다중 공선성 하에서 버스 승객 수요 예측 모델링

• Published in: 品質經營學會誌 Vol. 53; no. 2; pp. 109 - 119
• Main Authors: 정대원, Dae Won Jung, 황욱연, Wook-yeon Hwang
• Format: Journal Article
• Language: Korean
• Published: 한국품질경영학회 30.06.2025
• Subjects: Bus Passenger Demand; Deep Learning; Industrial Factors; Machine Learning; Multicollinearity; PCA; Predictive Modeling; Variable Selection; Predictive Modeling; Variable Selection; Bus Passenger Demand; Industrial Factors; Multicollinearity; Machine Learning; Deep Learning; PCA
• ISSN: 1229-1889; 2287-9005
• DOI: 10.7469/JKSQM.2025.53.2.109

Purpose: This study aims to first develop a bus passenger demand prediction model based on industrial factors, population, and traffic dataunder multicollinearity. It can help Busan bus operation. Methods: In orderto address the multicollinearity issues, the research mainly considers PCA (Principal Component Analysis), MLR (Multiple Linear Regression), machine learning (GBDT (Gradient Boosted Decision Trees), RF (Random Forest), and deep learning (MLP (Multi-Layer Perceptron), LSTM (Long Short-Term Memory)), and variable selection for predictive modeling. Results and Conclusion: The industrial factors, population and traffic datasignificantly explain the bus passenger demand. The RF provides the best prediction performance.