The choice of low-carbon travel modes by residents directly affects the low-carbon development of transportation. Whereas, the current study of behavior prediction models lacks the accuracy and interpretability. We propose the theoretical framework of residents' low-carbon travel choice behavior at the microlevel and construct the Stacking ensemble learning prediction model on basis of residents' low carbon travel data. Through integrating various data such as transportation facility data and real-time weather observation data, we utilize machine learning algorithms to improve the prediction accuracy of residents' low-carbon travel choice, and explore the impact of different influencing factors on their choice behaviors.


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    Title :

    Predictions of Low-carbon Travel Mode Choices for Residents Based on Stacking Ensemble Learning Prediction Model


    Contributors:
    Dong, Xiaoyang (author) / Li, He (author) / Dong, Gaofeng (author)


    Publication date :

    2024-08-07


    Size :

    432850 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English