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A principal axis factor analysis with promax rotation yielded four factors, or joint variations between the 20 items, that were inter-correlated with eigenvalues greater than 1. The ADBQ was also found to have high internal consistency (Cronbach's alpha = .86). The four factors were used to form four subscales of aggressive driving behavior that included anger/aggression, speeding/minor infractions, overt expression, and judgment of other drivers. The four subscales were found to correlate with self-reported biographical and driver history data, as well as, gender differences across scales. Additional analyses were conducted using data from the present sample from the University of Central Florida (N = 285) and the data from the previous study from Old Dominion University (N = 230) and Michigan Technological University (N = 265) for a combined sample of 780 undergraduate students. The findings in this present study provided additional support for the consistency, predictive validity, and factor structure of the ADBQ instrument. The Aggressive Driving Behavior Questionnaire proves to be a valuable measure in predicting the likelihood of a person engaging in aggressive driving behavior. The implications for driving behavior assessment, training, and instrument development are also discussed.; Over the past decade, aggressive driving behavior has become a topic of concern among the public, media, and researchers in the psychological community. Aggressive driving is a problematic pattern of social behavior that is not only a leading cause to motor vehicle accidents, but a serious threat to public safety. One instrument that has been developed to assess aggressive driving behavior is the Aggressive Driving Behavior Questionnaire (ADBQ). The ADBQ is a 20-item paper and pencil questionnaire intended to measure a driver's likelihood for engaging in aggressive driving behavior. The ADBQ was developed using a factor-analytic approach that combined five previously developed aggressive driving behavior scales (Brill, Mouloua & Shirkey, 2007). Of the 81 items of the five combined scales, nineteen latent variables were extracted and accounted for 67.4% of the explained variance for the observed responses. The final 20th item was developed by splitting one of the latent variables. A previous study, conducted at Old Dominion University (N = 230) and Michigan Technological University (N = 265), examined the ADBQ's factor structure and internal consistency, and found relatively high internal consistency (Cronbach's alpha = .77) and the identification of six factors using a principal axis factor analysis (Brill & Mouloua, 2011). The ADBQ was also tested in a controlled laboratory environment and found significant evidence that suggest the ADBQ is a valid predictor of aggressive driving behavior in a simulated environment (Brill, Mouloua & Shirkey 2009). The purpose of the present study was to further investigate the psychometric properties of the ADBQ. Based on a sample of 285 undergraduates (170 women and 115 men) from the University of Central Florida, the study examined the internal consistency, predictive and construct validity, and factor structure of the new questionnaire.

Very little information is known about the impact electrification has on driving behavior, or how drivers charge their electrified vehicles. The recent influx of electrified vehicles presents a new market of vehicles which allow drivers the option between electrical or conventional gasoline energy sources. The current battery capacity in full battery electric vehicles requires planning of routes not required of conventional vehicles, due to the limited range, extended charging times, and limited charging infrastructure. There is currently little information on how drivers react to these limitations. A number of current models of fully electric and plug-in hybrid electric vehicles, transmit data wirelessly on key-on, key-off, and charging events. The data includes battery state of charge, distance of miles driven on gasoline and electric, energy consumed, and many other parameters associated to driving and charging behavior. In this thesis, this data was then processed and analyzed to benchmark the performance and characteristics of driving and charging patterns. Vehicles were analyzed and contrasted based on model type, geographic location, length of ownership and other variables. This data was able to show benchmarks and parameters in aggregate for 56 weeks of electrified vehicle tracking. These parameters were compared to the EV Project, a large scale electrified vehicle study performed by Idaho National Labs, to confirm patterns of expected behavior. New parameters which were not present in the EV Project were analyzed and provided insight to charging and driving behavior not examined in any previous study on a large scale. This study provides benchmarks and conclusions on this new driving behavior, such as large scale analysis of brake regeneration performance and degradation of range anxiety. Analysis of the differences on charging and driving behavior between geographic regions and experience were examined, providing insight to how these variables affect performance and driving and charging patterns. Comparison of parameters established by the EV Project and new parameters analyzed in this report will help build a benchmark for future studies of electrified vehicles.

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While new road lighting technologies, either LED or adaptive road lighting systems, offer a wide range of unique potential benefits (mainly in terms of energy savings), it is necessary to evaluate the safety impacts of these technologies on road users. The literature survey shows that providing light on previous unlit roads has a positive effect on traffic safety. Reducing the amount of light has the opposite effect. These studies are usually conducted by using crash numbers, which makes it impossible to draw conclusions on changes in driving behaviour. Driving behaviour analyses need special approaches and indicators. Therefore indirect indicators such as speed and safety relationship, jerky driving and traffic conflict parameters are presented. The individual character of such data is difficult to deal with and limits big scale analyses. In order to have a practical example of such indicators a case study is conducted. Floating car data collected in Vienna is used to analyse travel speeds of taxi drivers at two LED test sites. A simple before-after analysis is used with data from January 2011 to May 2012 in order to examine an expected increase in speed due to a better visual performance of LED light. However the results show either no changes at all or a trend in speed reduction of 1km/h in average. Unfavourable test site locations might limit the significance of the results.

abstract: Vehicular automation and autonomy are emerging fields that are growing at an exponential rate, expected to alter the very foundations of our transportation system within the next 10-25 years. A crucial interaction has been born out this new technology: Human and automated drivers operating within the same environment. Despite the well- known dangers of automobiles and driving, autonomous vehicles and their consequences on driving environments are not well understood by the population who will soon be interacting with them every day. Will an improvement in the understanding of autonomous vehicles have an effect on how humans behave when driving around them? And furthermore, will this improvement in the understanding of autonomous vehicles lead to higher levels of trust in them? This study addressed these questions by conducting a survey to measure participant’s driving behavior and trust when in the presence of autonomous vehicles. Participants were given several pre-tests to measure existing knowledge and trust of autonomous vehicles, as well as to see their driving behavior when in close proximity to autonomous vehicles. Then participants were presented with an educational intervention, detailing how autonomous vehicles work, including their decision processes. After examining the intervention, participants were asked to repeat post-tests identical to the ones administered before the intervention. Though a significant difference in self-reported driving behavior was measure between the pre-test and post- test, there was no significant relation found between improvement in scores on the education intervention knowledge check and driving behavior. There was also no significant relation found between improvement in scores on the education intervention knowledge check and the change in trust scores. These findings can be used to inform autonomous vehicle and infrastructure design as well as future studies of the effects of autonomous vehicles on human drivers in experimental settings. / Dissertation/Thesis / Masters Thesis Human Systems Engineering 2019

Due to comfort, convenience, and flexibility, taxis become more and more prevalent in China, especially in large cities. According to a survey reported by Beijing Traffic Development Research Center, there were 696 million taxi person-rides in Beijing in 2011. However, many violations and road crashes that were related to taxi drivers occurred more frequently. The survey showed that there were a total of 17,242 taxi violations happened in Beijing in only one month in 2003, which accounted for 56% of all drivers' violations. Besides, taxi drivers also had a larger accident rate than other drivers, which showed that nearly 20% of taxi drivers had accidents each year. This study mainly focuses on investigating differences in driving behavior between taxi drivers and non-professional drivers. To examine the overall characteristics of taxi drivers and non-professional drivers, this study applied a hierarchical driving behavior assessment method to evaluate driving behaviors. This method is divided into three levels, including low-risk level, medium-risk level, and high-risk level. Low-risk level means the basic vehicle control. Medium-risk level refers to the vehicle dynamic decision. High-risk level represents the driver avoidance behavior when facing a potential crash. The Beijing Jiatong University (BJTU) driving simulator was applied to test different risk level scenarios which purpose is to find out the differences between taxi drivers and non-professional drivers on driving behaviors. Nearly 60 subjects, which include taxi drivers and non-professional drivers, were recruited in this experiment. Some statistical methods were applied to analyze the data and a logistic regression model was used to perform the high-risk level. The results showed that taxi drivers have more driving experience and their driving style is more conservative in the basic vehicle control level. For the car following behavior, taxi drivers have smaller following speed and larger gap compared to other drivers. For the yellow indication judgment behavior, although taxi drivers are slower than non-professional drivers when getting into the intersection, taxi drivers are more likely to run red light. For the lane changing behavior, taxi drivers' lane changing time is longer than others and lane changing average speed of taxi drivers is lower than other drivers. Another different behavior in high-risk level is that taxi drivers are more inclined to turn the steering wheel when facing a potential crash compared to non-professional drivers. However, non-professional drivers have more abrupt deceleration behaviors if they have the same situation. According to the experiment results, taxi drivers have a smaller crash rate compared to non-professional drivers. Taxi drivers spend a large amount of time on the road so that their driving experience must exceed that of non-professional drivers, which may bring them more skills. It is also speculated that because taxi drivers spend long hours on the job they probably have developed a more relaxed attitude about congestion and they are less likely to be candidates for road rage and over aggressive driving habits.

Variable speed limits (VSL) are dynamic traffic management systems designed to increase the efficiency and safety of highways. While the macroscopic performance of VSL systems is well explored in the existing literature, there is a need to further understand the microscopic behavior of vehicles driving in VSL zones. Specifically, driver compliance to advisory VSL systems is quantified based on a driving-simulation experiment and introduced into a broader microscopic behavior model. Statistical analysis indicates that VSL compliance can be predicted based upon several VSL design parameters. The developed two-state microscopic model is calibrated to driving-simulation trajectory data. A calibrated VSL microscopic model can be utilized for new VSL control and macroscopic performance studies, adding an increased dimension of realism to simulation work. As an example, the microscopic model is implemented within VISSIM (overriding the default car-following model) and utilized for a safety-mobility performance assessment of an incident-responsive VSL control algorithm implemented in a MATLAB COM interface. Examination of the multi-objective optimization frontier reveals an inverse relationship between safety and mobility under different control algorithm parameters. Engineers are thus faced with a decision between performing multi-objective optimization and selecting a dominant VSL control objective (e.g. maximizing safety versus mobility performance). / Master of Science

Freeway merging VISSIM calibration techniques were developed for the analysis of ramp metering in Georgia. An analysis of VISSIM’s advanced merging and cooperative lane change settings was undertaken to determine their effects on merging behavior. Another analysis was performed to determine the effects of the safety reduction factor and the maximum deceleration for cooperative braking parameter on the simulated merging behavior. Results indicated that having both the advanced merging and cooperative lane change setting active produced the best results and that the safety reduction factor had more influence on the merging behavior than the maximum deceleration for cooperative braking parameter. Results also indicated that the on-ramp experienced unrealistic congestion when on-ramp traffic was unable to immediately find an acceptable gap when entering the acceleration lane. These vehicles would form a queue at the end of the acceleration lane and then be unable to merge into the freeway lane due to the speed differential between the freeway and the queued ramp traffic. An Incremental Desired Speed algorithm was developed to maintain an acceptable speed differential between the merging traffic and the freeway traffic. The Incremental Desired Speed algorithm resulted in a smoother merging behavior. Lastly, a ramp meter was introduced and an increase in both the freeway throughput and overall speeds was found. Implications of these findings on the future research is discussed.

The purpose of the current study was to assess the efficacy of mental models as a predictor of driving outcomes. In contrast to more traditional measures of knowledge, mental models capture the configural property of knowledge, that is, an individual's understanding of the interrelationships that exist among critical concepts within a particular knowledge domain. Given that research has consistently shown the usefulness of mental models for the prediction of performance in a number of settings, it was hypothesized that the development of accurate driving mental models would also play an important role in the prediction of driving outcomes, especially in comparison to traditional measures of driving knowledge—such as the multiple-choice type tests typically required to obtain a driver license. Mental models of 130 college students (52 percent females) between 17 and 21 years-old (M = 18.68, SD = 0.80) were analyzed and compared to a subject matter expert (SME) referent structure using Pathfinder. A statistically significant correlation was found for mental model accuracy and moving violations (r = –.18, p <.05), but not for at-fault crashes. Evidence of incremental validity of mental models over commonly used predictors of moving violations (but not for at-fault crashes) was also found. Exploratory analyses revealed that driving knowledge, general mental ability (GMA), and emotional stability were the best predictors of mental model accuracy. Issues related to the measurement of mental models were extensively addressed. First, statistically significant correlations between GMA and several mental model properties (i.e., accuracy scores, within participant similarity, and within participant correlation) suggest that challenges inherent to the task for eliciting mental models may influence mental model scores which, in turn, may lower mental model reliability estimates. Also, the selection of model components (i.e., terms) and the identification of the "best" reference structure for deriving mental model accuracy scores are undoubtedly critical aspects of mental model-related research. Along with illustrating the decisions made in the context of this particular study, some suggestions for conducting mental model-related research are provided.