Eustace (Advisor); Maher Qumsiyeh (Committee Member); Philip Appiah-Kubi (Committee Member) Subjects: Civil Engineering; Transportation

Master of Science (MS), Ohio University, 2019, Electrical Engineering & Computer Science (Engineering and Technology)

Bird strikes represent a serious economic and safety risk to aircraft operations, especially near airports where aircraft are in critical stages of flight with little room for error. The United States Federal Aviation Administration (FAA) continues to research ways of mitigating the risk to aircraft posed by bird targets which include surveillance of birds with specialized radar systems. This thesis presents an algorithm that can utilize data from an avian radar, Automatic Dependent Surveillance – Broadcast (ADS-B) aircraft positioning data, and other sources to determine which birds constitute a significant risk to aircraft. It is envisioned that this algorithm could be added into a system which then alerts air traffic control (ATC) and/or pilots through communication protocols such as ADS-B and the ATC ground network. For this thesis, avian radar and ADS-B data was analyzed and tested through the prototype algorithm with a simulated aircraft track to illustrate example scenarios of this algorithm working. Additionally, multiple scenarios with a single simulated bird and simulated aircraft track were tested to verify operation of the algorithm when a known collision occurs.

Committee: Chris Bartone Ph.D. (Advisor); Michael Braasch Ph.D. (Committee Member); Frank van Graas Ph.D. (Committee Member); Donald Miles Ph.D. (Committee Member) Subjects: Aerospace Engineering; Electrical Engineering; Engineering; Systems Design

Doctor of Philosophy, The Ohio State University, 1986, Graduate School

Committee: Not Provided (Other) Subjects: Education

Doctor of Philosophy, The Ohio State University, 1985, Graduate School

Committee: Not Provided (Other) Subjects: Education

Doctor of Philosophy, University of Akron, 2016, Civil Engineering

Pavement conditions may have a significant impact on traffic safety, a correlation which needs to be studied and quantified. The research reported in this dissertation investigates the relationship between pavement conditions (specifically, ride quality and distresses of the pavement surface) and crash rates (CR) by examining traffic safety performance over time under changing pavement conditions for selected asphalt concrete (AC) and Portland cement concrete (PCC) pavement sections on in-service U.S. highways. This dissertation covers four topics on the relationship between pavement conditions and CR on highways. The first study examines different model formulations to investigate the effect of roadway (rigid pavement) surface conditions on traffic safety for highways in Ohio. In another study, conducted on U.S. highways for both AC and PCC pavement, various regression models are used to investigate whether pavement conditions correlate with CR. For both studies, a quadratic relationship is identified as the most effective for linking the CR with the International Roughness Index (IRI). The IRI values for PCC pavement corroborate those in the Ohio study. In the third study, an effort is made to better understand the potential effects of two different measurements, IRI and pavement Surface Distresses Indices (SDIs), on traffic crashes on U.S highways. The results show that a quadratic model may best fit the data for both types of pavement when including IRI and SDIs. In the fourth study, overall cracking indices are developed based on SDIs for AC and PCC pavements. The results show a relationship between the developed overall indices and CR with second-order (polynomial) models. The findings of these studies suggest two major conclusions. First, there is a clear correlation between CR and pavement conditions. Second, worsening pavement conditions can lead to more accidents in a normal driving environment. The models developed in these studies can be used to pred (open full item for complete abstract)

Committee: Ping Yi Dr (Advisor); Ala Abbas Dr (Advisor); Junliang Tao Dr (Committee Member); Jiang Zhe Dr (Committee Member); Richard Einsporn Dr (Committee Member); Linda Barrett Dr (Committee Member) Subjects: Civil Engineering; Transportation

Doctor of Philosophy, The Ohio State University, 2015, City and Regional Planning

This study investigates the effects of the surrounding environment on crashes, with a focus on crash severity and at-fault drivers characterized by gender and age. Crashes where a vehicle is the guilty party are investigated. The study adopts two approaches: aggregate and disaggregate. In the aggregate approach, the numbers of crashes, classified in terms of severity (fatalities, injuries, property damages only), and gender and age of the driver (with several age groups covering the 15-100 age span), represent the variables to be investigated, and have been derived for the Central Ohio Region from the multiple files of the crash database of the Ohio Department of Public Safety, over the period 2006-2011. These data are aggregated at the level of Traffic Analysis Zones (TAZ). OLS models are first estimated, but spatial autocorrelation tests point the existence of spatial autocorrelation (SA). Spatial econometrics models are then used to eliminate the SA bias: the Spatial Autoregressive Model (SAR) and the Spatial Error Model (SEM). Subsequent analyses are conducted using the SEM estimates, as the SEM model is successful in completely eliminating spatial autocorrelation. The aggregate approach uses a large set of explanatory variables classified into six groups: Regional and Locational, Socio-Economic, Land-Use, Public Transit and Traffic Flow, Circulation and Network, and Physical Characteristics. The results show that variables in all these groups have significant impacts on crash severity and frequencies. The disaggregate approach accounts for more variables that influence crash severity, but cannot be captured in the aggregate approach, such as weather conditions, light conditions, road conditions, type of intersection, and type of vehicle. All these variables are directly related to an individual crash. The logit model is used to explain the probability of a Bodily Injury (BI) crash at the crash scene, where the alternative is Property Damage Only (PDO) crash. (open full item for complete abstract)

Committee: Jean-Michel Guldmann (Advisor); Burkhard von Rabenau (Advisor); Philip Viton (Committee Member) Subjects: Behavioral Sciences; Land Use Planning; Transportation; Transportation Planning; Urban Planning

Master of Science (M.S.), University of Dayton, 2014, Civil Engineering

As everybody knows, there are many traffic crashes happening every day. Traffic crashes may result in injury, death, and property damage. A number of factors contribute to the risk of a crash, including vehicle design, speed of operation, road design, road environment, driver skill and/or impairment, and driver behavior. Worldwide, motor vehicle crashes lead to death and disability as well as financial costs to both society and the individuals involved. The objective of this study was to analyze crash data of motorcycle-motor vehicle collisions to identify possibly influential factors that cause these crashes and to study the magnitude of influence of each factor to these crashes. This study tested appropriate regression models to accurately model the factors that significantly influence motorcycle-motor vehicle crashes. A nominal multinomial logistic regression model was built. From stepwise selection procedure, the influential factors included age, time of crash, number of units, vehicle in error, road contour, collision type, alcohol used, posted speed, and helmet used. Number of units involved in a crash impacts the crash severity level, such as two units mostly result in injury and three or more units mostly result in fatal. If the driver of the motor vehicle causes the crash it will more likely result into injury than if the driver of the motorcycle causes the crash. Driver of motorcycle or vehicle that uses alcohol will certainly increase the chance of a fatality or injury. Crashes that occur on highways or freeways with higher speed limits are more likely to result in injuries and fatalities. The occupants of motorcycle use helmet will significantly be protected in the crash. These factors can be applied to reduce the severity of motorcycle-motor vehicle crashes.

Committee: Deogratias Eustace (Advisor); Peter Hovey (Committee Member); Gary Shoup (Committee Member) Subjects: Civil Engineering; Transportation

Master of Science (M.S.), University of Dayton, 2014, Civil Engineering

Several studies have determined the use of seat belts to be one of the major contributing factors in the reduction of fatalities and injury severities associated with motor vehicle crashes. Some studies have found that there is a relationship between drivers and their front passengers in terms of seat belts usage. The objective of this thesis study was to evaluate the seat belts usage rates in Dayton, Ohio based on vehicle type, gender, age, day of the week, time of observation, and person type driver or passenger. Data for this thesis was collected from thirteen sites in Greater Dayton, Ohio by direct observations at interchange ramps and intersections. The binary logistic regression model was used to investigate some independent variables of seat belt usage rates of drivers and their outboard (front seat) passengers in Dayton, Ohio, that is, the binary logistic regression model was used to identify factors that may play a role in relation to seat belt usage. The results from the binary logistic regression modeling show that the person type and vehicle type are significant factors affecting the likelihood of seat belt usage. There were no significant interactions identified between the factors studied. The odds of using seat belt by drivers are higher than the odds of using seat belt by their passengers. Also, the odds of occupants of passenger cars and sport utility vehicles to be belted are higher than the odds of using seat belt by pickup truck occupants. There is no statistically significant difference between van and pickup truck occupants in terms of their seat belt use. Moreover, the pickup truck and van occupants have the lowest seat belt usage rates. In order to increase seat belt usage rates, this study recommends for enforcement officials to pay more attention with pickup truck and van occupants when checking out unbelted vehicle occupants. This persistence will make them increase their seat belt usage, which eventually will increase their (open full item for complete abstract)

Committee: Deogratias Eustace Dr. (Advisor); Peter Hovey Dr. (Committee Member); Gary Shoup Mr. (Committee Member) Subjects: Civil Engineering; Engineering; Transportation