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Introduction: While improved safety is a highly cited potential benefit of autonomous vehicles (AVs), at the same time a frequently cited concern is the new safety challenges that AVs introduce. The literature lacks a rigorous exploration of the safety perceptions of road users who will interact with AVs, including vulnerable road users. Addressing this gap is essential because the successful integration of AVs into transportation systems hinges on an understanding of how all road users will react to their presence. Methods: A stated preference survey of the Phoenix, Arizona, metropolitan statistical area (Phoenix MSA) was conducted in July 2018. A series of ordered probit models was estimated to analyze the survey responses and identify differences between various population groups with respect to the perceived safety of driving, cycling, and walking near AVs. Results: Greater exposure to and awareness of AVs are not uniformly associated with increases in perceived safety. Various attitudinal factors, level of AV automation, and other intrinsic and extrinsic factors are related to safety perceptions of driving, walking, and cycling near AVs. Socioeconomic and demographic characteristics, such as gender, age, income, employment, and automobile usage and ownership, have various relationships with perceived safety. Conclusions: Cycling near an AV was perceived as the least safe activity, followed by walking and then driving near an AV. Both similarities and differences were observed among the factors associated with the perceived safety of different travel alternatives. Practical Applications: Public perception will guide the development and adoption of AVs directly and indirectly. To help maintain control of public perception, transportation planners, decision makers, and other stakeholders should consider more deliberate and targeted messaging to address the concerns of different road users. In addition, more careful pilot testing and more direct attention to vulnerable road users may help avoid a backlash that could delay the rollout of this technology.

Introduction: Predicting crash counts by severity plays a dominant role in identifying roadway sites that experience overrepresented crashes, or an increase in the potential for crashes with higher severity levels. Valid and reliable methodologies for predicting highway accidents by severity are necessary in assessing contributing factors to severe highway crashes, and assisting the practitioners in allocating safety improvement resources. Methods: This paper uses urban and suburban intersection data in Connecticut, along with two sophisticated modeling approaches, i.e. a Multivariate Poisson-Lognormal (MVPLN) model and a Joint Negative Binomial-Generalized Ordered Probit Fractional Split (NB-GOPFS) model to assess the methodological rationality and accuracy by accommodating for the unobserved factors in predicting crash counts by severity level. Furthermore, crash prediction models based on vehicle damage level are estimated using the same two methodologies to supplement the injury severity in estimating crashes by severity when the sample mean of severe injury crashes (e.g., fatal crashes) is very low. Results: The model estimation results highlight the presence of correlations of crash counts among severity levels, as well as the crash counts in total and crash proportions by different severity levels. A comparison of results indicates that injury severity and vehicle damage are highly consistent. Conclusions: Crash severity counts are significantly correlated and should be accommodated in crash prediction models. Practical application: The findings of this research could help select sound and reliable methodologies for predicting highway accidents by injury severity. When crash data samples have challenges associated with the low observed sampling rates for severe injury crashes, this research also confirmed that vehicle damage can be appropriate as an alternative to injury severity in crash prediction by severity.

Introduction: This study identifies determinants of safety climate at agricultural cooperatives. Methods: An extensive survey was designed to build upon past research done in collaboration with DuPont (Risch et al., 2014). In 2014 and 2015, the survey was administered to 1930 employees at 14 different agricultural cooperatives with 154 locations. Injury incidence data were also collected from each location to better understand the overall health and safety environment in this sector. An ordered probit model is used to identify variables that are associated with better safety climates. Results: Safety system components such as discipline programs, inspection programs, modified duty programs, off-the-job safety training programs, and recognition programs are positively related to individual safety climate for both managerial employees and nonmanagerial employees. Variables representing an employee’s agricultural background, distance between their workplace and childhood home, and formal education are not associated with managerial safety climate. However, agricultural background and childhood home distance are associated with nonmanagerial safety climate. Conclusions: Improving occupational health and safety is a priority for many agricultural cooperatives. Lower safety climate emerges as nonmanagerial employees have more experience with production agriculture and work nearer to their home community. Practical applications: Employees of agricultural cooperatives face a host of health and safety challenges that are likely to persist into the future. The safety system components associated with safety climate indicate that continuous feedback is important for improving occupational health and safety. Occupational health and safety programming should also acknowledge that many employees have experiences that influence their attitudes and behaviors.

Introduction This study presents multiple approaches to the analysis of crash injury severity at three- and four-legged unsignalized intersections in the state of Florida from 2003 until 2006. An extensive data collection process was conducted for this study. Method The dataset used in the analysis included 2,043 unsignalized intersections in six counties in the state of Florida. For the scope of this study, there were three approaches explored. The first approach dealt with the five injury levels, and an ordered probit model was fitted. The second approach was an aggregated one, and dealt with only the severe versus non-severe crash levels, and a binary probit model was used. The third approach dealt with fitting a nested logit model. Results from the three fitted approaches were shown and discussed, and a comparison between the three approaches was shown. Results Several important factors affecting crash severity at unsignalized intersections were identified. These include the traffic volume on the major approach, and the number of through lanes on the minor approach (surrogate measure for traffic volume), and among the geometric factors, the upstream and downstream distance to the nearest signalized intersection, left and right shoulder width, number of left turn movements on the minor approach, and number of right and left turn lanes on the major approach. As for driver factors, young and very young at-fault drivers were associated with the least fatal probability compared to other age groups. Impact on industry The analysis identified some countermeasures to reduce injury severity at unsignalized intersections. The spatial covariates showed the importance of including safety awareness campaigns for speeding enforcement. Also, having a 90-degree intersection design is the most appropriate safety design for reducing severity. Moreover, the assurance of marking stop lines at unsignalized intersections is very essential.

Introduction Young male drivers are over-represented in traffic accidents; they were involved in 14% of fatal accidents from 1991 to 2003 while holding only 8% of all drivers licenses in the UK. In this study, a subset of the UK national road accident data from 1991 to 2003 has been analyzed. The primary aim is to determine how to best use monetary and progressive resources to understand how road safety measures will reduce the severity of accidents involving young male drivers in both London and Great Britain. Method Ordered probit models were used to identify specific accident characteristics that increase the likelihood of one of three categorical outcomes of accident severity: slight, serious, or fatal. Results Characteristics found to lead to a higher likelihood of serious and fatal injuries are generally similar across Great Britain and London but are different from those predicted to lead to a higher likelihood of slight injuries. Those characteristics predicted to lead to serious and fatal injuries include driving in darkness, between Friday and Sunday, on roads with a speed limit of 60 mph, on single carriageways, overtaking, skidding, hitting an object off the carriageway, and when passing the site of a previous accident. Characteristics predicted to lead to slight injuries include driving in daylight, between Monday and Thursday, on roads with a speed limit of 30 mph or less, at a roundabout, waiting to move, and when an animal is on the carriageway. Impact on Industry These results aid the selection of policy options that are most likely to reduce the severity of accidents involving young male drivers.

INTRODUCTION: Many studies have examined different factors contributing to the injury severity of crashes; however, relatively few studies have focused on the crashes by considering the specific effects of lighting conditions. This research investigates lighting condition differences in the injury severity of crashes using 3-year (2009-2011) crash data of two-lane rural roads of the state of Washington. METHOD: Separate ordered-probit models were developed to predict the effects of a set of factors expected to influence injury severity in three lighting conditions; daylight, dark, and dark with street lights. A series of likelihood ratio tests were conducted to determine if these lighting condition models were justified. RESULTS: The modeling results suggest that injury severity in specific lighting conditions are associated with contributing factors in different ways, and that such differences cannot be uncovered by focusing merely on one aggregate model. Key differences include crash location, speed limit, shoulder width, driver action, and three collision types (head-on, rear-end, and right-side impact collisions). PRACTICAL APPLICATIONS: This paper highlights the importance of deploying street lights at and near intersections (or access points) on two-lane rural roads because injury severity highly increases when crashes occur at these points in dark conditions. Language: en

Problem : The occurrence and outcome of traffic crashes have long been recognized as complex events involving interactions between many factors, including the roadway, driver, traffic characteristics, and the environment. This study is concerned with the outcome of the crash. Method : Driver injury severity levels are analyzed using the ordered probit modeling methodology. Models were developed for roadway sections, signalized intersections, and toll plazas in Central Florida. All models showed the significance of driver's age, gender, seat belt use, point of impact, speed, and vehicle type on the injury severity level. Other variables were found significant only in specific cases. Results : A driver's violation was significant in the case of signalized intersections. Alcohol, lighting conditions, and the existence of a horizontal curve affected the likelihood of injuries in the roadway sections' model. A variable specific to toll plazas, vehicles equipped with Electronic Toll Collection (ETC), had a positive effect on the probability of higher injury severity at toll plazas. Other variables that entered into some of the models were weather condition, area type, and some interaction factors. This study illustrates the similarities and the differences in the factors that affect injury severity between different locations.

Problem: Motorcycles constitute about 19% of all motorized vehicles in Singapore and are generally overrepresented in traffic accidents, accounting for 40% of total fatalities. Method: In this paper, an ordered probit model is used to examine factors that affect the injury severity of motorcycle accidents and the severity of damage to the vehicle for those crashes. Nine years of motorcycle accident data were obtained for Singapore through police reports. These data included categorical assessments of the severity of accidents based on three levels. Damage severity to the vehicle was also assessed and categorized into four levels. Categorical data of this type are best analyzed using ordered probit models because they require no assumptions regarding the ordinality of the dependent variable, which in this case is the severity score. Various models are examined to determine what factors are related to increased injury and damage severity of motorcycle accidents. Results: Factors found to lead to increases in the probability of severe injuries include the motorcyclist having non-Singaporean nationality, increased engine capacity, headlight not turned on during daytime, collisions with pedestrians and stationary objects, driving during early morning hours, having a pillion passenger, and when the motorcyclist is determined to be at fault for the accident. Factors leading to increased probability of vehicle damage include some similar factors but also show some differences, such as less damage associated with pedestrian collisions and with female drivers. In addition, it was also found that both injury severity and vehicle damage severity levels are decreasing over time.

Introduction Based on the Federal Railway Administration (FRA) database, there were 25,945 highway-rail crossing accidents in the United States between 2002 and 2011. With an extensive database of highway-rail grade crossing accidents in the United States from 2002 to 2011, estimation results showed that there were substantial differences across age/gender groups for driver's injury severity. Method The study applied an ordered probit model to explore the determinants of driver injury severity for motor vehicle drivers at highway-rail grade crossings. Results The analysis found that there are important behavioral and physical differences between male and female drivers given a highway-rail grade crossing accident happened. Practical applications Older drivers have higher fatality probabilities when driving in open space under passive control especially during bad weather condition. Younger male drivers are found to be more likely to have severe injuries at rush hour with high vehicle speed passing unpaved highway-rail grade crossings under passive control. Synthesizing these results led to the conclusion that the primary problem with young is risk-taking and lack of vehicle handling skills. The strength of older drivers lies in their aversion to risk, but physical degradation issues which result in longer reaction/perception times and degradation in vision and hearing often counterbalance this attribute.

PROBLEM: The expected substantial increase in people aged 65 or older is important for those concerned about transportation injuries. However, much of the previous research concentrates on older drivers and overlooks the fact that vehicle and crash factors may provide significant explanations of older occupant injury rates. METHOD: Differences across age groups are explored using two nationwide travel surveys, crash involvement, fatalities, and injuries from crash databases and an ordered probit model of injury severity. RESULTS AND DISCUSSION: Two noticeable differences that help explain injury risk are that older people are more likely to travel in passenger cars than younger people who frequently use light trucks, and that seriously injured older occupants are more likely to be involved in side-impact crashes than their younger counterparts. IMPACT: Increased attention to vehicle engagement in side-impact crashes and to vehicle technologies that can help drivers avoid side collisions would be particularly helpful for older occupants.

Evaluating motorists through self-assessment has attracted much interest in recent literature, which is mainly due to the profound impact various parameters of self-assessment can have on the way motorists deal with hazardous traffic situations. Much of the previous work in this area has been hampered both by the lack of adequate sample sizes and, because of the small samples, the evaluation methodologies used.This paper extends previous research in two significant directions: (a) it uses the SARTRE 2 database, which provides more than 17,000 questionnaires from most European countries; and (b) it employs the ordered probit modeling approach, which recognizes the latent nature of self-assessment and explicitly links its dimensions to a set of relevant explanatory variables such as age, gender, region, and income.The results indicate that drivers who rate themselves as both more dangerous and faster than others are, generally, younger men, with higher incomes, break the speed limit more frequently, avoid wearing seat belts, and have been involved in more accidents in the past than other drivers. Interestingly, more experienced and more highly educated drivers assess their driving as less dangerous, but admit to driving faster than other drivers.The methodology used and the results obtained can be a significant help in identifying drivers with high and low self-assessment ratings, which can be useful in planning and implementing road safety information campaigns.