Forecasting the Economic Growth Trend of All Types of Companies in the Transportation Sector by Using Artificial Intelligence

The economic growth and performance of companies operating in the transportation industry are always influenced by a set of internal and external factors, from management structure and internal efficiency to macroeconomic variables such as crude oil prices and governance policies. Since each company's stock chart is a reflection of all these factors, stock price behavior analysis can be an efficient tool for evaluating the economic growth trend of companies. In this study, using deep learning models in artificial intelligence, the stock price trend of 28 companies operating in seven transportation subsectors (air, sea, rail, road, postal and logistics, miscellaneous services, and car rental) was examined and forecasted during the period 2020 to 2024. Data from the first four years were used to train the model and data from 2024 were used to test the prediction. The results showed that the designed model was able to predict real prices with an average accuracy of 97.98 percent, and by adding the WTI crude oil price variable, the model accuracy increased to 98.61 percent. This improvement was more significant in subsectors such as airlines and shipping that are directly dependent on fuel costs, while no significant change was observed in sectors such as smart transportation services. Qualitative analyses also showed that the model, in addition to accurately predicting values, has the ability to correctly identify market trends. Thus, the presented model, by combining financial and economic data, has been able to provide a practical tool for capital market analysis and strategic decision-making.