Safe Travel Research Articles

Cybersecurity in Aviation: A Case-Based Approach to Preparedness

With interconnected systems governing everything from flight management to passenger services, cybersecurity has emerged as a critical concern. This paper presents a comprehensive case-based review of cybersecurity preparedness in the aviation industry. Through an analysis of notable incidents such as data breaches, system outages, and cyber-attacks, the study explores the vulnerabilities inherent in aviation systems. It examines the impact of these incidents on passenger safety, operational continuity, regulatory compliance, and economic stability. Furthermore, the paper evaluates the effectiveness of existing cybersecurity measures and identifies areas for improvement. Drawing insights from real-world cases, the study aims to provide valuable lessons and recommendations to enhance cybersecurity preparedness in the aviation sector. By delving into the complexities of cybersecurity challenges faced by the industry, this review seeks to contribute to a deeper understanding of the evolving landscape of cybersecurity in aviation and the imperative for proactive measures to safeguard critical infrastructure and ensure the safety, security, and reliability of air travel in an increasingly digitalized world.

Physical Distance and Face Mask Wearing Surveillance System with Deep Learning

The COVID-19 pandemic has resulted in the world’s most critical global health catastrophe. To prevent the spread of COVID-19, people are encouraged to maintain 1 meter of physical distance and wear a face mask. However, many people refuse and forget to practice minimum physical distancing and wear their face masks. Besides, manual monitoring of physical distance and wearing face masks are impractical for a large population with insufficient manpower and resources. Hence, this project introduced a physical distance and face mask-wearing surveillance system utilizing deep learning at R&R Malaysia to ensure the safety of travelers during this COVID-19 pandemic. In this project, the system is implemented using the YOLOv4 algorithm to detect masked, non-masked, and incorrect mask-wearing faces and to calculate the physical distance between people. A total of 3,800 custom datasets were prepared to train the face mask detection model. As a result, this model achieved an average mAP of 95.86%, an F1-score of 0.93, and an average loss of 1.3972. The physical distancing detection model is employed on a pre-trained YOLOv4 algorithm to detect people. The Euclidean distance is calculated between the detected bounding boxes to compute the real distance between people.

An Integrated Risk Management Model for Performance Assessment of Airport Pavements: The Case of Istanbul Airport

Effective management of airport pavements is essential for maintaining safety and operational efficiency in air travel. An airport pavement management system (APMS) operates at two levels: the network level, which monitors overall pavement performance across the airport, and the project level, which conducts detailed inspections of individual pavements. However, pavement assessments are often costly and labor intensive, necessitating the development of cost-effective and practical models. This paper introduces the Airport Pavement Integrated Risk Management (APIRM) model, which integrates pavement condition assessment criteria with safety risk management (SRM) methodologies. The model was applied at Istanbul Airport. By using APIRM, airports can prioritize high-risk areas, optimizing resource allocation and enhancing safety. The model encourages coordination among various airport departments, offering a holistic approach to pavement management that integrates maintenance requirements with safety considerations.

The The influence of the grain noise on the recognition of baggage X-ray security check – a digital twin study

Artificial intelligence-assisted recognition of dangerous items at baggage inspection is essential for traveler safety. In our research, we used a digital twin of a suitcase with three dangerous items placed among ordinary items: a knife, a revolver, and a grenade. We aimed to investigate the impact of granular noise on the quality of recognizing dangerous objects, depending on the adopted color model. For our research, we used the YOLOv7 neural network, which was trained on images of individual color models undistorted by noise for 300 epochs. Test sets with different noise levels were then recognized by the trained network. The confusion matrix for the trained images from the digital twin revealed high-quality recognition of individual objects. Research into the influence of grain noise on the recognition of dangerous objects revealed poor resistance of colored images on a white background to such noise. The best results for recognizing dangerous objects were recorded for black-and-white negative images, which are commonly used in medical x-rays.

Research on Object Detection Based on YOLO and Its Application in Tactile Paving Recognition

The YOLO model, as an advanced real-time object detection algorithm, has made significant progress in the field of object detection since its inception. It achieves fast and accurate object detection by transforming the object detection task into a regression problem and using a single neural network to predict multiple bounding boxes and category probabilities. The YOLO model has significant advantages in processing speed, detection accuracy, and real-time performance, and is widely used in fields such as autonomous driving, intelligent monitoring, and medical image analysis. This paper adopts a tactile paving detection method based on YOLOv8 and CBAM attention mechanism, and improves the model's ability to extract tactile paving features by introducing CBAM attention mechanism and loss function. This method shows good adaptability when dealing with the tactile paving environment under different lighting and weather conditions, can effectively identify diverse obstacles, and provides a strong guarantee for the travel safety of the visually impaired. In conclusion, YOLO model has broad prospects in object detection and tactile paving detection. The tactile paving detection method based on YOLOv8 and CBAM attention mechanism adopted in this paper provides new ideas and methods for the development of tactile paving detection technology, and has important practical application value. In the future, with the continuous optimization of performance and technological innovation of YOLO series models, further improvement of hardware performance, and the fusion of YOLO and other sensor data to form a multimodal detection system, YOLO models are expected to play an important role in more practical application scenarios.

Alternate Ramp Merging Strategy for Connected Vehicles Based on Roadside Unit Support

Background: Uncooperative driving behavior in merging areas significantly increases traffic congestion and conflicts, resulting in reduced safety and comfort of vehicle travel. With the advancement of technologies for intelligent connected environments, vehicle-to-infrastructure (V2I) technologies have been implemented to enable real-time data exchange between vehicles and infrastructure. This exchange allows vehicles to make driving strategies and motion plans based on roadside information. Therefore, it becomes particularly important to develop effective merging strategies. Various methods, both patented and non-patented, have been proposed to solve the merging challenges, aiming to optimize data exchange and enhance the formulation of driving strategies. Objective: In this paper, a strategy is proposed using roadside information to guide vehicles alternately merging, aiming at solving the safety and traffic efficiency problems in the ramp merging area, which has two main objectives: guiding vehicles to merge smoothly and safely under high traffic flow to reduce vehicle conflicts and calculating their recommended velocity through connected information to minimize congestion. Methods: A roadside system was designed to guide vehicles to merge alternately. GPS information from connected vehicles is utilized to achieve cooperative perception and localization. A time-tomerge (TTM) algorithm was developed to optimize vehicle trajectories combined with the responsibility- sensitive-safety (RSS) theory, which assigns merge sequence and recommended velocity to each vehicle based on the actual traffic conditions. Results: Simulation results indicate that this merging strategy eliminates conflicts and suppresses the formation of deceleration waves compared to the first-in-first-out (FIFO) strategy. Compared to the natural merging of independently controlled vehicles, it results in a 33.71% increase in average vehicle velocity and a 14.68% reduction in average travel time through the merge area. Conclusion: This strategy improves driving safety and efficiency by enabling vehicles to reach a common understanding of the merging sequence, and the more congested the traffic, the more significant improvement in merging efficiency.

Development of an optimal route planning algorithm using hot zones

The authors in the article address the problem of planning optimal routes in a dynamic environment using OpenStreetMap (OSM) geographic data for the territory of Ukraine. Amid conflict situations and active hostilities, there is a need to adapt classical route-finding algorithms to account for specific terrain constraints. The main aim of the research is to develop an adaptive A* algorithm that considers high-risk areas ("hot zones") which can significantly impact the efficiency and safety of the calculated route. To achieve this goal, the authors conducted an analysis of existing shortest path search algorithms using the classic A* algorithm and its heuristic function, which considers the distance between route points. The study explores a modified version of the A* algorithm that includes an additional cost factor for "hot zones." The proposed approach introduces an adaptive multiplier that restricts route planning through dangerous areas. To validate the proposed method, OSM data containing only basic road information, without military restrictions, was used. This limitation highlights the importance of additional analysis of factors such as safety and risk in route planning. The results demonstrate the effectiveness of the developed adaptive A* algorithm in constructing routes in a dynamic environment, particularly with the ability to avoid dangerous ("hot") zones. The study's conclusions confirm that adding a cost factor in "hot zones" improves route accuracy and enhances travel safety. The developed algorithm can be applied to automated route planning in scenarios where it is crucial to consider variable risk factors and specific terrain conditions.

Experiences of Women who Faced Insecurity in Public Transport and its Impact on their Emotional Well-being: A Qualitative Study from Urban Area of Tamil Nadu, India

Introduction: Harassment in public transport faced by women passengers has been pushed to the sidelines failing to acknowledge the impact it has on both physical and mental health. Most incidents do not reach the authorities or the media, who often turn a blind eye to the existing problems. As a result, women develop a sense of insecurity that prevents them from using the public transport system. Women from economically challenged backgrounds often have no choice but to use public transport, as it is the cheapest and sometimes the only available mode of transportation. Aim: To understand the experiences of women passengers commuting by public transport in Chennai city, Tamil Nadu, and the incidents that led to their insecurity about using the public transport system. Materials and Methods: This qualitative study was conducted in the Department of Community Medicine, Sree Balaji Medical College and Hospital, Chennai, Tamil Nadu, India between November 2023 and March 2024 among women over 18 years of age who had been using public transport for daily transit for atleast six months. Qualitative data were collected through indepth interviews based on grounded theory among women who faced insecurity while traveling alone on public transport. Data saturation was reached after interviewing 20 participants. The transcribed data were analysed using Qualcoder software to identify relevant themes and subthemes. Results: In the current study, the majority of the women interviewed were between the ages of 18 and 50 years. Four major themes and 18 subthemes were identified through qualitative analysis. The four major themes were factors related to fear, social life, personal factors, and travel safety. Conclusion: The themes and subthemes highlight the gaps that have been overlooked by the authorities in the public transport system. It is crucial to understand that harassment occurring during transit, if not addressed immediately, will leave a lasting impression on women travelers, leading them to develop an irrational fear of crime that can have detrimental effects on their mental, physical, and social well-being. Further, research can be conducted using these themes, subthemes, and codes to develop a relevant research hypothesis that could be tested using quantitative study designs such as case-control and cohort studies.

Space cybersecurity challenges, mitigation techniques, anticipated readiness, and future directions

Space Cybersecurity (SC) is becoming critical due to the essential role of space in global critical infrastructure – enabling communication, safe air travel, maritime trade, weather monitoring, environmental surveillance, financial services, and defence systems. Simultaneously, involving diverse stakeholders in space operations further amplifies this criticality. Similarly, previous research has identified isolated vulnerabilities in SC and proposed individual solutions to mitigate them. While such studies have provided useful insights, they do not offer a comprehensive analysis of space cyber-attack vectors and a critical evaluation of the effectiveness of mitigation strategies. This study addresses this problem by holistically examining the scope of potential space cyber-attack vectors, encompassing the ground, space, user, cloud, communication channels, and supply chain segments. Furthermore, the study evaluates the effectiveness of legacy security controls and frameworks and outlines SC-vector-aligned counterstrategies and mitigation techniques to tackle the unique SC threats. Based on the analysis, the study proposes future research directions to develop and test advanced technological solutions and regulatory and operational frameworks to establish international standards policies and foster stakeholder collaboration. The study contributes a multi-disciplinary foundation and roadmap that researchers, technology developers, and decision-makers can draw on in shaping a robust and sustainable SC framework.

Simulation and application research of rubblization for aged cement concrete pavement

The long-serving old cement concrete pavement has suffered severe damage, affecting travel safety. When conducting rubblization construction to reconstruct the damaged pavement, it needs to consider the influence of dynamic compaction force on the original roadbed. This paper is based on vibration theory to analyze the resonance rubblization mechanism of elastic foundation slabs. Four types of constrained plate models have been established to study the frequency response characteristics of plates under different constraint conditions and the characteristics of subgrade response under dynamic compaction force. Based on actual engineering, the particle size distribution and strength mechanism of the broken layer have been revealed. The results show that the structure of the broken layer after resonance rubblization treatment exhibits obvious characteristics of a flexible base layer. When used as a base layer for overlaying, the broken layer shows an overall compressed state, which can better utilize the bending and tensile resistance of the overlaid asphalt surface layer, thus helping to prevent the occurrence of reflective cracks. The experimental results of this study can provide theoretical support and reference for subsequent resonance rubblization construction, ensuring the service quality of the asphalt pavement after rubblization and avoiding or delaying the occurrence of reflective crack diseases.

Human Perception of Collision and Safety in Densely Populated Airspaces

In the very near future, our skies may be occupied with various types of Unmanned Aerial Vehicles (UAVs) assigned with low-altitude missions. Given that uncrewed and crewed aircraft are expected to share the same airspace, addressing passenger perceptions of UAVs is crucial for maintaining confidence in air travel safety. Autonomous and remotely piloted UAVs operating in a human-integrated dense airspace must operate in a manner that is both safe and easily predictable by human observers. In this study, simulations of various UAV collision-avoidance scenarios were generated, which were then used to produce realistic video clips representing the scenarios. Human participants were then asked to rate the scenarios in terms of their perception of likelihood of collision and sense of safety. These ratings were used to assess the accuracy of human perception of near collisions and flight safety, both as a passenger inside an aircraft and as a ground observer. The results highlight some important trends in human perception of safety in the airspace. Human observers feel less safe when observing the airspace from the ground compared to being inside an aircraft. They also feel more unsafe in the presence of uncooperative aircraft in the airspace.

Survey and Research on Accessible Slow Walking Space in Fayuan Temple Historical Neighborhood of Xicheng District, Beijing, China

As society progresses and civilization develops, the construction of accessible environments is increasingly receiving widespread public attention. As an important part of urban space, optimizing the accessibility of slow-moving spaces to provide a more convenient, safe and comfortable travel environment for groups such as the elderly and people with travel disabilities has become an important initiative to promote friendly and inclusive development for the whole society. However, the barrier-free transformation of slow-moving spaces in historic districts faces multiple challenges, such as traffic congestion, spatial limitations, and landscape coordination. In order to analyze and identify the problems, and to better balance the protection of historic districts and modern accessibility construction, this project takes the Fayuan Temple Historic District in Xicheng District of Beijing as a case study, and comprehensively evaluates the accessibility environment of the district through various research methods such as experiential surveys, on-site surveys, in-depth interviews, and so on. The study found that: 1) the roads in the historic district are generally narrow and uneven; 2) there is a lack of necessary barrier-free facilities and parking spaces at the entrances and exits of the buildings; 3) there is a scarcity of barrier-free service facilities and they are not in harmony with the neighborhood's style; and 4) the willingness of the people with mobility impairments to travel is affected by the physical environment. This paper puts forward the corresponding policy suggestions for the above problems to provide useful references for the relevant government departments and related designs.

Pharmacological Innovations in Space: Challenges and Future Perspectives.

Since the first human experience in space, the interest in space research and medicine to explore universe is growing day by day. The extreme space conditions mainly radiation and microgravity effects on human physiology, antimicrobial susceptibility, and efficacy, safety, and stability of drugs. Therefore, the aim of this review is to address the impact of extreme space conditions, mainly microgravity and radiation, on human physiology and highlights the need for future approaches by evaluating the effectiveness of strategies to prevent or mitigate health problems. Published papers and NASA technical documents were searched in Pubmed and Google Scholar databases using the keywords ''antimicrobial susceptibility or drug resistance or drug stability or innovations or pharmacokinetic or pharmacodynamics'' and ''radiation or microgravity or space environments or space medicine or space pharmacy'' to prepare this review. In this review, the challenges regarding physiological effects and drug-related problems are examined through the evaluation of extreme conditions in space. Medications used in spaceflight are summarized, and the role of pharmacists specializing in space medicine is briefly explained. Last but not least, to overcome the aforementioned issues, novel approaches have been addressed, such as personalised treatments, development of space-resistant formulations and various microbial applications. Further research in the space medicine is required to facilitate the safe and healthy travel of humans to the Moon, Mars and other extraterrestrial destinations. One bear in mind that space research will contribute not only to the exploration of the universe, but also to the advancement of health and technological discoveries on Earth.

Analysis of emotions of online car-hailing drivers under different driving conditions and scenarios

Emotion is an important factor that affects driving behavior, and thus, drivers’ emotions are closely related to overall traffic safety. We investigated the emotional expressions of online car-hailing drivers under two driving conditions: with passenger(s) and without passenger(s). We recruited 16 male car-hailing drivers and collected a total of 91.5 h of data using non-contact equipment. We employed FaceReader 8.0 software to analyze the collected video data and extract the drivers’ facial expression information, thereby identifying six emotions expressed by the drivers. We then compared the frequency of the occurrence of the six emotions between the two conditions. The frequency rates indicate that the drivers exhibited more emotions when no passengers were in the vehicle. The chi-square test results indicate significant differences in the drivers’ emotions under the two conditions. For example, happiness is related to chatting with passengers. Also, the drivers exhibited more aggressive driving behavior during trips without passengers, and such behavior often was accompanied by negative emotions, such as anger. We also investigated drivers’ emotions under three scenarios that often occur while online car-hailing drivers are working: driver distractions, passenger interaction, and the traffic environment. The understanding of drivers’ emotions and the relationships between those emotions and scenarios that take place under different driving conditions can facilitate the identification of drivers’ intentions and provide guidance for the development of safe driving assistance warning systems. Dangerous driving behavior can be reduced through intervention and the monitoring of drivers’ emotions for the enhanced overall safety of roadway travel.

Optimization-based trajectory planning for reconfigurable unmanned vehicle units with multi-steering modes during the reconfiguration process

Reconfigurable unmanned vehicle unit (RUVU) exhibits three distinct steering modes: Ackermann steering, Diagonal steering, and In-Situ steering. This arises from its capacity for All-Wheel-Independent-Steering (AWIS) and All-Wheel-Independent-Driving (AWID). Given its unique motion characteristics, there is currently a lack of established methods to assist a RUVU in autonomously planning trajectories. In response to this deficiency, this paper proposes the Multi-Steering Modes Trajectory Planning algorithm (MSMTP) based on the framework of optimal control problem (OCP). The approach innovatively formulates the kinematics and Steering-Mode-Switch theory for vehicles with multi-steering modes. Additionally, a safe traveling corridor (STC) is implemented to guarantee non-conflicting interaction with the surrounding environment. Ultimately, the Bonmin solver is utilized to derive optimal results. Our findings demonstrate the effectiveness of the proposed algorithm in RUVU’s trajectory planning. When comparing the trajectory of RUVU to that of a vehicle only with Ackermann steering, the results also reveal that RUVU has the advantage of reaching its destination in a shorter time, especially in narrow environments.

Application of Artificial Intelligence Technology in New Energy Vehicles

Due to environmental pollution caused by excessive exploitation of world resources, countries are now advocating for green travel, which has led to the rapid development of the new energy vehicle industry. Compared to traditional cars, the safety, maneuverability, and convenience of new energy vehicles have made the application of artificial intelligence technology increasingly widespread in this field. Therefore, this article provides a systematic review and analysis of the application of artificial intelligence technology in new energy vehicles. Firstly, the article outlined the main application scenarios of artificial intelligence in the field of new energy vehicles, including autonomous driving, energy management optimization, vehicle intelligent interconnection, and predictive maintenance. On this basis, this paper analyzed the role of artificial intelligence technology in improving the performance, safety, and reliability of new energy vehicles, as well as the technological challenges and development trends it faces. For example, issues such as data privacy protection and real-time performance need to be further addressed. In the future, artificial intelligence and new energy vehicles will further integrate, making important contributions to achieving intelligent, green, and safe future travel.

Current State of Serious Games in Human Trafficking: Evaluation, Gaps, and Future Research Directions

Addressing human trafficking is crucial due to its severe impact on human rights, dignity, and well-being. Serious games refer to digital games that are designed to entertain while also accomplishing at least one additional objective, such as learning or health promotion. Serious games play a significant role in raising awareness, training professionals, fostering empathy, and advocating for policy improvements related to human trafficking. In this study, we systematically examine and assess the current landscape of serious games addressing human trafficking to unveil the existing state, pinpoint gaps, and propose future research avenues. Our investigation encompassed academic publications, gray literature, and commercial games related to human trafficking. Furthermore, we conducted a thorough review of evaluation criteria and heuristics for the comprehensive assessment of serious games. Subsequently, incorporating these evaluation metrics and heuristics, the games were subjected to evaluation by both players and experts. Following a combined qualitative and quantitative analysis, the results were deliberated upon, and their implications were expounded. Five serious games related to human trafficking were identified and evaluated using the SGES and EGameFlow scales, along with both game-specific and serious game heuristics. Player and expert evaluations ranked “(Un)TRAFFICKED” and “Missing” as the best-performing games, while “SAFE Travel” received the lowest ratings. Players generally rated the games higher than experts, particularly in usability, feedback, and goal clarity, although the games scored poorly in audiovisual quality and relevance. Experts highlighted deficiencies in motivation, challenge, and learning outcomes. The lack of personalization and the absence of social gaming elements point to the need for more targeted human trafficking games adapted to different demographics, cultures, and player types.

Enhancing Situational Awareness with VAS-Compass Net for the Recognition of Directional Vehicle Alert Sounds.

People with hearing impairments often face increased risks related to traffic accidents due to their reduced ability to perceive surrounding sounds. Given the cost and usage limitations of traditional hearing aids and cochlear implants, this study aims to develop a sound alert assistance system (SAAS) to enhance situational awareness and improve travel safety for people with hearing impairments. We proposed the VAS-Compass Net (Vehicle Alert Sound-Compass Net), which integrates three lightweight convolutional neural networks: EfficientNet-lite0, MobileNetV3-Small, and GhostNet. Through employing a fuzzy ranking ensemble technique, our proposed model can identify different categories of vehicle alert sounds and directions of sound sources on an edge computing device. The experimental dataset consisted of images derived from the sounds of approaching police cars, ambulances, fire trucks, and car horns from various directions. The audio signals were converted into spectrogram images and Mel-frequency cepstral coefficient images, and they were fused into a complete image using image stitching techniques. We successfully deployed our proposed model on a Raspberry Pi 5 microcomputer, paired with a customized smartwatch to realize an SAAS. Our experimental results demonstrated that VAS-Compass Net achieved an accuracy of 84.38% based on server-based computing and an accuracy of 83.01% based on edge computing. Our proposed SAAS has the potential to significantly enhance the situational awareness, alertness, and safety of people with hearing impairments on the road.

Flight in Transition: Navigating the Skies of Automation and Human Judgement

Integrating automation systems within the aviation sector signifies a critical juncture, presenting significant advancements in operational efficiency and safety protocols. However, this technological evolution mandates a comprehensive reassessment of decision-making processes, particularly as pilots navigate the intricate interaction between human cognition and automated support. This study aims to enhance academic discourse by conducting a thorough analysis of the implications of automation systems on decision-making in aviation, with a specific focus on the tragic incidents involving Lion Air Flight 610 on October 29, 2018, and Ethiopian Airlines Flight 302 on March 10, 2019. The impact of automation systems on decision-making in aviation will be examined in detail to understand their role in these incidents. Employing a rigorous case analysis methodology upon a diverse array of secondary sources, this study seeks to unravel the multifaceted dynamics at play and shed light on the influence of automation on pilot actions and responses. Anticipated outcomes of this study include providing invaluable guidance to aviation stakeholders, encompassing regulatory bodies, aircraft manufacturers, airlines, and pilots, concerning the challenges and opportunities inherent in automation integration. By identifying knowledge gaps and delineating avenues for enhancement, this research endeavours to inform evidence-based strategies and optimal practices for enhancing safety and resilience in contemporary air travel. Ultimately, the significance of this scholarly endeavour lies in its potential to advance scholarly understanding and facilitate informed decision-making processes, thereby contributing to the pursuit of safer and more efficient aviation operations on a global scale.

A Lightweight Blind Obstacle Detection Network for Mobile Side

Abstract. China has the longest and widest distribution of blind corridors in the world, but in many cities they are virtually non-existent, with all kinds of obstacles affecting the movement of the blind. Thus, guaranteeing safe traveling for the blind has become an increasingly important topic. Some existing assistive traveling devices for the blind have problems such as poor portability and low-cost performance. With the rise of the mobility era and the rapid development of deep learning technology, the target detection of blind obstacles on cell phones has become feasible. In this paper, we take the target detection model YOLOv8n as the base network, redesign the neck of the network, use GS convolution as the basis, adopt one-time aggregation and cross-channel branching to build a lightweight module, and use the CARAFE operator as the up-sampling method, and stack the lightweight module with CARAFE operator to build a new feature fusion layer, forming a lightweight feature-aware enhanced target detection network. The results show that the accuracy still reaches 99.5% of the original network under the premise that the model size is reduced by 1.2%, and the improved network achieves a balance between accuracy and lightweight.