Partial proportional odds model-an alternate choice for analyzing pedestrian crash injury severities

Investigating the influence of streetscape environmental characteristics on pedestrian crashes at intersections using street view images and explainable machine learning

Examining the relationship between streetscape features and road traffic accidents is pivotal for enhancing roadway safety. While previous studies have primarily focused on the influence of street design characteristics, sociodemographic features, and land use features on crash occurrence, the impact of streetscape features on pedestrian crashes has not been thoroughly investigated. Furthermore, while machine learning models demonstrate high accuracy in prediction and are increasingly utilized in traffic safety research, understanding the prediction results poses challenges. To address these gaps, this study extracts streetscape environment characteristics from street view images (SVIs) using a combination of semantic segmentation and object detection deep learning networks. These characteristics are then incorporated into the eXtreme Gradient Boosting (XGBoost) algorithm, along with a set of control variables, to model the occurrence of pedestrian crashes at intersections. Subsequently, the SHapley Additive exPlanations (SHAP) method is integrated with XGBoost to establish an interpretable framework for exploring the association between pedestrian crash occurrence and the surrounding streetscape built environment. The results are interpreted from global, local, and regional perspectives. The findings indicate that, from a global perspective, traffic volume and commercial land use are significant contributors to pedestrian-vehicle collisions at intersections, while road, person, and vehicle elements extracted from SVIs are associated with higher risks of pedestrian crash onset. At a local level, the XGBoost-SHAP framework enables quantification of features' local contributions for individual intersections, revealing spatial heterogeneity in factors influencing pedestrian crashes. From a regional perspective, similar intersections can be grouped to define geographical regions, facilitating the formulation of spatially responsive strategies for distinct regions to reduce traffic accidents. This approach can potentially enhance the quality and accuracy of local policy making. These findings underscore the underlying relationship between streetscape-level environmental characteristics and vehicle-pedestrian crashes. The integration of SVIs and deep learning techniques offers a visually descriptive portrayal of the streetscape environment at locations where traffic crashes occur at eye level. The proposed framework not only achieves excellent prediction performance but also enhances understanding of traffic crash occurrences, offering guidance for optimizing traffic accident prevention and treatment programs.

Quantifying the heterogeneity impact of risk factors on regional bicycle crash frequency: A hybrid approach of clustering and random parameter model

The existence of internal and external heterogeneity has been established by numerous studies across various fields, including transportation and safety analysis. The findings from these studies underscore the complexity of crash data and the multifaceted nature of risk factors involved in accidents. However, most studies consider the effects of unobserved heterogeneity from one perspective -- either within clusters (internal) or between clusters (external) -- and do not investigate the biases from both simultaneously on crash frequency analysis. To fill this gap, this study proposes a hybrid approach combining latent class cluster analysis with the random parameter negative binomial regression model (LCA-RPNB) to explore the association between risk factors and bicycle crash frequency. First, the bicycle crash data is categorized into three clusters using LCA based on crash features such as gender, trip purposes, weather, and light conditions. Then, the separated crash frequency models for different clusters and the overall model are developed based on RPNB using regional factors of crash locations as independent variables and the crash frequency of different clusters respectively as dependent variables. The hybrid approach enables a comprehensive examination of internal and external heterogeneities among bicycle crash frequency factors simultaneously. Results suggest that the proposed hybrid approach exhibits superior fitting and predictive performance compared to the model only considers the effects of unobserved heterogeneity from one perspective with the lower values of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This approach can help policymakers and urban planners to design more effective safety interventions by understanding the distinct needs of different bicyclist clusters and the specific factors that contribute to crash risk in each group.

Fatal crashes involving vehicles with driver warning systems: Identifying risk factors using a correlated random parameters ordered logit modeling approach

Recent advancements in vehicular technology are expected to enhance traffic safety by either warning the drivers or by automating the tasks related to driving to reduce the human driver's involvement. The driver warning systems (DWSs) are designed to warn drivers in unsafe situations such as forward collision, lane departure, or when changing lanes with vehicles in blind spot areas. Although these features are designed to enhance safety, recent crash data shows vehicles with these features are still getting involved in crashes, making it necessary to identify the contributing factors. Further, it also requires research to quantify the benefits of vehicles with one or multiple DWS in terms of safety during multivehicle crashes. This study presents a methodological framework to compare factors affecting fatal crashes involving vehicles with no, one and two DWSs. A three-step method is proposed to incorporate unobserved heterogeneity while modeling. Fixed parameter and correlated random parameter order logit models are employed. The results shows that correlated random parameters ordered logit model outperforms traditional fixed parameter ordered logit model. Vehicles equipped with DWSs are safer than vehicles without DWSs during wet or snowy road conditions, when the vehicle skids laterally or longitudinally, and at intersections. Vehicles with one or both DWSs can reduce drink-and-drive and speeding-related crash involvement than vehicles without DWSs. Female and elderly drivers are at a higher risk while driving a vehicle with one or both DWSs compared to driving a vehicle without DWSs, demanding vehicular modifications.

Biosecurity indemnification and attitudes of United States swine producers towards the prevention of an african swine fever outbreak

Animal disease outbreaks, such as the recent outbreak of African Swine Fever in 2018, are a major concern for stakeholders across the food supply chain due to their potential to disrupt global food security, cause economic losses, and threaten animal welfare. As a result of their transboundary nature, discussions have shifted to preventive measures aimed at protecting livestock while ensuring food security and safety. Emergency assistance has been a critical response option during pandemics. However, this may not be sustainable in the long run because the expectation of government bailout may encourage risk taking behaviours. Our hypothesis is that an indemnity policy that is conditioned on showing biosecurity practices would increase compliance and reduce government expenditure during disease outbreaks. We developed and launched a survey from March to July 2022 targeted at swine producers across the US. From the survey, we examined livestock farmers' attitudes and intentions regarding biosecurity investment and assessed their attitudes towards the purchase of livestock insurance and reporting suspected infected livestock on their farm. We used a partial proportion odds model analysis to examine the model. Our analysis revealed that intention to call a veterinarian, trust in government agencies and risk perception of farmers were instrumental in the willingness to self-invest in biosecurity, purchase livestock insurance, and promptly report infected livestock on their farms. This provides evidence that biosecurity compliance would increase if indemnification was tied to a demonstration of effort to adopt biosecurity practices. We also show that individuals who have been in the industry for a longer period may become complacent and less likely to report outbreaks. Farmers with a higher share of income from their production operations bear a greater risk from their operational income and are more willing to report any suspected infections on their farms. The data suggest that motivating the willingness of farmers to invest in biosecurity while overcoming cost concerns is achievable.

Investigating effective factors on rural crash severity at marginal areas around cities in Iran: a partial proportional odds modelling approach

Over the past decade, rural crashes have been responsible for an average of 65% of crash-induced casualties in Iran. Evidence from prior studies reveals that a significant number of these rural crashes occur at marginal areas around cities. Thus, Exclusive crash severity models should be developed to identify the factors associated with higher injury and fatality probabilities in these areas. In this study, a partial proportional odds (PPO) model was formulated using the rural crash data collected from roads leading to the city of Isfahan. The PPO model holds the ordinal nature of crash observations and allows for different influences of independent variables on various crash severity levels. Insights derived from the results reveal that factors such as vehicle traffic maintaining an average speed exceeding 95 km/h, the occurrence of multi-vehicle crashes, the incidence of overturn-type crashes, the at-fault vehicle being a truck/trailer and at-fault or not-at-fault vehicle being a motorcycle, increase the likelihood of more severe rural crashes. Conversely, a foreign vehicle being at-fault, and the driver of the at-fault vehicle aged between 30 and 40 years, tend to diminish the occurrence of severe crashes at marginal areas around cities.

Impact of bushfires on Australian livestock health, welfare and carcase quality

The 2019/2020 Australian bushfires were unprecedented in terms of total area burned and impact on livestock and wildlife populations. However, there is currently limited literature available relating the consequences of bushfire or smoke exposure to livestock health, welfare, and carcase quality. A retrospective cross-sectional study was conducted using historical monitoring data from a Meat Standards Australia (MSA) accredited abattoir located on the mid-north coast of New South Wales, Australia. The spatiotemporal association between exposure to bushfire smoke (specifically the duration of the bushfires, distance from closest bushfire, annual bushfire season, proportion of the property of origin burned during the bushfires and time frame since exposure to bushfires) and effects on carcase meat quality metrics and pathology were measured, by building linear, generalised linear and cumulative link mixed models. Our findings indicate that hot carcase weight increased as the distance between the property of origin and the closest bushfire became greater and decreased with exposure to bushfires of longer duration or when greater proportions of the property of origin were burnt during bushfires. Subcutaneous rib fat of carcases also increased with an increasing distance of properties from the closest recorded bushfire and decreased with exposure to bushfires during the 2019/2020 season. Higher meat colour scores (darker meat colour) were associated with exposure to bushfires during the 2019/2020 season and exposure to bushfires of longer durations. There was only a weak association between increasing distance to the closest bushfire and higher marbling scores. Evidence of pneumonia in carcases was associated with exposure to bushfires of longer duration, specifically increasing risk of pneumonia was associated with fires of longer durations. Greater periods of time since exposure (i.e., >6 months) to bushfires were also associated with a higher risk of evidence of pneumonia at the time of processing. With increasing incidence of bushfires in Australia forecasted as a result of climate change, there is an urgent need to understand the impact of bushfires on livestock, to limit the effects on livestock health and mitigate the risk of significant socioeconomic impacts to the livestock industry. By providing a greater understanding of the impact of bushfires, the findings of this study can support producers to make informed decisions to mitigate the effects of bushfires on livestock health and carcase meat quality.

Comparative analysis of pedestrian crash severity at United Kingdom rural road intersections and Non-Intersections using latent class clustering and ordered probit model

Pedestrian safety is a critical issue in the United Kingdom (UK) as pedestrians are the most vulnerable road users. Despite numerous studies on pedestrian-vehicle crashes globally, limited research has been conducted to explore the factors contributing to such incidents in the UK, especially on rural roads. Therefore, this study aimed to investigate the severity of pedestrian injuries sustained on rural roads in the UK, including crashes at intersections and non-intersections. We utilized the STATS19 dataset, which provided comprehensive road safety data from 2015 to 2019. To overcome the challenges posed by heterogeneity in the data, we employed a Latent Class Analysis to identify homogeneous clusters of crashes. Additionally, we utilized the Ordered Probit model to identify contributing factors within each cluster. Our findings revealed that various factors had distinct effects on the severity of pedestrian injuries at intersections and non-intersections. Several parameters like the pedestrian location in footway and one-way roads are only statistically significant in the intersection section. Certain factors such as the day of the week, the pedestrian's location in a refuge, and minor roads (class B roads) were found to be significant only in the non-intersection section.Parameters includingpedestrians aged over 65 years and under 15 years, drivers under 25 years, male drivers and pedestrians, darkness, heavy vehicles, speed limits exceeding 96 km/h (60 mph), major roads (class A roads), and single carriageway roadsare significant in both sections. The study proposes various measures to mitigate the severity of pedestrian-vehicle crashes, such as improving lighting conditions, enhancing pedestrian infrastructure, reducing speed limits in crash-prone areas, and promoting education and awareness among pedestrians and drivers. The findings and suggested measures could help policymakers and practitioners develop effective strategies and interventions to reduce the severity of these incidents and enhance pedestrian safety.

Characterizing the effects of contributing factors on crash severity involving e-bicycles: a study based on police-reported data

Mitigating e-bicycle crash occurrence has become a great challenge across the world. It is of paramount importance for improving traffic safety to characterize the relationship between e-bicycle crash injury severities and contributing factors. This study positions itself at clarifying the roles of the factors in e-bicycle crashes from time, space, road, environment, rider and object characteristics. The partial proportional odds (PPOs) model as well as its elasticity analysis was employed to identify the influences based on 15,138 police-reported e-bicycle crashes in Shangyu District of Shaoxin City, China. The results evidenced that there were 12 factors having significant effects. Especially, the results emphasized the greater influences of rider gender, age, object hit and road type. Their maximum of the absolutes of elasticities was greater than 24%. Increased crash severity was associated with females, younger riders, and higher speed collisions. However, the remaining significant variables had minor effects (no more than 10%). The findings provide meaningful insights for advancing e-bicycle development, when making related policies and prioritizing safety countermeasures.

How yielding cameras affect consecutive pedestrian-vehicle conflicts at non-signalized crosswalks? A mixed bivariate generalized ordered approach

Yielding cameras are considered to be an effective means of preventing drivers' non-yielding behavior. Notably, as pedestrians' perceived risk and behavior change dynamically during the crossing, the safety effectiveness of such facility could also vary across the consecutive conflicts. This study contributes to the literature by assessing the safety effectiveness of yielding camera from a novel perspective, focusing on the consecutive pedestrian-vehicle conflicts (primary conflict and secondary conflict), using Unmanned Aerial Vehicle (UAV) and roadside camera data. Another key contribution lies in the consideration of primary conflict related factors in the secondary conflict analysis, providing new insights into conflict analysis. The mixed bivariate generalized ordered probit model is proposed to analyze the consecutive conflicts simultaneously. The model results indicate that the yielding camera could decrease both slight and severe conflict probability in primary conflict. However, in secondary conflict, the yielding camera would lower severe conflict probability but increase slight conflict probability. Moreover, several primary conflict related factors reveal significant effects on the secondary conflict severity. Specifically, higher pedestrian speed and driver's yielding behavior in primary conflict could lead to higher crossing risks in the secondary conflict. Conversely, more unsuccessful attempts before primary conflict could decrease the severity level of secondary conflict. Based on the results, several practical implications are provided to improve the effectiveness of yielding camera and enhance pedestrian safety.

Multimorbidity and health-related quality of life among patients attending chronic outpatient medical care in Bahir Dar, Northwest Ethiopia: The application of partial proportional odds model

BACKGROUND

Multimorbidity, the presence of two or more chronic non-communicable diseases (NCDs) in a given person affects all aspects of people's lives. Poor quality of life (QoL) is one of the major consequences of living with multimorbidity. Although healthcare should support multimorbid individuals to achieve a better quality of life, little is known about the effect of multimorbidity on the QoL of patients living with chronic conditions. This study aimed to determine the influence of multimorbidity on QoL among clients attending chronic outpatient medical care in Bahir Dar, Northwest Ethiopia.

METHODOLOGY

A multi-centered facility-based study was conducted among 1440 participants aged 40+ years. Two complementary methods were employed to collect sociodemographic and disease related data. We used the short form (SF-12 V2) instrument to measure quality of life (QoL). The data were analyzed by STATA V.16, and a multivariate partial proportional odds model was fitted to identify covariates associated with quality of life. Statistical significance was considered at p-value <0.05.

PRINCIPAL FINDINGS

Multimorbidity was identified in 54.8% (95% CI = 52.2%-57.4%) of the sample. A significant proportion (33.5%) of the study participants had poor QoL and a quarter (25.8%) of them had moderate QoL. Advanced age, obesity and living with multimorbidity were the factors associated with poor QoL. Conversely, perceived social support and satisfaction with care were the variables positively associated with better QoL.

CONCLUSION

The magnitude of multimorbidity in this study was high and individuals living with multimorbidity had a relatively poorer QoL than those without multimorbidity. Care of people with chronic multiple conditions has to be oriented to the realities of multimorbidity burden and its implication on QoL. It is also imperative to replicate the methods we employed to measure and analyze QoL data in this study for facilitating comparison and further development of the approaches.

Investigating the Risk Factors Associated with Injury Severity in Pedestrian Crashes in Santiago, Chile

Pedestrians are vulnerable road users that are directly exposed to road traffic crashes with high odds of resulting in serious injuries and fatalities. Therefore, there is a critical need to identify the risk factors associated with injury severity in pedestrian crashes to promote safe and friendly walking environments for pedestrians. This study investigates the risk factors related to pedestrian, crash, and built environment characteristics that contribute to different injury severity levels in pedestrian crashes in Santiago, Chile from a spatial and statistical perspective. First, a GIS kernel density technique was used to identify spatial clusters with high concentrations of pedestrian crash fatalities and severe injuries. Subsequently, partial proportional odds models were developed using the crash dataset for the whole city and the identified spatial clusters to examine and compare the risk factors that significantly affect pedestrian crash injury severity. The model results reveal higher increases in the fatality probability within the spatial clusters for statistically significant contributing factors related to drunk driving, traffic signage disobedience, and imprudence of the pedestrian. The findings may be utilized in the development and implementation of effective public policies and preventive measures to help improve pedestrian safety in Santiago.

Using ordered and unordered logistic regressions to investigate risk factors associated with pedestrian crash injury severity in Victoria, Australia

INTRODUCTION

The safety of pedestrians is a major concern in Victoria, Australia. Despite the considerable number of pedestrian fatalities and injuries in traffic crashes, a limited number of studies focused on pedestrian crash severity in Victoria.

METHODS

This study investigates and identifies the influential factors determining the severity of pedestrian injuries in traffic crashes in Victoria by using crash data from 2010 to 2019. An unordered multinomial logit model and an ordered logit model are developed for this purpose.

RESULTS

The results indicate that pedestrian crashes on weekends, in the period of 10 a.m. to 10 p.m., on dark streets, at intersections, in areas with a speed limit above 50 km/h, and on medians or footpaths are associated with a higher probability of severe and fatal injuries. Male pedestrians, children, and older adults (>59) were more likely to sustain a higher level of injury in crashes. Concerning the driver characteristics, no significant relationship was found between pedestrian injury severity and driver gender and license status, but older drivers were more likely to cause severe and fatal injuries. Pedestrian collisions with motorcycles, heavy vehicles, light commercial vehicles, bus/minibus/coach, and trams increase the probability of more severe injuries compared to cars. Moreover, older vehicles are associated with a higher probability of severe pedestrian injuries. Comparison of the model results illustrated that the MNL model was slightly better fitted on the data than the ordered logit model, but the conclusions inferred from these two models were generally similar.

PRACTICAL APPLICATION

To reduce the injuries of pedestrian crashes, we recommend improving lighting conditions and sidewalk design, implementing speed reduction strategies at high pedestrian activity areas, introducing more pedestrian crossings at midblock, installing warning signs to drivers, and discouraging the use of vehicles that are more than 20 years old.

An application of in-vehicle recording technologies to analyze injury severity in crashes between taxis and two-wheelers

Conventionally, the crash data used in traffic safety analysis have been collected by the police dispatched to the crash scene. Therefore, crash information inevitably includes errors that influence traffic safety analysis. Such errors can include the crash speed, crash time, crash location, and other crash characteristics. The advances in in-vehicle video recording (IVVR) technologies have recently enabled traffic safety professionals to use more accurate crash information based on crash data reconstruction methods. Although a few studies have been conducted to identify the factors affecting the crash injury severity using such detailed crash data, there was no effort to analyze the factors affecting the injury severity in crashes between taxis and two-wheelers (TWs), including bicycles and motorcycles. Therefore, this study analyzes the injury severity of TW riders in taxi-TW crashes with the accurate crash data collected by taxis equipped with IVVR devices in Incheon, Korea. Two hundred and forty-eight crash data from two years (2010-2011) were used to perform this objective. The factors affecting the injury severity to TW riders were identified based on a partial proportional odds model for these data. Seven variables were found to affect the injury severity significantly: crash speed, second collision, third collision, Delta-V, crashes that occurred with a non-helmeted motorcycle rider, crashes where the collision type was sideswipe, and crashes under rainy or snowy weather conditions. On the other hand, two variables regarding crashes, where the taxi driver behavior helped reduce visible and severe injuries, were changing lanes and the young TW riders (<18 years).

Identifying cow - level factors and farm characteristics associated with locomotion scores in dairy cows using cumulative link mixed models

Lameness is a tremendous problem in intensively managed dairy herds all over the world. It has been associated with considerable adverse effects on animal welfare and economic viability. The majority of studies have evaluated factors associated with gait disturbance by categorising cows into lame and non-lame. This procedure yet entails a loss of information and precision. In the present study, we extend the binomial response to five categories acknowledging the ordered categorical nature of locomotion assessments, which conserves a higher level of information. A cumulative link mixed modelling approach was used to identify factors associated with increasing locomotion scores. The analysis revealed that a low body condition, elevated somatic cell count, more severe hock lesions, increasing parity, absence of pasture access, and poor udder cleanliness were relevant variables associated with higher locomotion scores. Furthermore, distinct differences in the locomotion scores assigned were identified in regard to breed, observer, and season. Using locomotion scores rather than a dichotomised response variable uncovers more refined relationships between gait disturbances and associated factors. This will help to understand the intricate nature of gait disturbances in dairy cows more deeply.

An analysis of the novice motorcyclist crashes in Taiwan

OBJECTIVE

Motorcycles comprised over 60% of motor vehicles in Taiwan. There were still many motorcycle crashes in Taiwan, especially among young riders. This study investigated the characteristics of novice motorcyclist crashes in Taiwan over the period January 2011 to December 2016. Various risk factors affecting the severity of novice motorcyclist crashes, such as the rider characteristics, licensing conditions, and the environment, were examined.

METHODS

To model the count data with multiple crash severities, several regression models were considered. The multinomial logit (MNL) model, ordered logit (OL) model, and partial proportional odds (PPO) model were chosen and investigated for the relationships between the severity of novice motorcyclist crashes and potential risk factors.

RESULTS

The results showed that the novice rider who was underage or unlicensed had a higher probability of a fatal crash. Male sex, helmet use, drinking, college student, frontal impact, urban or dry road, and daytime all played significant roles in novice motorcyclist crashes.

CONCLUSIONS

Taiwan traffic safety needs further policy adjustments and public education toward novice motorcycle crashes. Adequate driving training and providing a user-friendly environment for novice riders could help. Taiwan should consider graduated driver licensing systems for skill-building and riding supervision for new motorcyclists.

An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety

More than 8000 pedestrians were killed due to road crashes in Australia over the last 30 years. Pedestrians are assumed to be the most vulnerable users of roads. This susceptibility of pedestrians to road crashes conflicts with sustainable transportation objectives. It is critical to know the causes of pedestrian injuries in order to enhance the safety of these vulnerable road users. To achieve this, traditional statistical models are used frequently. However, they have been criticized for their inflexibility in handling outliers and missing or noisy data, and their strict pre-assumptions. This study applied an advanced machine learning algorithm, a Bayesian neural network, which has the characters of both Bayesian theory and neural networks. Several structures of this model were built, and the best structure was selected, which included three hidden neuron layers—sixteen hidden nodes in the first layer and eight hidden nodes in the second and third layers. The performance of this model was compared with the performances of some other machine learning techniques, including standard Bayesian networks, a standard neural network, and a random forest model. The Bayesian neural network model outperformed the other models. In addition, a study on the importance of the features showed that the individuals’ characteristics, time, and circumstantial factors were essential. They greatly increased model performance if the model used them. This research lays the groundwork for using machine learning approaches to alleviate pedestrian deaths caused by road accidents.

Driving behavior at midblock crosswalks with Rectangular Rapid Flashing Beacons: Hidden Markov model approach using naturalistic data

Pedestrian fatalities have increased in the U.S. with the largest increase being observed on urban arterials and away from intersections. Rectangular Rapid Flashing Beacon (RRFB) has been widely implemented as a safety countermeasure to improve driver awareness and visibility of pedestrians, especially for midblock crosswalks. Studies show that drivers are more likely to yield to pedestrians at crosswalks with an RRFB. These studies are often based on a binary outcome of whether or not drivers yield to pedestrians. Nevertheless, it is also important to consider the drivers' deceleration behavior as a dynamic process at these crosswalks and the impact of pedestrians being present or not. Understanding this dynamic behavior and the related circumstances can provide information on the design of alerting systems that help drivers make more appropriate decisions at these crosswalks to avoid a vehicle-pedestrian crash. This study examined this research topic using Hidden Markov Models (HMMs) and data from a naturalistic study. More specifically, four HMMs were applied to the naturalistic brake and jerk data from the Safety Pilot Model Deployment (SPMD) program given drivers' intention to slow down, the RRFB activation status, and the presence of pedestrians. The time-based data sequence was converted to distance-based through a moving window to enhance result comparison and interpretation. Grid-search was used to select the best moving window parameters and the optimal number of hidden states. This study confirmed the high compliance at an activated RRFB when pedestrians were present. Even without pedestrians, one in five traversals showed drivers slowing down to less than 8.94 m/s (20 mph) within 35 m of the crosswalk. Model results further indicate that drivers started braking as far back as 180 m before the crosswalk and stopped braking from 70 m before the crosswalk at an activated RRFB without pedestrians. When there were pedestrians, drivers would start braking 20 to 30 m later but would brake more firmly and for longer. Finally, drivers were not likely to brake or decelerate when RRFB was off and no pedestrians were present.

What are the leading causes of fatal and severe injury crashes involving older pedestrian? Evidence from Bayesian network model

INTRODUCTION

Identifying factors contributing to the risk of older pedestrian fatal/severe injuries, along with their possible interdependency, is the first step towards improving safety. Several previous studies focused on identifying the influence of individual factors while ignoring their interdependencies. This study investigated the leading risk factors associated with older pedestrian fatalities/severe injuries by identifying the interdependency relationship among variables.

METHOD

A Bayesian Logistic Regression (BLR) model was developed to identify significant factors influencing pedestrian fatalities and severe injuries, followed by a Bayesian Network (BN) model to reveal the interdependency relationship among the statistically significant variables and crash severity. Furthermore, the probabilistic inference was conducted to identify the leading cause of fatal and severe injuries involving older pedestrians. The models were developed with data from 913 pedestrian crashes involving older pedestrians at signalized intersections in Florida from 2016 through 2018.

RESULTS

Vehicle maneuver, lighting condition, road type, and shoulder type were directly associated with older pedestrian fatality/severe injury. Vehicle maneuver (going straight ahead) was the most significant factor in influencing the severity of crashes involving older pedestrians. The interdependency of vehicle moving straight, nighttime condition, and two-way divided roadway with curbed shoulders was associated with the highest likelihood of fatal and severe-injury crashes involving older pedestrians.

CONCLUSIONS

The Bayesian Network revealed the interdependency between variables associated with fatal and severe injury-crashes involving older pedestrians. The interdependency relationship with the highest likelihood to cause fatalities/severe-injuries comprised factors with the significant individual contribution to the severity of crashes involving older pedestrians. Practical applications: The interdependencies among variables identified in this research could help devise targeted engineering, education, and enforcement strategies that could potentially have a greater effect on improving the safety of older pedestrians.

Exploring the role of soil types on defense and fitness traits of silverleaf nightshade (Solanum elaeagnifolium), a worldwide invasive species through a field survey in the native range

Silverleaf nightshade (Solanum elaeagnifolium) is a highly successful invasive weed that has caused agricultural losses both in its home and invaded ranges. Surveying 50 sub-populations over 36,000 km² in its native range in South Texas, we investigated the interactions among soil type, population size, plant height, herbivory, and plant defenses in its home range with the expectation that populations growing in the plant's preferred sandier soils would host larger colonies of healthier and better defended plants. At each sampling location, on randomly selected plants, we measured height, insect herbivore damage, and presence, and density of internode spines. Soil type was determined using the NRCS Web Soil Survey and primarily grouped into sand, clay, or urban. Our results show a tradeoff between growth and defense with larger colonies and taller plants in clay soils, but smaller colonies of shorter, spinier plants in sandy soils. We also observed decreased herbivory in urban soils, further confirming the plant's ability to survive and even be strengthened by highly disturbed conditions. This study is a starting point for a better understanding of silverleaf nightshade's ecology in its home range and complicates the assumption that it thrives best in sandy soils.

Safety effects of law enforcement cameras at non-signalized crosswalks: A case study in China

Pedestrians are vulnerable when crossing the street, especially at non-signalized crosswalks. In China, in spite of the priority that laws entitle the pedestrians, the yielding rates at non-signalized crosswalks are relatively low. In light of this situation, law enforcement cameras have been used to increase the percentage of drivers yielding to pedestrians. This study investigates the effectiveness of law enforcement cameras on drivers yielding behavior and vehicle-pedestrian conflicts at non-signalized crosswalks. Using Unmanned Aerial Vehicle (UAV) and roadside video recording, information including pedestrian characteristics, vehicular characteristics and environmental factors are collected. The conflict indicators used include Post-Encroachment Time (PET), Time to Collision (TTC), and Deceleration to Safety Time (DST). In this study, a conflict classification framework based on PET, TTC and DST using Support Vector Machine algorithm is employed. A multinomial logit regression model is used to identify the factors contributing to the conflicts. Then, binary logit regression models are constructed to analyze the effects of law enforcement cameras on drivers yielding behavior. Conflict study reveals that the implementation of law enforcement cameras would increase the probability of slight conflict but decrease the probability of serious conflict. Yielding behavior analysis shows that the illegitimate yielding behavior percentages are over 10 %, indicating the necessity of improving the awareness of yielding rules, and the implementation of law enforcement cameras would increase the yielding and legitimate yielding probability. Moreover, factors including the adjacent vehicle yielding behavior, number of lanes between pedestrian and vehicle, pedestrian speed change, pedestrian waiting time, pedestrian accepted gap time, vehicle upstream speed and vehicle speed change are significantly associated with conflict severity and drivers yielding behavior. We recommend that supplementary facilities and measures should be used to improve the safety performance of law enforcement cameras.

A novel investigation of the influence of corporate governance on firms' credit ratings

Corporate governance is the way of governing a firm in order to increase its accountability and to avoid any massive damage before it occurs. The aim of this paper is to investigate the impact of capital structure, firms’ size, and competitive advantages of firms as control variables on credit ratings. We investigate the role of corporate governance in improving the firms’ credit rating using a sample of Jordanian listed firms. We split firms into four categories according to WVB credit rating. We use both the binary logistic regression (LR) and the ordinal logistic regression (OLR) to model credit ratings in Jordanian environment. The empirical results show that the control variables are strong determinants of credit ratings. When we evaluate the relationship between the governance variables and credit ratings, we found interesting results. The board stockholders and board expertise are moderately significant. The board independence and role duality are weakly significant, while board size is insignificant.

Exploring bicyclist injury severity in bicycle-vehicle crashes using latent class clustering analysis and partial proportional odds models

INTRODUCTION

Bicyclists are more vulnerable compared to other road users. Therefore, it is critical to investigate the contributing factors to bicyclist injury severity to help provide better biking environment and improve biking safety. According to the data provided by National Highway Traffic Safety Administration (NHTSA), a total of 8,028 bicyclists were killed in bicycle-vehicle crashes from 2007 to 2017. The number of fatal bicyclists had increased rapidly by approximately 11.70% during the past 10 years (NHTSA, 2019).

METHODS

This paper conducts a latent class clustering analysis based on the police reported bicycle-vehicle crash data collected from 2007 to 2014 in North Carolina to identify the heterogeneity inherent in the crash data. First, the most appropriate number of clusters is determined in which each cluster has been characterized by the distribution of the featured variables. Then, partial proportional odds models are developed for each cluster to further analyze the impacts on bicyclist injury severity for specific crash patterns.

RESULTS

Marginal effects are calculated and used to evaluate and interpret the effect of each significant explanatory variable. The model results reveal that variables could have different influence on the bicyclist injury severity between clusters, and that some variables only have significant impacts on particular clusters.

CONCLUSIONS

The results clearly indicate that it is essential to conduct latent class clustering analysis to investigate the impact of explanatory variables on bicyclist injury severity considering unobserved or latent features. In addition, the latent class clustering is found to be able to provide more accurate and insightful information on the bicyclist injury severity analysis. Practical Applications: In order to improve biking safety, regulations need to be established to prevent drinking and lights need to be provided since alcohol and lighting condition are significant factors in severe injuries according to the modeling results.

Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers

INTRODUCTION

Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved.

METHOD

To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, ''middle-aged and elderly drivers with low risk of driving violations and high historical crash records," ''drivers with high risk of driving violations and high historical crash records," and ''middle-aged drivers with no driving violations and conviction records." Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned.

A systematic assessment of the use of opponent variables, data subsetting and hierarchical specification in two-party crash severity analysis

Road crashes impose an important burden on health and the economy. Numerous efforts have been undertaken to understand the factors that affect road collisions in general, and the severity of crashes in particular. In this literature several strategies have been proposed to model interactions between parties in a crash, including the use of variables regarding the other party (or parties) in the collision, data subsetting, and estimating models with hierarchical components. Since no systematic assessment has been conducted of the performance of these strategies, they appear to be used in an ad-hoc fashion in the literature. The objective of this paper is to empirically evaluate ways to model party interactions in the context of crashes involving two parties. To this end, a series of models are estimated using data from Canada's National Collision Database. Three levels of crash severity (no injury/injury/fatality) are analyzed using ordered probit models and covariates for the parties in the crash and the conditions of the crash. The models are assessed using predicted shares and classes of outcomes, and the results highlight the importance of considering opponent effects in crash severity analysis. The study also suggests that hierarchical (i.e., multi-level) specifications and subsetting do not necessarily perform better than a relatively simple single-level model with opponent-related factors. The results of this study provide insights regarding the performance of different modelling strategies, and should be informative to researchers in the field of crash severity.

Combined latent class and partial proportional odds model approach to exploring the heterogeneities in truck-involved severities at cross and T-intersections

Although the fatal rate of passenger vehicle-involved crashes has decreased in the United States, the fatal rate of truck-involved crashes has increased. This has, in recent years, become a more severe problem than that caused by passenger vehicle-involved crashes. More studies need to be conducted in order to investigate factors that impact the severity of truck-involved crashes within specific scenarios. This study identifies and evaluates the factors that affect the severity of the truck-involved crashes at cross and T-intersections in North Carolina from 2005 to 2017. A latent class clustering for data segmentation is implemented to mitigate unobserved heterogeneity inherent in the crash data. Four partial proportional odds models, which include fixed and unfixed parameters, are developed considering the heterogeneous and ordinal nature inherent in severities. Estimated parameters and marginal effects are further investigated for better interpreting the impacts. Results show heterogeneous explanatory variables and associated coefficients for different classes and severity levels, which indicate the superiority of this combined approach to obtaining more specific factors and accurate coefficients that are estimated in different scenarios. Many factors are found to contribute to the severities, and crossroad scenarios are found to be more severe than T-intersections. The top five driving behaviors at intersections that contribute to the severity include disregarded signs, improper lane use, followed too closely, ignored signals, and failure to yield. These behaviors arouse a necessity to amend the traffic laws and strengthen drivers' education while giving further insights to engineering practitioners and researchers.

Multilevel ordinal model for CD4 count trends in seroconversion among South Africa women

BACKGROUND

Ordinal health longitudinal response variables have distributions that make them unsuitable for many popular statistical models that assume normality. We present a multilevel growth model that may be more suitable for medical ordinal longitudinal outcomes than are statistical models that assume normality and continuous measurements.

METHODS

The data is from an ongoing prospective cohort study conducted amongst adult women who are HIV-infected patients in Kwazulu-Natal, South Africa. Participants were enrolled into the acute infection, then into early infection subsequently into established infection and afterward on cART. Generalized linear multilevel models were applied.

RESULTS

Multilevel ordinal non-proportional and proportional-odds growth models were presented and compared. We observed that the effects of covariates can't be assumed identical across the three cumulative logits. Our analyses also revealed that the rate of change of immune recovery of patients increased as the follow-up time increases. Patients with stable sexual partners, middle-aged, cART initiation, and higher educational levels were more likely to have better immunological stages with time. Similarly, patients having high electrolytes component scores, higher red blood cell indices scores, higher physical health scores, higher psychological well-being scores, a higher level of independence scores, and lower viral load more likely to have better immunological stages through the follow-up time.

CONCLUSION

It can be concluded that the multilevel non-proportional-odds method provides a flexible modeling alternative when the proportional-odds assumption of equal effects of the predictor variables at every stage of the response variable is violated. Having higher clinical parameter scores, higher QoL scores, higher educational levels, and stable sexual partners were found to be the significant factors for trends of CD4 count recovery.

Challenges for rotational grazing practice: Views from non-adopters across the Great Plains, USA

Many ranchers who practice rotational grazing have experienced economic and ecological benefits. However, the adoption rate of rotational grazing has stagnated. To identify major challenges faced by non-adopters of rotational grazing as well as factors that affect the perceptions about different challenges, we conducted a mail survey of 4250 eligible ranchers in North Dakota, South Dakota and Texas, USA. Key categories of information obtained included basic ranch information, rotational grazing adoption status, and related information. Among 875 respondents, 40.4% identified themselves as non-adopters and perceived labor and water source constraints as the two major challenges, followed by high initial investment costs. This indicates the need for technical support and educational programs to address producers' concerns in addition to the monetary support from government subsidy programs. Findings from logistic regression analyses further indicate that landowners with higher quality soil, relatively more grassland (in both acres and percentage) and more owned land, generally perceive lower barriers to choosing rotational grazing practices and, therefore, may be a suitable target group for more effective outreach efforts and public fund investments to enhance the adoption of beneficial rotational grazing practices.

Modelling severity of pedestrian-injury in pedestrian-vehicle crashes with latent class clustering and partial proportional odds model: A case study of North Carolina

There are more than 2000 pedestrians reported to be involved in traffic crashes with vehicles in North Carolina every year. 10%-20% of them are killed or severely injured. Research studies need to be conducted in order to identify the contributing factors and develop countermeasures to improve safety for pedestrians. However, due to the heterogeneity inherent in crash data, which arises from unobservable factors that are not reported by law enforcement agencies and/or cannot be collected from state crash records, it is not easy to identify and evaluate factors that affect the injury severity of pedestrians in such crashes. By taking advantage of the latent class clustering (LCC), this research firstly applies the LCC approach to identify the latent classes and classify the crashes with different distribution characteristics of contributing factors to the pedestrian-vehicle crashes. By considering the inherent ordered nature of the traffic crash severity data, a partial proportional odds (PPO) model is then developed and utilized to explore the major factors that significantly affect the pedestrian injury severities resulting from pedestrian-vehicle crashes for each latent class previously obtained in the LCC. This study uses police reported pedestrian crash data collected from 2007 to 2014 in North Carolina, containing a variety of features of motorist, pedestrian, environmental, roadway characteristics. Parameter estimates and associated marginal effects are mainly used to interpret the models and evaluate the significance of each independent variable. Lastly, policy recommendations are made and future research directions are also given.

Comparison of univariate and two-stage approaches for estimating crash frequency by severity-Case study for horizontal curves on two-lane rural roads

The Highway Safety Manual (HSM) procedures apply specific safety performance functions (SPFs) and crash modification factors (CMFs) appropriate for estimating the safety effects of design and operational changes to a roadway. Although the applicability of the SPFs and CMFs may significantly vary by crash severity, they are mainly based on total crash counts, with different approaches for estimation of crashes by crash severity. The variety of approaches in the HSM and in the literature in general suggests that there may be no one best approach for all situations, and that there is a need to develop and compare alternative approaches for each potential application. This paper addresses this need by demonstrating the development and comparison of alternative approaches using horizontal curves on two-lane roads as a case study. This site type was chosen because of the high propensity for severe crashes and the potential for exploring a wide range of variables that affect this propensity. To facilitate this investigation, a two-stage modeling approach is developed whereby the proportion of crashes for each severity level is estimated as a function of roadway-specific factors and traffic attributes and then applied to an estimate of total crashes from an SPF. Using Highway Safety Information System (HSIS) curve data for Washington state, a heterogeneous negative binomial (HTNB) regression model is estimated for total crash counts and then applied with severity distribution functions (SDFs) developed by a generalized ordered probit model (GOP). To evaluate the performance of this two-stage approach, a comparison is made with predictions directly obtained from estimated univariate SPFs for crash frequency by severity and also from a fixed proportion method that has been suggested in the HSM. The results revealed that, while the two-stage SDF approach and univariate approach adopt different procedures for model estimation, their prediction accuracies are similar, and both are superior to the fixed proportion method. In short, this study highlights the potential of the two-stage SDF approach in accounting for crash frequency variations by severity levels, at least for curved two-lane road sections, and especially for the all too frequent cases where samples are too small to estimate viable univariate crash severity models.

A survey of nurse practitioner controlled drugs and substances prescribing in three Canadian provinces

AIMS AND OBJECTIVES

In Canada, nurse practitioners (NP) were legally authorised to prescribe controlled drugs and substances (CDS) in 2012. The objective of this study was to understand current NP-CDS prescribing in Alberta, Manitoba and Saskatchewan, Canada. This study is a component of a larger three-phase survey of NP practice patterns in these same provinces.

BACKGROUND

Nurse practitioners are nurses with a graduate degree who have the legal authority to perform expanded functions in health systems, including prescribing CDS. Given the novelty of CDS prescribing for NPs in Canada, little is known about this component of their role.

DESIGN

A secondary analysis of survey data collected between March 2016 and May 2017 was used to examine NP-CDS-prescribing patterns and identify potential associated factors.

METHODS

Nurse practitioners in Alberta, Manitoba and Saskatchewan were invited to complete a professional practice pattern survey. The survey was administered through a secure electronic data collection software application (redcap). In the practice pattern survey, 42 variables from 15 distinct conceptual questions were analysed in this study as potential predictors of NP-CDS prescribing within a purposeful selection ordinal logistic regression model. This scientific submission has been assessed for accuracy and completeness using the Equator STROBE guideline criteria (see Appendix S1).

RESULTS/FINDINGS

Five variables were found to be associated with an increased odds of more frequent NP-CDS prescribing in addition to three confounders/clinically relevant variables. Factors commonly associated with an increased frequency of NP-CDS prescribing relate to location of practice, area of practice, previous nursing experience, team environments and common diagnoses.

CONCLUSION

Little is known about NP-CDS prescribing. Understanding this important component of the NPs emerging legal scope of professional practice can contribute to the continued refinement of this role as well as support ongoing enquiry into the causes of, and potential interventions to prevent, the present opioid overdose deaths occurring while under an active prescription.

RELEVANCE TO CLINICAL PRACTICE

Understanding factors that influence NP-CDS prescribing has relevance to the current drug-related prescription fatalities crisis in all countries.

Spatial Factors Affecting the Frequency of Pedestrian Traffic Crashes: A Systematic Review

Context

Considering the importance of pedestrian traffic crashes and the role of environmental factors in the frequency of crashes, this paper aimed to review the published evidence and synthesize the results of related studies for the associations between environmental factors and distribution of pedestrian-vehicular traffic crashes.

Evidence Acquisition

We searched all epidemiological studies from 1966 to 2015 in electronic databases. We found 2,828 studies. Only 15 observational studies out of these studies met the inclusion criteria of the study. The quality of the included studies was assessed using the strengthening the reporting of observational studies in epidemiology (STROBE) checklist.

Results

A review of the studies showed significant correlations between a large number of spatial variables including student population and the number of schools, population density, traffic volume, roadway density, socio-economic status, number of intersections, and the pedestrian volume and the dependent variable of the frequency of pedestrian traffic crashes. In the studies, some spatial factors that play an important role in determining the frequency of pedestrian traffic crashes, such as facilities for increasing the pedestrians’ safety were ignored.

Conclusions

It is proposed that the needed research be conducted at national and regional levels in coordination and cooperation with international organizations active in the field of traffic crashes in various parts of the world, especially in Asian, African and Latin American developing countries, where a greater proportion of pedestrian traffic crashes occur.

A hybrid finite mixture model for exploring heterogeneous ordering patterns of driver injury severity

Debates on the ordering patterns of crash injury severity are ongoing in the literature. Models without proper econometrical structures for accommodating the complex ordering patterns of injury severity could result in biased estimations and misinterpretations of factors. This study proposes a hybrid finite mixture (HFM) model aiming to capture heterogeneous ordering patterns of driver injury severity while enhancing modeling flexibility. It attempts to probabilistically partition samples into two groups in which one group represents an unordered/nominal data-generating process while the other represents an ordered data-generating process. Conceptually, the newly developed model offers flexible coefficient settings for mining additional information from crash data, and more importantly it allows the coexistence of multiple ordering patterns for the dependent variable. A thorough modeling performance comparison is conducted between the HFM model, and the multinomial logit (MNL), ordered logit (OL), finite mixture multinomial logit (FMMNL) and finite mixture ordered logit (FMOL) models. According to the empirical results, the HFM model presents a strong ability to extract information from the data, and more importantly to uncover heterogeneous ordering relationships between factors and driver injury severity. In addition, the estimated weight parameter associated with the MNL component in the HFM model is greater than the one associated with the OL component, which indicates a larger likelihood of the unordered pattern than the ordered pattern for driver injury severity.

Pedestrian fatality risk in accidents at unsignalized zebra crosswalks in Poland

Poland has the second worst pedestrian fatality rate in the European Union. In the years 2007-2012, 9101 pedestrians were killed and 71328 injured on Polish roads. Almost 30% of pedestrian injury accidents took place at unsignalized zebra crosswalks. Based on police accident database, the worst problem in terms of numbers of fatalities occurs in built-up areas, on two-way undivided roads and at mid-block locations. Especially at risk are older people - almost 73% of pedestrians killed were 55 years or older. In order to show the effect of various factors on pedestrian fatality risk, a binary logit model with interaction terms was developed. The model shows that the following factors increase the probability of pedestrian's death at unsignalized zebra crosswalks: darkness, especially with no street lighting, divided road, two-way road, non built-up area, mid-block crosswalk location and summer time period. Speed limit is a crucial factor: probability of death increases by 37% with every 10km/h rise in the speed limit. Fatality risk increases also with victim's age and is higher for male pedestrians.