Pedestrian Vehicle Research Articles

Analysis of Pedestrian–Vehicle Interaction Dynamics at Signalized Urban Intersections: A Surrogate Safety Measure Approach

Increased pedestrian–vehicle interactions, especially at signalized intersection crosswalks, have resulted in more pedestrian fatalities, and this situation worsens under heterogeneous traffic conditions. Therefore, evaluating pedestrian road-crossing safety is crucial, especially at signalized intersection crosswalks. This article aims to study pedestrian crossing safety at signalized intersection crosswalks based on the safety margin. Video graphic data were collected from eight signalized intersections. Data were extracted, and significant variables influencing the pedestrian safety margin (PSM) were identified. A random-intercept multiple linear regression model for predicting the PSM was estimated. Data included site, demographic, behavioral, exposure, the state of the crossing (distraction), glance, and traffic characteristics as variables. The model results revealed that pedestrians used the largest safety margin against heavy vehicles while crossing in a group. Pedestrians’ crossing speed has a significant influence on the PSM. It was also found that pedestrians showed the highest level of caution (used the largest safety margin) against right-turning vehicles compared with through vehicles. The glance variable estimates revealed that the “during-crossing glance” is an important determinant of the PSM, while the “before-crossing glance” is insignificant. In addition, unsafe pedestrian crossing behavior concerning distractors highlighted that the use of headphones significantly influences the PSM. The results of the current PSM model could aid in the efficient design of future pedestrian infrastructure and suggest appropriate remedial measures for existing intersection crosswalks to enhance pedestrian safety.

Investigating streetscape environmental characteristics associated with road traffic crashes using street view imagery and computer vision

Examining the relationship between streetscape features and road traffic crashes is vital for enhancing roadway safety. Traditional field surveys are often inefficient and lack comprehensive spatial coverage. Leveraging street view images (SVIs) and deep learning techniques provides a cost-effective alternative for extracting streetscape features. However, prior studies often rely solely on semantic segmentation, overlooking distinctions in feature shapes and contours. This study addresses these limitations by combining semantic segmentation and object detection networks to comprehensively measure streetscape features from Baidu SVIs. Semantic segmentation identifies pixel-level proportions of features such as roads, sidewalks, buildings, fences, trees, and grass, while object detection captures discrete elements like vehicles, pedestrians, and traffic lights. Zero-inflated negative binomial regression models are employed to analyze the impact of these features on three crash types: vehicle-vehicle (VCV), vehicle–pedestrian (VCP), and single-vehicle crashes (SVC). Results show that incorporating streetscape features from combined deep learning methods significantly improves crash prediction. Vehicles have a significant impact on VCV and SVC crashes, whereas pedestrians predominantly affect VCP crashes. Road surfaces, sidewalks, and plants are associated with increased crash risks, while buildings and trees correlate with reduced vehicle crash frequencies. This study highlights the advantages of integrating semantic segmentation and object detection for streetscape analysis and underscores the critical role of environmental characteristics in road traffic crashes. The findings provide actionable insights for urban planning and traffic safety strategies.

Graph Convolution Network Combining Spatiotemporal Self-Attention and Mutual Information for Pedestrian-Trajectory Prediction

Accurate pedestrian-trajectory prediction is significant and challenging for intelligent driving. There are two challenges in this field: most existing methods ignore pedestrian–vehicle interaction in the multiple pedestrian and vehicle scenario; and research on interaction modes and objects during the interaction process remains relatively limited in depth and scope. To tackle these challenges, a novel graph convolution network combining spatiotemporal self-attention and mutual information (STSAMI-GCN) is designed for predicting pedestrian trajectories. First, to model the spatiotemporal interaction between pedestrians and vehicles, we build a spatiotemporal graph interaction module that integrates graph convolutional networks and self-attention mechanisms. Second, to filter redundant interactions and optimize feature extraction in the spatiotemporal graph interaction module, a mutual information graph network module is designed as an auxiliary network, which can reduce information redundancy and simplify graph complexity. In addition, to address the problem of propagation and accumulation of prediction errors, we design a trajectory decoder to generate future trajectories. Finally, a suitable dataset for our research scenario is built to validate the proposed model. We also evaluate it on two publicly available datasets. Experimental results show that, in the multiple pedestrian and vehicle scenario, STSAMI-GCN can be used to predict the future trajectories of multiple pedestrians, and to predict socially acceptable future trajectories. Specifically, STSAMI-GCN achieves 0.25 and 0.27 average and final deviation error, respectively, and outperforms various mainstream methods. Furthermore, ablation studies demonstrate the effectiveness of the spatiotemporal dual self-attention mechanism and mutual information graph network in improving pedestrian-trajectory prediction accuracy.

Age-Friendly Urban Design for Older Pedestrian Road Safety: A Street Segment Level Analysis in Madrid

Walking benefits older pedestrians but exposes them to traffic crashes. With an aging population, designing age-friendly cities is crucial, yet research on older pedestrian safety at a micro-level is limited. This study aims to reduce older pedestrian–vehicle collisions and create more livable environments through infrastructure policies derived from statistical data analysis. Special attention is focused on collecting a holistic set of infrastructure variables to reflect most of the street built environment elements, which helps policymakers implement short-term safety measures. Using Bayesian Poisson regression, this study analyzes factors contributing to the occurrence of crashes involving older and non-older pedestrians on road segments in Madrid, Spain. The results indicate that different factors affect the occurrence of crashes for all pedestrians versus older pedestrians specifically. Traffic crashes involving all pedestrians are affected by leisure points of interest, bus stops, and crosswalk density. Older pedestrian traffic crashes are influenced by population density, the presence of trees and trash containers, and contour complexity. Proposed measures include relocating trees and trash containers, modifying bus stops, and adding crosswalks and traffic lights. This paper also shows that these countermeasures, aimed at creating age-friendly streets for older pedestrians, are not expected to worsen the road safety of other pedestrians.

Impact of Refuge Island in Two-Lane Roads on Pedestrian Crossing Behavior: A Virtual Reality Study

Crashes involving pedestrians are particularly common at unsignalized mid-block crosswalks, where, sometimes, vehicles do not yield to pedestrians. A refuge island (RI) is a countermeasure at mid-block crosswalks on two-lane roads to improve safety in pedestrian–vehicle conflicts. This study investigates the impact of RI at unsignalized mid-block crosswalks on pedestrian crossing behavior and conflict severity, considering vehicle approach speed and approach direction. A virtual reality experiment was designed to compare pedestrian crossing behaviors in different scenarios. Results revealed that at higher vehicle approaching speeds, pedestrians tended to accept shorter gaps, leading to a higher rate of unsafe crossings. Generally, in RI setting, pedestrians accepted significantly shorter gaps, which increases the operational efficiency of the crosswalk without significantly affecting the severity of conflicts with vehicles. Furthermore, the subjective evaluation of the pedestrians after the experiment clearly indicated their preference for the RI setting, as they reported a safer crossing experience.

Multiscale Analysis of Pedestrian Crossing Distance

Problem, research strategy, and findings Crossing distance is considered a factor in both pedestrian safety and behavior. However, it has seldom been quantified within or across entire cities or related to walking outcomes. Combining data encoded in OpenStreetMap and drawn from high-resolution satellite imagery, we measured formal pedestrian crossings throughout a dense European city (Paris [France]), a dense American city (San Francisco [CA]), and a less-dense, more car-centric American city (Irvine [CA]). This granular approach—covering roughly 49,000 total crossings—identified inter- and intraurban spatial patterns in the distribution of pedestrian crossing distance, including clusters of long crossings that likely deter walking and increase its risk. By overlaying recent pedestrian–vehicle collisions on these novel data sets we found that longer crossing distance correlated with increased likelihood of collisions, raising the salience of traffic-calming interventions. Takeaway for practice Quantifying pedestrian crossing distance at the scale of entire municipalities empowers transportation planners to identify pedestrian-hostile crossings (individuals and clusters), add context to collision trends, and geographically target locations for traffic calming. These cases collectively demonstrate how the increasing prioritization of the automobile in city planning quantitatively changes (and degrades) the pedestrian environment, as well as how low-tech investments, such as sidewalk extensions and refuge islands, can mitigate these trends.

Improved Pedestrian Vehicle Detection for Small Objects Based on Attention Mechanism

Abstract This study aims to solve the low detection accuracy and susceptibility to false detection and omission in pedestrian and vehicle detection by proposing an improved YOLOv5s algorithm. Firstly, a small target detection module is added to better acquire and determine the information of pedestrians from long-range vehicles. Secondly, the multi-scale channel attention CBAM attention module is added, and the dual attention mechanism is not only flexible and convenient, but also improves the computational efficiency. Finally, the MPDIoU loss function based on minimum point distance is introduced to replace the original GIoU loss function, and this change not only enhances the regression accuracy of the model. At the same time, the convergence speed of the model is accelerated. KITTI data set was used for experiments, and the experimental results showed that the average accuracy of the model trained by the improved YOLOv5s algorithm on the data set reached 84.9%, which was 3.7% higher than that of the original YOLOv5s algorithm. It is verified that the model is suitable for high accuracy of pedestrian and vehicle recognition in complex environments, and has high value for promotion.

Autonomous Vehicle–Pedestrian Interaction Modeling Platform: A Case Study in Four Major Cities

Autonomous Vehicle–Pedestrian Interaction Modeling Platform: A Case Study in Four Major Cities

Investigating LiDAR Sensor Accuracy for V2V and V2P Conflict Detection at Signalized Intersections

This paper examined the accuracy of six installed LiDAR sensors at six different signalized intersections in Trois-Rivières City, Quebec, Canada. At each intersection, the crucial leading and following movements that cause vehicle–vehicle (V2V) and vehicle–pedestrian (V2P) conflicts were identified, and the LiDAR results were compared to crash reports recorded by police, insurance companies, and other reliable resources. Furthermore, the intersection crash rates were calculated based on the daily entering vehicle traffic and the frequency of crashes at each intersection. Convolutional Neural Networks (CNNs) were utilized over 970,000 V2V and V2P conflicts based on the post encroachment time (PET) and time-to-collision (TTC) safety assessment measures. Bayesian models were used to assess the relationships between different intersection characteristics and the occurrence of conflicts, providing insights into the factors influencing V2V and V2P conflict occurrences. Additionally, a developed image-processing algorithm was utilized to examine the conflicts’ trajectories. The intersections’ crash rates indicated that safety considerations should be implemented at intersections #3, #6, #4, #1, #5, and #2, respectively. Additionally, intersections #6, #4, and #3 were the intersections with the highest rates of vehicle–pedestrian conflicts. Analysis revealed the intricate nature of vehicle and pedestrian interactions, demonstrating the potential of LiDAR sensors in discerning conflict-prone areas at intersections.

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.

Vehicle–Pedestrian Detection Method Based on Improved YOLOv8

The YOLO series of target detection networks are widely used in transportation targets due to the advantages of high detection accuracy and good real-time performance. However, it also has some limitations, such as poor detection in scenes with large-scale variations, a large number of computational resources being consumed, and occupation of more storage space. To address these issues, this study uses the YOLOv8n model as the benchmark and makes the following four improvements: (1) embedding the BiFormer attention mechanism in the Neck layer to capture the associations and dependencies between the features more efficiently; (2) adding a 160 × 160 small-scale target detection header in the Head layer of the network to enhance the pedestrian and motorcycle detection capability; (3) adopting a weighted bidirectional feature pyramid structure to enhance the feature fusion capability of the network; and (4) making WIoUv3 as a loss function to enhance the focus on common quality anchor frames. Based on the improvement strategies, the evaluation metrics of the model have improved significantly. Compared to the original YOLOv8n, the mAP reaches 95.9%, representing an increase of 4.7 percentage points, and the mAP50:95 reaches 74.5%, reflecting an improvement of 6.2 percentage points.

The Development of Modeling Shared Spaces to Support Sustainable Transport Systems: Introduction to the Integrated Pedestrian–Vehicle Model (IPVM)

The significance of developing shared road infrastructure in cities throughout the world is growing. Driven by the need to improve traffic management in ways that enhance multiple sustainability outcomes, developing the tools needed to test shared space proposals is becoming more sought after by responsible agencies. This paper reviews approaches to simulation modeling focused on representing and assessing shared spaces, culminating in a new approach presented here called the Integrated Pedestrian–Vehicle Model (IPVM)—a novel framework that combines social force models, car-following models and other algorithms from the robotics domain to better describe both mobility and activity within a shared space. The IPVM recognizes that while shared spaces are inherently multimodal, past efforts have tended to use pedestrian models as a starting point. Most consider the interaction of pedestrians with other pedestrians and static road infrastructure. Shared space models are generally microscopic models that integrate a social force model with a variety of car-following models to describe the interaction between vehicles and pedestrians. However, there is little research and few practical methodologies that address the long-range conflict avoidance between vehicles and pedestrians. This aspect is crucial for accurately representing the desire lines and pathways of pedestrians and active transport users in complex environments like shared spaces. The IPVM describes and visualizes shared road infrastructure with an absence of separating infrastructure between users and outputs. It generates metrics that can be used in conjunction with the latest evaluation approaches to gauge the sustainability credentials of shared space road proposals. Enhanced modeling of shared space solutions can lead to more effective implementation, which can potentially reduce the presence of cars, increase public and active transport use and lead to a more sustainable transport system.

Assessing Traffic Characteristics for Safe Pedestrian Crossings: Developing Warrants for Sustainable Urban Safety

The escalating urbanisation fuelled by population growth and economic expansion has triggered a notable surge in vehicular and pedestrian traffic, amplifying their interaction. Nonetheless, inadequate research, investment, and prioritisation have engendered inefficient pedestrian crossing infrastructures. This study endeavours to bridge this gap by crafting tailored warrants suited to Indian traffic dynamics, facilitating the implementation of pedestrian crossing facilities. Employing PV2 threshold value analysis, this study scrutinises pedestrian behavioural traits, such as gap acceptance and waiting time. Additionally, K-means clustering analysis delineates distinct levels of severity (LOSe), grounded in variables encompassing vehicular and pedestrian flow, gap acceptance, and waiting time. By establishing the nexus between vehicular volume and gap acceptance and vehicular volume and waiting time, a spectrum of PV2 threshold values is delineated. These LOS categories guide the selection of pedestrian facilities, ensuring secure pedestrian–vehicle interactions. Leveraging PV2 charts and vehicular volume assessments, our research identifies fitting pedestrian crossing infrastructures, thereby bolstering road safety for pedestrians and vehicles, underpinning sustainable urban mobility.

A Collision Avoidance Strategy Based on Entropy-Increasing Risk Perception in a Vehicle–Pedestrian-Integrated Reaction Space

Ensuring pedestrian safety is one of the most significant challenges for autonomous driving systems in urban scenarios due to the non-cooperative and unpredictable nature of pedestrian movements. To tackle this problem, firstly, we propose a collision avoidance strategy based on entropy-increasing risk perception in a vehicle–pedestrian reaction space. Our approach combines a limited range of reaction space regions with entropy to quantify the risk of pedestrian–vehicle collision. Then, multi-vehicle candidate trajectories are generated using the path and speed sequence method, and the uncertain states of pedestrians are predicted based on the social force model and Markov model accordingly. Finally, to determine the optimal collision avoidance trajectory, we use quantitative reaction-space entropy as a new “cost function” to measure potential risk and perform multi-objective trajectory optimization based on the elitist non-dominated-sorting genetic algorithm region-focused (NSGA-RF) approach. Simulation results show that our proposed strategy can enhance the safety of the planned trajectory interaction between vehicles and pedestrians for autonomous driving under normal and emergency conditions.

Is Elastic Stable Intramedullary Nailing a Viable Treatment Option for Diaphyseal Fractures of the Humerus in Adults? A 29-case Series Prospective Study.

Humerus shaft fractures are common in orthopaedic practice. The emphasis in treatment has shifted from prolonged immobilisation to early mobilisation and internal fixation when needed for a quicker return to normal function. Internal fixation methods include plate osteosynthesis and intramedullary nailing. This study specifically evaluated the effectiveness of flexible intramedullary nails in treating diaphyseal humeral fractures. Between April 2007 and January 2010, Alexandria University Hospital treated 29 patients (21 males, 8 females) with diaphyseal humeral fractures. Treatment involved closed reduction and percutaneous fixation using two flexible nails. Patients, aged 17 to 65 with a mean age of 32, were included if they did not have pathological fractures, were above 16 years old, and were medically suitable. Causes included pedestrian vehicle accidents (17 cases) and falls or sports-related activities (12 cases). Surgical intervention occurred 1 to 8 days after the injury. Between April 2007 and January 2010, Alexandria University Hospital treated 29 patients (21 males, 8 females) for diaphyseal humeral fractures using closed reduction and percutaneous fixation with two flexible nails. Patients, aged 17 to 65, were monitored for an average of 18 months. Radiographic assessments showed complete union in nineteen fractures within 13 to 21 weeks. One patient experienced non-union but was successfully treated with bone grafting and plating. There were no significant intraoperative complications. Notably, four patients with preoperative radial nerve injuries recovered within 6 to 8 weeks. Evaluation using the Stewart and Hundley Scoring technique showed excellent outcomes for 60% of patients, good outcomes for 30%, fair outcomes for 5%, and poor outcomes for 5%. 1.The Elastic Stable Intramedullary Nailing (ESIN) technique shows promise in the treatment of humeral shaft fractures. 2. However, the success of treatment may depend on various factors, including patient age, fracture characteristics, and the presence of complications such as open fractures and radial nerve palsy. 3. Careful consideration of these factors is necessary when selecting a treatment approach for humeral shaft fractures.

Unified strategy for cooperative optimization of pedestrian control patterns and signal timing plans at intersections

Pedestrian traffic management and control at intersections is crucial for ensuring traffic safety and efficiency while promoting green transportation development. Numerous studies have been conducted on optimizing signal timing of various pedestrian control patterns, such as exclusive pedestrian phases (EPPs) and leading pedestrian intervals (LPIs). However, cooperative optimization of these patterns and the corresponding signal timing is lacking. Hence, this study proposes a unified strategy for the cooperative optimization of pedestrian control patterns and signal timing plans to improve the efficiency and safety of pedestrian–vehicle mixed traffic flow. The existing control patterns, such as EPPs, LPIs, and two-way crossing (TWC), are unified. The safety and efficiency costs are monetized, and the minimization of average costs per traffic participant is taken as the optimization objective. Additionally, decision variables for diagonal crossing at intersections and pedestrian–vehicle priority are introduced to achieve cooperative optimization of the pedestrian control patterns and signal timing plans. The proposed model parameters were calibrated and validated using a real-world case study, and the applicable boundaries of different pedestrian control patterns under different pedestrian and vehicle flow scenarios were identified based on cost difference analysis. The results indicate that the vehicle turn ratio, average vehicle carrying rate, and unit cost ratio dynamically change the applicable boundaries. On average, the proposed method reduced the cost by 2.62% compared with separately optimized EPPs, LPIs, and TWC across various scenarios.

Pedestrians’ road-crossing decisions: Comparing different drift-diffusion models

The decision of whether to cross a road or wait for a car to pass, humans make frequently and effortlessly. Recently, the application of drift-diffusion models (DDMs) on pedestrians’ decision-making has proven useful in modelling crossing behaviour in pedestrian–vehicle interactions. These models consider binary decision-making as an incremental accumulation of noisy evidence over time until one of two choice thresholds (to cross or not) is reached. One open question is whether the assumption of a kinematics-dependent drift-diffusion process, which was made in previous pedestrian crossing DDMs, is justified, with DDM-parameters varying over time according to the developing traffic situation. It is currently unknown whether kinematics-dependent DDMs provide a better model fit than conventional DDMs, which are fitted per condition. Furthermore, previous DDMs have not considered reaction times for the not-crossing option. We address these issues by a novel experimental design combined with modelling. Experimentally, we use a 2-alternative-forced-choice paradigm, where participants view videos of approaching cars from a pedestrian’s perspective and respond whether they want to cross before the car or to wait until the car has passed. Using these data, we perform thorough model comparison between kinematics-dependent and condition-wise fitted DDMs. Our results demonstrate that condition-wise fitted DDMs can show better model fits than kinematics-dependent DDMs as reflected in the mean-squared-errors. The condition-wise fitted models need considerably more parameters, but in some cases still outperform kinematics-dependent DDMs in measures that penalize the parameter number (e.g., Akaike information criterion). Introducing a starting point bias provides support for the novel hypothesis of rapid early evidence build-up from the initial view of the vehicle distance. The drift rates obtained for the condition-wise fitted models align with the assumptions in the kinematics-dependent models, confirming that pedestrians’ decision processes are kinematics-dependent. However, the partial preference for condition-wise fitted models in the model selection suggests that the correct form of kinematics-dependence has not yet been identified for all DDM-parameters, indicating room for improvement of current pedestrian crossing DDMs. Developing more accurate models of human cognitive processes will likely facilitate autonomous vehicles to understand pedestrians’ intentions as well as to show unambiguous human-like behaviour in future traffic interactions with humans.

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.

EPIDEMIOLOGY AND PATTERN OF ORTHOPAEDIC INJURIES CAUSED BY DELIVERY MOTORBIKE ACCIDENTS (DMBA) AT A SOUTH AFRICAN TERTIARY HOSPITAL

Trauma patients present with different injuries and some of them require emergency surgical procedures. Orthopaedic department at Steve Biko Academic Hospital (SBAH) have seen an increasing number of patients presenting with serious orthopaedic injuries due to Delivery Motorbike Accidents (DMBA). The aim of the study was to establish the epidemiology and pattern of orthopaedic injuries sustained following Delivery Motorbike Accident.Motor Vehicle Accident (MVA) administration office at SBAH in emergency department was approached for patients registers used between 1 January 2020 and 31 December 2022. There patients were registered as Motor Vehicle Accident (MVA), Motorbike Accident (MBA) and Pedestrian Vehicle Accident (PVA) by the administration office. The details of patients classified as MBA were collected and used to obtain clinical data from medical and radiological records in the form of patient's files and PACS respectively.Approximately 240 patients presented to Emergency department with orthopaedic injuries following a motorbike accident. About 78 files could not be retrieved from the patient's records department. About 74patients had their occupation recorded as unknown or unemployed. Approximately 70 patients had their occupation indicated in the file by the Clinicians or Administration Clerks. About 40 patients had their occupation as delivery man or working for a delivery company. 16 patients did not have images on the PACS system. Most of the patients were males, presented after hours and sustained multiple injuries that were managed surgically. Very few patients had their helmet status indicated in the file.The study suggests that accidents due to delivery motorbikes are prevalent at SBAH and result in orthopaedic injuries. Almost 50% of patients who had their occupation indicated in the file were delivery employees. A follow up prospective study is recommended to ensure complete collection of data from patients at presentation to ED.

OUTCOMES OF OPEN TIBIA FRACTURES IN CHILDREN

Open tibia fractures are common injuries in our paediatric population and are often associated with high-energy trauma such as pedestrian-vehicle accidents. At our institution, these injuries are routinely treated with debridement and mono-lateral external fixation. The purpose of this study was to determine the outcome of open tibia fractures treated according to this protocol, as well as the complication rate and factors contributing to the development of complications.We performed a retrospective folder review of all patients with open tibia fractures that were treated according to our protocol from 2015–2019. Patients treated by other means, who received primary treatment elsewhere, and with insufficient data, were excluded. Data was collected on presenting demographics, injury characteristics, management, and clinical course. Complications were defined as pin tract infections, delayed- or non-union, malunion, growth arrest, and neurovascular injury. Appropriate statistical analysis was performed.One-hundred-and-fifteen fractures in 114 children (82 males) with a median age of 7 years (IQR 6–9) were included in the analysis. Pedestrian vehicle accidents (PVA's) accounted for 101 (88%) of fractures, and the tibial diaphysis was affected in 74 cases (64%). Fracture severity was equally distributed among the Gustillo-Anderson grades. The median Abbreviated Injury Score was 4 (IQR 4;5). Ninety-five fractures (83%) progressed to uneventful union within 7 weeks. Twenty patients (17%) developed complications, with delayed union and fracture site infections being the most common complications. Gustillo-Anderson Grade 3 fractures, an increased Abbreviated Injury Score, and the need for advanced wound closure techniques were risk factors for developing complications.Surgical debridement and external fixation in a simple mono-lateral frame is an effective treatment for open tibia fractures in children and good outcomes were seen in 83% of patients. More severe injuries requiring advanced wound closure were associated with the development of complications.