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The Peer Exchange Program is a three-part webinar series for individuals, community agencies, and governmental agencies interested in furthering ideas and actions that surfaced in the past Community Pedestrian and Bicycle Safety Trainings (CPBST) or Comunidades Activas y Seguras (Active and Safe Communities) (CAyS) program trainings. As of 2023, SafeTREC and California Walks have conducted 126 Community Pedestrian and Bicycle Safety Program (CPBSP) workshops statewide.

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]

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 lane crossing (LC), the number 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

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 purpose of this study was to assess sociodemographic and behavioral risk factors associated with driving after marijuana use (DAMU) among West Virginia college students. Participants were recruited from West Virginia University between September and November 2022. The study sample was restricted to students who were ≥18 years of age; reported recently driving; possessed a current, valid driver's license from any US state; and were enrolled for at least one credit hour in the Fall 2022 semester. Among respondents (N = 772), 28.9% reported DAMU. Students who had a GPA of B (adjusted odds ratio [AOR]: 2.17, 95% confidence interval [CI]: 1.06–4.42), smoked or ingested marijuana in the past year (AOR: 26.51, 95% CI: 10.27–68.39), drove after drinking (AOR: 2.38, 95% CI: 1.18–4.79), and used both marijuana and alcohol concurrently and then drove (AOR: 10.39, 95% CI: 2.32–46.54) associated with DAMU. Individuals who felt the behavior was somewhat dangerous or not dangerous or thought their peers approved of DAMU showed significant associations with DAMU. As DAMU was prevalent, future interventions that raise awareness of the danger and potential consequences of DAMU may be needed to reduce this risky behavior on college campuses. [ABSTRACT FROM AUTHOR]

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The object of research is the process of cargo transportation by road. The problem of efficient loading and securing of hazardous goods in box containers during their transportation by road is considered. The basic principles of the voxel-based interpretation of the model of loading box containers on road transport are presented, and a general principle for calculating the fractal dimension of such three-dimensional objects has been developed. The calculation is based on the procedure of reducing the dimensionality of space by cutting the object into separate layers and determining the fractal dimensionality of two-dimensional slices. The proposed principle could be used to estimate the fractal dimension of three-dimensional objects in practical tasks in any industry. A method for simplified calculation of fractal characteristics of three-dimensional bill of lading models of cargo stowage has been devised. The method is based on the assessment of the quality of blocking of the constituent elements of the spatial system in three coordinate directions by the fractal dimension of two-dimensional images of their frames. The method provides opportunities for calculating the quantitative characteristics of the quality of cargo stowage from the standpoint of its transportation safety. A method for fractal stowing of goods in box containers on a truck platform has been proposed. This method of fractal stowage provides for the absence of slippage and displacement of boxes in the package and makes it impossible for them to overturn in extreme situations. The use of the fractal stowage method allows for an efficient and low-cost technology of securing the cargo as it involves only a circular bandage of the top layer of the loaded package of boxes and its fastening to the vehicle platform at four points [ABSTRACT FROM AUTHOR]

The main objective of this manuscript is to build a model for the distribution of LNG as a marine fuel in the southern Baltic Sea based on a genetic algorithm in terms of cost. In order to achieve this, it was necessary to develop, in detail, research sub-objectives like analysis of the intensity of ship traffic in the indicated area and analysis of LNG demand in maritime transport. In the first part of this study, the authors use data from the IALA IWRAP Mk2 and the Statistical Office in Szczecin to analyse the marine traffic density (by type of vessel) in the southern part of the Baltic Sea. LNG used as marine fuel reduces toxic emissions into the atmosphere. The authors specify the LNG fleet size and locations of LNG storage facilities in a way to ensure that the defined LNG bunker vessels can supply fuel to LNG-powered vessels within the shortest possible time period. The database contains a set of traits necessary to determine the optimal demand for LNG. The traits were developed based on an existing LNG fleet and appropriately selected infrastructure, and they represent existing LNG-powered vessels as well as LNG bunker vessels and their specifications. Based on the created LNG distribution model, were performed in Matlab R2019a software. An LNG distribution model was developed, which uses a genetic algorithm to solve the task. The demand for LNG for the sea area under analysis was determined based on data on the capacity of LNG-powered vessels (by type of vessel) and their distance from the specified port. [ABSTRACT FROM AUTHOR]

Traffic accidents are a nationwide public health concern, but autonomous vehicles (AVs) have the potential to significantly reduce accident severity and frequency by eliminating their most common cause, human error. By analyzing the data published by California's Department of Motor Vehicles, researchers have identified the factors that influence AV crash severity, however, none do so through a literature review. This paper's aims are multi-faceted: to understand AVs' operation on public roadways by identifying and classifying the factors contributing to accident severity, to develop a list of strategies that address the public's safety concerns, and to acknowledge the ethics of unavoidable collisions. To fulfill these objectives, a comprehensive literature review was conducted based on a keyword search. Following a multi-step screening and exclusion process, detailed review was performed of 107 relevant publications, and the factors contributing to increased crash severity were classified into 14 categories. The literature revealed that AVs are not at fault in most accidents, although they have a higher propensity than conventional cars to be involved in rear-end collisions, and they are specifically designed to minimize the number of accidents, but may face unavoidable ones. For the benefit of policymakers and manufacturers, 11 strategies that address the moral dilemma of these accidents and 7 strategies that address concerns about AV safety to improve public perception were identified. [ABSTRACT FROM AUTHOR]

Introduction: The Centers for Disease Control and Prevention's (CDC) National Center for Injury Prevention and Control (NCIPC) funds Injury Control Research Centers (ICRCs). These centers study injury and violence prevention through three core areas: (1) Research conducts cutting-edge, multidisciplinary research in the injury and violence prevention field; (2) Outreach translates injury and violence prevention research into action; and (3) Training educates and trains the next generation of injury and violence prevention professionals. We examined ICRC work from 2012 to 2019 to determine whether they fulfilled their goal of furthering injury and violence prevention research and practice. Methods: We created a database of core area accomplishments reported through annual and interim progress reports. These reports track core area accomplishments by injury and violence prevention topic area, publications, partnerships, and trainings. Results: From 2012 to 2019, ten ICRCs from two funding cycles received approximately $49 million. ICRCs reported 703 research, 1,432 outreach, and 660 training accomplishments. There were also 342 accomplishments contributing to a special tool or resource. These accomplishments focused on preventing traumatic brain injury, suicide, adverse childhood experiences, and transportation safety. ICRCs produced over 3,500 peer-reviewed publications. ICRCs reported over 3,600 accomplishments partnered with academic institutions, public health agencies, healthcare, and non-profit organizations. ICRCs created resources for audiences such as students, law enforcement, and policy makers. ICRCs trained 3,131 students and faculty. Practical Applications: ICRCs are the hubs of modern research and practice in the injury and violence prevention field. They successfully bring together stakeholders from disparate disciplines, perspectives, and agencies to join forces and tackle critical public health problems. Conclusion: ICRCs are an integral component of NCIPC's, CDC's and the Department of Health and Human Service's missions to protect and enhance the health of Americans. Research covered NCIPC research priorities over the funding period, furthering injury and violence prevention research and working as a foundation to practice and policy. Outreach and partnerships with an array of organizations put research into action. Trainings educated the new generation of injury and violence prevention professionals. [ABSTRACT FROM AUTHOR]

Traffic conflict techniques enable a comprehensive assessment of traffic safety analysis. Formal methods allow the identification of factors that contribute to traffic safety issues and provide evidence of potential safety degradation. As such, formal methods provide a novel way to model traffic rules and verify road users' compliance. The paper proposes formalizing a traffic safety rule in differential dynamic logic and using KeYmaera theorem prover for verification. This rule considers time-to-collision (TTC), space headway (SHW), and shockwave speed (SWV). To validate the effectiveness of this rule in realistic traffic scenarios, we conducted a study using calibrated microsimulation data from the SR528 highway in Orlando, Florida. Our analysis examined the TTC, SHW, and SWV values for vehicle platoons on the highway and demonstrated how smaller TTC and SHW values indicate shockwaves and subsequent conflicts. Furthermore, we observed that shockwave speed could contribute to traffic conflicts by enabling evasive actions such as sudden braking or lane changes as the risk of collisions increases. By highlighting these findings, we aim to provide valuable insights into the real-world applicability of formal methods for traffic safety and their potential in promoting safer driving practices that can help create reliable autonomous vehicle control systems.

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.

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.

Green polymer poly‐l‐proline, as a new type of biodegradable polymer material, has a wide application prospect in the field of environmental protection. Recent studies have shown that poly‐l‐proline can also act as a green inhibitor that is highly effective in inhibiting the production of gas hydrates in oil–water systems. As a natural polymer material, poly‐l‐proline has good biocompatibility and biodegradability and will not cause pollution to the environment. It is found that adding an appropriate amount of poly‐l‐proline to the oil–water system can significantly inhibit the formation of gas hydrates. This is because poly‐l‐proline can reduce the concentration of dissolved gases in water by adsorbing and dispersing ions and molecules in water, thereby reducing the chance of gas forming hydrates with water. In addition, poly‐l‐proline can also prevent gas molecules from entering water molecules to form hydrates by forming hydrogen bonds with water molecules. This study provides a green, environmentally friendly and efficient solution for controlling the formation of gas hydrates in oil–water mixtures. In the future, the application prospects of poly‐l‐proline in oil–water separation, marine engineering, natural gas exploitation, and other fields will be broader. [ABSTRACT FROM AUTHOR]

This article presents a review of cutting-edge technologies poised to shape the future of railway transportation systems, focusing on enhancing their intelligence, safety, and environmental sustainability. It illustrates key aspects of the energy-transport-information/communication system nexus as a framework for future railway systems development. Initially, we provide a review of the existing challenges within the realm of railway transportation. Subsequently, we delve into the realm of emerging propulsion technologies, which are pivotal for ensuring the sustainability of transportation. These include innovative solutions such as alternative fuel-based systems, hydrogen fuel cells, and energy storage technologies geared towards harnessing kinetic energy and facilitating power transfer. In the following section, we turn our attention to emerging information and telecommunication systems, including Long-Term Evolution (LTE) and fifth generation New Radio (5G NR) networks tailored for railway applications. Additionally, we delve into the integral role played by the Industrial Internet of Things (Industrial IoT) in this evolving landscape. Concluding our analysis, we examine the integration of information and communication technologies and remote sensor networks within the context of Industry 4.0. This leveraging of information pertaining to transportation infrastructure promises to bolster energy efficiency, safety, and resilience in the transportation ecosystem. Furthermore, we examine the significance of the smart grid in the realm of railway transport, along with the indispensable resources required to bring forth the vision of energy-smart railways. [ABSTRACT FROM AUTHOR]

Traffic accidents are a nationwide public health concern, but autonomous vehicles (AVs) have the potential to significantly reduce accident severity and frequency by eliminating their most common cause, human error. By analyzing the data published by California’s Department of Motor Vehicles, researchers have identified the factors that influence AV crash severity, however, none do so through a literature review. This paper’s aims are multi-faceted: to understand AVs’ operation on public roadways by identifying and classifying the factors contributing to accident severity, to develop a list of strategies that address the public’s safety concerns, and to acknowledge the ethics of unavoidable collisions. To fulfill these objectives, a comprehensive literature review was conducted based on a keyword search. Following a multi-step screening and exclusion process, detailed review was performed of 107 relevant publications, and the factors contributing to increased crash severity were classified into 14 categories. The literature revealed that AVs are not at fault in most accidents, although they have a higher propensity than conventional cars to be involved in rear-end collisions, and they are specifically designed to minimize the number of accidents, but may face unavoidable ones. For the benefit of policymakers and manufacturers, 11 strategies that address the moral dilemma of these accidents and 7 strategies that address concerns about AV safety to improve public perception were identified.

To address the low-light image (LLI) problem in train driving scenarios, this paper proposes a progressive and lightweight network called BrightsightNet for LLI enhancement. First, to overcome the problem of insufficient local exposure level, two structurally identical light curve parameter estimation sub-networks are used for light enhancement in turn. Second, for real-time inference, an efficient feature extraction operator is proposed that combines depth-separable convolution and attention mechanism. Third, the overall network uses encoder–decoder architecture. For the encoder, the output features of the three layers are fused through skip connections to form an information-rich feature map. For the decoder, a hierarchical decoding approach is used to predict the light curve parameters through the three convolution layers sequentially. Experimental results show that BrightsightNet achieves a user study score (USR) of 4.43 on the proposed dataset, outperforming Zero-DCE++, SCI, RetinexDIP, and RUAS by 0.51, 0.86, 0.64, and 1.39, respectively. Moreover, BrightsightNet has parameters of only 2.6K and a single inference time of 0.052 s, which is an innovative and practical solution for low-light image enhancement in train driving scenarios, contributing to safer and more reliable train operations.

Transportation safety continues to be a daily challenge as it results in enormous losses to human life and to the economy. This research targets safe mobility at intersections in an urban setting by analyzing drivers' behavior at the stop bar during red-light phases with an unprecedented focus on drivers' psychological and demographic attributes. To do so, three main scenarios are utilized to test drivers' responses using a state-of-the-art driving simulator, which include pedestrians crossing the crosswalk, police enforcement personnel and adjacent driver encroaching on the stop bar promoting the imitation behavior. A survey assessing demographics, including standardized questionnaires for individual psychological traits and driving rituals is used to complement the simulator experience for a total of 178 participants. Real life observations and monitoring at intersections are conducted to confirm drivers' behaviors with respect to stopping at the stop bar in the presence of various triggers. Our results show that younger males with a history of at least one severe accident are more likely to exhibit aggressive behavior such as speeding and committing violations. Participants scoring high on the Attitudes Towards Traffic Safety and Driving Behavior Survey scales are safer drivers who show more concern about traffic laws. High scores on mindfulness and agreeableness (Big Five Personality Inventory) are associated with less violations whereas extraversion and neuroticism linked with impulsivity and frustration are associated with higher acceleration rates and overall speed. Taken together, driving scenarios and psychological variables better profile differential safe driving behaviors. Thus, it could be highly effective to include trainings for mindfulness and emotional management in addition to driving risk-awareness to enhance safe driving behavior and focus on improving the existing driving education system by focusing on young drivers' attitudes towards safety as it has shown to impact their actual driving behavior. • Younger males with an accident history are more likely to be aggressive drivers. • High scores on mindfulness and agreeableness are linked to less violations. • Extraversion and neuroticism are related to high acceleration rates and speeding. • Drivers imitate other drivers violating the stop bar of signalized intersections. • Less violations are recorded when pedestrians and police are simultaneously present. [ABSTRACT FROM AUTHOR]

With the introduction of various carbon reduction policies around the world, hydrogen energy, as a kind of clean energy with zero carbon emission, has attracted much attention. The safe and economical transportation of hydrogen is of great significance to the development of hydrogen energy industries. Utilizing natural gas pipelines to transport hydrogen is considered to be an efficient and economical way. However, hydrogen has a higher risk of leakage due to its strong diffusion capacity and lower explosive limit than conventional natural gas. Therefore, it is of great significance to study the leakage and diffusion law of hydrogen-enriched natural gas (HENG) pipelines for the safe transportation of hydrogen energy. In this study, the leakage and diffusion characteristics of urban buried HENG pipelines are investigated numerically, and the dangerous degree of leakage is analyzed based on the time and area when the gas concentration reaches the lower explosive limit. The influences of hydrogen blending ratio (HBR), operating pressure, leakage hole size and direction, as well as soil type on the leakage and diffusion law of HENG are analyzed. Results show that the hydrogen mixing is not the key factor in increasing the degree of risk after gas leakage for urban buried HENG pipelines. When the HBR is 5%, 10%, 15% and 20%, the corresponding first dangerous time is 1053, 1041, 1019 and 998 s, respectively. This work is expected to provide a valuable reference for the safe operation and risk prevention of HENG pipelines in the future. [ABSTRACT FROM AUTHOR]

The main objective of this manuscript is to build a model for the distribution of LNG as a marine fuel in the southern Baltic Sea based on a genetic algorithm in terms of cost. In order to achieve this, it was necessary to develop, in detail, research sub-objectives like analysis of the intensity of ship traffic in the indicated area and analysis of LNG demand in maritime transport. In the first part of this study, the authors use data from the IALA IWRAP Mk2 and the Statistical Office in Szczecin to analyse the marine traffic density (by type of vessel) in the southern part of the Baltic Sea. LNG used as marine fuel reduces toxic emissions into the atmosphere. The authors specify the LNG fleet size and locations of LNG storage facilities in a way to ensure that the defined LNG bunker vessels can supply fuel to LNG-powered vessels within the shortest possible time period. The database contains a set of traits necessary to determine the optimal demand for LNG. The traits were developed based on an existing LNG fleet and appropriately selected infrastructure, and they represent existing LNG-powered vessels as well as LNG bunker vessels and their specifications. Based on the created LNG distribution model, were performed in Matlab R2019a software. An LNG distribution model was developed, which uses a genetic algorithm to solve the task. The demand for LNG for the sea area under analysis was determined based on data on the capacity of LNG-powered vessels (by type of vessel) and their distance from the specified port.

Due to the increasing demand on road maintenance around the whole world, advanced techniques have been developed to automatically detect and segment pavement cracks. However, most of methods suffer from background noise or fail in fine crack segmentation. This paper proposes a generative adversarial network (GAN)-based neural network named CrackSegAN to segment pavement cracks automatically. The generator of CrackSegAN generates segmentation results, while the discriminator trains the generator adversarially. A joint loss function is proposed to optimize the generator with sufficient gradients and mitigate the high class imbalance in pavement crack images. Elastic deformation data augmentation method is applied to force CrackSegAN to learn the transformation invariance. The proposed CrackSegAN reaches an average F1 score of 0.9780 on CrackForest dataset and 0.8412 on Crack500 dataset. Ablation study shows that the most prominent difference is made by the proposed joint loss function which increases the average F1 score by 8.98% on CrackForest dataset. Besides, the comparison between using different data augmentation strategies validates the effectiveness of elastic deformation. Overall, the proposed CrackSegAN increases the F1 score by 1.91% on CrackForest dataset and 1.01% on Crack500 compared with state-of-the-art methods. Qualitatively, CrackSegAN is more robust to background noises and segments cracks with more details. Moreover, the test on field data proves a better generalizability of CrackSegAN on unseen background noises.

Ice cover on pavement may reduce the road adhesion coefficient and increase the crash risks, which might result in more traffic crashes. The primary factor utilized to assess whether the wet pavement is icy or not is the pavement temperature. Therefore, forecasting pavement temperature is an effective method to judge road conditions and improve traffic safety. This paper proposes a combination model based on the extreme gradient boosting (XGBoost) model and long- and short-term time-series network (LSTNet) model to predict pavement temperature. Pavement temperature and meteorological data were collected for the cities along the Shandong part of the Beijing-Taipei Expressway (G3). In this study, nine meteorological variables were used. Subsequently, after correlation analysis, five variables, including air temperature, dew point temperature, relative humidity, evaporation, and potential evaporation, were selected for prediction. The method proposed in this study comprises the following steps. First, the XGBoost and the LSTNet models are respectively formulated based on the time-varying characteristics of pavement temperatures. Then, using the preset weight of the variable, the XGBoost model is used for preliminary prediction to add features. Finally, the experimental analysis is performed on the Qihe data set after the two models have been integrated using the inverse variance method. As revealed by the experimental results, the mean absolute error (MAE) and root-mean-square error (RMSE) of the proposed XGBoost-LSTNet model are 0.8235 and 1.2412, respectively. Compared with the long short-term memory (LSTM) model, random forest (RF) model, XGBoost model, and LSTNet model, the XGBoost-LSTNet model proposed in this paper has higher accuracy. The study’s findings can successfully increase wintertime expressway traffic safety and serve as a guide for managing maintenance and preventing icing-related accidents. [ABSTRACT FROM AUTHOR]

The share of road vehicles in urban transport systems is a challenge for modern and dynamically developing urban areas in accordance with the concept of sustainable and Smart Cities. Increasingly, there is a need to promote and adapt urban space to the movement of vulnerable road users (VRU). As part of a clear emphasis on the issue of pedestrians and other vulnerable road users, the aim of the article is to define the typology and hierarchy of solutions contributing to the increase in VRU safety in cities. The research process was based on the use of grounded theory. In the adopted research methodology, the use of the Delphi method made it possible to identify the approach of various European cities to the use and implementation of technical, technological and organizational solutions affecting safety. The research made it possible to evaluate individual solutions in VRU safety management and to indicate a list of recommendations for improving security, taking into account the views of international experts. In addition, the results of this study may enrich the current literature, helping to understand the perception of solutions implemented in urban transport systems as a holistic set of interrelated elements supporting pedestrian safety and increasing their role in cities. [ABSTRACT FROM AUTHOR]

As the global elderly population continues to rise, the risk of severe crashes among elderly drivers has become a pressing concern. This study presents a comprehensive examination of crash severity among this demographic, employing machine learning models and data gathered from Virginia, United States of America, between 2014 and 2021. The analysis integrates parametric models, namely logistic regression and linear discriminant analysis (LDA), as well as non-parametric models like random forest (RF) and extreme gradient boosting (XGBoost). Central to this study is the application of resampling techniques, specifically, random over-sampling examples (ROSE) and the synthetic minority over-sampling technique (SMOTE), to address the dataset's inherent imbalance and enhance the models' predictive performance. Our findings reveal that the inclusion of these resampling techniques significantly improves the predictive power of parametric models, notably increasing the true positive rate for severe crash prediction from 6% to 60% and boosting the geometric mean from 25% to 69% in logistic regression. Likewise, employing SMOTE resulted in a notable improvement in the non-parametric models' performance, leading to a true positive rate increase from 8% to 36% in XGBoost. Moreover, the study established the superiority of parametric models over non-parametric counterparts when balanced resampling techniques are utilized. Beyond predictive modeling, the study delves into the effects of various contributing factors on crash severity, enhancing the understanding of how these factors influence elderly road safety. Ultimately, these findings underscore the immense potential of machine learning models in analyzing complex crash data, pinpointing factors that heighten crash severity, and informing targeted interventions to mitigate the risks of elderly driving. [ABSTRACT FROM AUTHOR]

We review the practice of safety benefits analysis for federal transportation regulations in the USA. Using a case-study approach, we explore the linkages between risk assessment and benefits analysis, adding to previous work exploring these linkages for environmental health regulations. Challenges for calculating the benefits of transportation safety regulations arise because safety outcomes, like many noncancer health effects, typically do not have formal risk relationships like dose–response functions established for them. Analysts often rely on engineering or other expert judgments or resort to qualitative discussions to connect a regulatory intervention to its intended outcome. Challenges also arise when regulatory outcomes are intangible or do not have established metrics. Safety outcomes are not always measurable in concrete terms like mortality risk and may include difficult-to-operationalize concepts like "safety culture." If the outcome is not measurable, then quantifying or monetizing the expected effects of a regulation is not possible, and the ability to conduct robust qualitative discussions also may be limited. Economists evaluating benefits for safety regulations encounter limitations analogous to difficulties found in health regulations. To inform policymaking effectively, economists and safety experts could look to the relationship developed in environmental economics between economists and health scientists. [ABSTRACT FROM AUTHOR]

This study updates the prevalence of operator fatigue as a causative factor in accidents investigated by the National Transportation Safety Board (NTSB) and the associated injury severity in fatigue-related accidents. In total, 394 investigations were analyzed and 12% of them identified fatigue. The prevalence of fatigue varied among the transportation modes, ranging from 28% of aviation to 7% of marine. Most fatigue-related accidents (48%) occurred during late night or morning. Compared to non-fatigued operators, fatigued operators were more involved in severe or fatal injuries (odds ratio [OR] 2.30; 95% confidence interval [CI] [1.66, 2.95]) and injuries to non-operators (OR 3.32; 95% CI [2.70, 3.95]). Obstructive sleep apnea (OSA) was identified as a probable cause, contributing cause or finding in 15% of fatigue-related accidents, and in 85.7% of these accidents the operator met OSA screening criteria. Thus, opportunities remain for preventing fatigue-related accidents, including through more systematic operator screening for OSA. [ABSTRACT FROM AUTHOR]

The transportation system undergoes severe impacts due to potholes and the presence of stray animals on the roads resulting in accidents and fatal injuries. The utilization of intelligent transportation systems would reduce accidents and impart safety to the overall transportation network. This research aims to impart transportation safety through a real-time alert warning system for avoiding accidents due to potholes and the presence of stray animals. The study incorporates real-time detection of transportation entities like vehicles, animals, and pedestrians through a YOLO v3 computer vision algorithm processed on the GPU environment for a higher frame rate. The potholes and animal hotspots are mapped to form a geospatial database on which the buffer tool of geographic information system (GIS) is applied. The buffer zone was implemented on the geospatial layer to alert the driver in real-time, while the vehicle approaches the buffer zone. The system yields high precision of 0.976 mean average precision (mAP) score of entity detection and the real-time alert warning alerts the driver to ensure transportation safety while avoiding any possible accidents or fatal crashes. [ABSTRACT FROM AUTHOR]

Leans is a common type of Spatial Disorientation (SD) illusion that causes pilots to be confused about the position of the aircraft during a flight. This illusion could lead to serious adverse effects and even flight mishaps. Therefore, an effective means to deal with leans is crucial for flight safety. This study aims to investigate the effects of Galvanic Vestibular Stimulation (GVS) technology with different waveforms as a tool to mitigate the negative effects of leans. 20 Air Force pilots participated in leans-induced flight simulation experiment with three GVS conditions (without-GVS, step-GVS, ramped-GVS). Bank angle error, subjective SD, perceived strength, and annoyance were measured as the dependent variables. Analysis revealed that step-GVS and ramped-GVS yielded lower bank angle errors and subjective SD than without-GVS. In addition, annoyance ratings were lower for ramped-GVS than step-GVS. This study suggests that GVS has the potential to be utilised as a counteracting tool to cope with leans. Practitioner summary: Galvanic Vestibular Stimulation (GVS) can be utilised as a tool to counteract the detrimental effects of leans illusion, specifically the ramped style GVS, considering that it is less annoying and distracting for the pilots. In general, GVS induces a roll sensation that can offset the false sensation caused by the leans, which can potentially help maintain flight safety and avoid spatial disorientation-related accidents. Abbreviations: SD: spatial disorientation; GVS: galvanic vestibular stimulation; MSSQ: motion sickness susceptibility questionniare; SSQ: simulator sickness questionnaire; BLE: bluetooth low energy; PCB: printed circuit board; RPM: revolution per minute [ABSTRACT FROM AUTHOR]

What makes a vehicle user buckle up? Considering the strong effect of seat belt use in reducing injuries and fatalities in a vehicle crash, we investigated the role of the health belief model on seat belt use among front-seat passengers on urban and rural roads. A questionnaire based on the theory components was randomly distributed in public areas of Sari, Iran. Structural equation model was used to test the study hypotheses. The results revealed that anticipated severity and perceived susceptibility directly affected seat belt use on urban roads, whereas perceived barriers had a reverse effect on seat belt use on urban roads. Perceived barriers with an indirect and perceived susceptibility with a direct effect, played an essential role in explaining seat belts use on rural roads. Outcomes of this study extend the knowledge of seat belts use behavior among front seat passengers by introducing new factors of potential influence, which could lead to practical solutions aimed to enhance seat belts utilization among these vehicle users and decrease the rate of injuries and fatalities in road crashes. [ABSTRACT FROM AUTHOR]

• E-scooter fatalities involving a motor vehicle were identified in National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System (FARS) data and compared to the fatalities in different modes of transportation using statistical tests of proportions. • E-scooter fatalities have a similar proportion of men as motorcyclist and pedalcyclist fatalities, but skew younger than any other mode of transportation analyzed. • Fatally injured e-scooter users had the highest rate of alcohol involvement (41%) of any mode, but it was not statistically significant compared to pedestrians (30%) or motorcyclists (29%) • 82% of fatal e-scooter motor-vehicle collisions occurred at night, similar to the rate for pedestrian fatalities, and significantly more than other modes of transportation. • Fatalities involving an e-scooter or other mode of personal conveyance were significantly more likely to have been struck in a crosswalk compared to pedestrians. Introduction: Shared e-scooters are an emerging mode of transportation with many features that make their physical properties, behavior, and travel patterns unique. Safety concerns have been raised concerning their usage, but it is difficult to understand effective interventions with so little data available. Methods: Using media and police reports, a crash dataset was developed of rented dockless e-scooter fatalities in crashes involving motor vehicles that occurred in the United States in 2018–2019 (n = 17) and the corresponding records from the National Highway Traffic Safety Administration data were identified. The dataset was used to perform a comparative analysis with other traffic fatalities during the same time period. Results: Compared to fatalities from other modes of transportation, e-scooter fatality victims are younger and more likely male. More e-scooter fatalities occur at night than any other mode, except pedestrians. E-scooter users are comparatively as likely as other unmotorized vulnerable road users to be killed in a hit-and-run crash. While e-scooter fatalities had the highest proportion of alcohol involvement of any mode, this was not significantly higher than the rate seen in pedestrian and motorcyclist fatalities. E-scooter fatalities were more likely than pedestrian fatalities to be intersection-related, and to involve crosswalks or traffic signals. Conclusions: E-scooter users share a mix of the same vulnerabilities as both pedestrians and cyclists. Although e-scooter fatalities are demographically most similar to motorcycle fatalities, crash circumstances share more similarities with pedestrian or cyclist fatalities. Other characteristics of e-scooter fatalities are notably distinct from other modes. Practical Applications: E-scooter use must be understood by users and policymakers to be a distinct mode of transportation. This research highlights the similarities and differences between similar modes, like walking and cycling. By using this information on comparative risk, e-scooter riders and policymakers can take strategic action to minimize the number of fatal crashes. [ABSTRACT FROM AUTHOR]

The automated inspection and detection of foreign objects help prevent potential accidents and train derailments. Most existing approaches focus on the detection with prior labels, such as categories and locations of objects, and do not directly address detecting foreign objects of unknown categories, which can appear anytime on the rail track site. In this article, we develop a deep generative approach for detecting foreign objects without predefining the scope of objects. The detection procedure consists of the following three steps: first, the model composed of an autoencoder and a discriminator is developed via adversarial training based on normal rail images only; second, the detection of abnormal rail images is implemented based on the anomaly score obtained via the trained autoencoder; and finally, foreign objects are detected by filtering the subtle dissimilarity in normal areas and highlighting abnormal areas. The effectiveness of the proposed framework for the rail foreign object detection is validated with images collected by a train equipped with visual sensors. Computational results demonstrate that our proposal is capable to achieve an impressive performance on detecting numerous foreign objects. Moreover, two groups of benchmarking methods are employed to verify the superiority of the proposed framework. [ABSTRACT FROM AUTHOR]

Today, the use of smart transportation systems has become widespread due to the change in the structure of cities, technological developments, and the increase in the number of vehicles. It aims at targets such as reducing the damage to people and the environment by increasing traffic safety with smart transportation systems. In addition, one of the big problems of long-distance drivers is falling asleep while driving. This is an event that puts both the driver's life, the passengers in the vehicle, and the vehicles on the road in danger, and puts the company in a difficult situation if it works for a company. In this direction, the driver sleep alarm project has been developed. This project is a control system based on monitoring the user, measuring, and analyzing their movements. Literature research, which is the first stage of the project, was conducted and research continued throughout the project process. Within the scope of this project, a headset was designed for the driver's sleep alarm and a mobile application was created. The first prototype of the project was built. First, in the project, the angle values made by the driver's head were produced with the IMU connected to the microcontroller. 10° to 20° margins identified as sleeping state for roll, pitch, and yaw angles. Later, a wearable headset was designed for the driver. Based on these angles produced in the microcontroller, an algorithm has been created that detects whether the driver is asleep. These generated angle values and status information were transferred from the headset to the mobile application via Bluetooth. This data transferred in the mobile application is reflected on the screen. In addition, an algorithm has been created in the mobile application, which sends a notification to the driver when sleep status is detected and sends an informative SMS to the headquarters if the driver is unresponsive to this notification. For the second prototype, it was aimed to produce a PCB for the headset and thus make a new mechanical design. Data mining applications are planned with the collected data from drivers for future works. [ABSTRACT FROM AUTHOR]

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Shared e-scooters have the potential to increase access, complement transit, and replace automobiles, all while reducing emissions and congestion. However, there are concerns worldwide over the mode's safety issues and risks. In this paper, we explore both the motivations and barriers to using e-scooters. Data are collected from a stated preference survey, using a sample consisting of mostly university staff and students in Singapore. Three logit models with varying specifications of e-scooters' speed and lane use and one's prior experience of conflict with a personal mobility device (PMD) are estimated. Overall, the three models have a very comparable fit (adjusted R2 of about 0.55) and consistent results. The results indicate preferences for e-scooters if they are faster and off the sidewalk. However, a bad or unsafe experience with a PMD would negatively affect use to a greater degree, although it varies across individuals. Our study suggests diverting scooters off the sidewalk and increasing the speed may not always be effective in encouraging behavioral shifts toward this alternative mode. Other solutions such as improving the services and enhancing traffic safety should be explored and considered instead. [ABSTRACT FROM AUTHOR]

This article presents a review of cutting-edge technologies poised to shape the future of railway transportation systems, focusing on enhancing their intelligence, safety, and environmental sustainability. It illustrates key aspects of the energy-transport-information/communication system nexus as a framework for future railway systems development. Initially, we provide a review of the existing challenges within the realm of railway transportation. Subsequently, we delve into the realm of emerging propulsion technologies, which are pivotal for ensuring the sustainability of transportation. These include innovative solutions such as alternative fuel-based systems, hydrogen fuel cells, and energy storage technologies geared towards harnessing kinetic energy and facilitating power transfer. In the following section, we turn our attention to emerging information and telecommunication systems, including Long-Term Evolution (LTE) and fifth generation New Radio (5G NR) networks tailored for railway applications. Additionally, we delve into the integral role played by the Industrial Internet of Things (Industrial IoT) in this evolving landscape. Concluding our analysis, we examine the integration of information and communication technologies and remote sensor networks within the context of Industry 4.0. This leveraging of information pertaining to transportation infrastructure promises to bolster energy efficiency, safety, and resilience in the transportation ecosystem. Furthermore, we examine the significance of the smart grid in the realm of railway transport, along with the indispensable resources required to bring forth the vision of energy-smart railways.

Railroad is one of the primary modes to transport hazardous materials (hazmat) in North America. For instance, Canadian railroads carried around 50 million tons of hazmat in 2018. Given the inherent danger of trains carrying hazmat, this study aimed at exploring a novel way towards mitigation of the associated risk. This study sought to investigate whether proper rail track infrastructure investment can mitigate the risk from hazmat shipments. To this end, a methodology was developed and then applied to the Canadian railroad network. The proposed three-step methodology captured the differing perspectives of rail carriers and regulatory agencies, and entailed (1) ascertaining the risk-level of various yards and links in the given railroad network, (2) specifying potential candidates for infrastructure investment, and (3) finding the optimum set of investment decisions. The proposed methodology was then applied to the Canadian railroad network to demonstrate that significant risk-reduction can be achieved by adding alternative rail-links around the riskiest locations (i.e. the network hot-spots), and also to show that risk-reduction function is non-linear with non-monotonous behavior. The study showed the possibility of significant hazmat risk reduction through alternative rail-links that could take traffic away from the network hot-spots. The methodology and the results from the Canadian case can be used by railroad companies and policy makers to estimate the value of potentially risk-reducing infrastructure investments.

This study examines the latent influence of spatial locations on the relative risks of crash injuries associated with distracted driving (DD) and identifies regions of excess risks for policy intervention. Using a sample of aggregated injury and fatal DD crash records for the period 2015–2019 across 1,024 census block groups in Central Ohio (i.e., the Columbus Metropolitan Area) in the United States, we investigate the role of latent effects along with several covariates such as land-use mix, sociodemographic features, and the built environment. To this end, we specifically leverage a full Bayesian hierarchical formulation with conditional autoregressive priors to account for uncertainty (i.e., spatially structured random effects) stemming from adjacent census block groups. Furthermore, we consider uncorrelated random effects from upper-level administrative units within which each block group is nested (i.e., census tracts and counties). Our analysis reveals that (1) addressing spatial correlation improves the model's performance, (2) block-group-level variability substantially explains the residual random fluctuation, and (3) intersection density appears negatively associated with the relative risks of crash injuries, while more diversified land use can increase injury risk. Based on these findings, we present spatial clusters with twice the relative risks compared to other block groups, suggesting that policies be devised to mitigate severe injuries due to DD and therefore enhance public health.

In direct line with the evolution of technology, but also with the density of vehicles that create congestion and often road accidents, traffic monitoring systems are parts that integrate intelligent transport systems (ITS). This is one of the most critical elements within transport infrastructures, an aspect that involves extremely important financial investments in order to collect and analyze traffic data with the aim of designing systems capable of properly managing traffic. Technological progress in the field of wireless communications is advancing, highlighting new traffic monitoring solutions, and the need for major classification, but proposing a real-time analysis model to guide the new systems is a challenge addressed in this manuscript. The involvement of classifiers and computerized detection applied to traffic monitoring cameras can outline extremely vital systems for the future of logistic transport. Analyzing and debating vehicle classification systems, examining problems and challenges, as well as designing a software project capable of being the basis of new developments in the field of ITS systems are the aim of this study. The outline of a method based on intelligent algorithms and improved YOLOv3 can have a major impact on the effort to reduce the negative impact created by chaotic traffic and the outline of safety protocols in the field of transport. The reduction of waiting times and decongestion by up to 80% is a valid aspect, which we can deduce from the study carried out. [ABSTRACT FROM AUTHOR]

Freight vehicle crashes are more serious than regular vehicle crashes because they are likely to lead to major damage and injury once they occur; therefore, countermeasures are needed. The fatality rate from freight vehicle crashes is 1.5 times higher than that of all other accidents, and the death rate from expressway freight vehicle crashes continues to increase. In this study, the ten-freight-vehicle crash severity models (the ordered logit and probit model, the multinomial logit and probit model, mixed-effects logit and probit model, random-effects ordered logit and probit model, and multilevel mixed-effects ordered logit and probit model) are used to analyze the freight vehicle crash severity factors. The model was constructed using data collected from expressways over eight years, and 13 factors were derived to increase the severity of crashes and 7 factors to reduce the severity of crashes. As a result of comparing the 10 constructed models using AIC and BIC, the multilevel mixed-effects ordered probit model showed the best performance. It is expected that it can contribute to improving the safety of freight vehicles in the expressway section by utilizing factors related to the severity of crashes derived from this study. [ABSTRACT FROM AUTHOR]

This study modeled the crash severity of elderly drivers using data from the state of Virginia, United States, for the period of 2014 through to 2021. The impact of several exogenous variables on the level of crash severity was investigated. A multilevel ordinal logistic regression model (M-OLR) was utilized to account for the spatial heterogeneity across different physical jurisdictions. The findings discussed herein indicate that the M-OLR can handle the spatial heterogeneity and lead to a better fit in comparison to a standard ordinal logistic regression model (OLR), as the likelihood-ratio statistics comparing the OLR and M-OLR models were found to be statistically significant, with p-value of <0.001. The results showed that crashes occurring on two-way roads are likely to be more severe than those on one-way roads. Moreover, the risks for older, distracted, and/or drowsy drivers to be involved in more severe crashes escalate than undistracted and nondrowsy drivers. The data also confirmed that the consequences of crashes involving unbelted drivers are prone to be more severe than those for belted drivers and their passengers. Furthermore, the crash severity on higher-speed roads or when linked to high-speed violations is more extreme than on low-speed roads or when operating in compliance with stated speed limits. Crashes that involve animals are likely to lead to property damage only, rather than result in severe injuries. These findings provide insights into the contributing factors for crash severity among older drivers in Virginia and support better designs of Virginia road networks. [ABSTRACT FROM AUTHOR]