Transportation Safety Research Articles

Digital Traffic Lights: UAS Collision Avoidance Strategy for Advanced Air Mobility Services

With the advancing development of Advanced Air Mobility (AAM), there is a collaborative effort to increase safety in the airspace. AAM is an advancing field of aviation that aims to contribute to the safe transportation of goods and people using aerial vehicles. When aerial vehicles are operating in high-density locations such as urban areas, it can become crucial to incorporate collision avoidance systems. Currently, there are available pilot advisory systems such as Traffic Collision and Avoidance Systems (TCAS) providing assistance to manned aircraft, although there are currently no collision avoidance systems for autonomous flights. Standards Organizations such as the Institute of Electrical and Electronics Engineers (IEEE), Radio Technical Commission for Aeronautics (RTCA), and General Aviation Manufacturers Association (GAMA) are working to develop cooperative autonomous flights using UAS-to-UAS Communication in structured and unstructured airspaces. This paper presents a new approach for collision avoidance strategies within structured airspace known as “digital traffic lights”. The digital traffic lights are deployed over an area of land, controlling all UAVs that enter a potential collision zone and providing specific directions to mitigate a collision in the airspace. This strategy is proven through the results demonstrated through simulation in a Cesium Environment. With the deployment of the system, collision avoidance can be achieved for autonomous flights in all airspaces.

Maintenance and Supply of Post and Telegraph Stations in the Semirechye

The article explores the material supply of post and telegraph stations in the Semirechye region during the late 19th and early 20th centuries. The management of postal routes, transport safety, and the construction and equipment of stations were determined by the state, private entrepreneurs, and the local Kazakh population, who were taxed for these services. In addressing these challenges, the Russian Empire drew from European experiences, directly applying the postal station management model from Russia. Postal routes facilitated the transportation of correspondence, cargo, and passengers, thus solving essential communication issues within society. Government officials and soldiers also relied on these stations as key support points. Although the station attendants (overseers) were under the authority of post offices, they independently managed operations and relied on station-generated income. This arrangement was cost-effective for the state, allowing it to minimize expenses on postal institutions. The Russian government consistently worked to improve postal communication, and the system was subject to ongoing changes. However, despite some positive developments, both the Russian Empire and the postal system in Semirechye lagged behind European standards of postal transport.

Transport Safety and Quality of Life in Developing Cities: An Analytical Study of Amman's Urban Transport System

Transport safety is crucial for social sustainability, especially in rapidly urbanizing developing cities. This study evaluates the safety of Amman, Jordan's urban transport system, and its impact on residents' quality of life. Using the Analytic Hierarchy Process (AHP), we identified and prioritized five key safety indicators: accident rates, fatalities and injuries, pedestrian safety, public perceptions of safety, and the presence of safety infrastructure. The findings revealed high accident rates, inadequate pedestrian infrastructure, and poor public perceptions of safety. These issues lead to high-stress levels, limited mobility, and increased reliance on private vehicles, exacerbating traffic congestion and environmental pollution. Recommendations include stricter road safety measures, upgraded pedestrian infrastructure, public awareness campaigns, and enhanced public transport services. This research highlights the need for coordinated efforts among government agencies, private sector partners, and the community to improve transport safety and quality of life in developing cities.

Selective neglect of viral risk: Tokyo’s commuter trains during the COVID-19 pandemic

This article asks why Tokyo’s crowded trains were largely absent from official discourses of viral risk in Japan during the COVID-19 pandemic. While public transport environments were often framed as spaces of contagion in other countries, Japanese public health discourses instead tended to emphasize the viral safety of urban railway transport. Drawing on an analysis of media and expert discourse, auto-ethnographic fieldwork, and repeat interviews with commuters, this article examines the processes through which Tokyo’s urban railway network was extracted from discourses of viral risk. It identifies four factors that contributed to this: (1) dominant definitions of risk spaces in public health messaging, (2) trains’ integral role in the operation of socio-economic life, (3) elusiveness of public transport in the epidemiological data, and (4) a focus on “unruly” passenger conduct. This analysis of the selective treatment of viral risks is tied to a critical engagement with theoretical distinctions between “essential” and “existential” mobilities developed in prior anthropological scholarship on the pandemic. Exploring the discursive framing of pandemic mobility practices on one of the world’s most extensive urban railway networks, the article thus contributes to the anthropological study of mobilities and transport in Japan and beyond.

Research Progress on Novel Inspection of Oil and Gas Pipeline Integrity

Oil and gas pipeline integrity is the key to ensure the safety of energy transportation, with the development of science and technology, the research and application of new detection technology has been increasingly emphasized. This paper firstly describes the background and practical significance of the selected topic, aiming at exploring more efficient pipeline inspection methods to ensure the stable operation of energy networks. In terms of theoretical framework, we have sorted out the theoretical system of integrity management, including the core elements of risk assessment, inspection technology, and maintenance strategy, which provides a theoretical basis for the subsequent research. Then, we systematically review the latest progress of oil and gas pipeline integrity testing at home and abroad. From the dimensions of physical principle and application practice, we analyze the advantages and disadvantages of conventional ultrasonic and magnetic particle flaw detection, as well as the emerging acoustic emission and fiber grating inspection technologies, and show the application cases of each technology in actual projects. Through comparative analysis, we find that although conventional technologies meet the inspection needs to a certain extent, new inspection technologies such as remote monitoring based on the Internet of Things (IoT) and artificial intelligence-assisted diagnosis are gradually becoming a research hotspot due to their high precision, high efficiency and intelligent features. The development of these technologies not only improves the comprehensiveness and accuracy of inspection, but also provides strong support for realizing the whole life cycle management of pipelines. In the summary review section, we emphasize the future development trend of oil and gas pipeline integrity inspection, including the integration and intelligence of technology, as well as the deep mining and application of data. At the same time, we also point out the challenges in the current research, such as the unification of inspection standards and the establishment of data sharing mechanisms, which require the joint efforts of the research community and the industry to achieve the continuous improvement of oil and gas pipeline safety. To summarize, this paper aims to comprehensively show the research progress of new detection technology of oil and gas pipeline integrity, provide reference for researchers and engineering practitioners in related fields, and promote the further improvement of China's oil and gas pipeline safety management level.

Collecting population-representative bike-riding GPS data to understand bike-riding activity and patterns using smartphones and Bluetooth beacons

Bike-riding GPS data offers detailed insights and individual-level mobility information which are critical for understanding bike-riding travel behaviour, enhancing transportation safety and equity, and developing models to estimate bike route choice and volumes at high spatio-temporal resolution. Yet, large-scale bicycling-specific GPS data collection studies are infrequent, with many existing studies lacking robust spatial and/or temporal coverage, or have been influenced by sampling biases leading to these data lacking representativeness. Additionally, accurately detecting bike-riding trips from continuously collected raw GPS data without human intervention remains a challenge. This study presents a novel GPS data collection approach by leveraging the combination of a smartphone application with a Bluetooth beacon attached to a participant’s bike. Aided by minimal heuristic post-processing, our method limits data collection to trips taken by bike without the need for participant intervention, carefully optimising between survey participation, privacy challenges, participant workload, and robust bike-riding trip detection. Our method is applied to collect 19,782 bike trips from 673 adults spanning eight months and three seasons in Greater Melbourne, Australia. The collected dataset is shown to represent the underlying adult bike-riding population in terms of demographics (sex, occupation and employment type), temporal and spatial patterns. The average trip length (median = 4.8 km), duration (median = 20.9 min), and frequency of bicycling trips (median = 2.7 trips/week) were greater among men, middle-aged and older adults. The ‘Interested but Concerned’ riders (classified using Geller typology) rode more frequently, while the ‘Strong and Fearless’ and ‘Enthused and Confident’ groups rode greater distances and for longer. Participants rode on roads/streets without bike infrastructure for more than half of their trips by distance, while spending 24% and 17% on off-road paths and bike lanes respectively. This population-representative dataset will be key in the context of urban planning and policymaking.

Enhancing Sustainability in Transportation Education: A Multimodal Curriculum Approach

This research critically examines the integration of sustainability principles into transportation education, focusing on multimodal curriculum development. Qualitative research methods are employed to investigate the perspectives of 39 cadets studying multimodal transportation, logistics, transportation safety, and law and road management at a transportation institution. The findings reveal varying levels of awareness and integration of sustainability in education, with participants highlighting the need for a more comprehensive and practical curriculum. Participants also emphasize the importance of industry collaboration, research, and policy advocacy in enhancing sustainability focus. The research underscores the urgency of preparing future professionals for the globalized, environmentally conscious transportation industry. By incorporating participants' suggestions, educational institutions can enhance sustainability education and contribute to a more sustainable and resilient transportation sector.

A Review of Aircraft Engine Fault Prediction and Performance Optimization Based on Deep Learning

This paper is aimed at presenting a systematic review of the recent studies on the use of deep learning techniques in aircraft engine health management for fault detection and performance enhancement. This comes in the light of increased growth in the air transport industry, the efficiency of aircraft engines and the efficiency of their maintenance have emerged as key issues in the safety and efficiency of air transport. This paper also identifies some of the key issues emerging from this area such as the data issues and the need to develop very accurate models for faulting and performance enhancement. Some of the most advanced techniques, namely the Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) are discussed as potential solutions to these challenges. The theoretical descriptions of the working principles, as well as the strengths and weaknesses of the methods in question with regard to the problems of aircraft engine health management, are elaborated. Furthermore, this paper introduces the application of Reinforcement Learning as a new technique in the area of optimal control of engine parameters with the hope of improving the dependability and maintainability of aircraft engines and in the process of achieving sustainable growth in the aviation industry. The issues on model interpretability, model generalization and model deployment are also discussed and research guidelines for the future work in this field are also outlined. In this paper, the state of the art and the importance of deep learning as well as the applications of deep learning in aircraft engine health management are discussed based on the literature and case studies.

A class-center fine-tuning prototypical network for few-shot fault diagnosis of turnout switch machine driven by multi-source signals

The turnout switch machine is mainly responsible for controlling the switch converting and locking, which is a crucial component of the rail transit signal system, and its reliability directly impacts the safety of rail transit transportation. However, many existing methods for diagnosing faults in turnout switch machines suffer from low accuracy and poor generalization capabilities. The primary issue is that the fault data of turnout switch machines are scarce, and the single-source data cannot fully reflect the fault characteristics. To address these issues, this paper introduces a fault diagnosis approach for the turnout switch machine based on a class-center fine-tuning prototype network (CFPN). Firstly, current signals of three phases are fused with different weights through an adaptive data fusion strategy, using the fused signal as input data. Secondly, a multiple weighted feature fusion network (MWFFN) based on the activation function Meta-ACON that can learn whether to activate neurons is proposed, which can effectively extract and fuse features at different levels in the fusion signal. In addition, a fine-tuning center balance strategy is proposed to effectively expand the distance between prototypes and enhance intra-class aggregation. Finally, a few-shot sample experiment is conducted to verify the performance of CFPN, using fused current data collected from’ a switch machine. In the comparative experiment, CFPN shows good fault diagnosis performance, the fault diagnosis accuracy reaches up to 99.08%. At the same time, fused vibration data are used to conduct a cross-domain experiment on CFPN, and the results indicate that CFPN still shows good classification effect and has good generalization performance, demonstrating its high practical application value.

Optimizing neutron shielding: A specialised container approach for Disused Sealed Radioactive Sources and orphan neutron emitters

Shielding Disused Sealed Radioactive Sources (DSRSs) and orphan neutron sources, such as 252Cf, pose challenges due to the high penetration of neutrons, induced radioactivity in shielding materials, and the limited availability of compact, effective shielding materials. This study addresses these challenges by proposing a specialized container for the safe transport, conditioning, and disposal of neutron-emitting DSRSs and orphan sources. The computational tool that was used in this study is the Monte Carlo N-Particle (MCNP). The investigation begins with an examination of moderation materials, including high-density polyethylene (HDPE), paraffin, and water-extended polyester (WEP). Subsequently, four concrete types, namely Ordinary, Barite, Portland, and Serpentine Concrete, are scrutinized. Employing a comprehensive methodology, the design of the container is optimized to effectively moderate and absorb neutron emissions. The findings of this study demonstrate that the designed compact container can safely handle activities exceeding those studied, effectively managing up to 10 μg (about 0.2 GBq) 252Cf without exceeding 25 μSv/h at the outer container contact. This suggests a promising solution for the secure management of high-activity neutron-emitting sources.

Toxic marriage – Boeing and McDonnell Douglas

Research methodology Public data such as news reports, interviews and memos were used to craft the case. In addition, the technical reports released by the National Transportation Safety Board (NTSB), along with secondary data in the form of expert accounts and congressional hearings were used to round out the synopsis of the case study. Case overview/synopsis This case explores the Boeing–McDonnell Douglas merger and its impact on Boeing’s corporate culture, ethics and strategic decision-making. After the merger, Boeing shifted from a culture focused on engineering excellence to one emphasizing cost-cutting and shareholder value. This cultural shift contributed to the development failures and ethical lapses that resulted in the 737 MAX crisis, which involved two fatal crashes. The case is designed for courses in Strategic Management or Organizational Behavior. Complexity academic level Strategic Management or Organizational Behavior

Investigation of Deep Learning Model for Vehicle Classification

The usage of automobiles in cities and metropolitan areas has increased drastically throughout the years and there is a need to monitor the flow of road traffic to improve the traffic congestion and safety. One of the best ways to monitor the traffic is using an artificial intelligence and machine learning. An automatic vehicle tracking system based on artificial intelligence and machine learning can offers capability to analyse the real-time traffic video data for the purpose of traffic surveillance. The computer vision is one of the subsets in machine learning that can train the computer to understand the visual data and perform specific tasks such as object detection and classification. A Vision-based system can be proposed to detect road accidents, predict traffic congestion and further road traffic analytics. This can improve the safety in transportation where it can recognize types of vehicles on the road, detecting road accidents, predicting the traffic congestion and further road traffic analytics. In the context of road traffic monitoring, the parameters of the traffic such as the type and number of vehicles that passes through must be recorded in order to gain valuable insights and make prediction such as the occurrence of traffic congestion. However, this requires reliable informative and accurate data as input for analytics. Therefore, in this research the deep learning model for vehicle classification is investigated to detect, classify types of vehicles and further predictive analytics. The vehicle classification is proposed based on Single Shot Detector (SSD) architecture model. The proposed model is tested on five different classes of vehicles with a total of 1263 images. Experimental results show that SSD model able to achieve 0.721 of precision, 0.741 of recall and 0.731 of F1 Score. Finally, the result show that the SSD model is more accurate among all the models for all the performance measure with the difference of more than 0.052 of precision, 0.706 of recall and 0.05 of F1 Score.

Research progress of hydrogen blocking coatings

In the context of carbon peak and carbon neutrality, the use of clean energy has become the focus of attention. Hydrogen energy as the most potential secondary energy has a very important position in various fields, so the storage and transport of hydrogen energy is one of our most important concerns. Long distance pipeline is regarded as an effective means to transport hydrogen, but the high pressure hydrogen environment is easy to cause hydrogen embrittleness and other problems in the pipeline, which seriously threatens the safety of pipeline transportation. Hydrogen blocking coatings are one of the most effective ways to solve such problems. In this paper, the research results in recent years are reviewed and summarized. The article points out the hydrogen barrier mechanism, advantages and disadvantages of various types of hydrogen barrier coatings, the current status of research, as well as future research focus and development trend.

Research on Collision Warning Method for Ship-Bridge Based on Safety Potential Field

In order to ensure the safety of navigation in a bridge area, and based on the theory of the safety potential field, a method of ship safety assessment and early warning in an inland river bridge area is proposed. Firstly, the risk elements associated with ship collisions in a bridge area are classified. Secondly, these risks are quantified using the potential energy field, the boundary potential field and the behavioural field, and then the ship state under the influence of wind flow, predicted by the Kalman filter, is quantified using the kinetic energy field. Then, the above four potential energy fields are merged to obtain a superposition field, and the magnitude of the instantaneous risk in the bridge area is obtained based on its magnitude. Finally, the change of field strength values under different moments is used for early warning. The results of the simulation of a ship passing through the piers of the Baijusi Bridge show that the model can effectively quantify the risk of a ship–bridge collision in the inland bridge area and provide real-time warning of the risk of a ship–bridge collision in the bridge area, which is of great significance for improving the safety of the inland bridge area.

Occupational health and safety among farmers: a comprehensive study in Central Anatolia, Turkey

BackgroundThe agricultural sector is vital for food production, economic stability, and environmental sustainability. However, it also poses significant occupational health and safety challenges due to factors such as heavy machinery usage, exposure to chemicals, and challenging environmental conditions.MethodThis cross-sectional study involved 366 farmers affiliated with the Yozgat Chamber of Agriculture in Turkey. Data on demographic characteristics, occupational factors, and work accidents were collected with interview technique between May and August 2023. While descriptive data were expressed as categories with numbers and percentages, chi-square analysis was used to compare the obtained answers with the status of having or not having a work accident. Binary logistic regression analysis was used to estimate the effect of independent variables on the probability of having a work accident.ResultsThe study revealed that 33.0% of farmers experienced work accidents in the last year. Most accidents occurred during work shifts (28.8%) and in the fall season (34.8%). Factors contributing to accidents included lack of personal protective equipment (51.5%) and transportation-related issues (36.4%). Logistic regression analysis indicated that working more than 8 h daily, working 5 days or more per week, and using bus/minibus transportation significantly increased the likelihood of experiencing work accidents.ConclusionFindings underscore the importance of implementing effective occupational health and safety measures, including proper training, provision of personal protective equipment, and improving transportation safety. Addressing these issues can enhance the well-being of agricultural workers and contribute to a safer working environment in the sector.

Research on Pipeline Stress Detection Method Based on Double Magnetic Coupling Technology.

Oil and gas pipelines are subject to soil corrosion and medium pressure factors, resulting in stress concentration and pipe rupture and explosion. Non-destructive testing technology can identify the stress concentration and defect corrosion area of the pipeline to ensure the safety of pipeline transportation. In view of the problem that the traditional pipeline inspection cannot identify the stress signal at the defect, this paper proposes a detection method using strong and weak magnetic coupling technology. Based on the traditional J-A (Jiles-Atherton) model, the pinning coefficient is optimized and the stress demagnetization factor is added to establish the defect of the ferromagnetic material. The force-magnetic relationship optimization model is used to calculate the best detection magnetic field strength. The force-magnetic coupling simulation of Q235 steel material is carried out by ANSYS 2019 R1 software based on the improved J-A force-magnetic model. The results show that the effect of the stress on the pipe on the magnetic induction increases first and then decreases with the increase in the excitation magnetic field strength, and the magnetic signal has the maximum proportion of the stress signal during the excitation process; the magnetic induction at the pipe defect increases linearly with the increase in the stress trend. Through the strong and weak magnetic scanning detection of cracked pipeline materials, the correctness of the theoretical analysis and the validity of the engineering application of the strong and weak magnetic detection method are verified.

Identifying Veterans with a Higher Risk of Social Needs Using Cluster Analysis.

Many social need screening to advance population health and reduce health disparities, but barriers to screening remain. Improved knowledge of patient populations at risk for social needs based on administrative data could facilitate more targeted practices, and by extension, feasible social need screening and referral efforts. To illustrate the use of cluster analysis to identify patient population segments at risk for social needs. We used clustering analysis to identify population segments among Veterans (N=2010) who participated in a survey assessing nine social needs (food, housing, utility, financial, employment, social disconnection, legal, transportation, and neighborhood safety). Clusters were based on eight variables (age, race, gender, comorbidity, region, no-show rate, rurality, and VA priority group). We used weighted logistic regression to assess association of clusters with the risk of experiencing social needs. National random sample of Veterans with and at risk for cardiovascular disease who responded to a mail survey (N=2010). Self-reported social needs defined as the risk of endorsing (1) each individual social need, (2) one or more needs, and (3) a higher total count of needs. From the clustering analysis process with sensitivity analysis, we identified a consistent population segment of Veterans. From regression modeling, we found that this cluster, with lower average age and higher proportions of women and racial minorities, was at higher risk of experiencing ≥ 1 unmet need (OR 1.74, CI 1.17-2.56). This cluster was also at a higher risk for several individual needs, especially utility needs (OR 3.78, CI 2.11-6.78). The identification of characteristics associated with increased unmet social needs may provide opportunities for targeted screenings. As this cluster was also younger and had fewer comorbidities, they may be less likely to be identified as experiencing need through interactions with healthcare providers.

Analisis Media Statement Kasus Lion Air Penerbangan Jt-610 Boeing 737 Max 8 Dengan Pendekatan Storytelling Michael L. Kent

The focus of this study is on how Lion Air managed crisis communication following the Boeing 737 MAX 8 crash of Flight JT-610. The research delves into the use of storytelling in media statements as a strategy for crisis communication during the aftermath of the crash. It outlines Lion Air's goals in sharing information and engaging with the public and media in the midst of this crisis. The study utilizes a qualitative text analysis to scrutinize three specific media statements released during the crisis. By applying Michael L. Kent's storytelling elements, the analysis of the media statements concentrates on factors like emplotment, narrative theory, identification, form, and masterplots. The selected statements cover the immediate aftermath of the crash on October 29, 2018; a subsequent statement on March 13, 2019, addressing broader safety concerns surrounding the Boeing 737 MAX 8; and a final statement on October 26, 2019, summarizing findings from the National Transportation Safety Committee's investigation. The research findings suggest that employing storytelling through straightforward text in media statements, incorporating credible character depiction, structured narrative, empathy, and compelling storytelling, can notably influence an organization's reputation recovery and improvement. Well-crafted and timely media releases are essential as primary information sources for the media, especially in crisis scenarios where prompt and precise information dissemination is critical. This method ensures that information, though disseminated quickly, maintains relevant and impactful storytelling aspects. The study concludes that storytelling, characterized by humanistic, empathetic, and coherent narratives across media statements, can be effectively utilized by Lion Air to uphold consistent and narrative-focused communication with the public through the media.

Improving port state control through a transfer learning-enhanced XGBoost model

With the advancements in modern information technology, Port State Control (PSC) inspections, as a crucial measure to protect ship safety and the marine environment, are undergoing an intelligent transformation. This paper aims to streamline the selection process for inspecting high-risk ships by employing a data-driven model to predict the number of deficiencies in ships arriving at ports. A transfer learning-enhanced eXtreme Gradient Boosting (XGBoost) model is proposed by innovatively incorporating sample similarity calculations to adapt the model to the unique characteristics of the target port. This novel model enables the integration of relevant data from other ports, enhancing the predictive performance of the model to specific port conditions. Utilizing PSC inspection records from ports within the Tokyo Memorandum of Understanding (MoU) and choosing the port of Singapore as the target, numerical experiments demonstrate that the proposed model achieves improvements of 1.81 %, 6.08 %, and 3.60 % in the mean absolute error, mean squared error and root mean squared error, respectively, compared to the standard XGBoost model. Furthermore, across various sizes of training samples, the proposed model outperforms other machine learning models. This work may service as a significant step towards exploring the potential of developing data-driven models based on comprehensive data to assess the risk level of foreign ships arriving at ports, ameliorating the PSC inspection process by aiding PSC officers in identifying substandard ships more effectively.

Multi-objective model predictive control for ship roll motion with gyrostabilizers

Ships are prone to significant roll motion while sailing in adverse conditions, posing a serious threat to ship safety and maneuverability. Therefore, effective ship motion control is crucial. Integrating the MPC control algorithm with a gyrostabilizer can effectively achieve this goal. To evaluate and compare control strategies, a multi-objective model predictive control is proposed that integrates considerations of ship motion, safety, and energy consumption to conduct the operation concurrently. By assigning different weights to these factors, the study aims to discern the varying impacts on control effectiveness. The response of ship roll motion in beam waves is evaluated through roll hydrodynamic modelling, accounting for wave memory effects. A state-space model of ship and gyrostabilizers is proposed to represent their dynamic interaction and response to external moment. Subsequently, the influence of different weightings in the multi-objective model predictive control is compared, and the control performances of a frigate under different wave conditions are analyzed respectively. The multi-objective model predictive control, with varied weight assignments, leads to distinct reductions in roll motions. This investigation offers valuable insights into controlling roll motion in beam wave conditions, effectively reducing motion under varying sea conditions, and providing alternative guidance tailored to user preferences.