Research Article
Optimization of Railway Track’s Preventive Maintenance Planning Based on Predicting the Increase in Maximum Defect Sizes (Vigral Method)
Gregory Krug*
Issue:
Volume 11, Issue 2, April 2026
Pages:
24-32
Received:
13 April 2026
Accepted:
3 May 2026
Published:
19 May 2026
Abstract: This article describes a solution for optimization of railway track’s preventive maintenance scheduling. Solution is based on predicting spontaneous increases of maximum defect sizes occurring between two consecutive track measurements. Predictions are made immediately after the latest measurement. The planning of preventive maintenance works is based on periodic measurements of geometric parameters’ values. Changes in the track condition manifest as an increase in the size of "large" defects and a decrease in "small" ones through superposition, and sometimes as an abrupt change in the maximum defect size. The process properties depend on the track's physical condition and the magnitude of train load. This process is associated both with changes in the size of existing defects and with the formation of new ones that exceed the existing ones in size. The abrupt appearance of defects exceeding the current maximum values, occurring at random times between measurements, significantly impacts the track's technical condition and must be considered when planning track maintenance works. Thus, optimizing preventive maintenance work requires obtaining information about the track condition in future immediately after each measurement. The problem of predicting the appearance of defects whose sizes exceed those recorded in the latest measurement has not been studied. Analysis has shown that this phenomenon occurs to varying degrees in 5-10% of track segments, when subsequent measurements register the appearance of new, larger defects that arose in the period between measurements. Information about the possible appearance of such defects allows optimization of the track maintenance process. The method for predicting changes in the track's technical condition described in this article allows, with high reliability, immediately after the latest measurement to predict the appearance, during interval before the next regular measurement, of defects whose sizes exceed the maximum recorded in the latest measurement. The method also allows identifying sudden spontaneous deterioration of the track. The method is based on analyzing the homogeneity (compactness) property of the values of the ISDF. This function shows the cumulative length of each-size track irregularity within a track segment. For classifying results and making decisions, the "nearest neighbor method" is used. The method has been tested for predicting track condition for future periods of 1, 2 and 3 months after the latest measurement. For surface defects, the probability of correctly predicting a spontaneous increase in maximum defect size is within 0.91-0.98 range, and the probability of false positives is between 0.03-0.09.
Abstract: This article describes a solution for optimization of railway track’s preventive maintenance scheduling. Solution is based on predicting spontaneous increases of maximum defect sizes occurring between two consecutive track measurements. Predictions are made immediately after the latest measurement. The planning of preventive maintenance works is ba...
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Research Article
A Probabilistic Model for Predicting Traffic Accidents Based on the Parameters of the Three-dimensional Geometry of the Road Profile
Irina Baghdasaryan*,
Anzhela Barseghyan
Issue:
Volume 11, Issue 2, April 2026
Pages:
33-38
Received:
24 February 2026
Accepted:
11 March 2026
Published:
22 July 2026
DOI:
10.11648/j.ajtte.20261102.12
Downloads:
Views:
Abstract: The article proposes a probabilistic model for assessing the risk of road accidents on sections of highways with three-dimensional pedestrian markings. The aim of the study is to develop an approach to quantifying the risk of an accident based on the parameters of the spatial geometry of the road profile and traffic conditions. The model is based on the integration of methods of three-dimensional geometric modeling of road infrastructure, analysis of curve radii, longitudinal and transverse slopes, visibility zones and statistical forecasting of accidents using logistic regression. The model also takes into account external factors, including the speed of vehicles, weather conditions, and time of day. To form a vector of features, key parameters of the road infrastructure were identified, the impact of which on the probability of accidents was assessed using weighting coefficients. The results show that small curve radii, significant longitudinal slopes and limited visibility range have the greatest impact on the risk of an accident. The practical significance of the study lies in the possibility of using the developed model in analyzing the safety of road infrastructure and identifying potentially dangerous sections of highways. The use of three-dimensional pedestrian markings can help reduce the speed of movement and increase the attention of drivers, which is confirmed by the results of the analysis.
Abstract: The article proposes a probabilistic model for assessing the risk of road accidents on sections of highways with three-dimensional pedestrian markings. The aim of the study is to develop an approach to quantifying the risk of an accident based on the parameters of the spatial geometry of the road profile and traffic conditions. The model is based o...
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