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This paper presents a finite element model of a railway crossing panel for use in multibody simulations (MBS). It is a two-layer track model with rails and sleepers represented by beam elements that use linear bushings for rail fastenings and non-linear bushings for ballast. The model is calibrated and validated to measurement data from a comprehensively instrumented switch and crossing demonstrator installed in the Austrian railway network as a part of the European research programme Shift2Rail. The validation concerns the capability of the model to capture the structural response of the crossing panel under traffic loading after calibration of physical track parameters to realistic values. The structural response is measured in the form of displacements, strains, and sleeper-ballast contact forces. It is shown that the developed model can represent the measured track responses well and that it was necessary to account for a varying sleeper-ballast gap distribution along the crossing transition sleeper to obtain good agreement. The calibration uses Latin hypercube samples to explore the parameter space in a sensitivity analysis before a parameter optimisation is performed using a gradient-based method on a response surface built from a polyharmonic spline. [ABSTRACT FROM AUTHOR]

Featured Application: Application as a decision-support tool in the management of level crossings. Railway level crossings pose risks to both train passengers and road users, worsened by the complex interaction between rail and road traffic, particularly on local railways. This study introduces a Risk Management (RM) framework designed to enhance safety at level crossings on local railways by providing a decision-support tool. By individually assessing risk factors, the framework allows for a precise evaluation of the current risk levels in terms of both Cumulated and Individual Risk levels, Individual Risk, and the impacts of potential modifications. The framework allows the quantitative assessment of risk and resource optimization, enabling infrastructure managers to identify high-risk crossings and select the most effective safety interventions. The framework was applied to 96 level crossings on an Italian local railway line, demonstrating its ability to support decision-making for targeted interventions as well as broader network-level actions, significantly improving system safety. [ABSTRACT FROM AUTHOR]

Cross measurement is an important foundational work for special surveying and survey of newly built railways. The existing cross measurement is mainly completed through manual field measurement methods such as angle shifting, polar coordinate method, and GNSS-RTK method, which has problems such as low operational efficiency, multiple rework measurements, and high safety hazards. Aiming at this technical problem, this paper proposed a new railway crossing survey method based on UAV laser point cloud and image fusion, including key technologies such as data acquisition and pre-processing of railway band UAV aerial photography, sorting of design line positions and determination of cross measurement positions, and extraction of cross measurement information. The purpose is to complete the cross measurement work of the newly built railway through non-contact means. The results show that, compared to traditional operation methods, this method avoids difficulties in entering complex areas, significant safety hazards, and inconvenient installation of conventional measuring instruments. The efficiency can be improved significantly in terms of the flexibility and safety of measurement field operations. Based on the fusion processing technology of drone laser point cloud and image, the characteristics of high density, high accuracy, rich image texture, and high visualization degree of drone laser point cloud are fully utilized, laying a good foundation for determining the cross measurement position and extracting information. The method for calculating cross measurement parameters can improve the accuracy and reliability of cross measurement, and can also provide reference for the research of other types of cross, cross measurement, or other technical means. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 pandemic could alter the injury severity pattern of highway-rail grade crossing (HRGC) crashes. To compare factors affecting injury severities before and during the pandemic, HRGC crashes occurring between 2018 and 2021 in the United States were collected. A random parameters logit model with heterogeneity in means was developed to investigate the impact of multiple factors during the pandemic (2020-2021). Besides, a multinomial logit model was developed for the pre-pandemic (2018-2019) crashes. The present study indicates that the existence of old drivers and illuminated crossings generated random parameters with heterogeneity in means during the pandemic, which were affected by the driver in vehicle, estimated train speed, and crossing AADT indicators. As for the pre-pandemic period, no significant random parameters and unobserved heterogeneity were captured. Significant variables also differed markedly between these two periods, with no significant driver-related or vehicle-related indicators identified from 2018 to 2019. [ABSTRACT FROM AUTHOR]

Road crashes, although random events, frequently occur on roads. However, certain characteristic infrastructural locations require detailed analysis regarding the frequency of road crashes. This study examines the dynamics of road crashes at characteristic infrastructural locations in Serbia from 2018 to 2022, focusing on bridges, tunnels, railroad crossings, and road work zones. Using data on road crashes from official reports, the analysis includes trends in crash rates, fatalities, injuries, and material damage during the above-mentioned time frame. In addition to the data analysis, 22 experts from the fields of traffic engineering ranked the mentioned characteristic infrastructural locations in terms of road safety. The same questions were asked to six different artificial intelligence software programs. The findings reveal significant variations in crash rates across different infrastructures, with bridges and road work zones having the highest number of crashes. Expert assessment is in line with the analysis of the results, while artificial intelligence gives a completely opposite assessment. [ABSTRACT FROM AUTHOR]

The high-speed railway project and the ultra-high-voltage transmission project represent two crucial components of China's "new infrastructure". As the construction of these two projects progresses rapidly, it is inevitable that instances of intersections will occur. Extreme conditions may cause damage to ultra-high voltage transmission cables. When a high-speed train passes by an ultra-high voltage transmission line, it poses a serious safety hazard. To address this issue, engineering examples were utilized to examine the protection structure scheme, protection distance, protection load, and construction procedures when a high-speed railway intersects a 1000 KV ultra-high voltage transmission line. A shed structure form and construction method for the electric power protection were proposed to ensure the safe operation of the high-speed railway while also achieving the safe and rapid construction of the high-speed railway protection structure in the safety zone of the approaching 1000 kV ultra-high voltage transmission line. The results indicated that the protection of high-speed railway crossings and 1000 kV ultra-high voltage transmission lines primarily focuses on line-break protection. The concrete shed structure with a straight wall and a flat roof was designed to meet the requirements of high-speed railway crossings. The line-break protection method enables the construction of an automatic warning protection corridor and a complete movable trolley quickly and safely within the safety zone near the transmission line. The implementation effect is, therefore, positive. It can be used as a reference point for other projects of a similar nature. [ABSTRACT FROM AUTHOR]

Beam bridges, as the primary structural form of medium and small-sized bridges, are extensively utilized for road and railway crossings over rivers and valleys. Ensuring their reliability during earthquakes is crucial not only for maintaining traffic flow but also for mitigating the seismic impact on the economy and society. Considering earthquake intensity and uncertain parameters, this paper proposes an innovative method for assessing the seismic reliability of simply-supported beam bridges under three different levels of seismic design: minor, moderate, and major earthquakes. The proposed method first estimates the probability of encountering three typical earthquake intensities during the design life of simply-supported beam bridges based on crowd intensity, benchmark intensity, and major earthquake intensity. It then introduces uncertain parameters and employs the point estimation method to calculate the probability of bridge passage under specific earthquake intensities. Finally, it combines these earthquake intensities to calculate the overall seismic reliability of simply-supported beam bridges. The effectiveness and efficiency of this method are demonstrated through calculations for a three-span, double-degree-of-freedom simply-supported beam bridge, and validated using Monte Carlo simulations. This research provides solid theoretical support for seismic assessment, design, and intensity-based reliability analysis of simply-supported beam bridges. [ABSTRACT FROM AUTHOR]

• Survey study (N = 1011) on sign messaging and design for pedestrian-rail crossings. • Action-conveying and emotionally-motivated more effective than information signs. • Square and black on yellow signs perceived as most effective in conveying safety. • Pedestrians who frequently cross railroad tracks exhibit higher risk-taking tendencies. • Relationship between likelihood to walk across tracks and risk-taking propensity. Introduction : Train-pedestrian conflicts result in a substantial number of serious and fatal injuries annually. Signs indicating safe and permissible behaviors near railroad rights of way are commonly relied upon to mitigate collisions. However, the effectiveness of these signs in preventing accidents often depends on clarity and interpretation of the sign. The objective of this study was to evaluate the (1) effectiveness of sign messaging strategies and designs at pedestrian-railroad crossings, and (2) effects of context and risk-taking propensity on decisions to cross tracks. Method : A survey study (N = 1011) was conducted comparing likeliness to cross for a variety of pedestrian-railroad scenarios. The DOSPERT scale was included to estimate an individuals' risk-taking. Results : Findings reveal that action-conveying and emotionally-motivated signs are more effective in discouraging railroad crossing in high-risk situations (i.e., train present, crossing gates down, or warning lights flashing), compared to information-only signs. However, decisions to cross are primarily influenced by the presence of a train and crossing gates' status, followed by sign type. MaxDiff analysis show that yellow signs with black symbols and square shaped signs are perceived as the clearest in conveying safety information, compared to black on white, red on white, and circular signs. Additionally, individuals who cross railroad tracks as pedestrians more frequently exhibit higher risk-taking tendencies, while there is no relationship between driving across tracks and risk-taking propensity. Males and younger individuals also have higher risk-taking tendencies. Conclusions and Practical Applications : These findings have implications for policy and practice, such as revising manuals to incorporate more effective sign designs and targeted educational campaigns for high-risk groups. It is also crucial to conduct ongoing monitoring of implemented interventions, which could follow the framework presented in this paper. The study emphasizes collaboration across sectors to improve overall safety at pedestrian-railroad crossings, contributing to safer transportation infrastructure for all. [ABSTRACT FROM AUTHOR]

A sleeper, or more generally a "bearer", moves vertically under a passing train load. The extent of this motion depends on the static and dynamic load of the train, the train speed, and the support conditions at the bearer and its neighbours. Excessive motion, typically from voiding see-sawing, low support stiffness or possibly excessive stiffness, or even too little stiffness, are all of interest to maintainers. Typically, problems arise around transition zones, switches and crossings, but plain track with poor support can also be a problem. Within the last decade, low-cost micro-electro-mechanical system (MEMS) accelerometers have been used to capture the time history of vertical motion for use in condition monitoring. Existing condition monitoring systems often overlook or sometimes even ignore the possibility of problematic data, which seem to be common in monitored locations. It is essential to understand whether such "bad" data require further attention. Three problematic sites are presented, focussing on examples where the acceleration was higher than expected or the computed displacement was not as expected. Potential causes include wheel defects, hammering of the ballast by a hanging bearer, or high acceleration at some structural resonant frequency. The present paper aims to show the challenges of using MEMS accelerometers to collect data for condition monitoring and offers insights into the sort of problematic data that may be collected from real sites. [ABSTRACT FROM AUTHOR]

With the tremendous increase in the number of vehicles, the dense traffic created can lead to accidents and fatalities. In a traffic system, the costs for accidents are immeasurable. Numerous studies have been carried out to predict the cost of fatal accidents but have provided the actual values. Therefore, in this study, a monkey-based modular neural system (MbMNS) is developed to identify accident cost. The accident cases and cost data were collected and preprocessed to remove the noise, and the required features were extracted using the spider monkey function. Based on the extracted features, the accidents and the costs were identified. For rail engineering, this will support evaluating the number of railroad crossing accidents with different time intervals. The impact of every accident was also measured with different cost analysis constraints, including insurance, medical, and legal and administrative costs. Therefore, the present study contributes to the field by collecting and organizing the present railroad level crossing accident data from crossing inventory dashboards. Then, the introduction of a novel MbMNS for the cost analysis is the primary contribution of this study to further enrich the railroad level crossing protection system. The third contribution is the tuning of the prediction layer of a modular neural network to the desired level to achieve the highest predictive exactness score. Hence, the designed MbMNS was tested in the Python environment, and the results were validated with regard to recall, accuracy, F-measure, precision, and error values; a comparative analysis was also conducted to confirm the improvement. The novel MbMNS recorded high accuracy of 96.29% for accident and cost analysis, which is better than that reported for other traditional methods. [ABSTRACT FROM AUTHOR]

This paper, based on a national key railway project crossing the high ground stress TBM construction section, studies the test results by means of the combination of refining three-dimensional numerical simulation and regression orthogonal design experiment and multiple linear regression method. The sensitivities of different supporting factors are studied, their advantages and disadvantages in controlling surrounding rock deformation are identified. At the same time, multiple linear regression equation of the experiment is obtained, which provides a fast calculation formula for structural optimization, so as to adjust the support parameters and optimizing the structure. In linear regression orthogonal experiment, the length of short anchor bolt, the length of long anchor bolt, the longitudinal spacing of steel arch, the circumferential and longitudinal spacing of anchor bolt, and the shotcrete grade and ground stress level are selected as the influencing factors. Regression orthogonal design experiment with L8(27) orthogonal table is conducted without considering interaction effect. The constant term and coefficients of partial regression term are calculated, and the multiple linear regression equation is established, and the results are analyzed in terms of variance and testing lose effectiveness of fit. The results show that the F value of ground stress is greater than F0.001, and the ground stress has a decisive influence on the deformation of the surrounding rock. The F values of the length of short anchor bolt, the longitudinal spacing of the anchor bolt and steel arch are all greater than F0.025, which have a significant impact on controlling the deformation of the surrounding rock. The established regression equation is significant, the test of misfit is not significant, so the regression equation has a good degree of fitting. Therefore, measures should be taken to release ground stress in design and construction first, and then the length of short anchor bolt, the spacing of steel arch and the longitudinal spacing of anchor bolt should be taken into account when selecting support parameters. [ABSTRACT FROM AUTHOR]

Road-railroad level crossing accidents pose serious safety risks to road users, and their significant increase requires more research efforts to propose substitute solutions. Such a solution must consider the impact of intersection geometry, user perception, traffic characteristics, driver behavior, environment, and seasonal variations on accidents. This study explores the considerable number of such accidents and develops a predictive model using all the factors that influence them. For these objectives, data were collected from databases maintained by the zonal head office of the East Central Railway (ECR) in India. Data included 175 level crossings that experienced at least one accident between 2006 and 2021 in the ECR region. This study presents two accident prediction models using logistic regression and ANN for the predominant factors of accidents in the ECR zone of Indian railways. The accuracy of fatal accident prediction was 96% for logistic regression and 98% for ANN. [ABSTRACT FROM AUTHOR]

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Railways are essential to the global transportation infrastructure, providing eco-friendly and economical solutions for the movement of freight and passengers. Inspecting and maintaining extensive rail networks timely poses significant challenges. My group and collaborators have focused on automated railroad inspection technologies, emphasizing the use of deep learning and computer vision to overcome the limitations of traditional manual inspections. Our research introduces groundbreaking real-time inspection methods, leveraging a specialized dataset of railroad components for enhanced instance segmentation models, achieving unprecedented accuracy and inference speeds. The developed computer vision systems efficiently detect track components and their changes over time, and also quantify rail surface defects. Additionally, our work extends to improving railroad crossing safety, utilizing deep learning frameworks for the detection of unusual pedestrian behaviors and object identification, aimed at reducing crossing incidents and improving emergency response times. Our future research directions aim to further refine the cost-effectiveness and autonomy of railroad inspection systems. Through these innovations, we hope to aid in the inspection and maintenance of railroads, offering practical solutions for railroad and other civil engineering applications. [ABSTRACT FROM AUTHOR]

Finding effective routes is critical to support the decision-making of first responders to save lives in emergency and accident situations. Effective routing planning requires accurate traffic information including railroad blockage that could lead to significant delays. This study aims to improve the decision-making efficiency of emergency responding by integrating railroad crossing blockage and dynamic traffic information into routing planning process. Specifically, we propose a data-driven algorithm that considers real-time train blockage time windows in the route planning process. Through a series of first responder vehicle dispatching scenarios in Columbia, South Carolina, we demonstrate that the framework can assist first responders in finding the best route with the minimum response time in an emergency. Results from the scenarios show that the method can save as much as 61.6% of response time compared with the time calculated based on existing practices. This study demonstrates the benefit and necessity of integrating train operation information into emergency responding vehicle route planning to save lives. [ABSTRACT FROM AUTHOR]

The object of research is pedestrian safety when crossing railroad tracks at pedestrian crossings. A method for improving the safety of pedestrian traffic when crossing railroad tracks within the area approaching the railroad pedestrian crossing has been developed, by introducing an information system for warning pedestrians about train movement with a fixed warning time. Experimental measurements of the speed of the rolling stock were carried out in the area approaching the pedestrian crossing over the railroad track. With the mixed movement of trains, it was found that the lowest speed of the train was 67.46 km/h, and the highest was the passenger train – 131.9 km/h. At the same time, the speed of freight trains ranged from 74 to 79 km/h. Such data are needed to determine the speed of the rolling stock on the approach to the crossing or transition, which will allow the light and sound signal to be turned on in time when the movement of a pedestrian across the track will be dangerous. The methodology for calculating the length of the approach section to the pedestrian crossing, depending on the speed of the rolling stock, has been given. It was established that the speed of the rolling stock has a significant effect on the length of the approach section. At the lowest measured speed of the train – 67.46 km/h, the length of the approach section was 317.46 m, and when the passenger train was moving at a speed of 131.9 km/h – 620.68 m. For a single-track section of the railroad, a fixed time has been established for warning pedestrians about the approach of a train to the crossing. It is a constant value that is included in the warning information system on a single-track section, and after it, a complex light and sound alarm with an information board is activated, which will warn the pedestrian about the movement of the train. The method for improving the safety of pedestrian traffic with a fixed time of the pedestrian’s anticipation of the train movement could be used at crossings with pedestrian traffic, as a supplement to the crossing signaling subsystem in railroad transport. It can also be used at equipped pedestrian crossings over railroad tracks in urbanized areas. The advantage of introducing such a method is a fixed time to warn the pedestrian, in each specific case of the train approaching the crossing. [ABSTRACT FROM AUTHOR]

A railway level crossing is a risk area despite installations of the warning signal and arm gate that indicate a train approach. Accidents may nevertheless occur if vehicle drivers violate or fail to conform to the rules. Such action is considered a risk behavior that may cause severe collision, injury, and death. Thus, the driver behaviors are the key factors causing accidents. The objective of this study was thus to investigate the risk behaviors and the factors influencing above behaviors. These behaviors were recorded by a high-resolution video camera and the initial data analysis was based on descriptive statistics. An in-depth analysis of the factors influencing the risk behaviors was performed by using Pearson Chi-square technique and Binary Logistic Regression Analysis. The results showed that the railway crossing behaviors of more than half of the drivers under the study were the risk-taking, followed by opportunistic violation. Most violations occurred during the beginning of the warning signal. Overall, the motorcycle riders were found to be the group with greater chance of violating across the railway during the warning signal than the 4-wheeled vehicle drivers. Especially, during the period between the beginning of the warning signal and the complete arm gate closure, as well as the greater width of the crossing road. This could be due to the higher agility and evasive capability of motorcycle riders than 4-wheeled drivers. Additionally, this study recommends approaches to initially prevent or reduce this risky behavior and can serve as a guideline for enhancing areas with physical characteristics similar to the study area. [ABSTRACT FROM AUTHOR]

Objective: This study aims to evaluate the effect of in-vehicle audio warning at flashing-light-controlled grade crossings based on driving simulation and eye-tracking systems. Background: Collisions at flashing-light-controlled grade crossings have severe consequences. In-vehicle audio warning has the potential to regulate driver behavior. However, whether this improvement occurs through priming drivers' visual search patterns is not yet clear. Method: Drivers' visual activity and behaviors were recorded. The effect of a warning was tested with a series of flashing light trigger times (FLTTs) ranging from 2s to 6s with a 1s increment. Different driving conditions (i.e., clear and fog) and driver experience were considered in the experiment design. Results: Warnings could guide the allocation of both overt and covert attention, as well as raise drivers' situation awareness, manifesting as the enhanced perception of signs and better understanding of the flashing red light. Significant improvement in the stop-compliance rate was found in warning scenarios, particularly with a late FLTT. The decreased saccade duration and increased fixation duration on the signal implied a dilemma-zone effect when the FLTT was lower than 4s. Furthermore, reduced fixation duration on signs and signals was found in foggy conditions. Non-professional drivers had a wider search range than their counterparts. Conclusion: In-vehicle audio warning is an effective countermeasure for improving crossing safety by optimizing visual search strategy. Application: In-vehicle audio warnings warrant promotion at grade crossings based on the driver assistance system. [ABSTRACT FROM AUTHOR]

The traditional safety protection measures for railway crossings can no longer meet the current development needs due to their low efficiency, high cost and potential safety hazards. To meet this challenge, this paper proposes an innovative intelligent centralized control system based on wireless communication for early warning of railway crossings, which aims to guarantee the life and property safety of the staff and traffic participants at railway crossings. The LoRa long-range radiofrequency communication technology, OpenMV-based vehicle and pedestrian target recognition technology and computer based interlocking technology are combined to design a low-power, easy-to-deploy remote centralized management system for unattended railway crossings, which can improve the efficiency of railway operation, and enhance the safety of railway transport. [ABSTRACT FROM AUTHOR]

The safety and reliability of undivided two-way highway–rail grade crossings (HRGCs) are of paramount importance in transportation systems. Utilizing crash data from the Federal Railroad Administration between 2020 and 2021, this study aims to predict crash injury severity outcomes and investigate various factors influencing injury severities. The χ2 test was first used to select variables that were significantly associated with injury outcomes. By employing the eXtreme Gradient Boosting (XGBoost) model and interpretable SHapley Additive exPlanations (SHAP), a cross-category safety assessment that offers an evidence-based hierarchy and statistical inference of risk factors associated with crashes, crossings, vehicles, drivers, and environment was provided for killed, injured, and uninjured outcomes. Some significant predictors overlapped between the killed and injured models, such as old driver, driver was in vehicle, main track, went around the gate, adverse crossing surface, and truck, while the other different significant factors revealed that the model could distinguish between different severity levels. Additionally, the results suggested that the model has varying performances in predicting different injury severities, with the killed model having the highest accuracy of 93.36%. The SHAP dependency plots for the top three features also ensure reliable predictions and inform potential interventions aimed at strengthening traffic safety and risk management practices, such as enhanced warning systems and targeted educational campaigns for older drivers. [ABSTRACT FROM AUTHOR]

In this paper, we model the intensity of cross-border railway traffic on the Alpine-Western Balkan Rail Freight Corridor (AWB RFC). For each of the four border crossing points: Dimitrovgrad, Presevo, Sid, and Subotica, time series composed of 102 monthly export and import railway freight traffic observations are used for training and testing of alternative forecasting models. Traditional ARIMA, Long-Short-Term Memory (LSTM) neural network, hybrid ARIMA-LSTM and Singular Spectrum Analysis (SSA) models, are fitted to each of the time series. For all the considered time series, the best model was chosen based on the lowest values of commonly used metrics for measuring the performance of forecasting models. LSTM models outperformed all other models with the highest prediction accuracy while SSA models exhibited the lowest accuracy. By utilizing advanced forecasting models, this research contributes to finding effective solutions for addressing the issue of inadequate planning of border crossing procedures in railway traffic. [ABSTRACT FROM AUTHOR]

This paper is based on the reconstruction project of south section of National Highway No.310 (Xingyang Qiaolou-Cuimiao section) in Zhengzhou of National Highway No.234, which involves the construction of an overpass for National Highway No.234 to cross over Zhengzhou- Xi'an High-speed Railway. The main focus of this paper is to analyze the key and difficult points in the design of the foreign object intrusion monitoring system for the project and propose solutions. This project is the first foreign object intrusion monitoring system design for the operating high-speed railway overpass within the jurisdiction of Zhengzhou Bureau, providing reference and experience for the design and construction of similar projects in Henan and even throughout the whole country of China in the future. [ABSTRACT FROM AUTHOR]

Switches & crossings (S&Cs) are vital assets as they allow for increased railway capacity by introducing flexibility and connectivity in railway networks. At the same time, this makes them critical since they can cause costly delays and disruptions if they are not well maintained. This motivates studies to improve maintenance strategies of S&Cs, considering both the life-cycle costs (LCCs) of the assets and socio-economic transportation costs for passengers and freight customers. In this paper, the interdependence between deterioration mechanisms, maintenance activities, and expected LCC (including transportation costs) for the crossing panel -- an S&C subsystem -- is investigated using a combination of mechanical and econometric modelling. The interrelation between the degradation of contact geometry and track settlement is analysed using simulations of dynamic vehicle--turnout interaction. Long-term mechanical degradation of the crossing panel is simulated for different maintenance strategies that correspond to different timing of the associated maintenance measures (crossing repair welding and tamping). This provides the basis for analysing the interdependence between preventive and corrective activities using econometric modelling. Based on a case study of a common type of S&Cs in the Swedish infrastructure, the impact of different maintenance strategies on LCC and transportation costs is analysed. Opportunities and challenges in the development of more economically effective maintenance strategies of S&Cs are discussed. [ABSTRACT FROM AUTHOR]

• The probability of non-motorists' violation is significantly influenced by train, gate operational, and user characteristics. • Train category, train length, and carrying item have no significant impact on the non-motorists' violation. • Group violation and engagement in secondary activities do not influence bicyclists' choice to violate. • The geometrical attributes along with train vehicle unit impact various factors that significantly influence non-motorists' violation. • This research suggests various interventions to limit the violations at rail road grade crossings. Introduction : In order to enhance the existing body of research regarding the prevalence of violations committed by pedestrians and bicyclists, this observational study utilized personal-specific, train-specific, operational, and behavioral data extracted from recorded videos at seven railroad grade crossings (RRGCs). Method: A total of 7,332 pedestrians and 1,684 bicyclist crossing events were observed during 279 gate operation phases at seven study locations. First, two separate violation models (binary logit) for pedestrians and bicyclists were developed to determine the influence of personal-specific characteristics and other behavioral traits on probability of pedestrians' and bicyclists' violation. Later, the impact of various geometrical (railway track, distance between gates, distance between gate and nearest track, and road width) and operational (train vehicle unit) parameters on the factors that caused non-motorist violations was examined by developing distinct binary logit models. Results: The probability of pedestrians' and bicyclists' violation was found to be significantly influenced by train characteristics (train speed and train occupancy time), gate operational attributes (warning time and gate blockage duration), and personal-specific characteristics (gender, age, crossing speed, and looking both side). Furthermore, geometrical attributes along with train vehicle unit were observed to have impact over various factors that significantly influenced the behavior of pedestrians and bicyclists to violate. Practical Applications: The findings of this research provide essential information that could be applied in the initiatives of user awareness and enforcement program, emphasis on situational awareness and design of operational and geometrical elements to significantly limit the effects of risky walking and bicycling behavior at RRGCs. [ABSTRACT FROM AUTHOR]

Highway-rail grade crossing (HRGC) safety is one of the priority areas in the United States transportation system that requires for greater research efforts not just limited to crash analysis, but also to gain a deeper understanding of surrogate safety measures such as driver behavior-based traffic violations at HRGCs. This paper uses vehicle profile data to identify the key variables and develop prediction models for gate violations and examine the relationship between model accuracy and the key input variables. A data set of 256 vehicle-train events was collected at two HRGC testbeds in Lincoln, Nebraska. Among them, 76 events are gate violations, and 180 events are non-violations. Two tree-based ensemble techniques, the bootstrap forest and the boosted tree, were applied to the data set. It was found that once a vehicle is within 190 feet from the HRGC stop line, the model was approximately 80 percent accurate in predicting a gate violation. It was also found that as vehicles came closer to the HRGC, the prediction error decreased. With the advent of vehicle profile data collection, tree-based ensemble techniques are ideal for safety studies as they can utilize the highly non-linear vehicle profiles and relate these to safety surrogate metrics. [ABSTRACT FROM AUTHOR]

Road crashes, although random events, frequently occur on roads. However, certain characteristic infrastructural locations require detailed analysis regarding the frequency of road crashes. This study examines the dynamics of road crashes at characteristic infrastructural locations in Serbia from 2018 to 2022, focusing on bridges, tunnels, railroad crossings, and road work zones. Using data on road crashes from official reports, the analysis includes trends in crash rates, fatalities, injuries, and material damage during the above-mentioned time frame. In addition to the data analysis, 22 experts from the fields of traffic engineering ranked the mentioned characteristic infrastructural locations in terms of road safety. The same questions were asked to six different artificial intelligence software programs. The findings reveal significant variations in crash rates across different infrastructures, with bridges and road work zones having the highest number of crashes. Expert assessment is in line with the analysis of the results, while artificial intelligence gives a completely opposite assessment.

Featured Application: In this study, the impact of errors and missing information in the inventory data of highway–rail grade crossings (HRGCs) on the crash frequency prediction models currently used in the US is investigated. Transportation authorities and safety regulators can apply the insights gained from this research to enhance the accuracy of crash frequency prediction models for HRGCs. By utilizing more accurate and complete inventory data, these models provide more reliable crash frequency predictions, enabling better resource allocation and more targeted safety interventions. The highway–rail grade crossings (HRGCs) crash frequency models used in the US are based on the Federal Railroad Administration's (FRA) database for highway–rail crossing inventory. Inaccuracies or missing values within this database directly impact the estimated parameters of the crash models and subsequent crash predictions. Utilizing a set of 560 HRGCs in Nebraska, this research demonstrates variations in crash predictions estimated by the FRA's 2020 Accident Prediction (AP) model under two scenarios: firstly, employing the unchanged, original FRA HRGCs inventory dataset as the input, and secondly, utilizing a field-validated inventory dataset for the same 560 HRGCs as input to the FRA's 2020 Accident Prediction (AP) model. The findings indicated a significant statistical disparity in the predictions made with the two input datasets. Furthermore, two new Zero-inflated Negative Binomial (ZINB) models were estimated by employing 5-year reported HRGCs crashes and the two inventory datasets for the 560 HRGCs. These models facilitated the comparison of model parameter estimates and estimated marginal values. The results indicated that errors and missing values in the original FRA HRGCs inventory dataset resulted in crash predictions that statistically differed from those made using the more accurate and complete (validated in the field) HRGCs inventory dataset. Furthermore, the crash prediction model estimated upon the corrected inventory data demonstrated enhanced prediction performance, as measured by the statistical fitness criteria. The findings emphasize the importance of collecting complete and accurate inventory data when developing HRGCs crash frequency models. This will enhance models' precision, improve their predictive capabilities to aid in better resource allocation, and ultimately reduce HRGCs crashes. [ABSTRACT FROM AUTHOR]

This conference paper discusses a new analytical approach to the evaluation of traffic safety at the railway crossings. The new method is based on the use of ontological principles. The ontological tools present a comprehensive decision-making tool, will can be applied to any potential hazardous railway crossing on a road or rail network. The ontology can be considered as a knowledge base that not only stores the data itself, but also defines the relationships between these data, and can therefore be described as a clearly suitable tool for describing risks at level crossings. [ABSTRACT FROM AUTHOR]

Unexpected intrusions by pedestrians at grade crossings pose significant risks to the safety of railroad operations. Currently, there is no information processing system available for monitoring anomalies at grade crossings. Therefore, this paper presents a video processing pipeline and a generative adversarial network (GAN)-based deep learning framework to detect, localize, and analyze abnormal behaviors of pedestrians at grade crossings. First, the motions of pedestrians are represented by temporally-varying trajectories of key points identified by skeleton detection and tracking algorithms. A GAN model is developed to learn both global and local motion features of normal pedestrians only. The abnormal behaviors can then be detected as outliers by a discriminator during the testing phase. In contrast to existing efforts, several measures are taken to further boost model performance, including purposely exploiting overfitting in training to magnify the difference between the normal and the abnormal motion patterns and adding an appropriate amount of noise to enhance model generalization. The experiments conducted on a custom video dataset demonstrate the remarkable performance of our model, which successfully distinguishes motion patterns of squatting and lingering from normal walking. The model achieves a value of 0.89 in the AUC (Area Under the Curve) and notably outperforms the other seven benchmark models. The present method is also able to analyze multiple pedestrians in one video frame with a single run of the GAN model and requires no location-specific information, enabling salient robustness and field-deployability of the model at different locations without retraining. [ABSTRACT FROM AUTHOR]