Principal Component Random Forest for Passenger Demand Forecasting in Cooperative, Connected, and Automated Mobility

Cooperative, Connected, and Automated Mobility (CCAM) is set to play a key role in the future of transportation, contributing to the achievement of sustainable development goals. Moreover, Artificial Intelligence (AI), a transformative technology with applications across various industries, can significantly enhance CCAM operations. Additionally, passenger demand forecasting, a critical aspect of mobility research, will become even more essential as CCAM adoption continues to grow in the next years. Therefore, the present research study, in order to deal with the issue of passenger demand forecasting in CCAM, proposes the Principal Component Random Forest (PCRF) methodology, which is based on AI, as it leverages a well-established statistical methodology such as the Principal Components Analysis with a flagship traditional machine learning technique, which is Random Forest. The application of PCRF in four European pilot sites within the European Union-funded SHOW project demonstrated its high accuracy and effectiveness as reflected by the average normalized error of approximately 15%.