A Recommender Subsystem Construction for Calculating the Probability of a Violation by a Locomotive Driver using Machine-learning Algorithms
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Cited by 2 publications
References 10 publications
Aim. The aim of the paper is to examine the experience of reducing the effect of the human factor on business processes, to develop the structure and software of the decisionsupport system for preventing safety violations by train drivers using machine learning and to analyse the findings. Methods. The study presented in the paper uses machine learning, statistical analysis and expert analysis. In terms of machine learning, the following methods were used: logistical regression, random forests, gradient boosting over decision trees with frequency-domain representation of categorical features, neural networks. Results. A set of indicators characterizing a train driver’s operation were identified and are to be used as part of the system under development. The term “train driver’s reliability” was defined as the ability not to violate train traffic safety over a certain number of trips. Algorithms were designed and examined for predicting violations in a train driver’s operation that are used in defining reliability groups and lists of preventive measures recommended for the reduction of the number of safety violations in a train driver’s operation. Major violations with proven guilt of the driver that may be committed within the following 3, 7, 10, 20, 30, 60 days were chosen as attributes for the purpose of safety violation prediction. Analysis of the results on the test sample revealed that the model based on gradient boosting over decision trees with frequency-domain representation of categorical features shows the best results for binary classification on the prediction horizon of 30 and 60 days. The developed algorithm made a correct prediction in 76% of cases with the threshold value of 0.7 and horizon of 30 days and in 82% of cases with the threshold value of 0.9 and horizon of 60 days. The solution of the problem can be found in the integration of different approaches to predicting safety violations in a train driver’s operation. Additionally, 10 of the most significant indicators of a train driver’s operation were identified with the best of the considered models, i.e., gradient boosting over decision trees with frequency-domain representation of categorical features. Conclusion. The paper presents an overview of methods and systems of assessing human reliability and the effect of the human factor on the safety of transportation systems. It allowed choosing the most promising directions and methods of predictive analysis of a train driver’s operation, including methods of machine learning. The resulting set of indicators of a train driver’s operation that take into consideration the changes in the quality of such operation allowed obtaining initial data for training the models implemented as part of the system under development. The implemented models enabled the aggregation of information on train drivers and adoption of targeted and temporary preventive measures recommended for improving driver reliability. The resulting approach to the definition of preventive measures has been implemented in three depots of JSC RZD in trial operation mode.
Aim. The aim of the paper is to examine the experience of reducing the effect of the human factor on business processes, to develop the structure and software of the decisionsupport system for preventing safety violations by train drivers using machine learning and to analyse the findings. Methods. The study presented in the paper uses machine learning, statistical analysis and expert analysis. In terms of machine learning, the following methods were used: logistical regression, random forests, gradient boosting over decision trees with frequency-domain representation of categorical features, neural networks. Results. A set of indicators characterizing a train driver’s operation were identified and are to be used as part of the system under development. The term “train driver’s reliability” was defined as the ability not to violate train traffic safety over a certain number of trips. Algorithms were designed and examined for predicting violations in a train driver’s operation that are used in defining reliability groups and lists of preventive measures recommended for the reduction of the number of safety violations in a train driver’s operation. Major violations with proven guilt of the driver that may be committed within the following 3, 7, 10, 20, 30, 60 days were chosen as attributes for the purpose of safety violation prediction. Analysis of the results on the test sample revealed that the model based on gradient boosting over decision trees with frequency-domain representation of categorical features shows the best results for binary classification on the prediction horizon of 30 and 60 days. The developed algorithm made a correct prediction in 76% of cases with the threshold value of 0.7 and horizon of 30 days and in 82% of cases with the threshold value of 0.9 and horizon of 60 days. The solution of the problem can be found in the integration of different approaches to predicting safety violations in a train driver’s operation. Additionally, 10 of the most significant indicators of a train driver’s operation were identified with the best of the considered models, i.e., gradient boosting over decision trees with frequency-domain representation of categorical features. Conclusion. The paper presents an overview of methods and systems of assessing human reliability and the effect of the human factor on the safety of transportation systems. It allowed choosing the most promising directions and methods of predictive analysis of a train driver’s operation, including methods of machine learning. The resulting set of indicators of a train driver’s operation that take into consideration the changes in the quality of such operation allowed obtaining initial data for training the models implemented as part of the system under development. The implemented models enabled the aggregation of information on train drivers and adoption of targeted and temporary preventive measures recommended for improving driver reliability. The resulting approach to the definition of preventive measures has been implemented in three depots of JSC RZD in trial operation mode.
Evaluating the economic efficiency of preventive measures aimed at reducing the number of train control violations
Aim. The paper aims to examine the matters related to increasing the objectivity of evaluation of the quality of train control by train drivers. Methods. The study presented in the paper uses statistical analysis and linear algebra. Results. An algorithm was developed for defining preventive measures and their application efficiency was evaluated for drivers of rapid transit trains. The algorithm for defining preventive measures for drivers of rapid transit trains includes the following: violation prediction; definition of the factors that affect the onset of each type of violations; definition of the characteristics of the drivers that most deviate from the target values. The efficiency estimation is based on the assumption of correlation between the cost of a driving instructor’s work with a driver and the cost of losses that the company might incur in case of violations. The paper shows that the level of an error of the first kind in the train driver violation prediction model is justified, provided that the cost incurred as the result of gross train control violations is significantly greater than that associated with the training of such driver. The paper presents an analysis of the application of the AI-based system in four depots. Conclusion. The paper presents an algorithm for defining preventive measures for train drivers. An economic criterion was defined for evaluating the efficiency of application of the developed mathematical model for predicting gross violations of train control. The required and sufficient conditions of economic efficiency of the AI-based systems application were analysed. A comparative analysis was presented of the mean number of gross train driving violations in depots with and without the AI-based system.
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