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Drivers' hazardous physical and mental states (e.g., distraction, fatigue, stress, and high workload) have a major effect on driving performance and strongly contribute to 25-50% of all traffic accidents. They are caused by numerous factors, such as cell phone use or lack of sleep. However, while significant research has been done on detecting hazardous states, most studies have not tried to identify the causes of the hazardous states. Such information would be very useful, as it would allow intelligent vehicles to better respond to a detected hazardous state. Thus, this study examined whether the cause of a driver's hazardous state can be automatically identified using a combination of driver characteristics, vehicle kinematics, and physiological measures. Twenty-one healthy participants took part in four 45-min sessions of simulated driving, of which they were mildly sleep-deprived for two sessions. Within each session, there were eight different scenarios with different weather (sunny or snowy), traffic density and cell phone usage (with or without cell phone). During each scenario, four physiological (respiration, electrocardiogram, skin conductance, and body temperature) and eight vehicle kinematics measures were monitored. Additionally, three self-reported driver characteristics were obtained: personality, stress level, and mood. Three feature sets were formed based on driver characteristics, vehicle kinematics, and physiological signals. All possible combinations of the three feature sets were used to classify sleep deprivation (drowsy vs. alert), traffic density (low vs. high), cell phone use, and weather conditions (foggy/snowy vs. sunny) with highest accuracies of 98.8%, 91.4%, 82.3%, and 71.5%, respectively. Vehicle kinematics were most useful for classification of weather and traffic density while physiology and driver characteristics were useful for classification of sleep deprivation and cell phone use. Furthermore, a second classification scheme was tested that also incorporates information about whether or not other causes of hazardous states are present, though this did not result in higher classification accuracy. In the future, these classifiers could be used to identify both the presence and cause of a driver's hazardous state, which could serve as the basis for more intelligent intervention systems.

This study presents a novel examination of shared mobility’s viability and impact in Amman, Jordan, framed within the context of sustainable urban transportation. A rigorous methodological approach that integrates advanced statistical models including Probit and Decision Tree analyses was utilized to evaluate the propensity of Amman’s residents to adopt shared mobility solutions. Notably, the Ordered Probit Model provided superior model prediction compared to the multinomial logit model, evidenced by a better goodness of fit measure. The results showed that public transportation users would highly use shared mobility services based on cost and reliability, with service convenience emerging as a pivotal factor. The classification tree identified the convenience of the service as the most important factor in adopting shared mobility. The survey data revealed an initial adoption rate of 25.4%, indicating a significant inclination towards shared mobility among respondents. This is pivotal in understanding the current readiness and potential growth of shared mobility in the city. This study is one of the first to quantify the readiness and potential growth of shared mobility in a Middle Eastern urban setting. Furthermore, the impact of this adoption rate on CO2 emissions was conducted. Emission analysis is crucial for assessing the environmental benefits of transitioning towards shared mobility options and aligning with global sustainability goals. Finally, the study extrapolates strategic guidelines for advancing sustainable transportation in Amman, identifying shared mobility options with the highest potential for successful adoption and proposing strategies to foster their implementation. This research contributes a unique perspective to the discourse on urban mobility, particularly in developing urban contexts like Amman, offering valuable insights for policymakers and urban planners.

Shared mobility services furnish convenient transportation alternatives for individuals without vehicle ownership or a preference against driving. Shared mobility could benefit developing countries by providing a cost-effective alternative, enhancing accessibility, reducing congestion, and creating multiple job opportunities. In this study, a comprehensive analysis to assess shared mobility options as an avenue to sustainable transportation in Amman, Jordan, is presented. The study employs a multifaceted methodology, including a survey questionnaire, preliminary analysis, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis, and Structural Equation Model (SEM). The data were collected from a diverse group of Amman residents using a survey composed of 29 questions. The survey included demographic information, travel behavior, willingness to adopt shared mobility, perceived benefits, and possible barriers. These data were analyzed using Structural Equation Modeling (SEM), providing an in-depth understanding of the interrelationships among the variables studied. This study concludes by contributing to the ongoing discussion on sustainable urban transportation in Jordan and providing a road map for policymakers, urban planners, and transportation service providers. The presented findings provide an empirical basis for guiding future strategies and interventions toward sustainable urban development in Amman and potentially other urban contexts with comparable characteristics. Key findings reveal a significant potential for shared mobility to enhance urban transportation sustainability. Specifically, a notable positive perception among Amman residents was observed, with an average willingness to switch to shared mobility for daily commuting scoring 4.68 on a 7-point Likert scale. Moreover, a statistical analysis indicates that factors such as reduced costs, improved service reliability, and better environmental sustainability, notably influence the adoption of shared mobility services.

Distracted driving has been considered one of the main reasons for traffic crashes in recent times, especially among young drivers. The objectives of this study were to identify the distracting activities in which young drivers engage, assess the most distracting ones based on their experiences, and investigate the factors that might increase crash risk. The data were collected through a self-report questionnaire. Most participants reported frequent cell phone use while driving. Other reported activities include adjusting audio devices, chatting with passengers, smoking, eating, and drinking. A structural equation model was constructed to identify the latent variables that have a significant influence on crash risk. The analysis showed that in-vehicle distractions had a high effect on the crash likelihood. The results also indicated that dangerous driving behavior had a direct effect on the crash risk probability, as well as on the rash driving latent variables. The results provide insight into distracted driving behavior among young drivers and can be useful in developing enforcement and educational strategies to reduce this type of behavior.

PurposeThis paper aims to present a summary of the performance measurement and evaluation plan of the Wyoming connected vehicle (CV) Pilot Deployment Program (WYDOT Pilot).Design/methodology/approachThis paper identified 21 specific performance measures as well as approaches to measure the benefits of the WYDOT Pilot. An overview of the expected challenges that might introduce confounding factors to the evaluation effort was outlined in the performance management plan to guide the collection of system performance data.FindingsThis paper presented the data collection approaches and analytical methods that have been established for the real-life deployment of the WYDOT CV applications. Five methodologies for assessing 21 specific performance measures contained within eight performance categories for the operational and safety-related aspects. Analyses were conducted on data collected during the baseline period, and pre-deployment conditions were established for 1 performance measures. Additionally, microsimulation modeling was recommended to aid in evaluating the mobility and safety benefits of the WYDOT CV system, particularly when evaluating system performance under various CV penetration rates and/or CV strategies.Practical implicationsThe proposed performance evaluation framework can guide other researchers and practitioners identifying the best performance measures and evaluation methodologies when conducting similar research activities.Originality/valueTo the best of the authors’ knowledge, this is the first research that develops performance measures and evaluation plan for low-volume rural freeway CV system under adverse weather conditions. This paper raised some early insights into how CV technology might achieve the goal of improving safety and mobility and has the potential to guide similar research activities conducted by other agencies.

Trucking is considered the backbone of freight transportation in the U.S., because it significantly influences the U.S. economy. The National Highway System is handling a record volume of freight every year. The nation’s total freight transported by trucks is estimated as close to 60% (i.e., 30 million tons), and nearly 70% of total freight value, about $34 billion. In Wyoming, the high amount of energy-related activities led to an increase in heavy truck traffic. This increase of heavy trucks in remote and rural areas may pose safety and livability issues for all road users. Recent statistics show that high truck traffic is associated with an increase in the frequency and severity of crashes. Moreover, Wyoming has one of the key freight routes throughout the U.S.; Interstate 80 (I-80). This east-west 400-mile corridor has more than 50% heavy truck traffic where nearly 40% of all crashes occur on the corridor involve heavy trucks. The corridor is also characterized with a challenging roadway geometry as well as severe winter conditions. The U.S. Department of Transportation (USDOT) – Federal Highway Administration (FHWA) has selected I-80 as one of the three sites to deploy a Connected Vehicle (CV) Pilot, with a focus on commercial trucks. The main goal of the CV pilot program is to improve the safety, mobility, and productivity of traffic on that crucial freight corridor. A major task of the Wyoming CV Pilot is to assess the effectiveness of the emerging CV technology in mitigating safety issues related to heavy trucks. Car manufacturers and transportation agencies are rushing into smart driving technologies, where advanced technologies are taking over most of the driving tasks. Having Connected and Autonomous Vehicles (CAV) on our roadways will dramatically change the future of transportation. Research claims that CAV would significantly reduce crash frequencies as the vast majority of crashes are related to driver errors; however, CAV may increase crash severities due to potential software and hardware failures resulting in catastrophic crashes. CAV would also impact traffic operations, as it might lead to an increase in Vehicle Miles Traveled (VMT) due to the reduced driving tasks and workload. Roadway capacities, delay time, and fleet sizes will be affected with CAV as well. For a successful CAV adoption, enhancements and modifications to the existing infrastructure will be required (i.e., implementation of Dedicated Short Range Communication (DSRC) Roadside Units (RSU) better lane markings and signs for driverless cars, etc.) (1). Although CAV technologies might have a great potential to enhance traffic safety, their safety benefits are not easy to quantify. Traditional safety assessment methodologies may need to be revolutionized to reliably assess the safety benefits of these newly emerging technologies. While traditional safety approaches could be acceptable for baseline assessments, new directions toward integrating real-time data from CAV and micro-simulation modeling will be necessary to evaluate the safety and operations performances of CAV (2). The importance of Surrogate Measures of Safety (SMoS) is expected to be emphasized during the evolution of these emerging technologies. Moreover, real-time risk assessment using data mining techniques will be crucial to successfully analyzing the Big Data obtained from CAV in the near future. A main part of the CV pilot on I-80 is to assess the safety effectiveness of the connected vehicle applications. The first phase of this project will mainly focus on heavy commercial trucks. Therefore, modeling crashes using traditional safety as well as analyzing Surrogate Measures of Safety (SMoS) are valuable. Investigating factors influencing crash occurrence and predicting future crashes by introducing newly developed crash prediction models could be considered as an initial crucial step towards exploring the safety efficacy of the implemented CV technologies. Quantifying the safety performance and identifying factors that influence crashes is imperative to improve the traffic safety on the road network. Traffic safety issues could be assessed using multiple approaches, which could help in providing insights to reduce the frequency and severity of crashes. Therefore, the main focus of the research provided in this dissertation is to examine and analyze crashes to promote safety on Wyoming’s road network. The main dataset used in this research was the historical crash data on Wyoming highway and interstate roads, maintained by the Wyoming Department of Transportation (WYDOT). Other datasets, compromising roadway geometric characteristics, detailed weather information, implemented countermeasures, and traffic data, were combined with the main crash dataset. (Abstract shortened by ProQuest.)

Background We aimed at screening for subclinical psoriatic arthritis (PsA) among psoriatic patients without musculoskeletal complaints using ultrasonography of the lower limbs and finding the best predictor for its development. Results Subclinical inflammatory articular affection was found by ultrasound in 33 patients, among whom 26 had psoriatic nail affection. According to CASPAR criteria, those 26 patients could be diagnosed as PsA (subclinical). The only statistically significant difference between psoriatic patients with PsA and those without was the mean quadriceps tendon thickness as well as the presence of enthesophytes and bilateral quadriceps thickening. The best and only predictor for subclinical PsA was the presence of enthesophytes. Conclusion Ultrasound was more sensitive than clinical examination in detecting subclinical psoriatic arthritis which is highly prevalent in patients with psoriasis even in the absence of manifest arthritic complaints. The best and only predictor for subclinical PsA was the presence of enthesophytes.