Transportation Safety Research Articles

Multi-objective optimization study of LNG tank truck baffles under the most hazardous transport condition based on a combined approximate model

Baffles are essential safety devices for mobile pressure vessels on land and at sea. Under the most hazardous transport condition, Violent sloshing of the liquid inside the tank can damage the tank structure and baffles. To solve this problem, a multi-objective optimization method based on a combined approximate model and NSGA-II was proposed. The goals are to minimize the maximum impact force on the baffles and the front head of the LNG tank and to achieve a multi-objective optimization of the baffles. Firstly, based on the VOF method a dynamic model of liquid sloshing in the LNG tank was established. Then, the changes in the impact force on the baffles and front head of the LNG tank truck under different filling ratios and two extreme transport conditions were analyzed to determine the most hazardous transport condition. Finally, a novel multi-objective optimization method was proposed to reduce the maximum impact force of the baffles and the front head of the LNG tank. The results show that the two optimization goals were reduced by 117% and 85% respectively. These findings establish a theoretical foundation and provide data support for baffles design in mobile pressure vessels. This improves the safety of land and sea transportation.

Identifying periods impacted by sewer inflow and infiltration using time series anomaly detection

Accurate diagnosis of sewer inflow and infiltration (I/I) is crucial for ensuring the safe transportation of sewage and the stability of wastewater treatment processes. Identifying periods impacted by I/I is essential for I/I diagnosis, but current methods lack a standard criterion and require adaptation to specific conditions, resulting in low accuracy, complexity, and limited generalizability. This paper proposes a novel approach to distinguish I/I periods from time series of sewer measurements based on anomaly detection theory through an iterative use of a time-series reconstruction model. This method eliminates the need for external data such as rainfalls and avoids intensive manual data analysis. Operating directly on in-sewer data, it enhances accuracy compared to existing approaches and is applicable to various external factors such as rainfall, snowmelt, and seawater intrusion. The method can be applicable to a broad range of monitoring data, including flow rate, temperature, and conductivity. Validated through simulation studies and demonstrated via real-life applications, this method offers an efficient solution for I/I detection, facilitating further I/I diagnosis, including I/I quantification and location identification.

Crossing-Borders: Experiences With International Transports on Extracorporeal Membrane Oxygenation: Special Considerations and Challenges

Extracorporeal membrane oxygenation (ECMO) is a crucial support for patients with severe cardiac or respiratory failure, but its availability is limited, often requiring patient transport to specialized centers. Only a few centers provide mobile ECMO services, and international ECMO transports are rare. This study reviews a department’s experience with international ECMO transports from 1998 to 2022. Out of 1,277 ECMO transports, 357 (28%) were international. Most of these (52%) were directed to ECMO Center Karolinska, whereas others involved transfers due to a lack of beds or between foreign centers. The majority (79%) of patients were cannulated at the referring hospital, with 63% supported by venoarterial ECMO. Transport distances averaged 1,200 km, using fixed-wing aircraft 89% of the time. Hospital survival for those transported to Karolinska was 82%, and 36% of transports experienced complications, though no deaths occurred during transport. This study highlights the safety and effectiveness of international ECMO transport with highly trained teams.

A novel linetype recognition algorithm based on track geometry detection data

Abstract The detection and recognition of track geometry are crucial for ensuring railway transportation safety and maintenance efficiency. Traditional linetype recognition algorithms struggle to effectively handle the complexities of variable track geometry and noise interference. To address these challenges, a novel linetype recognition algorithm (NLRA) is proposed, which thoroughly analyzes the characteristics of track curvature across straight lines, transition curves, and circular segments. By employing partial differential equations for data preprocessing, NLRA effectively filters out noise. Utilizing curve design parameters and integrating line anti-noise recognition with multi-segment line fitting techniques, the algorithm accurately identifies each line segment and incorporates a single-point recognition method for data gaps. Experimental results demonstrate that NLRA outperforms traditional algorithms in terms of real-time performance, recognition accuracy, anti-interference capability, and noise handling. Notably, NLRA enhances accuracy by at least 35.1% and recall by at least 17.4% on selected typical routes. This work underscores the algorithm's potential to provide efficient and accurate tools for track maintenance, facilitate advancements in track detection software, and significantly contribute to railway transportation safety.

Societal Impact of Test Automation: Reducing Human Error in Critical Systems

This article explores the profound societal impact of test automation across critical sectors such as healthcare, finance, transportation, and energy. It examines how automated testing processes significantly reduce human error, enhance system reliability, and improve service quality. The article presents compelling evidence from various studies and reports, demonstrating substantial improvements in medical diagnostics, financial fraud detection, transportation safety, and energy distribution efficiency. Beyond error reduction, the article discusses broader societal benefits, including enhanced accuracy in data processing, faster emergency response times, improved service quality, and strategic resource allocation. The article underscores the crucial role of test automation in addressing the challenges posed by increasingly complex technological systems and its far-reaching implications for public safety, economic stability, and overall quality of life.

A Formally Integrated Adaptive Speed Management for Proactive Traffic Safety

The emergence of connected autonomous vehicles (CAVs) represents a key development in the quest to enhance traffic safety. CAVs hold significant promise for improving traffic safety and have great potential to contribute to transportation sustainability. However, their safety depends on the accuracy of the programmed rules and algorithms that guide their decision-making process. This paper introduces an adaptive traffic management system that integrates a formal traffic safety rule defined by pre-established bounds for Traffic Conflict Techniques. This system enables dynamic speed adjustments for vehicles violating the traffic safety rule in order to prevent potential collisions. To evaluate the effectiveness of our approach, we study a traffic flow on the SR528 highway in Orlando, Florida, and analyze the behavior of each vehicle in traffic based on extracted traffic safety indicators such as time-to-collision and space headway. This analysis is performed by the traffic safety rule to identify violating vehicles, and the speed update is achieved through the integration of the computer algebra system Mathematica and a micro-simulation tool called SUMO. Our study aims to improve the safety and efficiency of the traffic flow by combining simulation, TCTs analysis, and semi-formal tools like Mathematica to aid in the decision-making process of vehicles during traffic events, particularly shockwaves. Preliminary results demonstrate the efficacy of our approach in mitigating the impact of shockwaves. After applying the speed update, we observe an increase in time-to-collision and space headway. This indicates improved safety and reduced likelihood of collisions. Our findings highlight the potential of adaptive traffic management systems to enhance transportation safety and efficiency.

Application of simulated AIS data to study the collision risk system of ships

Abstract In previous research, several computational methods have been proposed to analyse the navigation, transportation safety and collision risks of maritime vessels. The objective of this study is to use Automatic Identification System (AIS) data to assess the collision risk between two vessels before an actual collision occurs. We introduce the concept of an angle interval in the model to enable real-time response to vessel collision risks. When predicting collision risks, we consider factors such as relative distance, relative velocity and phase between the vessels. Lastly, the collision risk is divided into different regions and represented by different colours. The green region represents a low-risk area, the yellow region serves as a cautionary zone and the red region indicates a high-alert zone. If a signal enters the red region, the vessel's control system will automatically intervene and initiate evasive manoeuvres. This reactive mechanism enhances the safety of vessel operations, ensuring the implementation of effective collision avoidance measures.

Assessment of Public Transportation Safety Measures in Yaoundé, Cameroon: Case of Collective Taxis

Yaoundé, the capital of Cameroon, is one of the cities in the country most affected by road traffic crashes. Despite the measures taken by authorities, the human factor remains a major cause of these crashes. This study aimed to evaluate the measures taken to reduce the risk-taking behaviors of collective taxi drivers in Yaoundé. A survey of 144 collective taxi drivers was conducted to gather information on their driving habits, adherence to, and perceived effects of safety regulations. The study revealed the following prevalence of risky driving behaviors among collective taxi drivers: 41.33% for impaired driving; 67% for speeding, 62% for disobeying traffic lights, 68.86% for distraction; and 67% for risky maneuvering on the road. Significant associations were found between risk perceptions and involvement in risky driving behaviors. Associations were also established between the frequency of police inspections and involvement in risky behaviors, between the participation in training programs on safety issues and using poorly maintained vehicles, and between the frequency of awareness campaigns and poor maneuvering on the road. To address these issues, it is essential to strengthen preventive measures on risk factors, raise awareness on a large scale and on a regular basis, and strictly enforce the existing regulations.

The Study of Risk Assessment Method for Ship Berthing Based on the “Human-Ship-Environment” Synergy

Berthing is one of the most dangerous phases in the process of ship navigation, and its risk assessment is crucial for both ship safety and port scheduling. To effectively enhance the safety and reliability of the berthing process, a berthing risk assessment method based on the synergy of “human-ship-environment” has been established. First, the impact of the human, ship, and environmental factors on berthing risk was analyzed, and a risk assessment index system for ship berthing was constructed. Then, the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) methods were employed for a comprehensive risk assessment. AHP was used to determine the weight of each factor reasonably, while FCE was applied for the evaluation of the berthing risk. Finally, the proposed method was applied to evaluate the berthing operations of two ships, namely the KCS ship type and the S-175 large passenger ship type, at the Qingdao Intelligent Ship Testing Field, China. The experiment’s results indicate that the evaluation results of the method proposed here have good consistency with the expert survey method.

Application Research of Infrared Technology in Safety Detection in Ship Field

Abstract. This paper studies infrared technology and its application in the field of ship detection, mainly studying infrared thermal imaging of failure equipment refueling oil spill and ship safety problems because infrared technology is a high precision contact thermal measurement method through infrared camera infrared objects, the infrared spectrum wavelength range of infrared radiation heat, on the receiver into electronic signals, through electronic attenuation and electronic scanning form the corresponding heat map or temperature field image, to intuitively reflect the temperature distribution and heat focus of the object. In addition, the infrared detection device can be reasonably installed in the ship without affecting the work of other equipment, and it has been found that infrared technology has significantly improved the solution to the safety problems of ships.

Analysis of shipping accident patterns among commercial and non-commercial vessels operating in ice-infested waters in Arctic Canada from 1990 to 2022

Over the past two decades, the Canadian Arctic has experienced a marked reduction in sea ice extent, coinciding with a significant rise in ship traffic. This study explores the relationship between ship traffic, shipping accidents, accident rates, and diminishing sea ice from 1990 to 2022 during the shipping season. The findings reveal that ship traffic has increased substantially along major Arctic routes, such as the Hudson Strait, Baffin Island, and the Northwest Passage, driven by the consistent decline in sea ice. Despite this rise in traffic, accident rates for commercial vessels, particularly General Cargo and Tanker ships, have significantly decreased, suggesting that current safety measures may be effective. However, the study also uncovered a significant positive correlation between all vessel accidents and sea ice concentration, indicating that certain ice conditions still pose substantial risks to vessels. Additionally, passenger vessel traffic has shown a notable positive correlation with accidents, pointing to emerging risks in the region. Non-commercial vessels, such as fishing vessels, have demonstrated stable accident rates, though they remain understudied. These results underscore the complexity of Arctic maritime operations in the face of climate change and highlight the urgent need for adaptive strategies, continuous monitoring, and targeted policy interventions to ensure the safety and sustainability of future Arctic shipping.

Improved composite network via bismuth iodide for efficient ice-nucleating application

The urgent demand for electricity requires more safe energy transportation. AgI-based weather modification agents are commonly employed to facilitate ice nucleation and remove the undesirable glaze icing to suppress the negative effects of natural disasters. However, the intrinsic nucleation potency of AgI strictly limits its further improvement. Here, a hexagonal BiI3 was added in AgI-based agents. The chemical interactions of BiI3 and precursors optimize the crystallization of the polymer composite, leading to an oriented composite network. In addition, BiI3 could alter the structure and morphology of the combustion products, therefore creating more possible heterogeneous nuclei sites. The resultant BiI3-modified AgI-based weather modification agent exhibits an ice nucleation ∼ 1014, which is nearly ten times higher than the sample without AgI-BiI3, leading to a 71.21 % enhancement of deicing efficiency compared to AgI sample. Our results indicate the effectiveness of BiI3 dopant to improve both nucleation and deicing performance for weather modification applications.

A literature review on the applications of artificial intelligence to European rail transport safety

A literature review on the applications of artificial intelligence to European rail transport safety

Analysis of 3k Experiments Applied to Railway Braking: Influence of Contaminants and Train Speed

The presence of contaminants influences braking efficiency in the railway system because it alters the adhesion at the wheel–rail interface. It is essential to study this phenomenon, as contaminants reduce the friction between wheels and rails, which impacts braking and transport safety. In addition, these contaminants increase the risk of derailments. The objective of the research was to determine the impact of different contaminants and operating speeds on the critical braking system’s responses. Using the 3k full factorial experimental design methodology, with analysis of variance (ANOVA) and linear and quadratic regressions, visualized using surface graphs, the effects of three operating conditions were studied: clean rails, with sand and sawdust, and driving the train at three operating speeds. These conditions gave rise to variations in braking distances, maximum creep, wheel slip times, and maximum peaks of electric current when braking in each experiment. The tests were carried out on the straight section of a β-shaped track and a railway vehicle, designed at a scale of 1:20. The analysis reveals that the braking distance increases significantly with surface roughness (clean track < sawdust < sand). At 0.75 m/s, the sawdust track reduces braking distance by 21% compared with the clean track; at 1.00 m/s, the reduction is 19%; and at 1.30 m/s, it is 35%.

Testing ADAS/ADS – from critical scenarios to automated testing oracles

AbstractAutomated and autonomous driving systems are increasingly integrated into modern vehicles to support the vision of safe, efficient, and comfortable transportation. Given the complexity of these systems, thorough testing and close monitoring of their behavior is inevitable to prevent hazardous situations and unexpected behaviors. This paper presents different approaches for testing ADAS/ADS, focusing on generating critical scenarios for virtual validation and monitoring approaches applied during operation. Specifically, we provide a comprehensive overview of our previous work on combinatorial and search-based testing methodologies, highlighting their application in generating robust test suites. Additionally, we summarize our work on intelligent monitoring approaches to detect operational issues. Our findings emphasize the necessity of advanced testing solutions and continuous monitoring to identify and mitigate potential failures, demonstrating their applicability in enhancing the safety and trustworthiness of ADAS/AD.

Investigation of the risk influential factors of maritime accidents: A novel topology and robustness analytical framework

This study aims to develop a novel and fully data-driven approach to analyse the maritime accidents risk influential factors (RIFs) by integrating Association Rule Mining (ARM) and Complex Network (CN) modelling. Firstly, a comprehensive dataset comprising 21,206 maritime accident records from Marine Accident Investigation Branch and Transportation Safety Board is collected and processed to serve as the foundational data source supporting the development of the new approach. Secondly, a novel Combined Association Rule Mining method is proposed to extract the interconnections among RIFs, with the mined results mapped into a CN framework. Finally, two importance ranking algorithms, namely the PageRank-Information-Entropy algorithm and edge betweenness centrality, are applied to identify the key RIFs and their information transmission paths. By simulating deliberate and random attacks within networks, a robustness analysis is conducted to further explore the evolution of RIFs. The findings reveal that ship-related factors demonstrate greater centrality and connectivity, exerting a more substantial influence on information propagation within the network structure. The robustness analysis illustrates that strategic node and edge removals are effective in preventing risk propagation. It therefore makes contributions to the development of a theoretical basis for stakeholders to develop cost-effective preventive measures against specific RIFs, ultimately enhancing maritime safety.

Physics-Informed Data-Driven Approaches to State of Health Prediction of Maritime Battery Systems

Battery systems are increasingly being used for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health (SOH) of the batteries can be verified by independent tests – annual capacity tests. However, this paper discusses physics-informed data-driven approaches to online diagnostics for state of health monitoring of maritime battery systems based on a combination of physical knowledge, physic-based models, insights from extensive characterization tests and operational sensor data collected from the batteries during actual operation. This represents an alternative approach to the annual capacity tests for electric ships that is found to be sufficiently robust and accurate under certain conditions. Previous attempts with purely data-driven models, including both cumulative and snapshot models, semi-supervised learning and simple models based on the state of charge did not achieve the required reliability and accuracy for them to be utilized in a ship classification perspective, as presented at previous PHM conferences. However, preliminary results from the physics-informed data-driven method presented in this paper indicate that it can be relied on for independent verification of state of health as an alternative to physical tests. It has been tested on battery cells cycled in laboratory degradation tests as well as on field returns from batteries onboard ships in service. Notwithstanding, further validation and verification of the method is recommended to further build confidence in the model predictions.

Artificial Intelligence based Leak Detection in Blended Hydrogen and Natural Gas Pipelines

In the transition towards a hydrogen-based energy system, the strategic use of pipelines is crucial for efficient hydrogen distribution. Leveraging existing natural gas pipelines to carry a mix of hydrogen and natural gas offers a cost-effective alternative to building new infrastructure. This study explores the development of a leak detection system compatible with existing pipelines, specifically tailored for the blended hydrogen and natural gas mix. Given the scarcity of leak data for blended hydrogen-natural gas pipelines, the study introduces a Real-Time Transient Model (RTTM) for blended gases, simulating leak dynamics to generate necessary data. Additionally, a leak detection system (LDS) is developed using a fusion of Convolutional Neural Network (CNN) and Explainable Artificial Intelligence (XAI) through Adaptive Neuro-Fuzzy Inference Systems (ANFIS). This LDS framework overcomes the “black box” issue common in AI-driven systems, enabling reliable detection. The integration of Explainable and traditional AI techniques holds promising implications for blended hydrogen pipelines by enhancing the safety and efficiency of hydrogen transportation, thereby mitigating economic and environmental impacts, and addressing public concerns.

Thermophysical Properties of the Hydrogen Carrier System Based on Aqueous Solutions of Isopropanol or Acetone

One concept for the safe storage and transport of molecular hydrogen (H2) is the use of hydrogen carrier systems which can bind and release hydrogen in repeating cycles. In this context, the liquid system based on isopropanol and its dehydrogenated counterpart acetone is particularly interesting for applications in direct isopropanol fuel cells that are operated with an excess of water. For a comprehensive characterization of diluted aqueous solutions of isopropanol or acetone with technically relevant solute amount fractions between 0.02 and 0.08, their liquid density, liquid viscosity, and interfacial tension were investigated using various light scattering and conventional techniques as well as equilibrium molecular dynamics (EMD) simulations between (283 and 403) K. Polarization-difference Raman spectroscopy (PDRS) was used to monitor the liquid-phase composition during surface light scattering (SLS) experiments on viscosity and interfacial tension. For comparison purposes and to expand the database, capillary viscometry and dynamic light scattering (DLS) from bulk fluids with dispersed particles were also applied to determine the viscosity while the pendant-drop (PD) method allowed access to the interfacial tension. By adding isopropanol or acetone to water, density and, in particular, interfacial tension decrease significantly, while viscosity shows a pronounced increase. The behavior of viscosity and interfacial tension is closely related to the strong hydrogen bonding between the unlike mixture components and the pronounced enrichment of both solutes at the vapor–liquid interface, as revealed by EMD simulations. For an aqueous solution with an isopropanol amount fraction of 0.04, minor variations in interfacial tension and viscosity were found in the presence of pressurized H2 up to 7.5 MPa. Overall, the results from this study contribute to an extended database for diluted aqueous solutions of isopropanol or acetone, especially at temperatures above 323 K.

Traffic Prevention and Security Enhancement in VANET Using Deep Learning With Trusted Routing Aided Blockchain Technology

ABSTRACTVehicular ad hoc networks (VANETs) in portable broadband networks are a revolutionary concept with enormous potential for developing safe and efficient transportation systems. Because VANETs are open networks that require regular information sharing, it might be difficult to ensure the security of data delivered through VANETs as well as driver privacy. This paper proposes a blockchain technology that supports trusted routing and deep learning for traffic prevention and security enhancement in VANETs. Initially, the proposed Feature Attention‐based Extended Convolutional Capsule Network (FA_ECCN) model predicts the driver's behaviors such as normal, drowsy, distracted, fatigued, aggressive, and impaired. Next, the Binary Fire Hawks‐based Optimized Link State Routing Protocol (BFH_OLSRP) is used to route traffic after trust values have been assessed. Furthermore, Binary Fire Hawks Optimization (BFHO) determines the best routing path based on criteria such as link stability and node stability degree. Finally, blockchain storage is supported by the Interplanetary File System (IPFS) technology to improve the security of VANET data. Additionally, the validation process is established by using Delegated Practical Byzantine Fault Tolerance (DPBFT). As a result, the proposed study employs the blockchain system to securely send data to neighboring vehicles via trust‐based routing, thereby accurately predicting the driver's behavior. The proposed method achieves a better outcome in terms of latency, packet delivery ratio (PDR), overhead packets, throughputs, end‐to‐end delay, transmission overhead, and computational cost. According to simulation results and efficiency evaluation, the proposed approach outperforms existing approaches and enhances vehicle communication security in an effective manner.