Forecasting the risks of an organization operating natural gas vehicles using a scoring model of logistic regression in the presence of expert restrictions

The paper proposes a method and describes a mathematical model for express analysis of the attractiveness of the operation of vehicles running on natural gas for a motor transport company. The proposed solution is based on a logistic regression scoring model used by banks to assess the creditworthiness of a borrower. To improve the quality of the results, the model is extended with a set of expert restrictions formulated in the form of rules. During the analysis, signs were identi ed that require quantization, since individual intervals of values turned out to be associated with risk in di erent ways. The developed mathematical model is implemented in the form of software in a high-level programming language, the information of the model is stored in a database management system and is integrated with an information system for supporting management decisions when operating vehicles on natural gas. The developed mathematical model was tested on a test training sample. The test results showed a satisfactory accuracy of the proposed model at the level of 77 % without the use of expert restrictions and 79 % with their use. At the same time, the share of Type II errors was 2.7 %, and Type I errors were 7.2 %, which indicates that the model is quite conservative, and a relatively high proportion of vehicles that meet the requirements were rejected.