Travel Mode Choice Based on Hyperparameter Optimization and Ensemble Learning
To address the challenges of low predict accuracy,complex hyperparameter optimization,and limited model interpretability in conventional travel mode choice models and machine learning models,this paper introduces the genetic algorithm and Bayesian optimization for hyperparameter optimization of the extreme gradient boosting machine model(XGBoost).Additionally,the SHAP(SHapley Additive exPlanations)model is integrated to visualize the nonlinear relationship between travel mode attributes and individual characteristics in the choice probability.The proposed model is trained using 5-fold cross-validation to prevent overfitting and is evaluated using Swissmetro dataset to demonstrate its superiority.The results indicate that enhancing the nonlinear representation of the utility function in discrete choice models improves model prediction performance,yet falls short compared to machine learning models.The optimized XGBoost model,employing genetic algorithm and Bayesian optimization,outperforms conventional multinomial Logit models with linear or nonlinear utility functions,as well as standard random forest and non-optimized XGBoost models in terms of accuracy,recall,and F1 score for travel choice predictions.The XGBoost model optimized by genetic algorithm exhibits the highest prediction accuracy of 0.781,surpassing models based on conventional multiple grid search.Moreover,hyperparameter optimization using genetic algorithm reduces training time by 81.4%compared to multiple grid search.Furthermore,the study reveals that the cost and time associated with different travel modes significantly influence the choice preferences,with trains and cars being more sensitive to time while the Swiss metro is more sensitive to cost.
万方数据
维普
