Improving road safety at horizontal curves using V2I speed warning messages.

INTRODUCTION: According to the Federal Highway Administration, a quarter of fatal collisions has occurred at horizontal curves. The average collision rate at horizontal curves was found to be three times higher than other types of highway segments. The lack of compliance with the speed limit and driver-related factors are among the main contributing factors to those collisions. Vehicle to Infrastructure (V2I) communications can address these limitations by providing drivers with valuable in-vehicle warning messages based on operational and safety data. There is limited effort investigating the impact of different types of V2I warning messages at horizontal curves and among different profiles of drivers. This study aims to thoroughly examine drivers' behavior and compliance with different V2I warning messages when approaching horizontal curves. METHODS: A driving simulator experiment and self-reported survey were conducted. Three main hypotheses were analyzed in this study. First, whether supplying drivers with in-vehicle V2I warning messages will positively affect drivers' behavior at horizontal curves compared to the standard road signs. Second, whether there will be a significant difference in drivers' behavior when receiving text and audio V2I warning messages. Third, whether seniors and female drivers will comply more with speed limit advisory provided through V2I message than younger and male drivers. RESULTS: The Analysis of Covariance confirmed the first two hypotheses. Two main measures of drivers' behavior found to be lower in the V2I communication scenarios compared to the base one. The audio warning message was found to be more promising in increasing drivers' compliance with speed limit advisory when approaching the curves. Analyzing the third hypothesis revealed that younger and male drivers had higher curve initiation speed compared to females and seniors. PRACTICAL APPLICATIONS: The findings of this study can be used by transportation researchers, authorities, and car manufacturers to improve the effectiveness of in-vehicle V2I warning messages among different profiles of drivers.

Near-term impact of COVID-19 pandemic on seniors' crash size and severity.

Recent research revealed that COVID-19 pandemic was associated with noticeable changes in travel demand, traffic volumes, and traffic safety measures. Despite the reduction of traffic volumes across the US, several recent studies indicated that crash rates increased across different states during COVID-19 pandemic. Although some recent studies have focused on examining the changes in traffic conditions and crash rates before and during the pandemic, not enough research has been conducted to identify risk factors to crash severity. Even the limited research addressing the contributing factors to crash severity were focused on the pool category of drivers and no insight is available regarding older drivers, one of the most vulnerable groups to traffic collision and coronavirus. Moreover, these studies investigated the early impact of the COVID-19 pandemic mostly using up to three months of data. However, near-term and long-term effects of the COVID-19 pandemic are still unknown on traffic collisions. Therefore, this study aims to contribute to the literature by studying the near-term impact of the COVID-19 pandemic on crash size and severity among older drivers. To this end, a relatively large sample of crash data with senior drivers at fault was obtained and analyzed. To identify the main contributing factors affecting crash outcomes, Exploratory Factor Analysis was conducted on a high-dimension data set to identify potential latent factors which were validated through Confirmatory Factor Analysis. After that, Structural Equation Modeling technique was performed to examine the associations among the identified independent latent factors and the dependent variable. Additionally, SEM model identified the impact of the COVID-19 pandemic on seniors' crash severity. The findings reveal that several latent variables were the significant predictors of crash severity of older drivers including "Driving maneuver & crash location", "Road features and traffic control devices", "Driver condition & behavior", "Road geometric characteristics", "Crash time and lighting", and "Road class" latent factors. The binary variable of "Pandemic" was found to be as highly significant as the last four latent factors mentioned above. This means not only were older drivers more likely to be involved in higher crash size with higher severity level during the pandemic period, but also "Pandemic" was a risk factor to seniors as much as "Driver condition & behavior", "Road geometric characteristics", "Crash time & lighting", and "Road class" factors. The results of this study provide useful insights that may improve road safety among senior drivers during pandemic periods like COVID-19.

Senior Americans' perceptions, attitudes, and safety concerns toward Autonomous Vehicles (AVs).

INTRODUCTION: Autonomous vehicles (AVs) are considered a promising solution to improve seniors' safety and mobility. However, to transition to fully automated transportation, especially among seniors, it is vital to assess their perception and attitude toward AVs. This paper investigates seniors' perceptions and attitudes to a wide range of AV options from the perspective of pedestrians and users in general, as well as during and after the COVID-19 pandemic. Underlying this objective is to examine older pedestrians' safety perceptions and behaviors at crosswalks in the presence of AVs. METHOD: A national survey collected data from a sample of 1,000 senior Americans. Using Principal Component Analysis (PCA) and Cluster Analysis, three clusters of seniors were identified with different demographic characteristics, perceptions, and attitudes toward AVs. RESULTS: PCA findings revealed that "risky pedestrian crossing behavior," "cautious pedestrian crossing behavior in the presence of AVs," "positive perception and attitude toward shared AVs," and "demographic characteristics" were the main components explaining most of the variation within the data, respectively. The PCA factor scores were used in the cluster analysis, which resulted in the identification of three distinctive groups of seniors. Cluster one included individuals with lower demographic scores and a negative perception and attitude toward AVs from the perspective of users and pedestrians. Clusters two and three included individuals with higher demographic scores. Cluster two included individuals with a positive perception toward shared AVs from the user perspective, but a negative attitude toward pedestrian-AV interaction. Cluster three included those with a negative perception toward shared AVs but a somewhat positive attitude toward pedestrian-AV interaction. The findings of this study provide valuable insights to transportation authorities, AV manufacturers, and researchers regarding older American's perception and attitude toward AVs as well as their willingness to pay and use Advanced Vehicle Technologies.

Exploring the determinants of older adults' susceptibility to pedestrians' incidents.

Older pedestrians (65 years and above) are among the most vulnerable road users. As the number and proportion of older adults grows in many countries, and awareness of the benefits of active travel becomes increasingly evident, it behooves researchers to further investigate the safety of older pedestrians. This study contributes to the literature by examining the factors that influence older adults' susceptibility to pedestrian incidents (falls incidents and pedestrian-vehicle collisions). To this end, a sample of older adults living in the Greater Golden Horseshoe, South Ontario, Canada was surveyed. The data were analyzed using descriptive statistics, Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM), to identify several latent factors that are significantly associated with older adults' involvement in pedestrian incidents. The results revealed that the factors that affect older adults' susceptibility to pedestrian incidents are level of difficulty while walking, fear of falling, and crossing evaluation capability. In contrast, level of pedestrian confidence and level of risk-taking crossing behavior are not among the significant factors. It was also found that older pedestrians aged 65-75 years, males, and those who are still working had a greater probability to be involved in pedestrian incidents. These findings shed light on some key constraints of older pedestrians, and the extent to which each factor could improve their safety. This information, in turn, can be used by transportation authorities in prioritizing their plans, policies, and programs towards improving the safety and mobility of older pedestrians.

A deep learning based traffic crash severity prediction framework.

Highway work zones are most vulnerable roadway segments for congestion and traffic collisions. Hence, providing accurate and timely prediction of the severity of traffic collisions at work zones is vital to reduce the response time for emergency units (e.g., medical aid), accordingly improve traffic safety and reduce congestion. In predicting the severity of traffic collisions, previous studies used different statistical and machine learning models with accuracy as the main evaluating factor. However, the performance of these models was generally not good, especially on fatal and injury crashes. Also, looking into the prediction accuracy only is misleading. This paper aims to propose a novel deep learning-based approach with a customized f1-loss function to predict the severity of traffic crashes. Underlying this objective is to compare the results of deep learning models with machine learning model considering two performance indicators, namely precision, and recall. The data used in the analysis include a sample of traffic crashes that occurred at work zones in Louisiana from 2014 to 2018. This dataset includes valuable information (features) related to road, vehicle, and human factors affecting the occurrence and severity of those crashes. The proposed methodology is based on transforming these features/variables into images. Image transformation is conducted using a nonlinear dimensionality reduction technique t-SNE and convex hull algorithm. A CNN based deep learning algorithm with a customized loss function was used to directly optimize the model for precision and recall. The results showed improved performance in predicting the crash severity of fatal and injury crashes using the deep learning approach, which can help to improve traffic safety as well as traffic congestion at work zones and possibly other roadways segments.

Serotyping, Genotyping and Virulence Genes Characterization of Pasteurella multocida and Mannheimia haemolytica Isolates Recovered from Pneumonic Cattle Calves in North Upper Egypt.

Pasteurella (P.) multocida and Mannheimia (M.) haemolytica are the most two common pathogenic bacterial agents causing pneumonia in calves. Both bacteria are associated with significant economic losses in the cattle industry due to high morbidity and mortality rates, especially in the case of severe infections. The objectives of the present study were to perform serotyping and genotyping, as well as characterization of the virulence-associated genes in 48 bacterial isolates; 33 P. multocida and 15 M. haemolytica. All strains were isolated from pneumonic cattle calves showing respiratory manifestations such as fever, nasal discharges, and rapid breathing in North Upper Egypt governorates (Beni-Suef and El-Fayoum). PCR was applied as a confirmatory test using a specific universal gene, kmt1, and rpt2 for P. multocida and M. haemolytica, respectively. The results show that 29 (87.9%) P. multocida and 15 (100%) M. haemolytica isolates were positive for the corresponding universal gene. The results of serotyping indicate that 86.2% of P. multocida isolates belonged to serotype B:2, while 13.8% were untyped. Meanwhile, 60% and 40% of M. haemolytica isolates belonged to serotype 2 and serotype 1, respectively. Investigation of virulence-associated genes showed that all the tested P. multocida isolates harbored nanB, omp87, and toxA genes. Four M. haemolytica isolates harbored both gcp and lktC genes and of these, three isolates harbored the ssa gene. Sequencing of toxA gene of P. multocida and lktC gene of M. haemolytica in the current strains indicated a great homology with strains uploaded in gene banks from different hosts and localities worldwide.

Investigation of drivers' behavior towards speeds using crash data and self-reported questionnaire.

Speeding is a key contributing factor in roadway crashes in the United Arab Emirates (UAE) and elsewhere. Understanding how drivers behave towards speed management devices (i.e., speed cameras, radars, speed limits and speed warning signs) as well as factors affecting drivers' involvement in speed-related crashes might help in improving traffic safety. This study aims to identify and quantify the factors that affect drivers' compliance with speed enforcement and management devices as well as drivers' involvement in at-fault speed-related crashes in the Emirate of Abu Dhabi (AD), UAE. Two different datasets were collected from the same drivers' population in AD to provide different valuable information regarding the speeding problem. The first dataset was obtained from crashes' reports while, the second dataset was obtained from a self-reported questionnaire survey that was carried out among a total of 442 drivers in AD. Three logistic regression models were developed to identify the significant variables that affect (1) the occurrence of speed related crash (using crashes reports data), (2) drivers' compliance with speed limits (using questionnaire data), and (3) involvement in at-fault speed related crashes (using questionnaire data). The findings revealed that drivers' factors (gender, age, and nationality), vehicle factor (vehicle type), roads and environment factors (weather, road type and speed limit) were the significant factors that affect the occurrence of speed-related crashes in AD. The questionnaire findings revealed that running late, low values of posted speed limits and no sufficient police enforcement were the three main reasons that make motorists drive over the speed limits. In addition, the results indicated that drivers' characteristics (i.e., gender, education and income), drivers' responses to speed enforcement and management devices, and drivers' awareness about the importance of such devices in improving traffic safety were the main factors that affecting both drivers' compliance with speed enforcement devices and drivers' involvement in at-fault speed-related crashes. A comparison between the analysis results of traffic crashes and questionnaire datasets as well as a comparison between the findings of this study and existing literature are also provided.

Profiling of proteins and proteases in the products of the salivary gland, digestive tract and excretions from larvae of the camel nasal botfly, Cephalopina titillator (Clark).

Proteins and proteolytic activities in the contents of the salivary gland (SGc), digestive tract (DTc) and excretory-secretory products (ESP) from larvae of the camel nasal botfly Cephalopina titillator were separated electrophoretically, and characterized. The protein profiles of the different samples were qualitatively quite similar in the larval stages L2 and L3. Zymogram analysis of proteases in the samples indicated that the digestive tract contained a greater variety of proteases than the salivary gland or the excretory-secretory products. They are mainly serine proteases. Proteases of ESP and DTc (especially of 3rd instar) contain trypsin- and chymotrypsin-like serine proteases, while the serine proteases of SGc are not of the trypsin- or chemotrypsin-type.

Phenotypic, antimicrobial susceptibility profile and virulence factors of Klebsiella pneumoniae isolated from buffalo and cow mastitic milk.

Studies on the prevalence and virulence genes of Klebsiella mastitis pathogens in a buffalo population are undocumented. Also, the association of rmpA kfu, uge, magA, Aerobactin, K1 and K2 virulent factors with K. pneumoniae buffalo, and cow mastitis is unreported. The virulence of K. pneumoniae was evaluated through both phenotypic and molecular assays. In vivo virulence was assessed by the Vero cell cytotoxicity, suckling mouse assay and mice lethality test. Antimicrobial susceptibility was tested by disk diffusion method. The 45 K. pneumoniae isolates from buffalo (n = 10/232) and cow (n = 35/293) milk were isolated (45/525; 8.6%) and screened via PCR for seven virulence genes encoding uridine diphosphate galactose 4 epimerase encoding gene responsible for capsule and smooth lipopolysaccharide synthesis (uge), siderophores (kfu and aerobactin), protectines or invasins (rmpA and magA), and the capsule and hypermucoviscosity (K1 and K2). The most common virulence genes were rmpA, kfu, uge, and magA (77.8% each). Aerobactin and K1 genes were found at medium rates of 66.7% each and K2 (55.6%). The Vero cell cytotoxicity and LD (50) in mice were found in 100% of isolates. A multidrug resistance pattern was observed for 40% of the antimicrobials. The distribution of virulence profiles indicate a role of rmpA, kfu, uge, magA, Aerobactin, and K1 and K2 in pathogenicity of K. pneumoniae in udder infections and invasiveness, and constitutes a threat for vulnerable animals, even more if they are in combination with antibiotic resistance.

Exploring the risk factors associated with the size and severity of roadway crashes in Riyadh.

INTRODUCTION: Recently, growing concern has been shifting toward the necessity of improving traffic safety in the Kingdom of Saudi Arabia (KSA). KSA has a unique traffic safety problem in that: (a) it can be classified as a developed country in terms of the magnitude and quality of the roadway networks available and its compatibility with international standards; however, (b) it can also be considered a developing country as the rate of increase in the number of road crashes is substantial compared with relevant figures of other developing countries and other countries of the Gulf region. Hence, more research efforts are still needed. OBJECTIVES: This paper examines the nature and causes of fatal and serious traffic crashes in KSA so that solutions and/or future studies can be suggested. METHOD: Data from 11,545 reported fatal and injury traffic crashes that occurred in Riyadh (the capital of KSA) during the period 2004-2011 were analyzed by alternative and complementary methods. A logistic regression model was estimated and the results revealed that crash reason (speeding), damages in public property, day of the week, crash location (non-intersection location), and point of collision (head-on) were the significant variables affecting the binary target variable (fatal and non-fatal crashes). Additionally, the structural equation modeling approach was developed to identify and quantify the impacts of significant variables influencing crash size (e.g., no. of injuries, no. of vehicles involved in the crash). Crash size is one of the important indices that measure the level of safety of transportation facilities. RESULTS: The results showed that road factor was the most significant factor affecting the size of the crash followed by the driver and environment factors. IMPACT ON INDUSTRY: Considering the results of this study, practical suggestions on how to improve traffic safety in KSA are also presented and discussed.

Immunological and molecular detection of digenetic infections in different species of Egyptian freshwater snails.

Due to the possibility of utilizing different snails in the combat of Schistosoma in Egypt; it is important to study the role it may play in transmitting other trematodes of medical and veterinary importance. Taking this background into consideration, polymerase chain reaction (PCR) assay was designed to identify trematode species at larval stages in intermediate hosts (cercariae in snails) using a combination of standard and molecular methods. This PCR assay was also applied to naturally infected molluscan in order to assess the use of the procedure for detection. The importance of the present study was to demonstrate the epidemiological situation and application in control.

Predicting reduced visibility related crashes on freeways using real-time traffic flow data.

OBJECTIVES: The main objective of this paper is to investigate whether real-time traffic flow data, collected from loop detectors and radar sensors on freeways, can be used to predict crashes occurring at reduced visibility conditions. In addition, it examines the difference between significant factors associated with reduced visibility related crashes to those factors correlated with crashes occurring at clear visibility conditions. METHOD: Random Forests and matched case-control logistic regression models were estimated. RESULTS: The findings indicated that real-time traffic variables can be used to predict visibility related crashes on freeways. The results showed that about 69% of reduced visibility related crashes were correctly identified. The results also indicated that traffic flow variables leading to visibility related crashes are slightly different from those variables leading to clear visibility crashes. IMPACT ON INDUSTRY: Using time slices 5-15 minutes before crashes might provide an opportunity for the appropriate traffic management centers for a proactive intervention to reduce crash risk in real-time.

Exploring the safety implications of young drivers' behavior, attitudes and perceptions.

The present study aims at identifying and quantifying significant factors (i.e., demographic, aberrant driving behavior) associated with young drivers' involvement in at-fault crashes or traffic citations at the ages of 16-17 (while having the Operational License) and 18-24 years old (while having the Full License). A second objective was to investigate the main reason(s) for involvement in risky driving behavior by young drivers. The data used for the analyses were obtained from a self-reported questionnaire survey carried out among 680 young drivers in Central Florida. To achieve these goals, the structural equation modeling approach was adopted. The results revealed that aggressive violations, in-vehicle distractions and demographic characteristics were the significant factors affecting young drivers' involvement in at-fault crashes or traffic violations at the age of 16-17. However, in-vehicle distractions, attitudes toward speeding and demographic characteristics were the significant factors affecting young drivers' crash risk at 18-24. Additionally, the majority of participants reported that "running late" is the main reason for taking risk while driving (i.e., speeding, accept short gaps, or drive so close to the car in front) followed by "racing other cars". Additionally, "exceed speed limits" was the main reason for receiving traffic citations at 16-17 and 18-24 age groups. Practical suggestions on how to reduce crash risk and promote safe driving among young drivers are also discussed.

Broccoli and turnip plants display contrasting responses to belowground induction by Delia radicum infestation and phytohormone applications.

Induced responses to insect herbivory are a common phenomenon in the plant kingdom. So far, induced responses have mostly investigated in aerial plant parts. Recently it was found that root herbivore may also elicit both local and systemic responses affecting aboveground herbivores and their natural enemies. Using broccoli (Brassica oleracea subsp. italica L.) and turnip (Brassica rapa subsp. rapa L.), two cultivated brassicaceaous plants differing in their chemistry and morphology, we analysed the local and systemic induced responses triggered by Delia radicum L. damage, JA and SA application. We also assessed whether the root induction treatments affected D. radicum larval performance. Both D. radicum damage and JA induced changes in glucosinolate and sugar content as well as affected D. radicum performance, while SA application did not. Despite the uniform chemical responses, the effect on larval performance on broccoli and turnip plants was very different. On broccoli, JA root treatment reduced herbivore performance, whereas in turnips the same treatment enhanced it. JA- and D. radicum-induced responses followed similar patterns, which suggests that the JA signalling pathway is involved in root-induced responses to larval feeding. Glucosinolate induction cannot fully explain the differences found in the performance of D. radicum on the different species. Changes in other resistance factors might significantly contribute to the induced resistance in these brassicaceaeous species as well.

The impact of staphylococcal mastitis on the level of milk IL-6, lysozyme and nitric oxide.

Mammary gland secretions derived from secretory cows infected with coagulase +ve Staphylococcus spp. was examined for the expression of IL-6, production of lysozyme and NO(x). The examined cows reflected 25 cases of subclinical mastitis and 15 cases of clinically mastitic animals. The IL-6 concentration in the subclinical animals was significantly higher (30.8 ng/ml) than the clinically manifested animals (18.0 ng/ml) and the normal cows (5.2n g/ml). On the other hand the level of lysozyme although significantly higher than the normal cows (6.9 microg/ml) yet its level in the subclinical animals (11.2 microg/ml) was lower than that estimated in the clinical animals (15.6 microg/ml). Similarly, the level of NO(x) in the normal animals was found to be 5.6 microM/ml to increase to 6.2 microM/ml in the subclinical mastitic animals and to significantly increase further to 11.5 microM/ml in the clinically affected cows. These results suggest the promising use of whey IL-6, lysozyme or/and NO concentration variabilities as prognostic parameters on the degree of the commencement of mastitis in cows.