Traffic Hazards Research Articles

Metaheuristic optimized complex-valued dilated recurrent neural network for attack detection in internet of vehicular communications

The Internet of Vehicles (IoV) is a specialized iteration of the Internet of Things (IoT) tailored to facilitate communication and connectivity among vehicles and their environment. It harnesses the power of advanced technologies such as cloud computing, wireless communication, and data analytics to seamlessly exchange real-time data among vehicles, road-side infrastructure, traffic management systems, and other entities. The primary objectives of this real-time data exchange include enhancing road safety, reducing traffic congestion, boosting traffic flow efficiency, and enriching the driving experience. Through the IoV, vehicles can share information about traffic conditions, weather forecasts, road hazards, and other relevant data, fostering smarter, safer, and more efficient transportation networks. Developing, implementing and maintaining sophisticated techniques for detecting attacks present significant challenges and costs, which might limit their deployment, especially in smaller settings or those with constrained resources. To overcome these drawbacks, this article outlines developing an innovative attack detection model for the IoV using advanced deep learning techniques. The model aims to enhance security in vehicular networks by efficiently identifying attacks. Initially, data is collected from online databases and subjected to an optimal feature extraction process. During this phase, the Enhanced Exploitation in Hybrid Leader-based Optimization (EEHLO) method is employed to select the optimal features. These features are utilized by a Complex-Valued Dilated Recurrent Neural Network (CV-DRNN) to detect attacks within vehicle networks accurately. The performance of this novel attack detection model is rigorously evaluated and compared with that of traditional models using a variety of metrics.

Traffic Hazards on Main Road’s Bridges: Real-Time Estimating and Managing the Overload Risk

Traffic Hazards on Main Road’s Bridges: Real-Time Estimating and Managing the Overload Risk

GC-YOLOv9: Innovative smart city traffic monitoring solution

In urban smart city environments, traffic hazards can lead to catastrophic outcomes, including significant property losses and severe threats to public safety. Conventional traffic monitoring systems are limited in terms of accuracy and speed, presenting significant challenges for real-time traffic surveillance. To tackle these challenges, this paper introduces the GC-YOLOv9 algorithm. Specifically, we have enhanced the YOLOv9 model by incorporating Ghost Convolution, markedly improving the model’s perceptual abilities and detection accuracy. Furthermore, this study designed an integrated smart city framework that includes layers for service applications, the Internet of Things, edge processing, and data centers. By deploying the enhanced YOLOv9 model within this framework, our method achieved mAP@0.5 scores of 77.15 and 74.95 on the BDD100K and Cityscapes datasets, respectively, surpassing existing technologies. Additionally, the potential applications of this method in public area fire safety management, forest fire monitoring, and intelligent security systems further underscore its significant value in improving the safety and efficiency of smart cities.

Income disparities and risk: Geographical manifestations of extreme inequities in the United States.

We examined hazard and risk-related metrics of the highest- and lowest-income counties and municipalities in each U.S. state. Indicators of natural and anthropogenic hazards, health outcomes, location of locally unwanted land uses, food insecurity, and other metrics were used to measure social and environmental justice. As expected, the highest-income places have better health outcomes, access to assets that protect health, and high municipal ratings of place quality compared with their poorest counterparts. Yet, they also have higher natural hazard risks and are more likely to live near concentrations of anthropogenic hazards. That is, high-income places have a lot to lose. Although the poorest jurisdictions demonstrate cumulative disadvantages, those in rural areas are exposed to less dense motor vehicle traffic and other hazards and risks associated with urban life. Relationships between income and the geography of hazards and risks are not simple. Even the highest-income areas face challenges. We suggest improvements in databases and tools to increase the focus on and monitoring of the breadth of risks people face in all areas.

Smart Traffic Management: Enhancing Urban Mobility through Predictive Analysis and AI-Driven Solutions

In urban environments, the issue of unauthorized parking in designated no-parking zones persists, leading to traffic congestion and safety hazards. inaccurate license plate recognition, License plate (LP) detection is a crucial task for Automatic License Plate Recognition (ALPR) systems. Most existing LP detection networks can detect License plates, but their accuracy suffers when license plates (LPs) are tilted or deformed due to perspective distortion. This leading to difficulties in identifying vehicle owners. To address this challenge, this project present TraceMe, a predictive system utilizing advanced machine learning algorithms. The system employs YOLOv8 for efficient object detection, focusing on identifying vehicles in no-parking zones, and Tesseract OCR for accurate license plate recognition. The extracted license plate information is then processed by a machine learning model trained to predict the owner of the vehicle. The proposed system involves collecting and annotating a diverse dataset, training YOLOv8 and LPRNet model for vehicle number plate detection, utilizing Tesseract OCR for license plate extraction, and implementing a machine learning model for owner identification. Real-time processing and integration with surveillance systems allow for immediate identification of unauthorized parking incidents. The system generates alerts or notifications, aiding law enforcement in enforcing parking regulations.TraceMe not only provides a technological solution to mitigate unauthorized parking but also contributes to improved traffic management and public safety.

Benefits, barriers and willingness to pay for bike-sharing service in tourism context

This study explores benefits, barriers and willingness to pay for bike-sharing service in tourism context. Based on a sample of 800 individuals who visited Da Nang, Vietnam between July and August 2023, trends in the barriers and benefits related to bike-sharing service from tourists’ point-of-view were explored. The results show that bike-sharing is appreciated for many reasons, notably for its fun/relaxing, cost saving, ease of city exploration, and promotion of better physical and mental health. However, bike-sharing services are considerably less likely to be viewed as options for faster transportation to a destination or reducing traffic hazards. Notably, eighty-six percent of non-riders indicated contentment with their existing transportation options and a lack of interest in bike-sharing services, a proportion significantly higher than any other group. Predictably, barriers related to the availability of bike-sharing and infrastructure, such as lack of sufficient number of shared bikes, far destination, and poor road conditions were notably more likely to be selected by one-time riders. The results are also evident that a significant portion of tourists is willing to pay to enhance their tourist experience with a bike-sharing service. On average, tourists were willing to pay $0.92 per hour (with a standard deviation of $0.24). This amount reflects the tourists’ recognition of the value added to their mode experience. These findings suggest that bike-sharing service play a significant role in fulfilling an essential transportation niche and have the potential to contribute to enhance tourists’ experience. Efforts aimed at addressing barriers associated with bike-sharing usage could further enhance their contribution to improve tourist satisfaction and boost attraction demand.

A risk-based quantitative approach for priority assessment of ageing bridges accounting for deterioration

The significant role of bridges in transportation networks requires continuous improvement of bridge management strategies, which has been even more exacerbated by the recent bridge collapses worldwide. This study proposes a framework for the prioritization and preliminary risk assessment of existing bridges subjected to seismic and traffic hazards. The proposed method adopts quantitative expressions to define risk in a simplified manner and, consequently, the priority associated with a certain bridge within a bridge stock. The three primary components of risk are considered: vulnerability, exposure, and hazard, which are specified according to the characteristics of the bridge, site, and road network. The bridge state of conservation is accounted for in the vulnerability component through average and critical defect condition indicators. The proposed method is implemented in a case-study of thirty real bridges in northern Italy. The impact of defects on vulnerability and priority ranking of bridges, the assumptions for defects condition and each subcomponent’s contribution to risk classification are discussed. The prioritization results are presented in absolute and normalized formats, with the latter providing a clearer view of the bridges’ relative priority within the stock. Finally, the proposed method’s findings are compared with the current Italian qualitative guidelines for bridge management.

Automated pavement marking integrity assessment using a UAV platform – a test case of public parking

ABSTRACT Intact pavement markings organize the utilization of the pavement surface area, increase the road capacity, and improve traffic safety. However, deterioration occurring over the service life will lead to traffic safety hazards. A detailed inspection is regarded as the central tenet of pavement marking management. Conventional manual assessment approaches are time-consuming, qualitative, and argued to be subjective. Thus, cost reduction, quantitative analysis, and automatization have been the focus of research. Unmanned Aerial Vehicle (UAV) platform has been studied wildly in civil tasks due to its low cost and high maneuverability. A UAV-based platform was developed in this research to address the inefficiency, access limitations, and image processing of existing road marking inspection systems. In this research, the resolution of the image is 5472 × 3080 pixels and is captured with a fixed focal length of 8.8 mm. Given intensity differences between pixels of paintings and asphalt backgrounds, the pixels belonging to painting regions are classified using the K-mean clustering algorithm. After detecting all parking lines from images, the Otsu method was employed to determine the threshold of 0.9 as the quantitative indicator of painting quality. The UAV-based platform exhibits expected performance in assessing the pavement markings in a parking area. Future research will explore the deployment in city-scale applications.

Safety analysis of pedestrians distracted by mobile phones at street crossings: Field study in Nanjing

The growing public concern over traffic safety hazards caused by pedestrians’ distracted behavior, particularly related to mobile phone usage at pedestrian crossings. Through video recording of pedestrians’ street-crossing behaviors on 12 sidewalks across 9 urban road intersections in Nanjing city, 1778 valid pedestrian samples were collected. The study categorizes mobile phone use during the crossing into five distinct types: no use, voice call, screen gaze, screen gaze with operation, and listening to music with headphones. Then, the effects of gender, age and companion conditions on the mobile phone during the crossing were examined by chi-square tests. Utilizing binomial logistic and Relogit regression models, the study analyzed the impact and safety risks of distraction on crossing behavior. Additionally, a random parameters (RP) logit model with heterogeneity in means was used to investigate the determinants affecting mobile phone usage. Notably, individuals aged 18 to 30 years were identified as the random parameter, while factors such as being under 18 years old, having a companion without interaction, having a companion with interaction, weekdays, sidewalk length, and the total number of other pedestrians were identified as fixed parameters. The study also presented significant variables affecting the probability of mobile phone usage through marginal effects, highlighting the potential safety risks associated with mobile phone usage during street crossing. These findings emphasize the need for heightened pedestrian safety awareness and a reduction in distracted behaviors to enhance overall traffic safety.

Driver Abnormal Expression Detection Method Based on Improved Lightweight YOLOv5

The rapid advancement of intelligent assisted driving technology has significantly enhanced transportation convenience in society and contributed to the mitigation of traffic safety hazards. Addressing the potential for drivers to experience abnormal physical conditions during the driving process, an enhanced lightweight network model based on YOLOv5 for detecting abnormal facial expressions of drivers is proposed in this paper. Initially, the lightweighting of the YOLOv5 backbone network is achieved by integrating the FasterNet Block, a lightweight module from the FasterNet network, with the C3 module in the main network. This combination forms the C3-faster module. Subsequently, the original convolutional modules in the YOLOv5 model are replaced with the improved GSConvns module to reduce computational load. Building upon the GSConvns module, the VoV-GSCSP module is constructed to ensure the lightweighting of the neck network while maintaining detection accuracy. Finally, channel pruning and fine-tuning operations are applied to the entire model. Channel pruning involves removing channels with minimal impact on output results, further reducing the model’s computational load, parameters, and size. The fine-tuning operation compensates for any potential loss in detection accuracy. Experimental results demonstrate that the proposed model achieves a substantial reduction in both parameter count and computational load while maintaining a high detection accuracy of 84.5%. The improved model has a compact size of only 4.6 MB, making it more conducive to the efficient operation of onboard computers.

Joint resource allocation and security redundancy for autonomous driving based on deep reinforcement learning algorithm

AbstractAutonomous vehicles navigating urban roads require technology that combines low latency with high computing power. The limited resources of the vehicle itself compel it to offload task requirements to edge server (ES) for processing assistance. However, as the number of vehicles continues to increase, how edge servers reasonably allocate limited resources to autonomous vehicles becomes critical to the success of urban intelligent transportation services. This paper establishes an urban road scenario with multiple autonomous vehicles and an edge computing server and considers two main driving behaviour transition resource requests, namely car‐following behaviour requests and lane‐changing behaviour requests. Simultaneously, acknowledging that vehicles may encounter unforeseen traffic hazards when switching driving behaviours, a safety redundancy setting strategy is employed to allocate additional resources to the vehicle to ensure safety and model the vehicle resource allocation problem in the autonomous driving system. Double‐deep Q‐network (DDQN) is then used to solve this model and maximize the total system utility by comprehensively considering resource costs, system revenue, and autonomous vehicle safety. Finally, results from the simulation experiment indicate that the proposed dynamic resource allocation scheme, based on deep reinforcement learning for autonomous vehicles under edge computing, not only greatly improves the system's benefits and reduces processing delays compared to traditional greedy algorithms and value iteration, but also effectively ensures security.

Optimization of hybrid platform for high-tech equipment and building vibration mitigation using evolutionary algorithms

In this paper, an ideal fuzzy logic control (FLC) algorithm is designed for micro vibration control of a hybrid platform which is placed on a second floor of the building subjected to traffic-induced ground vibrations. A hybrid platform is one that can be utilized to safeguard high precision equipment from vibrations caused by traffic, machinery, and natural hazards like earthquakes while it is situated with in the building structure. A fuzzy system is employed to obtain the appropriate control force of active actuators, and the fuzzy system is then optimized using four evolutionary algorithms (EAs), which are population-based metaheuristic algorithms that were inspired by the nature. A microgenetic algorithm (µ-GA), particle swarm optimization (PSO), differential evolutionary algorithm (DEA), and cuckoo search algorithm (CSA) are used to optimize the FLC knowledge base and rule base. To illustrate the capabilities of the proposed method in traffic-induced vibration control of a hybrid platform, a three-degree-of freedom (3-DOF) structure along with the platform is taken into account. Finally, utilizing the Bolt, Beranek and Newman (BBN)-vibration criteria and the absolute velocity of hybrid platform as the objective, the efficiency of the hybrid platform microvibration control is evaluated. The simulation results obtained from these novel EAs indicated that the actively controlled platform is efficacious in reducing microvibration of high-tech equipment. Based on the results, the PSO algorithm optimized fuzzy controller outperforms the other methods in terms of velocity levels of second floor of the building and hybrid platform.

Prediction of the severity of exceeding design traffic loads on highway bridges

Being a driver of failure consequences, forecasting the severity of events where design traffic load limits on bridges have been exceeded (DLEEs) is fundamental for road safety. Previous research has focused on estimating failure consequences by direct and indirect cost metrics. Only recently has research assessed severity unconventionally, in which the type of DLEEs was predicted by applying econometric models through Binomial Logistic Regression (BLR). Since machine learning models using Artificial Neural Networks (ANN) have not yet been explored, this study will enhance the literature as follows. First, two different ‘severity’ models were set up as a function of bridge-side, temporal-context, and traffic load hazard variables. Whilst the former relied on a BLR, the latter used an ANN. Second, the performance of these models was assessed using confusion matrixes, some performance indicators, and a cross-entropy parameter. Raw Weigh-In-Motion data on 7.4 M+ individual vehicle transits on a bridge along a primary roadway in Brescia (Italy) were processed. Although a similarly strong performance was achieved for BLR and ANN, the results indicated that ANN was able to predict severity records with a higher level of confidence than BLR on the case study dataset, with the cross-entropy of the ANN less than one third of that of the BLR. These analyses can support road authority traffic management to safeguard bridges from traffic load hazards. Finally, this study recommends future developments, such as considering the structural effects of traffic loads in the modelling, prioritizing traffic management actions among bridges at network level, and exploring the impact of ANN models in risk assessment.

Ranking Road Safety Hazardous Location: A Case Study of Chhorepatan–Machhapuchhre Viewpoint Road Section

The importance of road safety, involving pedestrians, cyclists, motorcyclists, and drivers adhering to rules and strategies to prevent accidents and fatalities, is emphasized, especially in Nepal, where road fatalities remain a pressing issue. The study focuses on the Chhorepatan – Machhapuchhre Viewpoint Road Section, which faces increasing traffic density and hazards. It proposes a six-stage methodological framework, incorporating the Analytical Hierarchy Process (AHP) and field surveys, to rank hazardous locations based on safety parameters, resulting in a Safety Hazardous Index (SHI). The research aims to identify and prioritize key safety factors by correlating SHI values with crash records, potentially serving as a model for assessing road sections in similar conditions.

Electromagnetic radiation from electrically driven vehicles

The problem of electromagnetic radiation of vehicles exists and worries people, because it is not clear what is the safe level of frequency, intensity, time spent by drivers and passengers in vehicles, etc. The article is devoted to the disclosure of the problems of adverse effects of electromagnetic fields (EMF) and electromagnetic radiation (EMR) of modern hybrid and electric vehicles on drivers, passengers, pedestrians and the environment. The purpose of the study was to investigate the process of occurrence, propagation and impact of negative functions of electromagnetic radiation on biological objects, their propagation speed, flicker, wavelength and frequency, and radiation devices in electric and hybrid vehicles. The methodology of the article is based on the results of research by scientists from around the world and our own research and experiments, the results of which are presented in this article. The results of the scientific work are reviewed from researchers on this topic and their own tests are described The authors point out the need to measure electromagnetic radiation at the stages of their operation, repair and manufacture, since these vehicles running on electric traction generate magnetic fields that can violate electromagnetic safety. This is because electric vehicles have power plants, sensors, control, information and communication systems. Electric currents flowing through electric motors, power circuits, and batteries while driving generate magnetic fields (MFs) in the low frequency ranges (ultra low frequency (ULF), 0.001 - 10 Hz; extremely low frequency (ELF), 10 - 300 Hz).Higher harmonics of the electric field in electric vehicles and hybrid vehicles are generated by various electronic devices on board, information and communication systems. For example, in hybrid vehicles, magnetic pulses of up to 5 kHz are generated when the internal combustion engine is switched to electric mode. In addition, all types of cars generate a low-frequency pulsating magnetic field when the steel wheel disks rotate. The frequency f of this field is determined by the wheel speed and is usually f 20 Hz, but the spectrum also contains harmonics. The scientific novelty is that for the first time the concept of modeling and optimization of electromagnetic hazard assessment of electric vehicles and hybrid vehicles is presented, which has a unified approach to the study of the magnetic field regardless of the structure and design schemes of their power plants at the stage of operation; for the first time methods for assessing important parameters of the magnetic field of electric vehicles and hybrid vehicles at the stage of operation are developed based on the concept of optimal control of the power plant depending on the operating conditions. The practical significance is the originality, which lies in the fact that the authors have considered the problems of determining the moment of occurrence of traffic hazards in various road and transport situations and the technical hazards of various types of electric vehicles and hybrid vehicles.

Effects of smartwatch usage on Children’s street-crossing behavior and visual attention allocation: An experimental study on street crossing in a real road environment

The use of smartwatches is facilitating the lives of children. However, smartwatch usage while crossing a street may lead to distractions and traffic safety hazards. The purpose of this study was to investigate the effects of smartwatch usage on the street-crossing behavior and visual strategies of children. Twenty-two children were recruited, and 220 street-crossing experiments were performed in real road environments, including at signalized and unsignalized intersections. ANOVA and Kruskal–Wallis H test calibrations were used to analyze the street-crossing behavior between the control group (who performed a task without smartwatch distraction) and the experimental group (who performed four different tasks with smartwatch distraction), and to visualize and compare the visual behavior data of children crossing the street. The results showed that children wearing smartwatches crossed the street more slowly and had a relatively narrow range of visual search, focusing on only a small area of the zebra crossing around them and the crosswalk area within their sight, whereas children who did not wear smartwatches additionally focused on areas with dangerous oncoming traffic on the left and right sides. Children wearing smartwatches were under various mental workloads and distraction levels as they performed the various distracting tasks, with listening to stories being the least distracting and least intensive, followed by answering phone calls and viewing images; a simple arithmetic operation was the most distracting and intensive task. Children while performing the distracting tasks showed a lower street-crossing speed and scanned left and right fewer times; viewing images and performing the simple arithmetic task also increased their fixation time on the smartwatches, further affecting their street-crossing behavior and thus increasing the risk of collision. At signalized intersections, children paid more attention to hazardous oncoming traffic areas and exhibited cautious crossing behaviors compared with that at unsignalized intersections. This study elucidated the characteristics of the effects of smartwatch usage on the street-crossing behavior of children and the influence pattern of the visual attention allocation and revealed the hazards of smartwatch usage on the street-crossing safety. It can contribute to improving the safety cognition of children distracted by smartwatch usage and reducing the occurrence of distracting behaviors while crossing streets. The results can provide a theoretical basis for scientific interventions and safety improvement measures.

The Effect of AR-HUD Takeover Assistance Types on Driver Situation Awareness in Highly Automated Driving: A 360-Degree Panorama Experiment

Human-machine co-driving presents a significant hurdle in automated driving system. The takeover process in automated driving system involves complex human factors, failure to takeover the vehicle and control driving behavior during the takeover process may lead to severe traffic safety hazards. An augmented reality head-up display (AR-HUD) takeover assistance information can provide real-time assistance information to the driving environment, enhancing drivers’ situation awareness (SA) and takeover decisions in highly automated driving system. This study investigated the impact of different AR-HUD types of takeover assistance information display. Three AR-HUD types, corresponding to the three pre-takeover behavioral processes (perception, understanding, and prediction), were evaluated: PSR (assistance in perceiving the source of risk), AS (assistance in analyzing situations), and MD (assistance in making decisions). The baseline (without assistance information) was used as the control group. In a driving simulation experiment using 360° panoramic video, seventy-nine participants performed SA assessment and visual tracking tasks. Questionnaire and eye-tracking data indicated that the type of AR-HUD displayed positively influenced drivers’ SA and takeover decisions, with AS being the most effective in enhancing SA and improving takeover performance. Additionally, this study compared the differences between the three types of AR-HUD and the baseline under two takeover request lead times (TORlt) of 5 seconds and 7 seconds. It was found that drivers’ SA was lower when TORlt was shorter (with the corresponding AR-HUD display also being shorter). This study provides insight concerning the impact of various types of AR-HUD takeover assistance information display and TORlt on driving safety. The findings support the further optimization of AR-HUD takeover assistance information design.

O.5.1-5 Influence of the parental perception of the neighborhood environment on physical activity and active school commuting in Ecuadorian children

Abstract Purpose Physical inactivity among children is considered a multidimensional problem. This study examines the relationships between parents' perceptions of the neighborhood environment with physical activity (PA) and active school commuting among urban children from Cuenca-Ecuador. Methods A cross-sectional study was conducted between October 2018 and March 2019 among 1028 children (8 to 12 years old) and their parents/guardians. Children's PA levels were objectively measured using accelerometers (Actigraph). Parent's perceptions of their neighborhood aesthetics, traffic, and crime hazards were assessed using the Neighborhood Environment Walkability Scale for Youth (NEWS-Y) parent version. Active school commuting (ASC) was evaluated using a validated tool applied to the children. Children who reported walking or using a bicycle to commute to and from school five days a week were classified as active commuters. Multivariate ordinary least squares and logistic regressions models were performed to determine associations between parents' perceptions of the neighborhood environment with PA and ASC, respectively. Results Complete data were available from 711 children (52% female) and 521 parents. The parental perceptions of the neighborhood aesthetics (Mean: 2.68; DE:0.75) and traffic hazards (Mean: 2.44; DE:0.44) were not associated with children's physical activity. Neighborhood crime safety (Mean:2.64; DE:0.74) score was negatively associated with the children's vigorous physical activity (Mean:14.50 minutes/day; DE:8.66) level (β = -1.52 minutes/day; DE = 0.52; p < 0.004). After adjusting for walking distance and car ownership, parental perception of neighborhood aesthetics, traffic, and crime hazards were not associated with ASC. Conclusions Crime hazard was identified as the main barrier to children's vigorous PA. The neighborhood environment seems not to be associated with children's ASC. Future studies should identify the determinants for ASC, and comprehensive policies are urgently needed to overcome crime hazards in Ecuador Funding source Universidad de Cuenca, Universidad Politécnica Salesiana Sede Cuenca.

Trauma Care

90% growth of technology and infrastructure has made our lives easier. The advent of technology increased the traffic hazards and the road accidents take place frequently which cause huge loss and property because of poor emergency facilities. We all have heard about the refusal on the part of private hospitals in Kollam and Trivandrum to admit an accident victim resulted in his tragic death, almost 7 hours after the accident. This project will provide an optimum solution to this drawback. This system will link all the hospitals in a locality. It will provide the current status about the blood banks, ICU, ventilator and casualty services of all hospitals, We can know whether a bed is available in an ICU or not. If an accident occurs in a particular area one can take a photograph and can inform the hospitals about the condition of the patient such that the hospitals can take immediate actions. Through the emergency button available in the system, the users can inform authorities on accidents occurred

Road safety hazards for children while commuting to school: Findings from a pilot study in Karachi, Pakistan

IntroductionRoad traffic injuries are a leading cause of mortality and morbidity among children. Travelling to and from school is a major risk exposure for children around the globe. ObjectiveThe purpose of this study was to assess road traffic injury hazards for school children during dropp-off or picked-up times. MethodsThis observational cross-sectional study included 94 public and private schools in Karachi, Pakistan. A structured observational tool was used to collect data on school demographics, the road traffic environment, infrastructure, injury hazards in vehicles used by school children, and child pedestrian injury risk and road use behaviors. ResultsA total of 860 observations of school children, drivers of vehicles transporting children, schools, and vehicles were recorded. Most schools (n = 83, 88%) did not have designated parking spaces around the school; only one public school had a parking area. Only one private school had a zebra crossing around the school premises. Very few schools (n = 13, 14%), mostly private (n = 12) had pedestrian sidewalks. Only 35 (18%) adult motorcyclists, out of 199, were wearing a helmet, and eight (6%), out of 145, car passengers were wearing seatbelts. Compressed natural gas (CNG) cylinders were installed in 83 (35%), out of 235, observed vehicles. The remaining 152 (65%) did not have CNG cylinders or they were not visible to our data collectors. In 55 (23%) observations, bus passengers stepped off the bus in the middle of the road. Most pedestrians (n = 266, 99.5%) did not use a Zebra crossing. More than a quarter (n = 74, 28%) of pedestrians looked left and right before crossing the road. ConclusionWhile traveling to school, either by walking or taking vehicular trips, children face many road traffic injury hazards in Karachi. Pedestrians and passengers exhibited risky behaviors while using roads. Further initiatives are advised from a public health viewpoint aiming at minimizing transport-related hazards.