Pedestrian Safety Research Articles

Implementasi Light Emitting Diode Sebagai Penerangan Jalan Umum Yang Hemat Daya

Public street lighting, also known as street lighting, refers to the outdoor lighting systems used to illuminate public roads. An effective street lighting system plays a crucial role in enhancing safety and security for pedestrians, cyclists, and motorists by providing improved visibility of the road and surrounding terrain during nighttime hours. The primary focus in street lighting planning is on economic factors. An analysis of light sources has highlighted the economic advantages of utilizing light emitting diodes for public street lighting. This choice proves to be more efficient when implemented in a sustainable manner. The installation of public street lighting can be the optimal solution to address the issue of inadequate lighting in the village of Pedurenan Gunung Sindur Bogor, ensuring enhanced comfort and security during nighttime activities. The lighting installation process commences with a thorough examination of the site, followed by diligently arranging and design of the electrical installations for the lighting system. Finally, the planned design was carried out and implemented. The implementation of public street lighting is a collaborative effort between college students and youth residents in the village. The outcome of this community service initiative is the implementation of public street lighting installations that offer optimal illumination, ensuring the convenience and safety for all people accessing the roads.

MOBILIDADE SEGURA: PROJETO DE INTERVENÇÃO VIÁRIA PARA UMA INTERSEÇÃO DA AVENIDA PRESIDENTE VARGAS DO MUNICÍPIO DE DOURADOS-MS

Urban mobility characterizes the conditions for moving people and loads through urban space, acting on the well-being and safety of the population. Considering the safety aspect, Vision Zero emerges as a set of techniques that help reduce accidents with deaths or serious injuries. This work presents the application of concepts of safe mobility and Vision Zero in the city of Dourados-MS through an intervention project in a stretch of one of its roads. The elaboration of the project required two main stages: determination of the study section and characterization of the traffic elements of that section. From the implementation of the bike path on Avenue Presidente Vargas, the safety of cyclists will increase. In addition, the correction of signs and the solution of problems related to accessibility when crossing the medians will result in greater safety for people with reduced mobility. Thus, it was found that the parameters of Vision Zero are strong allies to ensure the safety of pedestrians and cyclists and prevent serious accidents from occurring in the city.

Interactive soundscape design in enhancing perceived safety in urban spaces at night

Based on previous findings relating soundscape to the psychological perception of urban pedestrians at night, this study selected several typical nighttime urban spaces that are prone to trigger crime panic emotions for the experiment, and used an interactive soundscape adjustment method in which respondents independently mixed and adjusted six types of sound levels to obtain the combination of sound environments most conducive to a sense of safety, while completing the pre- and post-optimised sound environment safety The perception research questionnaire was also completed. The results show that the soundscape created by people has a positive effect on the perception of safety. Footsteps and conversations were the most popular sounds in the soundscape mix, and the results inform the development of a friendly sound environment for the city at night, increasing interaction with the urban public space by enhancing the perception of safety of pedestrians at night. Interactive soundscape design is more effective in enhancing safety in parkland than in street spaces and river walkways. Key words: Perceived safety, Interactive adjustment, Soundscape Creation

Smart Crosswalk: Machine Learning and Image Processing based Pedestrian and Vehicle Monitoring System

The conventional pedestrian crossing system's shortcomings require urgent reform to enhance the safety of pedestrians and improve urban mobility. Issues such as insufficient time for pedestrians to cross, prolong waiting times, neglection of emergency vehicles, and the absence of effective 24/7 response mechanisms at traditional crosswalks present significant safety concerns in urban areas. Our primary intention is to develop a cutting-edge pedestrian crossing system that relies on deep learning and image processing technologies as its foundation. This research addresses to innovate an advanced smart crosswalk consisting of four essential components: a real-time Pedestrian Detection and Priority System customized for individuals with special needs, a responsive system for detecting road conditions, vehicle availability and speed near crosswalks, a real-time Emergency Vehicle Detection and Priority System strengthened by rigorous verification procedures, and a robust framework for identifying pedestrian accidents and violations of crosswalk rules. The entire system has been meticulously designed not only to enhance pedestrian safety by identifying potential dangers but also to optimize traffic flow. In essence, it aims to provide an improved pedestrian crossing experience characterized by increased safety and efficiency.

Investigating Pedestrian Crossing Patterns at Crossing Locations Based on Trajectory Data Collected by UAV

Previous studies on pedestrian crossing have mostly focused on pedestrian crossing decisions; while as an important behavioral aspect, the pedestrian crossing process, i.e., their motions during the entire crossing process, has been narrowly studied. Understanding how pedestrian moves across the street during their entire crossing process helps identify risky movements and reasons for such movements, which can further help in the implementation of effective countermeasures. Therefore, this paper proposed a new and easily applied approach for investigating and understanding the pattern of the pedestrian crossing process at crosswalks based on vision-based trajectory tracking technology and UAV (unmanned aerial vehicle) data. This study uses UAV for collecting video data which is timesaving and has a sufficient coverage area, compared to other methods. For trajectory extraction, the vision-based Deep-SORT-Yolov5 architecture is applied. An improved DBSCAN (density-based spatial clustering of applications with noise) algorithm is introduced for clustering and identification of patterns of pedestrian crossing processes based on their trajectories. This approach is tested via a case study involving six marked crosswalks in Shanghai, China. By using the proposed method, different crossing patterns are extracted and compared. The results show reasonable outputs of trajectory patterns, which reasonably explain the potential instincts of the pedestrians and affecting factors on the behavior of the pedestrian crossing process. Suggestions are made based on the results. This paper contributes to a more comprehensive safety analysis of pedestrian crossings by considering the pedestrian crossing process. The model, along with the UAV-based trajectory observation method, provides an easily-applied and low-cost way of traffic data collection for the purpose of pedestrian safety evaluation.

Analysis of the Technical Condition of the Road Infrastructure of the Greater Poland Voivodship from the Aspect of Road Traffic Safety in 2010-2021

Analysis of the Technical Condition of the Road Infrastructure of the Greater Poland Voivodship from the Aspect of Road Traffic Safety in 2010-2021

MmDrive: Fine-grained Fatigue Driving Detection Using mmWave Radar

Early detection of fatigue driving is pivotal for the safety of drivers and pedestrians. Traditional approaches mainly employ cameras and wearable sensors to detect fatigue features, which are intrusive to drivers. Recent advances in radio frequency (RF) sensing enable non-intrusive fatigue feature detection from the signal reflected by driver’s body. However, existing RF-based solutions only detect partial or coarse-grained fatigue features, which reduces the detection accuracy. To tackle the above limitations, we propose a mmWave-based fatigue driving detection system, called mmDrive, which can detect multiple fine-grained fatigue features from different body parts. However, achieving accurate detection of various fatigue features during driving encounters practical challenges. Specifically, normal driving activities and driver’s involuntary facial movements inevitably cause interference to fatigue features. Thus, we exploit unique geometric and behavioral characteristics of fatigue features and design effective signal processing methods to remove noises from fatigue-irrelevant activities. Based on the detected fatigue features, we further develop a fatigue determination algorithm to decide the driver’s fatigue state. Extensive experiment results from both simulated and real driving environments show that the average accuracy for detecting nodding and yawning features is about 96%, and the average errors for estimating eye blink, respiration, and heartbeat rates are around 2.21 bpm , 0.54 bpm , and 2.52 bpm , respectively. And the accuracy of the fatigue detection algorithm we proposed reached 97.63%.

Using Sensor Fusion and Machine Learning to Distinguish Pedestrians in Artificial Intelligence-Enhanced Crosswalks

Pedestrian safety is a major concern in urban areas, and crosswalks are one of the most critical locations where accidents can occur. This research introduces an intelligent crosswalk, employing sensor fusion and machine learning techniques to distinguish the presence of pedestrians and drivers. Upon detecting a pedestrian, the system proactively activates a warning light signal. This approach aims to quickly alert nearby people and mitigate potential dangers, thereby strengthening pedestrian safety. The system integrates data from radio detection and ranging sensors and a magnetic field sensor, using a hierarchical classifier. The One-Class support vector machine algorithm is used to classify objects in the radio detection and ranging data, while fuzzy logic is used to filter out targets from the magnetic field sensor. Additionally, this work presents a novel method for the manufacture of the road signaling system, using mixtures of resins, aggregates, and reinforcing fibers that are cold-injected into an aluminum mold. The mechanical, optical, and electrical characteristics were subjected to standardized tests, validating its autonomous operation in real-world conditions. The results revealed the system’s effectiveness in detecting pedestrians with a 99.11% accuracy and a 0.0% false-positive rate, marking a substantial improvement over the previous fuzzy logic-based system with an 81.33% accuracy. Attitude testing revealed a significant 33.33% reduction in pedestrian erratic behavior and a substantial decrease in driver speed (32.83% during the day and 70.6% during the night) compared to conventional crossings. Consequently, this comprehensive work offers a unique solution to pedestrian safety at crosswalks by showcasing the potential of machine learning techniques, particularly the One-Class support vector machine algorithm, in advancing road safety through precise and reliable pattern recognition.

Epidemiology of head injuries in pedestrian-motor vehicle accidents

Despite efforts of many countries to improve pedestrian safety, international reports show an upward trend in pedestrian-motor vehicle accidents. Although the most common cause of death of pedestrians is head injuries, there is a lack of knowledge on the epidemiology and characteristics of head injury in terms of the Glasgow Outcome Scale to be used for prevention. However, this study aimed to determine the epidemiology of pedestrian-motor vehicle accidents, the characteristics of head injury, and differences in the Glasgow Outcome Scale in terms of gender. In this retrospective analytical study, the data of 917 eligible injured pedestrians were obtained from the two databases of the Trauma System and the Hospital Information System. The data were analyzed using SPSS software (Version 21). The mean age of all 917 injured pedestrians was 47.55 ± 19.47 years. Most of the injured pedestrians (42.10%) were in the age range of 41–69 years and 81.31% were male. Moreover, 83.07% did not have any acute lesions on the CT scan. The most common brain lesion was brain contusion (n = 33, 3.60%), subarachnoid hemorrhage (n = 33, 3.60%), and skull fracture (n = 29, 3.16%). Among all concurrent injuries, lower extremity/pelvic injuries were observed in 216 patients (23.56%). Outpatient treatment (n = 782, 85.27%), airway control/endotracheal intubation (n = 57, 6.22%), and resuscitation (n = 35, 3.82%) were the most applied treatments respectively. There were significant differences in the Glasgow Outcome Scale between men and women (P- value = 0. 012). The high rate of mortalities, disability, head injuries, contusion, subarachnoid hemorrhage, and skull fractures in pedestrians involved in MVAs emphasizes the need for developing and implementing prevention strategies including appropriate management and risk reduction. Male pedestrians were at higher risk of motor vehicle accidents and worse Glasgow Outcome Scale. The presented data identified the main types of pedestrian injuries and suggested the importance of adopting appropriate preventive strategies to achieve the most effective interventions for creating a safer community.

Factors associated with pedestrian-vehicle collision hotspots involving seniors and children: a deep learning analysis of street-level images

ABSTRACT This study aimed to examine the factors associated with pedestrian–vehicle collision hotspots involving seniors and children. For the empirical analysis, we first quantified street-level images of collision hotspots involving seniors and children and non-collision hotspots in the Seoul Metropolitan Area, Korea, using deep learning analysis. Thereafter, we examined the risk factors associated with collision hotspots through logistic analyses. This study has two major findings. First, the effects of risk factors (e.g. share of sky and green space) differ between collision hotspots involving seniors and children. Second, some pedestrian safety treatments (i.e. traffic lights and sidewalks) are positively associated with collision risks. The findings suggest that varied approaches to enhancing pedestrian safety among different age groups should be considered for more effective pedestrian safety interventions. In addition, the quality of pedestrian safety measures should be examined to improve pedestrian safety for seniors and children.

Comparison of Accommodation Speed and Reading Accuracy for Conventional and Digital Rear-View Mirrors with Emphasis on Overall Traffic Safety

Abstract The topic of this article is a comparison of the speed of accommodation and the reading accuracy of conventional and digital rear-view mirrors. The data for the research described in this article was obtained through laboratory measurements using precise measuring techniques on a medium-sized, relatively heterogeneous group of users, consisting mainly of university students and employees. In the experiment, all research participants performed a series of test tasks on two identical passenger vehicles, where one was equipped with conventional side mirrors, while the other had digital side mirrors installed. The main reason for carrying out this study is to try to imitate a common situation where the driver is forced to drive a car with these modern technologies in normal traffic and to understand the specific issues associated with the use of this technology. The speed of accommodation and reading accuracy is a very important parameter in this regard, which is also important for maximizing road traffic safety. The data obtained from this research was subsequently subjected to selected statistical analysis. Results confirmed statistically significant differences in the speed of accommodation when reading data from conventional and digital mirrors. It has been shown that the use of digital mirror technology can have a direct negative effect on road safety, especially in combination with other negative effects given by the technical nature of the technology under investigation, such as image latency in digital displays.

Examination of the Effects of Dynamic Speed Limit on Shock Waves with a Simulation Technique

The aim of motorway networks is to ensure smooth, high volume road traffic. The problem of anomalies in the system has been a long-standing concern for researchers, and with the widespread use of motorway networks a series of studies have been published on the subject. Speed limitation is one of the most important tools of mitigating disturbances. This study is looking for a solution to the problem known as the "shock wave effect". The research is based on a simulation method in a PTV VISSIM environment. The software can be used to examine certain traffic conditions, and then apply dynamic speed limitation based on these conditions. The Built Environment Information Platform (BENIP) is based on the idea that the built environment, the traffic, and the flow of information between them are closely related. The relevance of the study lies in the fact that a point-based notification of speed limits can be used for vehicles – a technology that is available in most cases at the infrastructure level on motorways – thus, improving road capacity and traffic safety.

Bioinspired Garra Rufa Optimization-Assisted Deep Learning Model for Object Classification on Pedestrian Walkways.

Object detection in pedestrian walkways is a crucial area of research that is widely used to improve the safety of pedestrians. It is not only challenging but also a tedious process to manually examine the labeling of abnormal actions, owing to its broad applications in video surveillance systems and the larger number of videos captured. Thus, an automatic surveillance system that identifies the anomalies has become indispensable for computer vision (CV) researcher workers. The recent advancements in deep learning (DL) algorithms have attracted wide attention for CV processes such as object detection and object classification based on supervised learning that requires labels. The current research study designs the bioinspired Garra rufa optimization-assisted deep learning model for object classification (BGRODL-OC) technique on pedestrian walkways. The objective of the BGRODL-OC technique is to recognize the presence of pedestrians and objects in the surveillance video. To achieve this goal, the BGRODL-OC technique primarily applies the GhostNet feature extractors to produce a set of feature vectors. In addition to this, the BGRODL-OC technique makes use of the GRO algorithm for hyperparameter tuning process. Finally, the object classification is performed via the attention-based long short-term memory (ALSTM) network. A wide range of experimental analysis was conducted to validate the superior performance of the BGRODL-OC technique. The experimental values established the superior performance of the BGRODL-OC algorithm over other existing approaches.

A ward level analysis of child pedestrian casualty frequencies in Greater London

Introduction: Child pedestrian safety remains a challenge despite the remarkable progress that has been attained in recent years, particularly, in high income jurisdictions such as London. This study sought to identify and quantify the magnitude of the effects of various explanatory variables, from the domains of transport, built and natural environment, socio-demographic and economic factors, on ward level child pedestrian injury frequencies in Greater London. Method: We adopted a multilevel random parameters model to investigate the factors associated with child pedestrian injuries given the hierarchical nature of the data comprising of wards nested within boroughs. Results: We found that crime, the Black, Asian, and Minority Ethnic (BAME) population, school enrollment, and the proportion of the population who walk five times a week had an increasing effect on the number of child pedestrian casualties. Conversely, the proportion of the population with a level 4 qualification and the number of cars per household had a decreasing effect. Conclusions: Our study identified high child pedestrian injury frequency wards and boroughs: Stratford and New Town had the highest expected child pedestrian injury frequencies followed by Selhurst, Westend, and Greenford Broadway. Some inner London boroughs are among the highest injury frequency areas; however, a higher number of high child pedestrian injury boroughs are in outer London. Practical Applications: The paper provides recommendations for policy makers for targeted child pedestrian safety improvement interventions and prioritization to optimize the utilization of often constrained resources. The study also highlights the importance of considering social inequities in policies that aim at improving child traffic safety.

МОЖЛИВОСТІ РЕВІТАЛІЗАЦІЇ ПРОМИСЛОВИХ ТЕРИТОРІЙ МІСТ НА ПРИКЛАДІ ВУЛИЦІ ЗАМКОВОЇ У МІСТІ ЛЬВІВ

Globally, cities have two options for growth: extensive and intensive. The former involves expanding the city's area to accommodate increased development or population. However, this option has significant drawbacks, including longer travel times within the city, loss of arable or recreational land around the city, and the need to construct new infrastructure. As a result, many cities opt for the intensive approach, which maximizes land use efficiency. The topic of revitalization has been under investigation since the mid-20th century, but it is relatively new for Ukraine. As a result, there is a lack of scientific articles that thoroughly explore the subject from all angles. From Ukrainian authors, I want to highlight Hnatiuk L., Melnyk M., Sych O., Levchenko O., Birkentale V. The aim of this article is to examine the global practices of breathing new life into industrial areas. Additionally, it will delve into the history and present state of an industrial area located on Zamkova Street in Lviv. The article seeks to identify the development prospects of the region and provide recommendations and principles for the revitalization of Zamkova Street. We have discovered a few examples of international projects that showcase the importance of experience: KingKros in London, UK, HafenCity in Hamburg, Germany, and Two Kings in Toronto, Canada. As part of our research, we have identified some challenges facing the industrial area on Zamkova Street. Based on our analysis of these challenges and similar projects, we have created a list of principles for revitalization. These include mixed-use buildings, height restrictions, infrastructure development, the idea of a "five-minute city" or "city of short distances," a broad range of housing prices, ample public spaces, new points of attraction, job opportunities close to homes, architecture competitions, preservation of historical buildings, emphasis on pedestrian safety, and the concept of a "sponge city." The issue of industrial areas in Ukrainian cities is complex, but not unique. Many cities have gone through this developmental phase. Fortunately, our cities have an opportunity to draw from successful revitalization experiences. By tailoring these practices to fit each unique situation, we can transform our communities for the better.

Who to sacrifice? Modeling the driver’s dilemma

The “driver’s dilemma” presents an ethical conundrum in Highly Automated Vehicles (HAVs), where a decision is made between saving ten pedestrians or the vehicle’s passengers in life-or-death situations. This study delves into the driver’s dilemma by assessing respondents’ beliefs on whether passengers should be sacrificed to save pedestrians, as well as their willingness to purchase such vehicles. We conducted an original survey that collected approximately 7,000 responses, taking into account factors such as ethical identities, technology familiarity, and practical HAV benefits. Using structural equation models, our findings emphasize the driver’s dilemma, with utilitarianism and co-ridership significantly affecting the decision. Altruists generally favor saving pedestrians; however, altruistic individuals who believe that saving family members in vehicles over ten pedestrians is ethical are more inclined to purchase HAVs that prioritize their family members’ safety, even if it means sacrificing pedestrians. Nonetheless, the majority view protecting pedestrians as the ethical choice, and our results indicate that a social consensus on pedestrian safety could encourage the adoption of ethical HAVs. Our study underlines the need to balance individual preferences with socially desirable outcomes in HAV policy.

Experimental Study on Pedestrian Behaviors during Fire Emergency Conditions with Minecraft: Case Studies in a Classroom

The comprehension of the fire evacuation process is crucial for developing effective evacuation management strategies to enhance pedestrian safety. In this study, we construct a classroom with internal obstacles forming intersecting pathways in Minecraft, and conduct a series of virtual evacuation experiments involving multiple pedestrians to investigate the pedestrian behaviors. Case studies in a single-exit classroom demonstrated that normal obstacles and fire in the main evacuation path prompt pedestrians to detour, and pedestrians exhibit fire-avoidance behavior in advance during fire emergency. In the two-exit classroom experiments, normal obstacles have a limited effect on the exit choices of pedestrians, as they primarily choose the nearest exit. Pedestrians positioned in the center of classroom are influenced by their initial orientations, and some pedestrians opt for exits in their initial facing directions. The presence of fire has a greater influence on pedestrians’ exit choices, with most opting for exits away from the fire. Furthermore, during fire emergencies, some pedestrians engage in risk-taking behavior by choosing higher-risk paths in pursuit of a faster evacuation. These adventurous pedestrians proactively plan routes that maximize their distance from the fire and exhibit orderly queuing behavior. These findings are helpful to reveal pedestrian behaviors during fire emergencies.

Pedestrian Safety in Frontal Tram Collision, Part 2: Laminated Glass as a Crucial Part of the Absorption and Deformation Zone-Its Impact Test and Analysis.

As was shown in the previous part of the study, windshields are an important part of the passive safety means of modern low-floor trams with an extraordinary effect on pedestrian safety in a pedestrian-tram collisions. Therefore, maximum attention must be paid to the definition of tram windshield characteristics. This article describes a windshield crash test, from which data are obtained to verify the feasibility of the applied computational approaches. A developed analytical model is utilised for a simple description of the energy balance during collision with an illustrative definition of the important parameters of laminated glass as well as their clear physical interpretations. The finite element analysis (FEA) performed in Ansys software using two versions of material definition, namely a simpler (*MAT_ELASTIC with nonlocal failure criterion) and a more complex (*MAT_GLASS with brittle stress-state-dependent failure) material model, which are presented as suitable for obtaining a detailed description of the shattering process of laminated glass, which can also be used effectively in windshield engineering.

Children's risk assessment in street crossing using virtual reality

Introduction: Crossing streets represents a risky task for children where they have to assess both the probability and harm severity of being hit by a vehicle. To cross streets safely, children must perceive and interpret the traffic environment and scale their movements to the flow of traffic. Their ability to gather information about the surrounding environment through visual search strategies is essential in this process. This study aimed to explore children's street crossing behaviors and to identify successful risk-assessment strategies. Method: Virtual reality (VR) with built-in eye tracking was used for this investigation; 55 children between 7 and 10 years old completed six street crossing tasks with varying complexity and difficulty. Results: Varying competencies in street crossing were demonstrated among the children. Those who crossed safely looked to the left and right more often to check for traffic and spent more time assessing the traffic environment by following oncoming vehicles with their gaze before crossing than those who crossed dangerously. No apparent differences between children who crossed safely and those who crossed dangerously were found while crossing. Conclusions: The findings suggest that dangerous street crossings were, on different levels, related to assessment time before crossing, visual search strategies during assessment time, and the tasks harm severity and probability risk. Practical Applications: Future research could suggestively include indicators such as assessment time and visual search strategies, and tasks could discern harm severity and probability risk. These indicators might also be considered for training programs aiming to enhance children's pedestrian safety.

The relationship between cognitive functioning and street-crossing behaviours in adults: A systematic review and meta-analysis

BackgroundPedestrians are vulnerable road users, with 26 % of global road fatalities being pedestrians (and other vulnerable road users). It is argued that pedestrians are involved in crashes due to errors in decision-making due to deficits in cognitive skills. To date there has limited research into pedestrian decision-making. There currently stands no theoretical model to help understand how cognitive function impacts on pedestrian street crossing decisions. ObjectivesThe aim of this review was to synthesize the literature on the relationship between cognitive functioning and street-crossing behaviours, in two population groups: all adults, and older adults with or without a cognitive impairment. Data sourcesPublished literature from three databases (PsycINFO, PubMed and Medline) was searched in February 2022. Studies were required to have investigated the relationship between cognitive functioning and street-crossing behaviours. ResultsNine studies were identified for the systematic review, with four cognitive domains and nine street-crossing behaviours examined. Findings from the systematic review suggested that poorer processing speed and visual attention predicted increased unsafe street-crossing behaviours across the two population groups. Additionally, most studies demonstrated a non-significant relationship between executive function and street-crossing behaviours. Results from the meta-analysis on all adults, demonstrated a small effect size for the strength of the relationship between overall cognitive functioning and street-crossing behaviours, with stronger effects noted in individual domains of selective attention and inhibition. LimitationsThe small number of studies in this space combined with considerable variability in cognitive domains measured, assessment tools utilized, and street-crossing behaviours examined across studies limit conclusions about patterns of the relationship between cognitive functioning and pedestrian safety. ConclusionsFindings highlight the important role of visual attention in enabling the engagement of safe street-crossing behaviours, which may assist in the development of targeted interventions to reduce risk of harm to pedestrians. Given unexpected findings regarding the influence of executive functioning, as well as limited findings on other cognitive domains such as mental status and memory, future research should aim to elucidate their role in pedestrian safety. Further research into cognitive function and pedestrian street crossing behaviours is critical if we are to develop a theoretical framework for how pedestrians make road-crossing decisions. If we can better understand the factors that influence street crossing decisions, roadway infrastructure and training programs can be developed to improve outcomes for this vulnerable population.