Transportation Safety Research Articles

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.

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.

Vehicle Traffic with Over Dimensions and Over Loads in Improving Transportation Safety

Vehicle Traffic with Over Dimensions and Over Loads in Improving Transportation Safety

Investigating pedestrians’ red light running intentions at urban intersections in different traffic Environments: A scenario-based analysis guided by theoretical frameworks

Pedestrian risky behaviors are one of the contributing factors to crashes involving pedestrians. Therefore, it is crucial to comprehend the mechanisms by which pedestrians interact with many influential components in the traffic environment. This study aimed to evaluate pedestrians’ red light running intentions and related factors under different traffic flow scenarios, including straight traffic flow, right-turning traffic flow, and left-turning traffic flow. A theoretical approach based on the theory of planned behavior (TPB) and the prototype willingness model (PWM) was employed. Data were collected from an online survey of 2250 participants in Tehran, Iran. Structural equation modeling (SEM) was used to identify the significant factors that explain intentions. All models successfully explained the behavioral intention for red light running violation; however, the findings revealed that the integrated model was the best-performing model to represent violation and, thus, was selected for interpreting the results and drawing relevant conclusions. Different traffic flow scenarios had varied effects on violation intentions for individual characteristics and model constructs. Previous crash experiences and driving-related background variables emerged to impact pedestrian violation intention across three scenarios. The findings also suggested that the rational constructs (attitude, perceived behavioral control, and facilitating conditions) had a more robust impact on violation intention compared to reactive constructs (prototype similarity, prototype favorability), with facilitating conditions being the strongest predictor of the model, followed by attitudes toward violation as a significant predictor of intention for red light violation. According to the results, the mechanism of risk-taking varies depending on the direction of the traffic flow. Higher risk was associated with the violation at the intersections with straight traffic flow compared to the intersections with turning traffic flow. Based on the findings of this study, several implications, including interventions focusing on individuals’ transportation safety attitudes, countermeasures to increase the risk perception of pedestrians toward turning vehicles, and countermeasures regarding the use of mobile phones while walking for the context of this study were proposed.

Coal-derived electrically conductive asphalt pavements for snow/ice melting: From laboratory to field

Timely removal of ice and snow from roads is critical to safe, fast, and uninterrupted transportation networks in cold regions. Constructing electrically conductive asphalt pavements to melt the ice and snow on the roads through resistive heating is an emerging alternative technology to traditional snow/ice removal approaches such as utilizing snowplow machines and deicing chemicals. Carbon-based fibers and fillers including carbon fiber and graphite have been widely reported to make electrically conductive hot mix asphalt mixtures for pavement snow/ice-melting applications. This study aimed to develop and demonstrate a novel type of electrically conductive asphalt pavements for snow/ice-melting, which utilizes electrically conductive cold mix asphalt (CMA) mixtures incorporating coal-derived carbon-based coke aggregate as resistive heating elements. Both laboratory experiments and field tests were conducted to investigate the electrical, mechanical, and thermal properties of such electrically conductive asphalt mixtures and pavements. The laboratory experiment results indicated that the electrically conductive CMA mixtures incorporating coke aggregate had sufficiently high electrical conductivity and satisfactory mechanical performance and the pavement prototype slab utilizing a thin layer of such CMA mixtures could successfully raise the pavement surface temperatures to 8.3–11.7 °C from a low temperature of −5 °C with an input power density of 473 W/m2. The field test results showed that the full-scale coal-derived electrically conductive asphalt pavements were able to increase the pavement surface temperatures when electricity was applied, but the magnitude of temperature increase was highly dependent on the power density. Therefore, it is promising to use coke aggregate to construct coal-derived electrically conductive asphalt pavements for snow/ice melting.

Towards Sustainable Transportation: Adaptive Trajectory Tracking Control Strategies of a Four-Wheel-Steering Autonomous Vehicle for Improved Stability and Efficacy

The objective of continuous increase in the evolution of autonomous and intelligent vehicles is to attain a trustworthy, economical, and safe transportation system. Four-wheel steering (4WS) vehicles are favored over traditional front-wheel steering (FWS) vehicles because they have excellent dynamic characteristics. This paper exhibits the trajectory tracking task of a two degree of freedom (2DOF) underactuated 4WS Autonomous Vehicle (AV). Because the system is underactuated, MIMO, and has a nontriangular form, the traditional adaptive backstepping control scheme cannot be utilized to control it. For the purpose of rectifying this issue, two-state feedback-based methods grounded on the hierarchical steps of the block backstepping controller are proposed and compared in this paper. In the first strategy, a modified block backstepping is applied for the entire dynamic system. Global stability of the overall system is manifested by Lyapunov theory and Barbalat’s Lemma. In the second strategy, a block backstepping controller has been applied after a reduction of the high-order model into various first-order subsystems, consisting of Lyapunov-based design and stability warranty. A trajectory tracking controller that can follow a double lane change path with high accuracy is designed, and then simulation experiments of the CarSim/Simulink connection are carried out against various vehicle longitudinal speeds and road surface roughness to demonstrate the effectiveness of the presented controllers. Furthermore, a PID driver model is introduced for comparison with the two proposed controllers. Simulation outcomes show that the proposed controllers can attain good response implementation and enhance the 4WS AV performance and stability. Indeed, enhancement of the stability and efficacy of 4WS autonomous vehicles would afford a sustainable transportation system by lessening fuel consumption and gas emissions.

Systematic review of cognitive impairment in drivers through mental workload using physiological measures of heart rate variability

The intricate interplay between driver cognitive dysfunction, mental workload (MWL), and heart rate variability (HRV) provides a captivating avenue for investigation within the domain of transportation safety studies. This article provides a systematic review and examines cognitive hindrance stemming from mental workload and heart rate variability. It scrutinizes the mental workload experienced by drivers by leveraging data gleaned from prior studies that employed heart rate monitoring systems and eye tracking technology, thereby illuminating the correlation between cognitive impairment, mental workload, and physiological indicators such as heart rate and ocular movements. The investigation is grounded in the premise that the mental workload of drivers can be assessed through physiological cues, such as heart rate and eye movements. The study discovered that HRV and infrared (IR) measurements played a crucial role in evaluating fatigue and workload for skilled drivers. However, the study overlooked potential factors contributing to cognitive impairment in drivers and could benefit from incorporating alternative indicators of cognitive workload for deeper insights. Furthermore, investigated driving simulators demonstrated that an eco-safe driving Human-Machine Interface (HMI) significantly promoted safe driving behaviors without imposing excessive mental and visual workload on drivers. Recommendations were made for future studies to consider additional indicators of cognitive workload, such as subjective assessments or task performance metrics, for a more comprehensive understanding.

A probabilistic assessment of ship blackout incident with Fault Tree Analysis into (FTA) Bayesian Network (BN)

Blackouts in maritime activities can cause propulsion loss and dangerous maritime conditions. Bayesian risk analysis is applied to ship blackout incidents in this study to improve understanding and reduce risks. Using Fault Tree Analysis (FTA), a Bayesian Network (BN) model incorporates fuel quality, lubricating oil quality, sensor error, injector error, and mechanical defects to estimate blackout probability. The model analyses how hazards and their interactions affect this situation using probabilistic inference. Sensitivity analysis identifies variables that affect blackout probabilities and prioritises risk mitigation solutions. Based on prior and posterior probabilities, ‘Automatic Voltage Regulator Failure’ (0.03 prior, 0.17 posterior), ‘Rotor Mechanical Fault’ (0.03 prior, 0.15 posterior), and ‘High Cooling Water Temperature’ (0.03 prior, 0.13 posterior) are the top three blackout causes. Other significant variables include ‘Switchboard Line Failure,’ ‘Faulty Fuel Pump,’ ‘Rotor Open Circuit,’ and ‘Temperature Sensor Failure’ in relative amounts. Bayesian risk analysis can identify and minimise marine blackout concerns, giving decision-makers a comprehensive framework for informed decision-making and proactive risk management. This research emphasises blackout accidents’ importance, improving maritime transportation safety and reliability.

Progress in Corrosion Protection Research for Supercritical CO2 Transportation Pipelines

Carbon Capture, Utilization, and Storage (CCUS) technology is an emergent field with the potential for substantial CO2 emissions reduction, enabling low-carbon utilization of fossil fuels. It is widely regarded as a critical technology for combating global climate change and controlling greenhouse gas emissions. According to recent studies, China has identified CCUS as a key emissions reduction technology in climate change response and carbon neutrality objectives. Within this framework, supercritical CO2 (SC-CO2) transport pipelines are an essential means for efficient and safe transportation of CO2. Corrosion protection of pipelines enhances the efficiency and safety of CCUS technology and supports broader implementation and application. This paper reviews the current research on corrosion protection for SC-CO2 transport pipelines, discusses effect factors, compares various corrosion protection strategies, and analyzes the challenges in corrosion protection of SC-CO2 transport pipelines. It concludes with a perspective on future research and development directions in this field. This paper is dedicated to providing new research strategies for pipeline corrosion protection in CCUS technology in the future, and providing technical support for pipeline corrosion protection in CCUS industrial applications.

Identifying possible ways for adapting an open wagon for transporting containers

The object of this study is the processes of perception and redistribution of loads in the supporting structure of a open wagon loaded with containers, taking into account the new scheme of their fastening. For safe transportation of containers in an open wagon, it is suggested to use a removable module. This module works according to the principle of an intermediate adapter between the container and the open wagon body. Fastening of the module itself in the open wagon is carried out through the fitting stops, which are placed on the floor of the open wagon. Mathematical modeling was carried out to determine the longitudinal dynamic load acting on the container fixed according to the new scheme in the open wagon. To this end, a mathematical model was built that characterizes the longitudinal movements of the "open wagon - removable module - container" system. Determination of the accelerations that act on the supporting structure of the open wagon loaded with containers was also carried out by means of computer simulation. Verification of the formed model of the dynamic load of the open wagon was carried out according to the F-criterion. Also, as part of the study, a modal analysis of the load-bearing structure of a open wagon loaded with containers was carried out, which made it possible to assess its traffic safety. A feature of the results obtained as part of the research is that the proposed design of the removable module could be used not only for fastening containers but also for transportation of other types of cargo. The field of practical application of the results is railroad transport, including other transportation industries. The conditions for the practical use of the results are a symmetrical scheme of loading the body of a open wagon with containers. The results of this study will contribute to increasing the efficiency of container transportation and the profitability of railroad transport

Experimental Investigation of Biofouled Ship Control Surface Performance

Abstract Marine biofouling threatens ship safety by causing unexpected control surface performance. To explore these adverse biofouling effects, this paper presents the results of wind tunnel testing using a model-scale, low aspect ratio NACA 0018 control surface. Simulated calcareous biofouling was introduced as coarse-grit sandpaper, and, uniquely, roughness locations and sandpaper grits were varied to better understand any resulting degradation in hydrodynamic performance. Direct experimentation at high (turbulent) chord Reynolds number values suggests that leading-edge biofouling with 36-grit sandpaper results in worst-case performance, with the control surface's average lift-to-drag ratio reduced by 63%. Additional findings are presented, and recommendations for future experimental work are also provided.

Aviation-BERT-NER: Named Entity Recognition for Aviation Safety Reports

This work introduces Aviation-BERT-NER, a Named Entity Recognition (NER) system tailored for aviation safety reports, building on the Aviation-BERT base model developed at the Georgia Institute of Technology’s Aerospace Systems Design Laboratory. This system integrates aviation domain-specific data, including aircraft types, manufacturers, quantities, and aviation terminology, to identify named entities critical for aviation safety analysis. A key innovation of Aviation-BERT-NER is its template-based approach to fine-tuning, which utilizes structured datasets to generate synthetic training data that mirror the complexity of real-world aviation safety reports. This method significantly improves the model’s generalizability and adaptability, enabling rapid updates and customization to meet evolving domain-specific requirements. The development process involved careful data preparation, including the synthesis of entity types and the generation of labeled datasets through template filling. Testing on real-world narratives from the National Transportation Safety Board (NTSB) database highlighted Aviation-BERT-NER’s robustness, with a precision of 95.34%, recall of 94.62%, and F1 score of 94.78% when evaluated over 50 manually annotated (BIO tagged) paragraphs. This work addresses a critical gap in English language NER models for aviation safety, promising substantial improvements in the analysis and understanding of aviation safety reports.

Development of a Comprehensive Safety Evaluation Mechanism for the Highway Bus Industry

A suitable mechanism for evaluating the safety of the highway bus industry is crucial for ensuring public transportation safety in a country. In this study, we have combined indices from the Compliance Safety Accountability (CSA) program of the Federal Motor Carrier Safety Administration (FMCSA) in the US, the safety evaluation of renting buses in Japan, and some existing safety evaluation methods in Taiwan to develop a new safety evaluation mechanism for the highway bus industry. This paper introduces a two-stage decision making approach that includes the use of fuzzy analytic hierarchy process (Fuzzy AHP) and machine learning techniques such as decision trees, support vector machines, and random forests to develop this mechanism. The data used in this research were collected from the internet and directly from highway bus transportation companies. We calculated the safety performance of each company and assigned them different safety ranks. Compared with the causes of accidents in Taiwan, the results of this study showed that work hours, driver fitness, and administrative penalties are the three most important sub-attributes that affect the safety rank of the company.

How CCTV shapes perceptions of safety in London’s public transport: a quantitative analysis

ABSTRACT The perception of safety while using public transportation varies among individuals, influenced by personal experiences and individual characteristics. Closed-circuit television (CCTV) systems are commonly employed to deter crime in public areas, including transit environments where safety concerns are prevalent. This study aims to scientifically assess the effectiveness of CCTV in enhancing the perceived safety of public transportation users. A comprehensive survey was conducted, involving over 200 students from various universities to ensure a representative sample. The survey targeted public transportation users and focused on their sense of security in relation to the presence of CCTV. Through quantitative analysis, three specific research questions were addressed. The results indicate that the presence of CCTV alone is insufficient to significantly improve feelings of safety among public transportation users. These findings suggest important policy implications: enhancing perceived safety in public transportation requires more than just the implementation of security measures. It is essential to inform and educate the public about actual crime rates, existing security measures, and the importance of crime reporting. This comprehensive approach can more effectively address the multifaceted nature of safety perception in public transportation.

Accident Prevention of BOXN Wagon Train

The safety and efficiency of railway freight transportation are critical to the functioning of many national economies, and India is no exception. BOXN wagons, widely used in the Indian railway system for transporting heavy goods such as coal, minerals, and other bulk materials, are essential for supporting industries across the country. However, due to their size, weight, and the volume of cargo they carry, these wagons are prone to accidents if not properly managed. Accidents involving BOXN trains can result in severe consequences, including loss of life, damage to property, and environmental degradation. One of the biggest and most significant industries in the world is the Indian goods railway sector. It is essential for the transportation of materials and goods across the nation. Indian Railways seeks to shorten commute times by boosting the average freight train speed efficiently from 22 mph to 50 mph. The adoption of new technologies is anticipated to have positive effects on the Indian goods railway sector as well. Smart Innovations, for instance, smart goods cars and digital signaling can contribute to reducing train accidents and help to increase railroad productivity using smart mobility.

Predictive Modeling of the Hydrate Formation Temperature in Highly Pressurized Natural Gas Pipelines

In this study, we aim to develop advanced machine learning regression models for the prediction of hydrate temperature based on the chemical composition of sweet gas mixtures. Data were collected in accordance with the BOTAS Gas Network Code specifications, approved by the Turkish Energy Market Regulatory Authority (EMRA), and generated using DNV GasVLe v3.10 software, which predicts the phase behavior and properties of hydrocarbon-based mixtures under various pressure and temperature conditions. We employed linear regression, decision tree regression, random forest regression, generalized additive models, and artificial neural networks to create prediction models for hydrate formation temperature (HFT). The performance of these models was evaluated using the hold-out cross-validation technique to ensure unbiased results. This study demonstrates the efficacy of ensemble learning methods, particularly random forest with an R2 and Adj. R2 of 0.998, for predicting hydrate formation conditions, thereby enhancing the safety and efficiency of gas transport and processing. This research illustrates the potential of machine learning techniques in advancing the predictive accuracy for hydrate formations in natural gas pipelines and suggests avenues for future optimizations through hybrid modeling approaches.

Application of Advanced Algorithms in Port State Control for Offshore Vessels Using a Classification Tree and Multi-Criteria Decision-Making

This article examines the methods and application of classification trees and multi-criteria decision-making in the process of holding offshore vessels in port (Port State Control—PSC). This work aims to improve the efficiency and precision of the control processes in the detention of offshore vessels by using advanced analytical methods. Methodologically, a classification decision tree was used to identify the most important risk factors, enabling a faster and more accurate assessment of the possibility of detaining offshore vessels in port. Multi-criteria decision-making (MCDM) also enabled the simultaneous assessment of multiple factors, ensuring a balanced, robust, accurate, and objective approach. The research results show that the integration of these methods into the PSC process can significantly increase the safety of shipping and reduce the operating costs of offshore vessels. The application of these analytical tools can lead to a more systematic and transparent inspection process. This paper suggests further research and training of inspectors in the use of these techniques to maximize their applicability and effectiveness. Finally, this paper emphasizes the potential of classification trees and MCDM for safer and more efficient maritime transport by improving PSC procedures.