Safe Travel Research Articles

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.

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.

Research on the Optimization Method of Bus Travel Safety Considering Drivers’ Risk Characteristics

Bus drivers have an important role in ensuring road safety, as their driving circumstances fluctuate due to the combined influence of physiological, psychological, and environmental dynamics, which can cause complex and varied driving dangers. Quantifying and assessing drivers’ risk characteristics under various scenarios, as well as finding the best fit with their work schedules, is critical for enhancing bus safety. This research first uses the entropy weight method, which is based on historical warning data, to examine the risk characteristics of bus drivers in various complicated contexts. It then creates an objective function targeted at minimizing the operational risk for a specific bus route. This function uses the quasi-Vogel approach and an improved simulated annealing algorithm to optimize and restructure the scheduling table, taking individual driver risk characteristics into account. Finally, the analysis is confirmed and examined with actual operational data from the Zhenjiang Bus Line 3. The data show that enhanced bus operations resulted in a 7.22% gain in overall safety and a 33.76% improvement in balancing levels. These insights provide valuable theoretical guidance as well as practical references for the safe operation and administration of public buses.

Changes in Travel Activities and Preferences in Gangwon Province, South Korea, Due to Social Distancing Measures during COVID-19

Social media data are increasingly used to assess public opinion dynamics and develop sustainable regional tourism policies. This study explored the changes in travel patterns and preferences in Gangwon Province before, during, and after the implementation of social distancing measures during the COVID-19 pandemic. Five hundred and twenty-six YouTube videos related to travel in Gangwon Province were collected using NodeXL, and content and statistical analyses were conducted on travel regions, main activities, and viewers’ reactions. The main findings indicated that as the intensity of social distancing measures increased, the activity of YouTube video creators also increased, particularly in the East Coast region, compared with other locations such as mountains, rivers, and traditional markets. Viewer engagement was the highest during the implementation of social distancing, showing a considerable interest in beach travel. These results have significant implications for planning safe travel during crises such as COVID-19 and for local governments to promote a responsible travel environment.

Intelligent Assisted Travel Wheelchair Based on Image Recognition Technology

This paper introduces an intelligent image recognition system integrated into a wheelchair based on deep learning in cold environments, aiming to improve the convenience and safety of disabled individuals. The system adopts advanced image recognition technology to monitor road conditions in real-time through the camera and to detect and measure distance to foreign objects on the road. The system visualizes the detection results on the wheelchair screen to assist the user in avoiding and improving the safety of their daily travel. In addition, the system also includes crawler tracks, seat heating, snow and rain protection, and other functions. The wheelchair has a wide range of application prospects and development potential. It is expected to be widely used in the future, providing a strong guarantee for the safe travel of disabled individuals in China.

Perceptions of Women’s Safety in Transient Environments and the Potential Role of AI in Enhancing Safety: An Inclusive Mobility Study in India

Travel safety for women is a concern, particularly in India, where gender-based violence and harassment are significant issues. This study examines how the perception of safety influences women’s travel behaviour and assesses the potential of technology solutions to ensure their safety. Additionally, it explores how AI and machine learning techniques may be leveraged to enhance women’s travel safety. A comprehensive mobility survey was designed to uncover the complex relationship between travel behaviour, reasons for mode choice, built environment, feelings, future mobility, and technological solutions. The responses revealed that security and safety are the most critical factors affecting women’s travel mode choices, with 54% and 41%, respectively. Moreover, over 80% of women indicated a willingness to change their travel behaviour after experiencing fear, anxiety, or danger during their everyday journeys. Participants were 24% less willing to use ride-sharing services than ride-hailing services, which could affect the transition towards more sustainable transportation options. Furthermore, AI-based sentiment analysis revealed that 46% of the respondents exhibited signs of ‘anger’ regarding what could help women feel safer in transient environments. The practical implications of this study’s findings are discussed, highlighting the potential of AI to enhance travel safety and optimise future sustainable transport planning.

Exploring intercity travel decision-making in a developing country: Insights from COVID-19 impacts in Iran

This study examines the impact of COVID-19 on intercity travel mode choices in Mazandaran province, Iran, addressing critical gaps in understanding how pandemics affect travel behavior in developing countries. The research focuses on how socio-economic factors, perceived health risks, and travel time influence individuals’ choice of transportation modes during the pandemic. Using a stated preference survey method with 669 participants, the study assessed how concerns about virus transmission and adherence to health protocols shape travel decisions. Discrete choice modeling was employed to predict travel mode shares between public transport and personal vehicles. The findings reveal that COVID-19 risk perception, socio-economic factors, and travel time significantly impact travel behavior. Specifically, heightened perceived risk of infection resulted in a 25% reduction in public transportation use, with individuals increasingly opting for personal vehicles. Additionally, strict adherence to health protocols, such as mask-wearing and vehicle cleaning, improved safety perception, leading to a 40% increase in confidence in public transport. The study also found that socio-economic factors like age, income, and education significantly shaped travel preferences. These insights provide valuable guidance for public health policymakers and transportation authorities to enhance the safety and management of intercity travel during ongoing and future pandemics.

Indang Konektayo Kiosk: An Interactive Menu System for Tourists

Every traveler wants to experience hassle-free and safe travel while maintaining an enjoyable journey with the help of user-friendly interfaces and an innovative technological menu system. An interactive information menu system that improves the traveler experience, commercial operations, and tourism information at the Municipality of Indang. It is a technological solution that is relevant to today's demand and interest, and that is readily available data sources and information, which benefit. Specifically, it possesses the following characteristics, including: (1) a place finder that provides registered places in Indang with the right information and allows users to discover new established places in Indang; (2) mapping routes that provide directions and generate routes; and (3) sending the Google Maps URL through a short messaging system (SMS) to the mobile phone of the user, which can be opened in digital maps such as Google Maps. The descriptive method of research was used in this study and was created with Cascading Style Sheet (CSS), Hypertext Markup Language (HTML), Hypertext Processor (PHP), JavaScript (JS) in web development, MySQL for its database, as well as an SMS API for sending map URLs based on the queries. The Agile software model was used, which was composed of sprints, each of which was meant to focus on the developmental adjustments as interactive kiosks were prone to updates and ran into serious changes. Using ISO 9126 software product quality standards, the system received an average rating of 4.71 from respondents, indicating excellent quality and that it served its functions properly and met all of the required requirements.

Aircraft Electrothermal Pulse Deicing

Abstract Ice formation and accumulation on aircraft is a major problem in aviation. Icing is directly responsible for aircraft incidents, limiting the safety of air travel and requiring expensive, and sometimes ineffective deicing strategies. Furthermore, electrification of aircraft platforms leads to difficulties with integration of legacy deicing methods such as pneumatic boots. In this work, we study electrothermal pulse deicing capable of efficient and rapid removal of ice from aircraft wings. The pulse approach enables the efficient melting of a thin (<100 μm) ice layer on the wing surface to limit parasitic heat losses. Only the interface is melted, with the rest of the ice sliding on the melt lubrication layer due to aerodynamic forces. To study pulse deicing, we developed a transient thermal-hydrodynamic numerical model that accounts for multiple phases and materials, specific and latent heating effects, melt layer hydrodynamics, as well as boundary layer effects. To identify optimal deicing strategies, we use our model to study the effects of heater thickness (50 μm < th < 1 mm), substrate electrical insulation thickness (10 μm < ti < 1 mm), pulse duration (0.4 s < Δtpulse < 4.5 s), and pulse energy. Optimum operating points are identified for large (Boeing-747), midsize (Embraer-E175), and small (Cessna-172) aircraft. The scale-dependent thermal-hydraulic model results are used to estimate input conditions required for deicing and integrated into an electrical model considering energy storage, power electronics, integration, and layout, to achieve overall volumetric and gravimetric power density optimization.

Travelling together: exploring the impact of support and service animals on travel experiences

ABSTRACT The phenomenon of multi-species families is increasingly linked to the growing awareness of the benefits of emotional support and service animals. As more people adopt various animals into their homes, it becomes crucial to understand how these family arrangements influence travel experiences. The presence of these animals during travel directly impacts the well-being and safety of travellers, contributing to the emotional and behavioural dynamics of multi-species families in transit. Consequently, the hospitality, tourism, and transportation industries must adapt to accommodate the growing number of tourists travelling with their animals, whether for work or leisure. The United States is the focus of this research due to its significant role in regulating emotional support and service animals, allowing for comparisons with the diverse regulations across European countries. This study aims to: (1) investigate current U.S. legislation regarding emotional support and service animals; (2) explore the challenges faced by owners of these animals during travel; and (3) highlight the practical experiences of owners travelling with emotional support and service animals. The methodology used is an exploratory qualitative review. The research highlights the experiences of Em and Jennifer, Brazilian students who travelled to the USA with their animals, illustrating the variations in travel experiences with emotional support and service animals. In conclusion, while acceptance and accommodation of these animals are evolving, particularly in the US hospitality industry and tourist attractions, challenges and a lack of understanding still exist in some establishments. This underscores the importance of studies like this to raise awareness and educate about the needs of families that rely on their animals. The increasing prevalence of multi-species families positively influences the availability of pet-friendly services, which can significantly impact travellers’ choice of accommodation when accompanied by their pets.

Trajectory Planning Design for Parallel Parking of Autonomous Ground Vehicles with Improved Safe Travel Corridor

In this paper, the concept of symmetry is utilized to design the trajectory planning for parallel parking of autonomous ground vehicles—that is, the construction and the solution of the optimization-based trajectory planning approach are symmetrical. Parking is the main factor that troubles most drivers for their daily driving travel, and it can even lead to traffic congestion in severe cases. With the rise of new intelligent and autonomous vehicles, automatic parking seems to have become a trend. Traditional geometric planning methods are less adaptable to parking scenarios, while the parking paths planned by graph search methods may only achieve local optimality. Additionally, significant computational time is often required by numerical optimization methods to find a parking path when a good initial solution is not available. This paper presents a hierarchical trajectory planning approach for high-quality parallel parking of autonomous ground vehicles. The approach begins with a graph search layer to roughly generate an initial solution, which is refined by a numerical optimization layer to produce a high-quality parallel parking trajectory. Considering the high dimensionality and difficulty of finding an optimal solution for the path planning optimization problem, this paper proposes an improved safe travel corridor (I-STC) with the construction of collision constraints isolated from surrounding environmental obstacles. By constructing collision constraints of the I-STC based on the initial solution, the proposed method avoids the complexities and non-differentiability of traditional obstacle avoidance constraints, and simplifies the problem modeling the subsequent numerical optimization process. The simulation results demonstrate that the I-STC is capable of generating parallel parking trajectories with both comfort and safety.

Fatal journeys: causes of death in international travellers in South America.

Understanding mortality among travellers is essential for mitigating risks and enhancing travel safety. However, limited evidence exists on severe illnesses and injuries leading to death among travellers, particularly in low- and middle-income countries and remote regions. We conducted a retrospective census study using country-level observational data from death certificates of travellers of seven South American countries (Argentina, Brazil, Chile, Colombia, Ecuador, Peru, and Uruguay) from 2017 to 2021. Causes of death were evaluated using ICD-10 codes, categorised into non-communicable diseases (NCDs), communicable diseases, and injuries. We quantified causes of death by demographic characteristics (e.g. age, sex), and geographical variables. Chi-square tests were used to assess differences between categories. We calculated crude mortality rates and incidence rate ratios (IRRs) per country's subregions. A total of 17 245 deaths were reported. NCDs (55%) were the most common cause of death, followed by communicable diseases (23.4%) and injuries (18.1%). NCD-associated deaths increased after age 55years and were highest among ≥85years. Communicable diseases were more common at younger age (<20years). Injury-associated deaths were more common in men (79.9%) and 25-29-year-olds (17.1%). Most deaths (68.2%) could have been avoided by prevention or treatment. Mortality risk was higher among travellers in bordering regions between countries. In Roraima [Brazil] and Norte de Santander [Colombia], locations bordering Venezuela, the death incidence rate ratio was 863 and 60, respectively. These countries' reference mortality rates in those regions were much lower. More than 80% of the deaths in these border regions of Brazil and Colombia involved Venezuelan citizens. Conclusion: The study identified risk factors and high-risk locations for deaths among travellers in seven countries of South America. Our findings underscore the need for specific health interventions tailored to traveller demographics and destination to optimise prevention of avoidable deaths in South America.

Enhancing autonomous pavement crack detection: Optimizing YOLOv5s algorithm with advanced deep learning techniques

To enhance the safety and comfort of vehicle travel, detecting pavement cracks is a critical task in road management. This article introduces an advanced single-stage target detection method utilizing the YOLOv5s algorithm to enhance real-time performance and accuracy. Initially, Squeeze-and-Excitation Networks are integrated into the model to facilitate self-learning for improved crack characterization. Subsequently, anchors computed through the K-means clustering algorithm are closely aligned with the fracture dataset, achieving an adaptation rate of 99.9 % and enhancing the recall rate of the model. Furthermore, the inclusion of the SimSPPF module from YOLOv6 diminishes memory usage and expedites detection speed. By replacing the original nearest up-sampling method with transposed convolution, optimization of up-sampling for crack datasets is achieved. Performance assessments reveal that the refined YOLOv5s algorithm attains an F1 score of 91 %, a mean Average Precision (mAP) of 93.6 %, and a 1.54 % increase in frames per second (fps) for pavement crack detection. This enhancement in detection technology signifies a substantial advancement in the maintenance and longevity of road infrastructure.

Graph neural network incorporating time-varying frequency domain features with application in spatial wind speed field prediction

Precise forecasting of wind speed is vital to guarantee safe travel on bridges. Nevertheless, current research primarily concentrates on single-point prediction. When it comes to forecasting spatial wind fields, graph neural networks prove to be powerful methods, but many of them have yet to take into account the impact of frequency domain attributes. To address this, a novel model called the graph neural network with time-varying frequency features (GTF) is proposed. Its core layer employs wavelet transforms in matrix form to extract various frequency sub-series, followed by the use of adaptive adjacency matrices to identify their spatial characteristics. Furthermore, aggregating all frequency band information and exploring spatial features further through multi-kernel convolutions. The predictive performance of different models for wind speed forecasting is assessed by utilizing spatial wind field data collected on a large-span bridge. Experimental results demonstrate that spatiotemporal model GTF outperforms the other models in terms of predictive accuracy. The quantity of wind observation points and their level of correlation exert a certain impact on the GTF model, while hyperparameters such as stacking depth and wavelet decomposition parameters have minimal effects. Finally, recommended parameters for the GTF model are provided.

Adopting smart technologies to encourage active transportation: a survey of pedestrians and cyclists in Perth, Western Australia

Cities are facing growing challenges and demand for urban transportation due to population growth and increased urbanization. Active transportation (AT), specifically walking and cycling are valuable modes of sustainable transportation that can mitigate the environmental impacts of motorized transportation and improve urban environment and people's health. Embracing technological solutions aimed at reducing obstacles to active transportation can create a more accommodating environment, that is not only ecologically sustainable, but also encourages individuals to opt for active modes of transportation. This paper investigates people's attitudes toward applying technology to increase AT. An online survey in Perth, Western Australia was conducted to explore pedestrians’ and cyclist’s attitudes toward using technology to facilitate AT. Findings suggest technological applications that improve safety and feasibility of active travel are the most impactful actors to encourage AT. These results can provide insight for designers, urban planners, and decision-makers when developing technologies or designing urban technological systems.

Tactile Paving Recognition Method Based on Improved YOLOv7

Tactile paving is a professional road facility to ensure the safe travel of people with visual impairment. However, there are many problems with tactile paving travel in practice. For one, some tactile paving is seriously damaged, and the other is the accumulation of obstacles. How to help visually impaired people recognize and locate obstacles in tactile paving is a problem worth studying. In this paper, image recognition technology is used to recognize the tactile paving pictures with obstacles, and an attention mechanism is used to optimize samples to improve recognition accuracy.

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.