Abstract Vehicle ownership patterns and their determinants play an important role in transportation policy-making. This issue has been paid even greater attention in developing countries that aspire to reach sustainable transportation development goals in the era of urbanization and globalization. In this study, the multinomial logit model, neural networks and random forest were applied to examine the features' impact level and to also predict vehicle ownership patterns in Phnom Penh city. The empirical results indicate that household income is the most powerful variable affecting motorization in Phnom Penh. Supplementation of individual trip characteristics such as total number of trips made, number of trips made for work purposes and overall travel distance all make effective contributions as classifiers. Furthermore, it is acknowledged that the machine-learning approach outperformed not only in terms of predicting accuracy, but also in dealing with unbalanced categories when compared with the statistical approach. This detection supplies the advantages of applying machine learning techniques in terms of, but not limited to, the field of vehicle ownership.

    Highlights Vehicle ownership determinants are evaluated by influence level and impact trend. Motorization of the urban and suburban area was sensitive to the different features. Machine learning outperforms statistical method in examining vehicle ownership. Personal trip characteristics are served as good explanatory variables.


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    Title :

    Determination of the influence factors on household vehicle ownership patterns in Phnom Penh using statistical and machine learning methods


    Contributors:

    Published in:

    Publication date :

    2019-05-20


    Size :

    17 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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