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One of the challenges in introducing automated service vehicles (ASVs) to parking areas is guiding pedestrians to make quick and appropriate crossing decisions based on an understanding of what the ASV typically does. This study aims to find effective communication strategies for ASVs for pedestrian crossings in Japanese parking areas. Virtual Reality experiments were conducted with 40 participants facilitating vehicle behavior (Normal Deceleration, Early Deceleration, Early Stop) and text-based external human-machine interfaces (eHMIs; No eHMI, "After you," "I'll stop," "In Automated Driving") as tools of communication with two types of ASVs (bus and golf cart) in parking areas. The results indicated that vehicle behavior significantly influenced pedestrian crossing decisions, and pedestrians began to cross when the ASV decelerated early and stopped not too far from them (Early Deceleration). Self-reported measure analyses revealed that text messages containing the intent of the ASV's actions or instruction (After you, I'll stop) with Early Deceleration would probably lead to pedestrians' favorable attitudes toward automated buses. These findings contribute to communication designs based on implicit communication for pedestrian crossing decision-making in parking areas with a clear interpretation of ASV's intentions. [ABSTRACT FROM AUTHOR]

Pine Island Academy, present in Nocatee Community, Jacksonville, Florida, uses various modes of transport for picking up and dropping off children at school. The parents using private vehicles to pick up and drop off their kids tend to take a long time due to delays and congestion in their routes. Parents who use golf carts (GC) for this activity tend to take a short time to pick up and drop off their kids. Due to this, it is necessary to analyze the use of GC as an alternative mode of transport for children pick up and drop off while reducing delay and congestion. This paper uses VISSIM microscopic simulation for analysis whereby the traffic flow in the morning for drop-off and the afternoon for pickup was observed at different GC traffic compositions regarding the relative delay, queue delay, and travel times. From the analysis, it was observed that the delays and travel times for private vehicles decrease as the GC composition increases in the network. These results show that a GC may be suitable for picking up and dropping off children at school. This factor is further supported by the ANOVA test of significance. Due to the results from this paper, recommendations to the transportation agencies responsible for school management include promoting GC usage, establishing designated GC lanes to ensure smooth traffic flow and minimal interaction with other vehicles, implementing a multi-modal approach that includes GCs, and emphasizing safety training and awareness for GC users. [ABSTRACT FROM AUTHOR]

The article presents a court case in which a Miami-Dade County trial court ruled that golf cart owners are liable for damages if they lend their golf carts and their drivers negligently cause injuries. In the case Gonzales v. Chiong, the court held that cart owners are liable and awarded over 50 million dollars in damages to the victim. Other topics include the Florida laws governing the use of golf carts.

Background: Golf is one of the most popular sports in the United States (US) and is played by participants of all ages and skill level. Given the popularity and sport-specific demands on the upper torso, golf poses a considerable risk for upper extremity (UE) injuries. Therefore, the aim of the current study was to (1) determine the incidence rate of UE golf injuries presenting to emergency departments (EDs) in the US, (2) determine the most commonly injured body parts and mechanisms of injury, and (3) compare current injury epidemiology with previous trends in the literature. Hypothesis: Male sex, bimodal age extremes (young and elderly), and utilization of golf carts (vs walking) are associated with a higher incidence of golf-related UE injuries. Study Design: Descriptive epidemiology study. Level of Evidence: Level 3. Methods: The National Electronic Injury Surveillance System (NEISS) is a statistically validated injury surveillance system that collects data from ED visits as a representative probability sample of hospitals in the US. We queried the NEISS for the years 2011 to 2020 to examine the following variables for golf-related UE injuries: sociodemographic, diagnosis, body part, and mechanism of injury. Results: From 2011 to 2020, there were a total of 1862 golf-related UE injuries presenting to participating EDs, which correlates to an estimated 70,868 total injuries. Overall, male golf players were disproportionately affected (69.2%) versus female golf players (30.8%) and the most commonly injured age groups were those aged >60 and 10 to 19 years. The most common injuries included fractures (26.8%), strains/sprains (23.4%), and soft tissue injuries (15.9%). The joints injured most frequently were the shoulder (24.8%), wrist (15.6%), and joints in the hand (12.0%). The most common mechanisms of injury were cart accidents (44.63%), falling/tripping (29.22%), and golf club swinging/mechanics (10.37%). Conclusion: Golf-related UE injuries can be acute or due to chronic overuse. Male athletes >60 years of age were the population most commonly presenting to the ED with a golf-related injury. Further, the shoulder, forearm, and wrist were most commonly injured. These findings are consistent with previous epidemiological trends in the literature. Interventions to reduce the incidence of injury should be sport-specific and focus primarily on equipment and golf cart safety and swing modification to optimize the biomechanical function of the UEs. Clinical Relevance: Our findings indicate that golf-related injury prevention programs should target UE injuries, particularly among young (<19) and older (>60 years) golfers with poor swing mechanics. [ABSTRACT FROM AUTHOR]

Crashes involving golf carts (GCs) have been on an increasing trend in recent years, particularly in the United States. This study focuses on analyzing GC crashes in the Florida community known as The Villages, one of the largest GC-oriented communities in the nation and worldwide. The objective was to evaluate the injury severity of crashes involving GCs in a retirement community where GCs are a common mode of transportation. The ordinal logistic regression (OLR) and Decision Tree Ensemble (DTE) models were used to analyze the injury severity of 616 GC-related crashes. Models' accuracy parameters were used to check their reliability. The analysis revealed that GC crash severity is influenced by various factors. Factors found to be significant by the OLR model in determining injury severity include ejection of one or more occupants from the GC, the extent of damage to the GC, GC speed prior to the crash, roadway characteristics (including divided roadways, traffic control devices, paved shoulders, and T-intersections), and roll-over incidents. The OLR model demonstrated an overall accuracy of approximately 71% in predicting injury severity. The DTE model performed better, with an overall accuracy of 78%. The OLR model's findings were supported by the DTE model, which identified estimated GC speed, occupant(s) ejection from the GC, estimated GC vehicle damage, intersection type, and type of shoulder as the most important factors influencing GC crash severity. Understanding these factors is vital for transportation agencies to develop effective strategies to reduce the severity of GC crashes, ensuring the safety of GC users. This study provides recommendations to transportation agencies on measures to improve the safety of GCs. [ABSTRACT FROM AUTHOR]

Background: Head and spine injuries sustained following golf cart accidents have been rarely analyzed. This study aimed to describe a series of patients sustaining golf cart injuries requiring neurosurgical management for head or spine injuries. Methods: The University of Puerto Rico Neurosurgery database was used to retrospectively identify and investigate patients who sustained a golf cart-related injury requiring a neurosurgical evaluation during 15 years. Results: The analysis identified 25 patients with golf cart-related injuries requiring neurosurgical management with a median age of 16 (interquartile range 13-34). Seventeen patients (68%) were female. The primary mechanism of injury was ejection from the cart in 84% of the patients (n = 21). The most frequent head injury was a skull fracture in 80% of patients (n = 20). Intracranial hemorrhage was present in 76% of patients (n = 19), with brain contusions (n = 16, 64%) being the most common. Eighteen patients (72%) were admitted for surgery or neurological monitoring. The median hospital length of stay among hospitalized patients was 5.5 days. Ten patients (40%) were admitted to the intensive care unit (ICU) with a median stay of 8.5 days. Four patients (16%) required surgery for their injuries. At discharge, 80% of patients (n = 20) had a good outcome. Conclusion: This study showed that children and adolescents are at high risk for golf cart-related neurosurgical injuries. This form of transportation can produce considerable neurological injuries, the primary mechanism of injury being ejection from the cart. Approximately three-quarters of the patients need hospital admission, with half requiring an ICU stay. [ABSTRACT FROM AUTHOR]

Wireless electric vehicle (EV) charging is an important operation for valuable EV options in modern life. Inductive wireless EV charging needs constant current and voltage (CC–CV) charge controllers. This paper presents 750 W variable frequency CC–CV inductive wireless charging for an e-golf cart 50 Ah 72 V Li-ion battery. Due to this system's low power, the system's efficiency may be weak; the secondary-side (SS) maximum efficiency-controlled (MEC) converter was validated. The golf cart's battery characteristics were evaluated to design and experiment with inductive wireless power transfer (IPT) coils and an integration system for a 42 kHz resonant frequency. The CC–CV charged control is an infrastructural part of the H-bridge inverter at varied frequencies from 50 kHz to 56 kHz when the DC input voltage is 310 V, and in the range of 44 kHz to 46 kHz at the 155 V input. The results found the charging of 9 A CC, 82 V CV and 730 W. The 310 V input voltage system without the SS MEC converter's efficiencies was 62% to 72% and it was improved to 65% to 81% using the SS MEC converter. Finally, the best cases were validated at the 155 V DC input voltage and the system with the SS MEC converter had 76% to 86% efficiency. [ABSTRACT FROM AUTHOR]

Car manufacturers around the globe are in a race to design and build driverless cars. The concept of driverless is also being applied to any moving vehicle such as wheelchairs, golf cars, tourism carts in recreational parks, etc. To achieve this ambition, vehicles must be able to drive safely on streets stay within required lanes, sense moving objects, sense obstacles, and be able to read traffic signs that are permanent and even temporary signs. It will be a completely integrated system of the Internet of Things (IoT), Global Positioning System (GPS), Machine Learning (ML)/Deep Learning (DL), and Smart Technologies. A lot of work has been done on traffic sign recognition in the English language, but little has been done for Arabic traffic sign recognition. The concepts used for traffic sign recognition can also be applied to indoor signage, smart cities, supermarket labels, and others. In this paper, we propose two optimized Residual Network (ResNet) models (ResNet V1 and ResNet V2) for automatic traffic sign recognition using the Arabic Traffic Signs (ArTS) dataset. Additionally, the authors developed a new dataset specifically for Arabic Traffic Sign recognition consisting of 2,718 images taken from random places in the Eastern province of Saudi Arabia. The optimized proposed ResNet V1 model achieved the highest training and validation accuracies of 99.18% and 96.14%, respectively. It should be noted here that the authors accounted for both overfitting and underfitting in the proposed models. It is also important to note that the results achieved using the proposed models outperform similar methods proposed in the extant literature for the same dataset or similar-size dataset. [ABSTRACT FROM AUTHOR]

Background: Golf is a moderate‐intensity physical activity that provides positive physical and mental health benefits. However, the inclusiveness of the sport for individuals with disabilities globally is unknown. Objective: To characterize the demographics and disability characteristics of individuals engaging in disability golf globally. It was hypothesized that the majority of participants would be middle‐aged, male, and from countries with higher gross domestic product, similar to the nondisabled population. Design: Descriptive, cross‐sectional analysis using European Disability Golf Association (EDGA) database. Setting Various international golf tournaments. Participants: Golfers (n = 1734) with disability from 44 countries registered with the EDGA (2017–2021). Interventions: Not applicable. Main Outcomes Descriptive analyses of frequencies, distributions, and means differences of demographic characteristics (age, gender, type of disability, level of handicap, golf cart use, and country of origin) of golfers with disability were performed. Data provided analysis of the association between number of participants and a country's gross domestic product (GDP). Results: Individuals had a mean age of 52.5 (±15.6) years: 1589 (91.6%) male and 145 (8.4%) female. Twenty‐three countries had female participation. The most commonly reported primary disability diagnoses were neurologic (24.8%), orthopedic (21.4%), and amputation below the knee (14.4%). Neurologic impairments (24.7%) were most common in men and orthopedic impairments (29.7%) were most common in women. Individuals with neurological impairments (27.4%) most frequently required golf carts to play. The GDP of a country had a positive correlation (r = 0.68) with the number of registered golfers with disability. Conclusion: Golf is played by individuals with a variety of disabilities and provides numerous benefits. However, there is an underrepresentation of youth, women, and individuals with certain impairments and from lower‐income countries. These are the potential areas of opportunity to improve engagement and inclusiveness of golf. [ABSTRACT FROM AUTHOR]

Low Speed Vehicle (LSV)/Golf Cart (GC) communities are those modern residential areas that fully or partially allow LSVs/GCs to travel within the community. Whereas LSV/GC communities are increasing rapidly, the safety impacts of LSV/GC on the community have rarely been investigated. Also, no national uniform manuals are available for guiding the proper design of signs, pavement markings, and signals in the community. In this study, five years (2016-2020) of fatal crash data that involved LSV/GC were extracted from the National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System (FARS) database to: (1) provide an overview of LSV/GC fatal crashes in the United States, (2) discuss characteristics of LSV/GC fatal crashes, and (3) delineate significant contributing factors. A case study in a large LSV/GC community in Florida was conducted to identify current issues and recommend improvements on: (1) driving behavior of LSV/GC drivers, (2) LSV/GC-related traffic control devices, and (3) LSV/GC community planning. The findings can help national and state agencies make proactive decisions to enhance traffic safety and livability for LSV/GC communities. [ABSTRACT FROM AUTHOR]