Household Vehicle Ownership Prediction Using Machine Learning Approach

Manjushree, NS and Samyama Gunjal, GH and Swamy, SC and Giridharan, A (2022) Household Vehicle Ownership Prediction Using Machine Learning Approach. In: 2022 International Conference for Advancement in Technology, ICONAT 2022, 21 January 2022, Virtual, Online.

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Abstract

Predictions of vehicle ownership and their influencing factors play an important role in transportation policy making. In the era of urbanization and globalization, vehicle ownership patterns have become a more relevant issue in developing countries attempting to achieve sustainable transportation development goals. Machine learning techniques facilitate tremendously in predicting the vehicle ownership patterns, required to achieve the above said goal. In the proposed work, the machine learning models such as decision tree, random forest and multinomial logistic regression models are applied over household datasets, to predict the household factors influencing vehicle ownership. According to the ML model, influencing factors on Household Vehicle Ownership (HVO) prediction are number of persons having DL in a household, number of persons drive in a household, number of persons using ride source service or public transport in a household. Analysis of datasets showed that, total income of a household, number of persons in household and distance travelled each day are positively associated with vehicle ownership of the household. The predictions of this study would be useful mainly for local governments, transportation agencies, planners and policy-makers. © 2022 IEEE.

Item Type:	Conference Proceedings
Publication:	2022 International Conference for Advancement in Technology, ICONAT 2022
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Decision trees; Developing countries; Economic and social effects; Logistic regression; Machine learning; Random forests; Vehicles, Household factor; Household numbers; Household vehicle ownership; Household vehicle ownership pattern; Machine learning approaches; ML technique; Policy making; Transportation policies; Vehicle ownership, Forecasting
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	18 May 2022 08:53
Last Modified:	18 May 2022 08:53
URI:	https://eprints.iisc.ac.in/id/eprint/71832

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