Selection of at-grade highway-rail crossings for grade separation

Yang, Xue; Li, Joshua; Liu, Wenyao; Wang, Kelvin C. P.; Hatt, Jim; Schwennesen, Jared

doi:10.1080/23248378.2022.2060359

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…Однорівневі перетини автомобільних доріг та залізниць (HRGC) є критичними просторовими об'єктами, що мають першорядне значення для безпеки руху транспорту. Використовуючи національну базу даних про HRGC та дані про аварії за 5 років в Оклахомі (штат на південному заході США), автори [30] Метою дослідження [11] став аналіз ризиків, пов'язаних із переїздами автомобільних доріг (HRC), й оцінка попередніх досліджень, проведених у США та Канаді. Щоб зменшити частоту аварій і тяжкість травм, було розглянуто моделі, які часто використовують для автомобільної та залізничної мережі.…”

Section: вступunclassified

Vehicle Traffic Safety Assessment at the Intersection of Highways and Railways at the Same Level

Kurhan¹,

Kurhan²,

Havrylov³

et al. 2022

STP

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Purpose. The purpose of the study is to assess the state of vehicle traffic safety at the intersection of highways and railways at the same level, as well as the subsequent development of measures to improve road traffic safety at railroad crossings. Assessing the degree of road traffic safety at railway crossings is one of the main tasks of both the road operation service and railway transport services. Therefore, it is advisable to implement a system for assessing the level of safety at railroad crossings, which is necessary to identify dangerous areas and develop measures to improve traffic conditions on them. Methodology. In order to obtain initial data and methods of assessing the state of vehicle traffic safety at the intersection of highways and railways at the same level, the authors conducted a review of the world literature on the topic of the study, as well as monitoring the safety of vehicle traffic at the intersection of highways with railway tracks. On the basis of the various methodological approaches analysis, the method of the final accident rate was used, which makes it possible to assess the state of safety as a result of the modernization or new design decisions, that is, in the absence of data on road accidents. Findings. It was found that according to the final coefficient of railway accident rate, crossings can be classified into one of four classes: safe, low-risk, dangerous, very dangerous. The issue of reducing the risk of an accident at the interaction of railway and road transport has been studied. Originality. Scientific approaches to evaluating the safety of vehicle movement at the intersection of highways and railways have gained further development, at the same level as the calculation of accident coefficients and danger indicators based on the data of the AIS Pereizd subsystem, which is being implemented on the railways of Ukraine. Practical value. The obtained results will be useful for carrying out measures to increase the safety of traffic through railway crossings, especially in the directions of the introduction of high-speed train traffic. The use of the AIS Pereizd automated information subsystem will allow using the method of the final accident rate and evaluating the safety of rail transport and motor vehicles in the area of railway crossings without additional labor costs.

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Section: вступunclassified

Vehicle Traffic Safety Assessment at the Intersection of Highways and Railways at the Same Level

Kurhan¹,

Kurhan²,

Havrylov³

et al. 2022

STP

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Cost Analysis and Prediction of Railroad Level Crossing Accidents for Indian Railways

Chhotu,

Suman

2024

Urban Rail Transit

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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.

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