Freight Rate and Demand Forecasting in Road Freight Transportation Using Econometric and Artificial Intelligence Methods

Abstract

The digitisation of the transportation sector and data availability have opened up new opportunities to implement data-driven methods for improving company performance. This article analyses demand and freight rate forecasting techniques in the context of the road freight transportation company. The European market was analysed in this research, and direction from the Netherlands to Italy was selected for the case study. Performed investigation showed that econometric models such as Auto-Regressive Integrated Moving Average (ARIMA) used for demand prognosis provide good results. Freight rate forecasting is different; econometric models, including multivariate models ARIMA with exogenous variables (ARIMAX) and Seasonal ARIMAX (SARIMAX), do not perform satisfactorily under specified time intervals, therefore MultiLayer Perceptron (MLP) was used as a solution. It can be seen that Artificial Intelligence (AI) based methods provide better results. Despite its success, the AI-based approach alone is not recommended for practical implementation since forecasted input parameters are necessary. Lastly, the study uncovers a valuable insight. A strong correlation (0.86) between spot and contract rates was found, and the article shows how current spot rates can be used for contract rate forecasting.