다중공선성하에서버스승객수요예측모델링
The Bus Passenger Demand Prediction Modeling Under Multicollinearity
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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 pas- senger demand. The RF provides the best prediction performance.
Abstract
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 pas- senger demand. The RF provides the best prediction performance.