Government reports indicate that, on average, more than 3000 people die due to distracted driving each year, accounting for nearly 10% of all fatal car crashes. Other reports claim that two-thirds of fatal car accidents result from aggressive driving. Previous research has been inconclusive regarding how personality impacts distracted and aggressive driving behaviors. Therefore, the goal of this current study is to fill the gap in the literature concerning the role that personality plays in distracted and aggressive driving behaviors. We also explored the role that distracted and aggressive driving behaviors played in accident involvement. A sample of (N=327) participants were recruited using social media and the UCF SONA System. They were asked to self-report their driving behaviors and personality traits by completing a series of online questionnaires (ADBQ, BFI, DBQ, DDQ, DEMO, and IPIP NEO PI-R). Using this data, bivariate correlations were run using the Pearson Correlation Coefficients to determine the role that personality (OCEAN) plays in distracted and aggressive driving behaviors. We used the DDQ and the IPIP NEO PI-R to evaluate the relationship between personality and distracted driving, and we found that personality traits: Agreeableness, Conscientiousness, Extraversion, and Neuroticism were all significant predictors of distracted driving. Openness was the only one of the five personality traits to have no significant correlation. We used the ADBQ and the IPIP NEO PI-R to assess the relationship between personality and aggressive driving, and we found the same four personality traits: Agreeableness, Conscientiousness, Extraversion, and Neuroticism were all significant predictors of aggressive driving. Openness was, again, the only one of the five personality traits to have no significant correlation. Backward regression analyses were performed to determine what caused these relationships. The regression analysis displayed trait subscales: Morality, Cooperation, Self Discipline, Activity Level, Excitement Seeking, Anger, Emotionality, and Liberalism, each significantly contributed to driver distraction. Another backward regression analysis reveals trait subscales: Morality, Self-Efficacy, Dutifulness, Self Discipline, Anger, and Artistic Interests, each significantly contributed to driver aggression.

While driver distraction has been extensively studied in laboratory and empirical field studies, the prevalence of driver distraction on our nation's highways and the relative crash risk is unknown. It has recently become technologically feasible to conduct unobtrusive large-scale naturalistic driving studies as the costs and size of computer equipment and sensor technology have both dramatically decreased. A large-scale naturalistic driving study was conducted using 100 instrumented vehicles (80 privately-owned and 20 leased vehicles). This data collection effort was conducted in the Washington DC metropolitan area on a variety of urban, suburban, and rural roadways over a span of 12-13 months. Five channels of video and kinematic data were collected on 69 crashes and 761 near-crashes during the course of this data collection effort. The analyses conducted here are the first to establish direct relationships between driving inattention and crash and near-crash involvement. Relative crash risk was calculated using both crash and near-crash data as well as normal, baseline driving data, for various sources of inattention. Additional analyses investigated the environmental conditions drivers choose to engage in secondary tasks or drive fatigued, assessed whether questionnaire data were indicative of an individual's propensity to engage in inattentive driving, and examined the impact of driver's eyes off the forward roadway. The results indicated that driving inattention was a contributing factor in 78% of all crashes and 65% of all near-crashes. Odds ratio calculations indicated that fatigued drivers have a 4 times higher crash risk than alert drivers. Drivers engaging in visually and/or manually complex tasks are at 7 times higher crash risk than alert drivers. There are specific environmental conditions in which engaging in secondary tasks or driving fatigued is deemed to be more dangerous, including intersections, wet roadways, undivided highways, curved roadways, and driving at dusk. Short, brief glances away from the forward roadway for the purpose of scanning the roadway environment (e.g., mirrors and blind spots) are safe and decrease crash risk, whereas such glances that total more than 2 seconds away from the forward roadway are dangerous and increase crash risk by 2 times over that of more typical driving. / Ph. D.

Traffic simulation has become an invaluable part of the traffic engineering toolbox. However, the majority of driver models are designed to recreate traffic performance based on interactions among vehicles. In keeping with this pursuit, most are fundamentally built to avoid collisions. This limits the applicability of using these models for addressing safety concerns, especially those regarding pedestrian safety performance. However, by explicitly including some of the sources of human error, these limitations can, in theory, be overcome. While much work has been done toward including these human factors in simulation platforms, one key aspect of human behavior has been largely ignored: driver distraction. This work presents a novel approach to inclusion of driver distraction in a microsimulation or agent-based model. Distributions of distraction events and inter-distraction periods are derived from eye-glance data collected during naturalistic driving studies. The developed model of distraction is implemented -- along with perception errors, visual obstructions, and driver reaction times -- in a simulated mid-block pedestrian crossing. The results of this simulation demonstrate that excluding any of these human factors from the implemented driver model significantly alters conflict rates observed in the simulation. This finding suggests that inclusion of human factors is important in any microsimulation platforms used to analyze pedestrian safety performance.

Despite recent public policy initiatives limiting or banning forms of distracted driving resultant from cellular phone use, crashes remain on the rise. Individuals from the millennial generation, ages 16 to 35, appear to be most susceptible to distracted driving. Understanding the behaviors, attitudes, and habits of millennials is critical to developing effective policy for behavior change. A dual task ethnographic study framed by Skinner's theory of behavior modification and Maslow's hierarchy of needs motivational model, was used to investigate to what extent millennials feel public policy has influenced their driving, and if additional policy initiatives are required to deter distracted driving behavior. Two phases of inquiry, first, naturalistic observation, and then focus group were conducted at a commuter university. Distracted driving behaviors including hand held cellular phone use, eating, drinking, and passenger interaction of 100 drivers entering or exiting campus were observed, tracked, and analyzed using a researcher-developed tracking form. Eighty-four percent exhibited at least one distracted driving behavior. After which, 12 enrolled and licensed students, aged 18-35, were recruited via social media for two focus group discussions. Focus group data were inductively coded and analyzed using semantical attribution analysis. The students revealed that millennial drivers felt distracted driving policy did not address behaviors they see as worthy of intervention, they did not perceive that cellular phone use while driving posed a significant threat, and they felt current law was difficult to enforce with penalties they regarded as non-prohibitive. Social change implications include improved distracted driving public policy, which may result in driving behavior changes and a potential reduction of death, injury, and property loss.

Diss. Tampereen teknillinen korkeakoulu, 2006. / Myös verkkojulkaisuna.

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Thesis (M.A. (Communication Studies)) -- University of Limpopo, 2017 / The use of communication technologies has brought changes to our daily ways of doing things. Youth use technology for different purposes at different locations. As technology grows everyday with various advantages, its benefits come along with some disadvantages. Road accidents are one of the major problems that South Africa experiences. The focus of this study was to explore the danger of texting and driving and its potentials for road accidents. This research focused on the negative impact of technology, mainly technological distraction, while driving. Broadly, this research looks at the social and cultural impact of texting and mobility, with specific focus on distracted driving. This study is significant in the social analysis of technology use and distraction; this significance is made more important considering that there is very limited study of this social issue in South Africa. The always-available communication culture (such as texting while driving) should be viewed as a problematic phenomenal. For data collection, the researcher conducted a survey with adults and youth drivers to explore the perceptions and attitudes of drivers towards cell phone use, texting and driving. The researcher also conducted roadside observations of drivers to investigate the occurrence of distracted driving due to technology use, and interviews were conducted with Traffic Officers to share their professional experience on observing incidences of texting and driving. The study reveals that technology use, texting and driving is a common problem among young drivers, while adults tend to be more careful and engage less in this activity. Aspect of the findings of the study shows that 60% of drivers report that they have sent a text while driving. Moreover, the study explores ways of curbing cell phone use texting and driving on South African roads. There should be more research on distracted driving and technology use, so that more suggestions on how to curb technology use while driving can be offered

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Logistics 4.0 suggests that increased automation can enhance performance, while Logistics 5.0 emphasizes the advantages of a modern workforce that combines humans and emerging technologies. However, the logistics industry needs a deeper understanding of human factors, an area that has been overlooked so far. To bridge this research gap, this dissertation investigated distracted driving behavior among individuals involved in transportation and logistics-based applications. This investigation employed both qualitative and quantitative research methods. Essay 1 focuses on a systematic literature review (SLR) that comprehensively analyzes published research on self-response studies regarding distracted driving behavior. The study identifies five overarching categories of distractions: (a) cell phone-related, (b) technology-related, (c) nontechnology-related, (d) psychological, and (e) personality. The findings underscore the substantial research conducted on self-reported distractions associated with cell phones and technology. Essay 2 employs the protection motivation theory (PMT) to develop hypotheses that predict the engagement of young drivers in texting while driving (TWD). In addition to TWD, the survey also included cognitive failure to examine the indirect effects of PMT on TWD within a mediation framework. The results, obtained through structural equation modeling with 674 respondents aged 18-25, indicate that several factors including response cost, threat vulnerability, cognitive failure, self-efficacy, and threat severity influence TWD behavior. Essay 3 investigates the influence of young drivers' respect for safety, neutralization techniques, and polychronicity on distracted driving behavior (DDB), based on the cognitive dissonance theory (CDT). The findings, drawn from 326 respondents aged 22-29 years, indicate that drivers who prioritize safety (respect for safety) are less likely to engage in DDB. Lastly, Essay 4 takes a survey-based approach to explore how factors such as respect for safety, polychronicity, and cognitive failure influence the likelihood of engagement in distractions among industrial operators, specifically forklift drivers, in warehouse and distribution centers (WDCs). The study's results indicate that cognitive failure has a negative impact on forklift drivers' distracted driving behavior, while polychronic tendencies help drivers avoid engaging in distracted behavior.

Security threats to cyber-physical systems are targeting institutions and infrastructure around the world, and the frequency and severity of attacks are on the rise. Healthcare manufacturing, financial services, education, government, and transportation are among the industries that are the most lucrative targets for adversaries. Hacking is not just about companies, organizations, or banks; it also includes critical infrastructure. Wireless Sensors Networks, Vehicle-to-everything communication (V2X), Dynamic Message Signs (DMS), and Traffic Signal Controllers are among major Intelligent Transportation Systems (ITS) infrastructure that has already been attacked or remain vulnerable to hacking. ITS has been deployed with a focus on increasing efficiency and safety in the face of dramatic increases in travel demand. Although many studies have been performed and many security primitives have been proposed, there are significant concerns about flawless performance in a dynamic environment. A holistic security approach, in which all infrastructure performs within the satisfactory level of security remains undiscovered. Previously, hacking of road infrastructure was a rare event, however, in recent years, field devices such as DMS are hacked with higher frequency. The primary reason that transportation assets are vulnerable to cyber-attacks is due to their location. A more dramatic scenario occurs when hackers attempt to convey tampered instructions to the public. Analyzing traveler behavior in response to the hacked messages sign on the basis of empirical data is a vital step toward operating a secure and reliable transportation system. There may be room for improvement by policymakers and program managers when considering critical infrastructure vulnerabilities. With cybersecurity issues escalating every day, road users' safety has been neglected. This dissertation overcomes these challenges and contributes to the nascent but growing literature of Intelligent Transportation System (ITS) security impact-oriented risk assessment in threefold. • First, I employ a risk-based approach to conduct a threat assessment. This threat assessment performs a qualitative vulnerability-oriented threat analysis. The objective is to scrutinize safety, security, reliability, and operation issues that are prompted by a compromised Dynamic Message Signs (DMS). • Second, I examine the impact of drivers' attitudes and behaviors on compliance, route diversion behavior, and speed change behavior, under a compromised DMS. We aim to assess the determinants that are likely to contribute to drivers' compliance with forged information. To this extent, this dissertation evaluates drivers' behavior under different unauthentic messages to assess in-depth the impact of an adversarial attack on the transportation network. • Third, I evaluate distracted driving under different scenarios to assess the in-depth impact of an adversarial attack on the transportation network. To this extent, this dissertation examines factors that are contributing to the manual, visual, and cognitive distractions when drivers encountering fabricated advisory information at a compromised DMS. The results of this dissertation support the original hypothesis and indicate that with respect to the forged information drivers tend to (1) change their planned route, (2) become involved in distracting activities, and (3) change their choice speed at the presence of a compromised DMS. The main findings of this dissertation are outlined below: 1. The DMS security vulnerabilities and predisposing conditions allow adversaries to compromise ITS functionality. The risk-based approach of this study delivers the impact-likelihood matrix, which maps the adverse impacts of the threat events onto a meaningful, visual, matrix. DMS hacking adverse impacts can be categorized mainly as high-risk and medium-risk clusters. The safety, operational (i.e., monetary losses) and behavioral impacts are associated with a high-risk cluster. While the security, reliability, efficiency, and operational (i.e., congestion) impacts are associated with the medium-risk cluster. 2. Tech friendly drivers are more likely to change their route under a compromised DMS. At the same time, while they are acquiring new information, they need to lowering their speed to respond to the higher information load. Under realistic-fabricated information, about 65% of the subjects would depart from their current route. The results indicate that females and subjects with a higher driving experience are more likely to change their route. In addition, those subjects who are more sensitive to the DMS's traffic-related messages and those who use DMS under congested traffic condition are more likely to divert. Interestingly, individuals with lower education level, Asians, those who live in urban areas, and those with trouble finding their direction in new routes are less likely to pick another route rather the one they planned for. 3. Regardless of the DMS hacking scenarios, drivers would engage in at least one of the distractive activities. Among the distractive activities, cognitive distraction has the highest impact on the distracted driving likelihood. Meaning, there is a high chance that drivers think of something other than driving, look at surrounding traffic and scenery, or talk to other passengers regarding the forged information they saw on the DMS. Drivers who rely and trust in technology, and those who check traffic condition before starting their trips tend to become distracted. In addition, the result identified that at the presence of bogus information, drivers tend to slow down or stop in order to react to the DMS. That is, they would either (1) become involved in activities through the means of their phone, (2) they would mind wander, look around, and talk to a passenger about the sign, and (3) search for extra information by means of their vehicle's radio or internet. 4. Females, black individuals, subjects with a disability, older, and those with high trust in DMS are less likely to ignore the fabricated messages. In contrary, white, those who drive long hours, and those who see driving as a tedious task are more likely to ignore the bogus messages. Drivers who comply with traffic regulations and have a good driving record are likely to slow down under the tampered messages. Furthermore, female drivers and those who live in rural areas are more likely to slow down under fabricated advisory information. Furthermore, this dissertation identifies that planning for alternative route and involvement in distractive activities cause speed variation behaviors under the compromised DMS. This dissertation is the first to investigate the adverse impact of a compromised DMS on the road users and operators. I attempt to address the current gap in the literature by assessing and evaluating the impact of ITS security vulnerabilities. Broader impacts of this study include (1) to systematically raising awareness among policy-makers and engineers, (2) motivating further simulations and real-world experiments to investigate this matter further, (3) to systematically assessing the adverse impact of a security breach on transportation reliability and safety, and drivers' behavior, and (4) providing insights for system operators and decision-makers to prioritize the risk of a compromised DMS. Additionally, the outcome can be integrated with the nationwide connected vehicle and V2X implementations and security design. / Doctor of Philosophy / Security threats are targeting institutions and infrastructure around the world, and the frequency and severity of security attacks are on the rise. Healthcare manufacturing, financial services, education, government, and transportation are among the industries that are the most lucrative targets for adversaries. Hacking is not just about companies, organizations, or banks; it also includes critical infrastructure. Intelligent Transportation Systems have been deployed with a focus on increasing efficiency and safety in the face of dramatic increases in traffic volume. Although many studies have been performed and many security primitives have been proposed, there are significant concerns about flawless performance in a dynamic environment. A holistic security approach, in which all infrastructure performs within the satisfactory level of security remains undiscovered. Previously, hacking of road infrastructure was a rare event, however, in recent years, field devices, such as dynamic message signs, are hacked with higher frequency. The primary reason that transportation assets are vulnerable to cyber-attacks is that of their location in public. A more dramatic scenario occurs when hackers attempt to convey tampered instructions to the public. Analyzing traveler behavior in response to the hacked messages sign on the basis of empirical data is a vital step toward operating a secure and reliable transportation system. This study is the first to investigate the adversarial impact of a compromised message sign on the road users and operators. I attempt to address the current gap in the literature by assessing and evaluating the impact of ITS security vulnerabilities.