The growth rate of cities is increasing due to urbanization, necessitating the management of resources such as public transport, private transport, roads, etc. Understanding the behavior of travel is also a crucial topic in managing and planning the resources in smart cities. Hence, in this research work an attempt is made to analyze the travel mode preference of an individual under different circumstances. The public database provided by Microsoft for tracking the travel mode of individuals is used for experimentation. The comparative analysis of different machine learning algorithms is performed for the prediction of travel mode. Experimentally, it has been observed that the Extra-Trees classifier outperforms the rest of the classifiers. The Extra-Trees classifier gained an overall accuracy of 97 % in predicting the travel mode of individuals. In this way, the proposed research work can be used in real-time applications to understand travel behavior and suggest the mode of travel for individuals under given constraints with great accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Comparative Analysis of Different Machine Learning Techniques for Travel Mode Prediction


    Contributors:


    Publication date :

    2024-05-23


    Size :

    441371 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Machine Learning-Based Travel Mode Prediction: A Comparative Methodological Approach

    Bhosle, Nilesh / Jagtap, Jayant / Svitek, Miroslav | IEEE | 2025


    Travel Mode Choice Prediction Using Imbalanced Machine Learning

    Chen, Huanfa / Cheng, Yan | IEEE | 2023

    Free access

    Travel mode choice prediction: developing new techniques to prioritize variables and interpret black-box machine learning techniques

    Naseri, Hamed / Waygood, E.O.D. / Patterson, Zachary et al. | Taylor & Francis Verlag | 2025


    A Comparative Analysis between Machine Learning and Econometric Approaches for Travel Mode Choice Modeling

    Javadinasr, Mohammadjavad / Asgharpour, Sina / Mohammadi, Motahare et al. | TIBKAT | 2023


    A Comparative Study of MNL and Machine Learning Methods for Travel Mode Choice of Medical Travel

    Huang, Pengpeng / Gong, Lei / Lei, Tian et al. | Springer Verlag | 2024