Work

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

Year: 2025

Type: article

Abstract: 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 EU-funded SHOW project demonstrated its high accuracy and effectiveness.

Source: Sustainability

DOI: 10.3390/su17062632

Authors: Georgios Spanos, Antonios Lalas, Konstantinos Votis, Dimitrios Tzovaras

Institutions: Information Technologies Institute, Centre for Research and Technology Hellas

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $2,896