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As one of the primary sources of carbon emissions, transportation sector has proposed various measures to reduce its carbon emissions. Introducing energy-efficient and low-carbon autonomous trucks into freight transportation is highly promising, but faces various challenges, especially safety issues. This study addresses eco-friendly lane reservation-based autonomous truck transportation network design for transportation safety and low carbon emissions. It aims to optimally implement dedicated truck lanes in an existing network and design dedicated routes for autonomous truck transportation to simultaneously minimise the negative impact caused by dedicated truck lanes and carbon emissions of the entire transportation system. We first formulate this problem into a bi-objective integer linear program. Then, an ϵ-constraint-based two-stage algorithm (ETSA) is proposed to solve it based on explored problem properties. A case study based on the well-known Sioux Falls network is conducted to demonstrate the applicability of the proposed model and algorithm. Computational results for 310 instances from the literature demonstrate that the proposed algorithm significantly outperforms the ϵ-constraint combined with the proposed ILP in obtaining the Pareto front. Moreover, helpful managerial insights are derived based on sensitivity analysis. [ABSTRACT FROM AUTHOR]

Today, in the global market of logistics services for the transportation of dangerous goods, there is a problem of balancing the commercial and safety conditions of these transportations. The system of international regulation of the transportation of dangerous goods is quite complex and its regulatory legal support includes conventions and agreements. The current scheme of actions of the main conventions and agreements regulating the transportation of dangerous goods by various modes of transport was created by an international organization and must be implemented in national legislation. The system of international regulation of the transportation of dangerous goods is quite complex and its regulatory legal support includes many conventions and agreements. Scheme of actions of the main conventions and agreements regulating the transportation of dangerous goods by various types of transport, and international organizations that ensure their development and promote their implementation into regional norms and national transport legislation, outlined in this article. In the international plan, the main source of the regulatory framework in the field of transportation of dangerous goods by all types of transport are the UN Recommendations on the Transportation of Dangerous Goods (Model Rules). The development of recommendations for the transportation of dangerous goods (Model Rules) is carried out by the Committee of Experts on the Transportation of Dangerous Goods in the globally agreed system of classification and labeling of chemical substances of the Economic and Social Council of the United Nations. On their basis, international organizations and national authorities of various countries are developing normative documents regulating the transportation of dangerous goods by various modes of transport, i.e., reflects the international legal basis, which should be implemented in national legislation. Today, the 18th edition of the Standard Rules for the Transportation of Dangerous Goods is in effect. Work on improving standards continues primarily because, according to UN data, the share of dangerous goods in world freight traffic is constantly growing and now reaches almost half of its total volume. The transportation of dangerous goods is associated with a significant potential for the occurrence of extraordinary events. There is an urgent need for measures to reduce this potential to the level of residual risks acceptable to society and the state. [ABSTRACT FROM AUTHOR]

Skid resistance of pavement plays a vital role in transportation safety, and it continuously deteriorates because the pavement was exposed to traffic and environmental loadings. Although the evaluation law of asphalt pavement skid resistance has been intensively studied, few has focused on its impact on driving safety. To close this knowledge gap, the current study used the CarSim/Simulink simulation and back-propagation (BP) neural network method to establish the relationship between pavement characteristics and vehicle driving safety. The skid resistance performances of nine types of asphalt mixture specimen were evaluated using a full life cycle dynamic friction testing machine (LCDFM), and results showed that the deterioration rate of the dynamic friction coefficient (μLCDFM) first increases rapidly, then slowly decreases and finally stabilizes. The early-stage increase resulted from the polish of asphalt film from the aggregate surface. The simulation studies indicated that driving speed exhibits the most significant impact on driving safety, followed by vehicle type, mean texture depth (MTD), and polishing time. The MTD is negatively correlated with μLCDFM , and the other factors are positively correlated. The driving safety rankings of the three pavement types and three gradations examined are open-graded friction coarse (OGFC) > stone mastic asphalt (SMA) > asphalt concrete (AC). Meanwhile, a greater value of nominal maximum aggregate size (NMAS) also induces better driving safety owing to the more abundant surface macrotexture. The research findings from this study provide reference to the materials design and pavement skid resistance prediction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the Variable Message Signs (VMS) mounted over the cab of emergency vehicles as a safety countermeasure to protect roadside incident and service personnel. The research team collaborated with the Alabama Department of Transportation's Safety Service Patrol (SSP) program, specifically the Alabama Service Assistance Patrol (ASAP) in the West Central Alabama region, to collect video data from their service vehicles. Deep learning techniques were employed to detect vehicles in the recorded videos. A total of 11,338 passing vehicles were detected in 135,946 frames of video footage, and their trajectories were extracted for analysis. The study focused on examining the behaviors and movements of passing vehicles, including their speed and lane change behaviors, and developed statistical models to systematically investigate the impact of VMS on these behaviors. Random intercept models were utilized to account for unobserved factors associated with different stop locations. The modeling results revealed significant relationships between the use of VMS and the behaviors of passing motorists. When the VMS was active, drivers were more likely to change lanes and reduce their speed compared to situations where the VMS was not active. The odds of a vehicle changing lanes were found to be 95% higher when the VMS was in use. These findings suggest that the utilization of VMS can have a positive impact on traffic, particularly for passenger vehicles. The study outcomes imply that the use of VMS could be an effective countermeasure in protecting roadside incidents and ensuring the safety of service personnel. [ABSTRACT FROM AUTHOR]

Transport safety campaigns are valuable for public health, as they prevent traffic fatalities and collisions. Authorities and nonprofit organizations attempt to promote transport safety by advertising via social media. Understanding the factors that make online transport safety campaigns successful is also important. However, studies exploring the impact of animated videos on transportation safety are scarce. We adopted the stimuli-organism-response (S-O-R) framework to examine the impact of music and graphics on transport users' emotional states and behaviors. We also considered how the reactions varied across cultures. The respondents were shown animated transport safety advertisements produced by Metro Trains in Melbourne, Australia. A total of 217 valid questionnaires were collected from transportation users in Taiwan and Vietnam. In contrast, we explored the spillover effects of an animated transport safety promotion campaign across cultures. The findings show that music significantly impacts people's emotions, which, in turn, are associated with positive behavioral intentions. Self-efficacy had the greatest effect on behavioral responses. We also found that transport users in Taiwan were more influenced by graphics, whereas those in Vietnam were more affected by music in the S-O path. Fear arousal affects the behavioral intentions of Vietnamese transportation users in the O-R path. Self-efficacy is the dominant factor affecting the ability of Taiwan's transport users to obey traffic rules. Transport safety advertising should be designed to evoke emotions, and specific stimulus elements should be adjusted according to cultural preferences. [ABSTRACT FROM AUTHOR]

Large container ships are the main means of maritime trade transportation, and transportation safety and efficiency directly affect global economic development. The complex maritime navigation conditions make it difficult to ensure the safety and efficiency of large container ships. In order to improve the safety and transportation efficiency of large container ships sailing at sea, a Maneuvering Motion Generator (MMG) is proposed. Based on the existing four degree of freedom model, a six degree of freedom ship maneuvering motion model is constructed to simulate and analyze the sea maneuvering of large container ships. The results showed that the constructed model accurately simulated the changes in pitch angle and roll angle during ship navigation, and the error between roll angle and pitch angle did not exceed 0.2°. When the relative wind direction was 120°, and the wind speed was 13m/s, wind interference had the greatest impact on ship navigation. At this time, the lateral deviation was more than 120m, and the heading deviation angle was more than 4°. When the wind speed was 16m/s, the influence of wind interference on the ship's heading was significantly reduced after the ship's steering, and the deviation angle reduced from 0.3° to almost no effect. The designed simulation model for large ship maneuvering compensates for the lack of upper and lower wave states in ship maneuvering simulation, accurately simulating the sea motion state of large ships. This result helps operators make optimal decisions in strong winds and other disruptive environments, improving the safety and efficiency of large ships navigating at sea. [ABSTRACT FROM AUTHOR]

Objectives: This article presents an analysis of the traffic-calming effects of bus rapid transit (BRT) by studying changes to motor vehicle speeds before and after implementation of Albuquerque Rapid Transit (ART) infrastructure in Albuquerque, New Mexico. Methods: While ART construction was completed in spring 2018, the BRT buses did not operate until December 2019; providing a unique opportunity to explore the influence of BRT infrastructure sans BRT buses (i.e., to tease apart the effects of BRT infrastructure and operations). We used validated data from StreetLight InSight to compare before/after changes to average motor vehicle speeds and 85th percentile motor vehicle speeds at 46 ART sites and 36 control sites. Results: Findings suggest that infrastructure associated with BRT systems can improve traffic safety by reducing vehicle speeds. Speed decreases at the ART sites were especially strong in terms of 85th percentile decreases, suggesting that the BRT infrastructure is especially effective at limiting excessive speeding. Motor vehicle 85th-percentile speeds along the ART corridor were reduced by 11.5% (compared to a 5.8% decrease at control sites). The 85th-percentile speeds at the ART sites decreased from 32.3 mph to 28.6 mph, which is an especially important range for vulnerable road-user safety outcomes. While ART intersections saw the largest decreases in absolute speeds (a reduction of 4.1 mph in 85th-percentile speeds), ART mid-block sites had larger decreases relative to the control mid-block sites (decreases in 85th-percentile speeds were 73.7% greater at ART mid-block sites than at control mid-block sites). BRT-related lane reductions were linked with particularly strong speed reductions; there were 85th-percentile speed reductions of 4.1 mph (12.6%) when general vehicle lanes were removed versus 2.2 mph (7.8%) when lanes were not removed. Conclusions: Speed reductions were experienced across the ART corridor even though 87.0% of BRT locations did not have a change in posted speed limit, suggesting that physical changes to the roadway associated with BRT were impactful in terms of speed reductions and in turn could possibly promote traffic injury prevention by decreasing the number and severity of crashes. [ABSTRACT FROM AUTHOR]

In recent years, longer and heavier trains have become more common, primarily driven by efficiency and cost‐saving measures in the railroad industry. Regulation of train length is currently under consideration in the United States at both the federal and state levels, because of concerns that longer trains may have a higher risk of derailment, but the relationship between train length and risk of derailment is not yet well understood. In this study, we use data on freight train accidents during the 2013–2022 period from the Federal Railroad Administration (FRA) Rail Equipment Accident and Highway‐Rail Grade Crossing Accident databases to estimate the relationship between freight train length and the risk of derailment. We determine that longer trains do have a greater risk of derailment. Based on our analysis, running 100‐car trains is associated with 1.11 (95% confidence interval: 1.10–1.12) times the derailment odds of running 50‐car trains (or a 11% increase), even accounting for the fact that only half as many 100‐car trains would need to run. For 200‐car trains, the odds increase by 24% (odds ratio 1.24, 95% confidence interval: 1.20–1.28), again accounting for the need for fewer trains. Understanding derailment risk is an important component for evaluating the overall safety of the rail system and for the future development and regulation of freight rail transportation. Given the limitations of the current data on freight train length, this study provides an important step toward such an understanding. [ABSTRACT FROM AUTHOR]

The intelligent design of complex transportation nodes is an important way to improve urban transportation efficiency and safety. However, currently, there are still problems with low data acquisition and processing accuracy and high-technology application costs in complex transportation nodes. Therefore, the research attempts to achieve the intelligent design of complex traffic node organizations based on digital analogue-driven and integrated building information models and optimization graph convolution algorithms. It aims to achieve real-time monitoring and analysis of traffic information to improve the overall performance of the transportation system. These experiments confirmed that the studied BIM could achieve full lifecycle management of urban interchanges, and could achieve strain analysis and trend prediction based on time series. When the organizational members were 122, the information channels for smart stations based on research technology were 241, which improved information efficiency by more than 90%. When the sensor node was 48, the predicted values of the research model based on the optimization graph convolution algorithm were consistent with the actual values, with a prediction accuracy of 95%. Therefore, the technology studied in this study can provide reliable solutions for the design of complex traffic nodes and support traffic management decisions. [ABSTRACT FROM AUTHOR]

Background: The 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. Method: This 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. Results: The 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. Conclusion: Findings 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. [ABSTRACT FROM AUTHOR]

Hazardous materials pose significant dangers during transportation due to their flammable and explosive properties. The consequences of accidents involving such materials are often severe and irreparable. A well‐designed hazardous materials transportation network can mitigate these risks. However, designing such a network presents two major challenges: quantifying the risk associated with hazardous materials transportation and addressing the hierarchical relationship between government and companies. To address these challenges, we enhance the accuracy of accident probability estimates and the comprehensiveness of accident consequence assessments, incorporating the uncertainty of accident outcomes. We propose a comprehensive risk assessment model and develop a bilevel programming model to reflect the hierarchical relationship. In this model, the government at the upper level aims to minimize the total risk, while companies at the lower level seek to minimize their total costs. The model is transformed using chance‐constrained programming and solved using heuristic algorithms. We apply the model to the highway network in Anhui province, China, to verify its validity. The results demonstrate that the model effectively manages the hierarchical relationship between government and companies, reduces the risk of hazardous materials transportation, and enhances the stability and safety of the transportation network. [ABSTRACT FROM AUTHOR]

This paper presents an investigation aimed at enhancing the snow resistance of bogies in a three-car grouped alpine high-speed train (HST) that operates in a snowy region. The study employs a combination of the unsteady Reynolds-averaged Navier–Stokes model, coupled with the discrete phase model. This modeling approach has been validated through wind tunnel tests, including flow field tests, two-phase flow tests, and snow-ice tests. The study comprehensively compares the characteristics of the wind-snow flow, spatial snow concentration, and the distribution of snow on the train's underbody between two scenarios: the original train's underbody structure and the multifactor collaborative optimization scheme (MCOS). The results indicate that the MCOS case facilitates the escape of snow particles from the bogie cavities by weakening the upward and reverse flows. Furthermore, the MCOS case significantly reduces the concentration of snow particles around the heat-producing elements and decreases the accumulation of snow on both the lower and upper surfaces of the bogies. As a result, it reduces snow accumulation on the bogies by 50.6%, 56.4%, 60.8%, and 62.4% at train speeds of 200, 250, 300, and 350 km/h, respectively. In summary, this research provides valuable insights for improving the snow resistance of HSTs operating in snowy regions, with potential applications in enhancing railway transportation safety and efficiency. [ABSTRACT FROM AUTHOR]

To lighten the workload of train drivers and enhance railway transportation safety, a novel and intelligent method for railway turnout identification is investigated based on semantic segmentation. More specifically, a railway turnout scene perception (RTSP) dataset is constructed and annotated manually in this paper, wherein the innovative concept of side rails is introduced as part of the labeling process. After that, based on the work of Deeplabv3+, combined with a lightweight design and an attention mechanism, a railway turnout identification network (RTINet) is proposed. Firstly, in consideration of the need for rapid response in the deployment of the identification model on high-speed trains, this paper selects the MobileNetV2 network, renowned for its suitability for lightweight deployment, as the backbone of the RTINet model. Secondly, to reduce the computational load of the model while ensuring accuracy, depth-separable convolutions are employed to replace the standard convolutions within the network architecture. Thirdly, the bottleneck attention module (BAM) is integrated into the model to enhance position and feature information perception, bolster the robustness and quality of the segmentation masks generated, and ensure that the outcomes are characterized by precision and reliability. Finally, to address the issue of foreground and background imbalance in turnout recognition, the Dice loss function is incorporated into the network training procedure. Both the quantitative and qualitative experimental results demonstrate that the proposed method is feasible for railway turnout identification, and it outperformed the compared baseline models. In particular, the RTINet was able to achieve a remarkable mIoU of 85.94%, coupled with an inference speed of 78 fps on the customized dataset. Furthermore, the effectiveness of each optimized component of the proposed RTINet is verified by an additional ablation study. [ABSTRACT FROM AUTHOR]

This paper aims to thoroughly examine and compare advanced driver-assistance systems (ADASs) in the context of their impact on safety and driving comfort. It also sought to determine the level of acceptance and trust drivers have in these systems. The first chapter of this document describes the sensory detectors used in ADASs, including radars, cameras, LiDAR, and ultrasonics. The subsequent chapter presents the most popular driver assistance systems, including adaptive cruise control (ACC), blind spot detection (BSD), lane keeping systems (LDW/LKS), intelligent headlamp control (IHC), and emergency brake assist (EBA). A key element of this work is the evaluation of the effectiveness of these systems in terms of safety and driving comfort, employing a survey conducted among drivers. Data analysis illustrates how these systems are perceived and identified areas requiring improvements. Overall, the paper shows drivers' positive reception of ADASs, with most respondents confirming that these technologies increase their sense of safety and driving comfort. These systems prove to be particularly helpful in avoiding accidents and hazardous situations. However, there is a need for their further development, especially in terms of increasing their precision, reducing false alarms, and improving the user interface. ADASs significantly contribute to enhancing safety and driving comfort. Yet, they are still in development and require continuous optimization and driver education to fully harness their potential. Technological advancements are expected to make these systems even more effective and user-friendly. [ABSTRACT FROM AUTHOR]

In the realm of intelligent cockpits, the predominant emphasis on enhancing driving safety and comfort through human‐vehicle interaction and driving behavior monitoring has conventionally centered on the upper body of the driver. Regrettably, the wealth of information inherent in the lower half of the driver's body, particularly the feet, tends to be systematically underestimated by researchers. A specialized intelligent carpet tailored for automotive cockpits that integrates sponge‐based triboelectric sensor to monitor driver's foot movement is proposed. The intelligent carpet contains two areas, namely the human‐vehicle interaction sensing area and the driving intention monitoring area. With the human‐vehicle interaction sensing area, the driver can interact with the vehicle through the left foot, resulting in reducing the occupation of visual resources and hand load. Furthermore, the driving intention monitoring area supported by the right foot can effectively identify driver's intentions and driver's behaviors, which are essential to trajectory prediction, optimal path planning, and driving safety. It is demonstrated that an intelligent carpet is potentially useful for human‐vehicle interaction, and transportation optimization and accident prevention. [ABSTRACT FROM AUTHOR]

The safety of the civil aviation system has been of increasing concern with several accidents in recent years. It is urgent to put forward a precise accident prediction model, which can systematically analyze safety from the perspective of accident mechanism to enhance training accuracy. Furthermore, the predictive model is critical for stakeholders to identify risk and implement the proactive safety paradigm. In this work, to mitigate casualties and economic losses arising from aviation accidents and improve system safety, the focus is on predicting the aircraft damage severity, the injury/death severity, and the flight phases in the sequence of identifying event risk sources. This work establishes a multi-task deep convolutional neural network (MTCNN) learning framework to accomplish this goal. An innovative prediction rule will be developed to refine prediction results from two approaches: handling imbalanced classes and Bayesian optimization. By comparing the performance of the proposed multi-task model with other single-task machine learning models with ten-fold cross-validation and statistical testing, the effectiveness of the developed model in predicting aviation accident severity and flight phase is demonstrated. [ABSTRACT FROM AUTHOR]

In the field of computer vision, the detection of road potholes at night represents a critical challenge in enhancing the safety of intelligent transportation systems. Ensuring road safety is of paramount importance, particularly in promptly repairing pothole issues. These abrupt road depressions can easily lead to vehicle skidding, loss of control, and even traffic accidents, especially when water has pooled in or submerged the potholes. Therefore, the detection and recognition of road potholes can significantly reduce vehicle damage and the incidence of safety incidents. However, research on road pothole detection lacks high-quality annotated datasets, particularly under low-light conditions at night. To address this issue, this study introduces a novel Nighttime Pothole Dataset (NPD), independently collected and comprising 3831 images that capture diverse scene variations. The construction of this dataset aims to counteract the insufficiency of existing data resources and strives to provide a richer and more realistic benchmark. Additionally, we develop a baseline detector, termed WT-YOLOv8, for the proposed dataset, based on YOLOv8. We also evaluate the performance of the improved WT-YOLOv8 method and eight state-of-the-art object detection methods on the NPD and the COCO dataset. The experimental results on the NPD demonstrate that WT-YOLOv8 achieves a 2.3% improvement in mean Average Precision (mAP) over YOLOv8. In terms of the key metrics—AP@0.5 and AP@0.75—it shows enhancements of 1.5% and 2.8%, respectively, compared to YOLOv8. The experimental results provide valuable insights into each method's strengths and weaknesses under low-light conditions. This analysis highlights the importance of a specialized dataset for nighttime pothole detection and shows variations in accuracy and robustness among methods, emphasizing the need for improved nighttime pothole detection techniques. The introduction of the NPD is expected to stimulate further research, encouraging the development of advanced algorithms for nighttime pothole detection, ultimately leading to more flexible and reliable road maintenance and road safety. [ABSTRACT FROM AUTHOR]

Ice formation is generally considered to exclude many particles and most solutes and thus be relatively pure compared to ambient waters. Because river ice forms by a combination of thermal and mechanical processes, some level of sediment entrainment in the ice column is likely, though reports of sediment in river ice are limited. We observed high and sporadic levels of silt and sand in ice of the Kuskokwim and Tanana rivers (Alaska, the United States) during routine field studies. These observations led us to make a more comprehensive survey of sediment entrainment in river ice of the Kuskokwim and Yukon rivers and several of their tributaries. We collected and subsampled 48 ice cores from 19 different river locations in March 2023, which included concurrent measurements of water turbidity, velocity, and depth. Approximately 60% of cores contained detectable levels of sediment, averaging 438 mg/L with median concentrations exceeding 1000 mg/L in three cores from the Yukon and Kuskokwim main stems. Many cores had even higher concentrations at certain intervals, with seven cores having subsamples exceeding 2000 mg/L; these were often located in the middle or lower portion of the ice column. Jumble ice, formed mechanically by frazil‐pan jamming during freeze‐up, was generally the best predictor of higher sediment entrainment, and these locations often had higher under‐ice velocities and depths. Our observation of high and widespread sediment entrainment in northern river ice, particularly in jumble‐ice fields, may have implications for sediment transport regimes, ice strength and transportation safety, and how rivers break up in the springtime. [ABSTRACT FROM AUTHOR]

This study presents a novel, machine-learning-based Automatic Incident Detection (AID) system for freeways. Through a comprehensive analysis of existing AID systems, the paper identifies their limitations and key performance metrics. VISSIM, a traffic simulation software, is employed to generate diverse, realistic traffic data incorporating factors significantly impacting AID performance. The developed system utilizes an Artificial Neural Network (ANN) trained via RapidMiner software. The ANN is designed to learn and differentiate normal and incident traffic patterns. Training yields a Detection Rate (DR) of 95.6%, a False Alarm Rate (FAR) of 1.01%, and a Mean Time to Detection (MTTD) of 0.89 min. Testing demonstrates continued effectiveness with a DR of 100%, a FAR of 1.29%, and a MTTD of 1.6 min. Furthermore, a sensitivity analysis is conducted to assess the influence of individual factors on system performance. Based on these findings, recommendations for enhancing AID systems are provided, promoting improved traffic safety and incident management. This research empowers transportation authorities with valuable insights to implement effective incident detection strategies, ultimately contributing to safer and more efficient freeways. [ABSTRACT FROM AUTHOR]

Copyright of Low-Carbon Chemistry & Chemical Engineering is the property of Low-Carbon Chemistry & Chemical Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Featured Application: Towing systems are largely used in the automotive industry. They must respect some requirements defined by the standards. These important systems for cars are studied in this paper in order to improve safety in transportation. The authors present the possibility to use other materials for towbars and formulate a series of practical recommendations obtained as a result of extended studies on towing systems. This paper aims, in the context of the need to reduce the weight and price of vehicles, to present theoretical and experimental results regarding towing systems made of alternative materials (fibrous composite materials and aluminum alloys). The study of the main element of the whole system, namely towbar research, was stressed, presenting comparative results. The FEM will be used to obtain the stress and strain field in the towball and the towing system. The experimental tests validate the theoretical results obtained. This paper studies five towing systems made by different materials and finally presents a series of practical recommendations, useful in the industry, regarding the improvement of the analyzed models. [ABSTRACT FROM AUTHOR]

Carbon capture and storage technologies play crucial roles in mitigating atmospheric greenhouse gases (GHGs). Pipeline transportation is the primary method of CO2 transportation, making pipeline safety a priority. In this study, Fluent software was used to create a model for annular edge leakage flanges, which significantly differs from the traditional pinhole leakage model. This study aims to examine the impact of CO2 pipeline flow and pressure on the diffusion of gas leaking from the flange and to develop a precise correlation between the diffusion distance and substance concentration. The results indicate that an increase in flow and pressure intensifies the diffusion of the flange leakage. Specifically, for a leakage lasting 96 s at flow rates of 0.7 and 10 m3/h, the diffusion ranges for the 5% concentration alarm threshold are 0.47 and 2.86 m, respectively. Furthermore, at a speed of 10 m/s and a pressure of 0.4 MPa, the diffusion ranges for 5 and 2% alarms are similar, spanning from 0.33 to 0.35 m. This study provides theoretical support and technical improvements to ensure the safe operation of pipelines. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Bridges are prone to damage from various factors, impacting the overall safety of transportation networks. Accurate damage identification is crucial for maintaining bridge integrity. This study proposes a novel method using encoded images and a convolutional neural network (CNN) for bridge damage identification. By converting raw acceleration data into encoded images, the data can be represented from multiple perspectives, enhancing the extraction of essential features related to bridge damage states. The method was validated using data simulated from a continuous rigid-frame bridge model. The results demonstrate that using encoded images as inputs yields a higher recall rate, precision, and F1-score compared to using acceleration responses as inputs, achieving a comprehensive accuracy of 92%. This study concludes that the combination of encoded images and CNN provides a robust approach for accurate and efficient bridge damage identification. [ABSTRACT FROM AUTHOR]

Monitoring pavement conditions using crowdsourced vehicular data can significantly contribute to real-time and cost-effective pavement maintenance decision-making. This paper presents the development of various machine learning models for predicting crucial pavement characteristics essential for ensuring roadway safety, including roadway longitudinal grade, cross slope, international roughness index, surface rutting, and pavement skid resistance. The study collected vehicular sensing data and paired it with field pavement characteristics obtained using innovative instruments on selected asphalt and concrete sections in Oklahoma. Seven machine learning models were trained using the AutoGluon platform, yielding highly accurate predictions for safety-related roadway characteristics. The weighted ensemble L2, random forest, and category boosting (CatBoost) models exhibited the highest accuracy, with R-squared values exceeding 0.9, while the k-nearest neighbor algorithm and LightGBM models showed lower competitiveness. The inference latency of the models varied, with CatBoost demonstrating the lowest latency and weighted ensemble L2 achieving the highest accuracy at the expense of slightly higher inference latency. The choice of model depends on the specific application, whether it be pavement network management or real-time roadway condition monitoring. The findings from this research empower transportation agencies to efficiently screen the pavement network for further inspection or maintenance, thus enhancing transportation safety by providing instant alerts to drivers about potential high-risk pavement sections, resulting in safer and more reliable transportation infrastructure. [ABSTRACT FROM AUTHOR]

The rapid development of high techniques in recent years has made the operation of intelligent ships a possible option in foreseeable future. This will inevitably change the navigation environment of ships. Both conventional and intelligent ships will sail in the same channels and water areas. This research proposed a framework to meet the needs of future waterway transportation safety in such a mixed environment. The proposed framework can model and assess the risk of inland waterway transportation in a mixed traffic environment of remote-control and conventional ships. It defined four stages of the mixed traffic scenario in inland rivers, including perception, cognition, decision, and control stage. On this basis, we explored the risk factors under mixed traffic scenario, and construct the system dynamics model of transportation risk accordingly. A case study of risk modelling and assessment of inland waterway transportation systems in continuous bridge areas under the mixed environment was conducted to validate the proposed method. The results showed that the proposed framework can identify the main stages and key factors affecting inland waterway transportation system safety under the mixed traffic environment. This research provides a theoretical basis for the safety supervision of remotely controlled ship operations in future. [ABSTRACT FROM AUTHOR]

Four‐dimensional trajectory prediction is a crucial component of air traffic management, and its accuracy is closely related to the efficiency and safety of air transportation. Although long short‐term memory (LSTM) or its variants have been widely used in recent studies, they may produce unacceptable results in long‐term prediction due to the iterative output that accumulates error. To address this issue, a transformer‐based long‐term trajectory prediction model is proposed here, which utilizes the self‐attention mechanism to extract time series features from historical trajectory data. For long‐term prediction scenarios, we a trajectory stabilization module is introduced to ensure the stationarity of the time series for better predictability. Additionally, the transformer output strategy is improved to generate the prediction sequence by a single step instead of serial dynamic decoding, thus effectively enhancing the precision and inference speed. The proposed model is validated using real data obtained from China's Southwest Air Traffic Management Bureau. The experimental results demonstrate that this model outperforms the benchmark model. Further ablation experiments and visualizations are performed to analyze the impact of trajectory stabilization and one‐step inference strategy. [ABSTRACT FROM AUTHOR]

Railway transportation of hazardous materials (HAZMAT) is common and is generally considered safe. However, transporting toxic, flammable, and explosive substances via railways carries significant risk due to their high volume, proximity to populated areas, low public awareness, and potential domino effect. Particularly, the practice has come into question after a Norfolk Southern train derailed in Ohio in February of 2023. This derailment continuously reminds the public and the industry that such incidents can profoundly affect a community, critical infrastructure, and the environment. To identify the root cause of the Ohio train derailment and discover the deficiency of the safety system applied, combining the cause mapping approach with the safety triad concept was employed in this study. Based on this approach and the preliminary incident investigation released by the National Transportation Safety Board, the incident sequence is established, and the causal events leading to this incident are identified in the three essential pillars of its safety system: prevention, mitigation, and response, respectively. The study subsequently develops recommendations to improve the safety system of HAZMAT freight trains. This is expected to lower further the probability and consequence of HAZMAT freight train incidents and ultimately result in long‐term changes in railroad transportation. [ABSTRACT FROM AUTHOR]

The fast growth of internet of vehicles (IoV) has created a new area of connectedness, with promising safety and efficiency in transportation. However, this advancement in vehicle technology has come with significant cybersecurity risks, specifically through control area network (CAN) protocol and other communication techniques within vehicles. This experimental study suggests a machine learning (ML) based security approach based on the extreme learning machine (ELM) algorithm to address these challenges. Unlike customary neural networks, ELM is known for its fast processing, minimal training time, and high accuracy, making it preferably suitable for dynamic IoV environments. The methodology involves data preprocessing, feature selection, and employing ELM for attack classification; the algorithm’s performance is evaluated using CARHacking, NSL-KDD, and EdgeIIoT datasets. We also examine the significance of distributed processing to enhance the computational efficiency of the model, obtaining 89% accuracy in 3 ms run-time for external networks, and 83% accuracy with 9 ms run-time for intra-vehical networks. This newly proposed security mechanism using ELM shows very accurate results in detecting intrusions with a high recall rate and reduced computation time through distributed processing. [ABSTRACT FROM AUTHOR]

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective of this study is to investigate the potential risks posed by snowdrifts, a prevalent cause of natural disasters in northern China, on railway subgrades, and to assess their risk level. As a wind-driven process of snow migration and redeposition, snowdrifts pose a significant threat to the safety of transportation infrastructures. This study focuses on the Afu Railway in Xinjiang, situated on the northern slopes of the Eastern Tianshan Mountains, where it experiences periodic snowdrifts. We employed a combination of the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation (FCE) to construct an integrated evaluation system for assessing the risk of snowdrift to railway subgrades. The results indicate that subgrade design parameters and regional snowfield conditions are two key metrics affecting the extent of snowdrift disasters, with topography, vegetation coverage, and wind speed also exerting certain impacts. The evaluation method of this study aligns with the results of on-site observations, verifying its accuracy and practicality, thereby providing a solid risk assessment framework for snowdrifts along the railway. The scientific and systematic hazard assessment method of railway subgrades developed in this research provides basic data and theoretical support for future research, and provides a scientific basis for relevant departments to formulate countermeasures, so as to improve the safety and reliability of railway operations. [ABSTRACT FROM AUTHOR]

Railway track defects pose significant safety risks and can lead to accidents, economic losses, and loss of life. Traditional manual inspection methods are either time-consuming, costly, or prone to human error. This paper proposes RailTrack-DaViT, a novel vision transformer-based approach for railway track defect classification. By leveraging the Dual Attention Vision Transformer (DaViT) architecture, RailTrack-DaViT effectively captures both global and local information, enabling accurate defect detection. The model is trained and evaluated on multiple datasets including rail, fastener and fishplate, multi-faults, and ThaiRailTrack. A comprehensive analysis of the model's performance is provided including confusion matrices, training visualizations, and classification metrics. RailTrack-DaViT demonstrates superior performance compared to state-of-the-art CNN-based methods, achieving the highest accuracies: 96.9% on the rail dataset, 98.9% on the fastener and fishplate dataset, and 98.8% on the multi-faults dataset. Moreover, RailTrack-DaViT outperforms baselines on the ThaiRailTrack dataset with 99.2% accuracy, quickly adapts to unseen images, and shows better model stability during fine-tuning. This capability can significantly reduce time consumption when applying the model to novel datasets in practical applications. [ABSTRACT FROM AUTHOR]

Higher efficiency of rail transportation at the present stage of development of the transport industry necessitates the creation and introduction of rail vehicles with improved technical and economic characteristics among which is reduced tare weight. The issue of reducing the tare weight of wagons is quite urgent. It deals not only with the sprung mass of the wagon but also with the load on the rail track, which is under the influence of constant cyclic loads. Therefore, the present study deals with the development of a wagon covered with tarpaulin for carrying goods requiring protection against the environment. The loads inherent for operation on 1520 mm gauge lines are considered. The covered wagon mod. 11-217 is chosen as a prototype. The profiles of the covered wagon frame components are selected according to the moment of resistance of their cross-sections. It is found that the proposed design has a 16% lower tare weight than that of the prototype. The results of the strength calculation for the wagon under the main design operating modes have proved the feasibility of its structural design. The motion of the covered wagon over a track irregularity has been assessed as 'excellent'. The results of the study will contribute to the creation of recommendations for the development of modern structures of covered wagons as well as improve the efficiency of railway transportation. [ABSTRACT FROM AUTHOR]

Roads are the most heavily affected aspect of urban infrastructure given the ever-increasing number of vehicles needed to provide mobility to residents, supply them with goods, and help sustain urban growth. An important indicator of degrading road infrastructure is the so-called bump features of the road surface (BFRS), which have affected transportation safety and driving experience. To collect BFRS, we can collect discrete-sampled, non-homogeneous multi-sensor stream data. We propose a BFRS detection method based on spectrum modeling and multi-dimensional features. With the sampling rate of GPS at 1 Hz and a gyroscope and accelerometer at 100 Hz, multi-sensor stream data are recorded at three different urban areas of Nanjing, China, using the smartphone mounted on a vehicle. The recorded stream data captures a geometric feature modeling movement and the respective driving conditions. Derived features also include acceleration, orientation, and speed information. To capture bump features, we develop a deep-learning-based approach based on so-called spectrum features. BFRS detection experiments using multi-sensor stream data from smartphones are conducted, and 4, 14, and 17 BFRS are correctly detected in three different areas, with the precision as 100%, 70.00%, and 77.27%, respectively. Then, comparisons are conducted between the proposed method and three other methods, and the F-score of the proposed method is computed as 1.0000, 0.6363, and 0.7555 at three different areas, which hold the highest value among all results. Finally, it shows that the proposed method performs well in different geographic areas. [ABSTRACT FROM AUTHOR]

This research investigates the integration of visual cues and astronomical learning methodologies into transportation management education to enhance maritime safety and navigation practices. Through qualitative inquiry and descriptive analysis involving professionals trained at the Maritime Institute, perceptions regarding these methodologies' effectiveness and alignment with international standards were explored. Findings revealed a gap between recognition and integration into educational curricula, highlighting the need for greater emphasis on innovative pedagogical approaches. Despite participants' recognition of their significance, limited exposure to these methodologies was reported. However, both visual cues and astronomical learning were perceived to closely align with international standards, suggesting their potential to promote global best practices within the maritime industry. Addressing professional needs, including enhancing operational efficiency and supporting decision-making processes, emerged as crucial considerations for integrating these methodologies. Moving forward, collaborative efforts between educational institutions, industry stakeholders, and regulatory bodies are imperative to establish standardized guidelines and best practices, ensuring the effectiveness of transportation management education in preparing professionals for the challenges of the maritime landscape. [ABSTRACT FROM AUTHOR]

In the online catering industry, delivery riders’ traffic violations significantly threaten public transportation safety, inadequately addressed by current regulations from the government and online platforms. This paper proposes a government-led collaborative regulatory mechanism with active platform participation. A tripartite evolutionary game model, including the government, platforms, and riders, evaluates this approach. Findings suggest that collaborative regulation forms when the combined start-up and subsidy costs of government-led regulation are less than the total benefits of collaboration and when the cost difference between active and passive platform participation is less than the total benefit of active participation. Effective regulation occurs when combined penalties for riders’ violations exceed the profit difference between illegal and compliant deliveries. Reducing government regulation start-up costs and increasing penalties and enforcement probabilities can promote compliance among riders. Excessive subsidies to platforms do not effectively control violations, indicating the need for balanced subsidy allocation for optimal regulatory effectiveness. [ABSTRACT FROM AUTHOR]

In order to evaluate the impact of emergencies on the resilience of highway transportation, a resilience network hierarchical model of the highway transportation system was constructed by analyzing the formation and emergence process of safety resilience in the highway transportation system. Four layers of networks were divided, including highway network, transport network, traffic network, and emergency network. Combined with the network hierarchical model, a resilience evaluation index system was designed, and an assessment method for highway transportation systems based on the fuzzy analytic hierarchy process(FAHP) was proposed. Finally, a case study of a public health emergency in a region of Hunan was carried out. The results show that the proposed method for evaluating the safety resilience of highway transportation systems can better reflect the overall resilience under public health emergencies, which is consistent with the quantitative analysis results through the system resilience curve. It helps to accurately evaluate the safety resilience of the system. At the same time, this method has the advantages of flexibility and simplicity in solving unstructured decision-making problems of the system, which helps to improve the safety production management and safety resilience level of highway transportation systems. In the future, the scope of research scenarios and regions can be expanded, and further analysis of the evolution of safety resilience and the ability of resilience development in different stages under external disturbances can be conducted in order to further explore and optimize the resilience of the system. [ABSTRACT FROM AUTHOR]

This paper introduces an anchorless deep learning model designed for efficient analysis and processing of large-scale 3D synthetic traffic sign board datasets. With an ever-increasing emphasis on autonomous driving systems and their reliance on precise environmental perception, the ability to accurately interpret traffic sign information is crucial. Our model seamlessly integrates object detection, depth estimation, deformable parts, and text character extraction functionalities, facilitating a comprehensive understanding of road signs in simulated environments that mimic the real world. The dataset used has a large number of artificially generated traffic signs for 183 different classes. The signs include place names in Japanese and English, expressway names in Japanese and English, distances and motorway numbers, and direction arrow marks with different lighting, occlusion, viewing angles, camera distortion, day and night cycles, and bad weather like rain, snow, and fog. This was done so that the model could be tested thoroughly in a wide range of difficult conditions. We developed a convolutional neural network with a modified lightweight hourglass backbone using depthwise spatial and pointwise convolutions, along with spatial and channel attention modules that produce resilient feature maps. We conducted experiments to benchmark our model against the baseline model, showing improved accuracy and efficiency in both depth estimation and text extraction tasks, crucial for real-time applications in autonomous navigation systems. With its model efficiency and partwise decoded predictions, along with Optical Character Recognition (OCR), our approach suggests its potential as a valuable tool for developers of Advanced Driver-Assistance Systems (ADAS), Autonomous Vehicle (AV) technologies, and transportation safety applications, ensuring reliable navigation solutions. [ABSTRACT FROM AUTHOR]

AbstractBulk granular cargoes would encounter strength failure during sea transportation, and serious accidents may happen. Limiting their moisture content is the most widely utilized preventative measure. However, using this method is not enough for their sea transportation safety. It is essential to have an in-depth comprehension of their mechanism to encourage the development of crucial technologies and devices. The application of theoretical, experimental and numerical methods to study strength failure mechanism in bulk granular cargoes is reviewed. The mechanism from macroscopic, mesoscopic, and microscopic perspectives are then summarized. Based on the present studies, however the macroscopic and microscopic interactions between particles and water during strength failure remain unclear. Microscopic alterations in cargo structure and water-air development remain unknown. As a result, the techniques that could be used to investigate cargo particle movement and moisture migration are advised. So the macroscopic and microscopic relationship could be established. Further, the direct ship stability assessment model of the sea conditions-cargo-ship coupled motion could also be established. The effects between wave loads, cargo wet granular structure and water migration could be understood more deeply. A more accurate transport scheme for bulk granular cargoes can be developed to ensure the safety of their sea transport. [ABSTRACT FROM AUTHOR]

Hydrogen (H2) is considered a suitable substitute for conventional energy sources because it is abundant and environmentally friendly. However, the widespread adoption of H2 as an energy source poses several challenges in H2 production, storage, safety, and transportation. Recent efforts to address these challenges have focused on improving the efficiency and cost-effectiveness of H2 production methods, developing advanced storage technologies to ensure safe handling and transportation of H2, and implementing comprehensive safety protocols. Furthermore, efforts are being made to integrate H2 into the existing energy infrastructure and explore new opportunities for its application in various sectors such as transportation, industry, and residential applications. Overall, recent developments in H2 production, storage, safety, and transportation have opened new avenues for the widespread adoption of H2 as a clean and sustainable energy source. This review highlights potential solutions to overcome the challenges associated with H2 production, storage, safety, and transportation. Additionally, it discusses opportunities to achieve a carbon-neutral society and reduce the dependence on fossil fuels. [ABSTRACT FROM AUTHOR]

Suicide is the deliberate act of ending a person's own life due to multifarious reasons. In the U.S., suicide is the 10th major cause of death. Nearly 45,000 people died by suicide in 2016 across the nation. It is anticipated that not all traffic crashes can be considered as accidents. Traffic crash related injuries are occasionally considered a means of suicide, and some crashes occur due to the suicidal attempts. These attempts can be made by operators of motor vehicles, jumpers into the pathway of trains, and pedestrians deliberately jumping into the vehicle trajectory. There are a handful of studies that have focused on traffic crashes (both railroad and roadway) related to suicidal incidents. This study aimed to explore the insights associated with suicide related traffic crashes (SRTCs) by collecting traffic data for seven years (2010–2016) from Louisiana. At first, exploratory data analysis was performed to examine the five Ws (who, what, why, when, and where) associated with SRTCs. Later, this study applied text network analysis, which was not performed in any of the previous studies, to provide additional contexts of these crashes. The findings of this study can shed lights on an unexplored arena of transportation safety research. [ABSTRACT FROM AUTHOR]

High-quality visibility forecasting benefits traffic transportation safety, public services, and tourism. For a more accurate forecast of the visibility in the Guizhou region of China, we constructed several visibility forecasting models via progressive refinements in different compositions of input observational variables and the adoption of the Unet architecture to perform hourly visibility forecasts with lead times ranging from 0 to 72 h over Guizhou, China. Three Unet-based visibility forecasting models were constructed according to different inputs of meteorological variables. The model training via multiple observational variables and visibility forecasts of a high-spatiotemporal-resolution numerical weather prediction model (China Meteorological Administration, Guangdong, CMA-GD) produced a higher threat score (TS), which led to substantial improvements for different thresholds of visibility compared to CMA-GD. However, the Unet-based models had a larger bias score (BS) than the CMA-GD model. By introducing the U2net architecture, there was a further improvement in the TS of the model by approximately a factor of two compared to the Unet model, along with a significant reduction in the BS, which enhanced the stability of the model forecast. In particular, the U2net-based model performed the best in terms of the TS below the visibility threshold of 200 m, with a more than eightfold increase over the CMA-GD model. Furthermore, the U2net-based model had some improvements in the TS, BS, and RMSE (root-mean-square error) compared to the LSTM_Attention model. The spatial distribution of the TS showed that the U2net-based model performed better at the model grid scale of 3 km than at the scale of individual weather stations. In summary, the visibility forecasting model based on the U2net algorithm, multiple observational variables, and visibility data from the CMA-GD model performed the best. The compositions of input observational variables were the key factor in improving the deep learning model's forecasting capability, and these improvements could improve the value of forecasts and support the socioeconomic needs of sectors reliant on visibility forecasting. [ABSTRACT FROM AUTHOR]

Background: Driver drowsiness is a significant factor contributing to road accidents and the overall increase in road mortality rates. This study investigates the relationship between number of lane crossing (LC), the amount of lateral position deviations and driver drowsiness. The proposed method, due to precision and convenience, has great potential for developing driver-assistant systems. Materials and Methods: In this experimental research, 34 sub-urban bus drivers participated in a 2-h driving session in a simulator designed based on visual reality. Sensors attached to the steering recorded right and left deviations and relevant information was matched with the receded videos and the amount of the standard deviation of lane position (SDLP). The number of LC was determined with the designed indicator on the road software. Then, the association between SDLP and the number of LC was compared with the results of the Karolinska sleepiness scale (KSS) and observer rating of drowsiness (ORD), which determined the level of drowsiness, by facial features. Results: The results of multivariate analysis of variance indicated that the time variable has a significant effect on both ORD and SDLP (P<0.05). These two variables provided over 99% of the variance. The same results were obtained for KSS and SDLP (P<0.05). Meanwhile, the linear combination of these two dependent variables over 12 periods of the research has significant variations. In addition, the results show the progression of KSS and ORD (P<0.05) and an increase in SDLP and LC (P<0.05). In the same manner, LC has a tight association with the level of drowsiness and other factors (P<0.05). Conclusion: Drowsiness increases the variation in line tracking. However, it is not an appropriate signal for drowsiness detection. The SDLP and the number of line crossings is an. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) revolutionizes vehicle communication in dynamic networks. Message dissemination in IoV involves sharing critical information for the safety and convenience of the IoV network. It is very crucial to secure message dissemination due to potential cyber‐attacks, traffic disruptions, and privacy breaches. Data integrity, authentication, and privacy are vital to maintaining trust and safety in the IoV network. This network consists of resource‐constrained IoV devices with limited resources due to the availability of embedded components in vehicular systems. Therefore, optimizing algorithms and protocols is crucial for efficient vehicle‐to‐everything (V2X) communication, enhancing safety and transportation efficiency. Solutions often include lightweight protocols and secure message exchange. This paper proposes a machine learning (ML) based secure and lightweight message dissemination framework for resource‐constrained IoV. First, we present an ML‐based threat classification model capable of effectively categorizing adversarial and nonadversarial data streams and delivering an optimized model with superior accuracy. Furthermore, we also propose a secure message dissemination scheme using lightweight cryptographic primitives, which significantly reduces computational, communication, and energy overhead. To validate the robustness of our proposed ML‐based secure and lightweight message dissemination framework, we evaluate it using various security parameters and performance measures such as computation cost, communication cost, energy cost, accuracy, precision, recall, and F1‐score. Our contributions promise to significantly enhance the security and efficiency of message dissemination in IoV environments and advance lightweight, secure, and reliable transportation systems for the future. [ABSTRACT FROM AUTHOR]

The development and integration of automated driving systems in vehicles hold substantial promise for fostering enhanced efficiency, environmental sustainability, and safety in transportation. Notably, at the lower levels of automation (LI, L2), the lane-keeping system emerges as a widely adopted automated driving feature, ensuring the vehicle's alignment within its designated lane. With the recent introduction of new European regulations mandating the inclusion of emergency lane-keeping systems in all new vehicles starting July 2022, a growing prevalence of such systems is anticipated in the forthcoming decades. The precision and reliability of these systems in accurately detecting road markings and their distinctive features are paramount for achieving safe and intelligent mobility solutions. To fully capitalize on the advantages these systems offer, they need to expand their operational design domain. This necessitates a comprehensive understanding of their performance across diverse road design and maintenance conditions, supporting road operators in updating standards and maintenance protocols. The primary objective of this study is to investigate how various road characteristics impact the performance of lane-keeping assistant systems. Within this framework, the paper presents an experimental evaluation of Lane-Keeping System (LSS) performance conducted on two-lane rural roads. Advanced technologies for road monitoring and LSS were employed under different road and driving conditions. Through rigorous data analysis and the application of statistical models, variables significant to the fault probability of LSS were identified, highlighting the role played by horizontal curvature and driving speed. Results underscore the relevance of horizontal curvature as a critical factor constraining the physical infrastructure, shaping the operational design domain of LSS. This research contributes valuable insights toward optimizing lane-keeping assistant systems, thereby advancing the development and deployment of safe and efficient automated driving systems in diverse road scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Erciyes Üniversitesi Hukuk Fakültesi Dergisi is the property of Erciyes University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)