Modeling Pricing in the Transport Services Market: Analysis and Forecasting

The automation and intellectualization of industrial, transport, and energy systems have become critical drivers of efficiency and innovation in the modern economy. This adoption of algorithms enables precise forecasting of demand and pricing, facilitating automated decision-making. This shift towards automated, data-driven approaches aligns with the broader trend of digital transformation in industrial and transport systems. The theoretical foundations of pricing in the transport services market have been research. Data have been generated and indicators that affect the cost of renting a wagon have been determined. New aspects of integrating linear regression with econometric models to improve forecast accuracy are presented. This hybrid approach uses the strengths of both methods: the interpretability of linear regression and the qualitative characterization of interdependencies between real economic phenomena. A statistical assessment of models for predicting the cost of wagon rental is presented. Prospects for the development of research have been identified. Our approach is comprehensive when applied to a dynamic pricing model in the transportation market, providing a more robust and adaptable forecasting system. The introduction of statistical criteria facilitates a precise forecasting of transport demand and cargo flow optimization, leveraging extensive econometric data. This approach underscores the importance of innovative statistical methods in forecasting and strategizing in the transportation sector, suggesting that such adaptive models are essential for operators to remain competitive and economically stable in a dynamic market environment.