Car Ownership Demand Modeling Using Machine Learning: Decision Trees and Neural Networks

Tanwanichkul, Ladda; Kaewwichian, Patiphan; Pitaksringkarn, Jumrus

doi:10.21660/2019.62.94618

Cited by 8 publications

(10 citation statements)

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“…The findings in this study throws more light on vehicle ownership in the context of a small to medium urban city in Ghana which is mainly influence by the respondents travel characteristics and average monthly income. This finding is largely in agreements with the studies by Ha et al [ 33 ], Kaewwichiann et al [ 34 ] and Paredes et al [ 32 ], however, with regards to the best performing ML algorithm, the finding in our study produced different results which might be as a result of the difference in the data structure and the methods used to find the best learning features for each algorithm, highlighting the case-specific nature of the application of the ML algorithms to vehicle ownership studies.…”

Section: 0 Conclusionsupporting

confidence: 94%

“…In other words, it is the measure of how scores of an estimator reduce when a feature is excluded from the model estimation. A reduction in the model scores indicates how important this feature is, in the overall performance of the model (see [ 23 , 34 , 48 ] for more details).…”

Section: 0 Dataset and Modeling Frameworkmentioning

confidence: 99%

“…Their results indicated that household income was the most prominent feature affecting vehicle ownership in Phnom Penh and that the RF produced the highest accuracy in terms of prediction. Kaewwichiann et al [ 34 ] also explore the use of DT and ANN for vehicle ownership modeling using household survey data from Khon Kaen Province, Thailand. Their results indicated that ANN outperforms DT.…”

Section: 0 Introductionmentioning

confidence: 99%

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Modeling vehicle ownership with machine learning techniques in the Greater Tamale Area, Ghana

2021

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Vehicle ownership modeling and prediction is a crucial task in the transportation planning processes which, traditionally, uses statistical models in the modeling process. However, with the advancement in computing power of computers and Artificial Intelligence, Machine Learning (ML) algorithms are becoming an alternative or a complement to the statistical models in modeling the transportation planning processes. Although the application of ML algorithms to the transportation planning processes—like mode choice, and traffic forecasting and demand modeling—have received much attention in research and abound in literature, scanty attention is paid to its application to vehicle ownership modeling especially in the context of small to medium cities in developing countries. Therefore, this study attempts to fill this gap by modeling vehicle ownership in the Greater Tamale Area (GTA), a typically small to medium city in Ghana. Using a cross sectional survey of formal sectors workers, data was collected between June–August 2018. The study applied nine different ML classification algorithms to the dataset using 10-fold cross-validation technique/s and the Cohen-Kappa static/statistic to evaluate the predictive performance of each of the algorithms, and the Permutation Feature Importance to examine the features that contribute significantly to the prediction of vehicle ownership in GTA. The results showed that Linear Support Vector Classification (LinearSVC) classifier performed well in comparison with the other classifiers with regards to the overall predictive ability of the classifiers. In terms of class predictions, K- Nearest Neighbors (KNN) classifier performs well for no-vehicle class whiles Linear Support Vector Classification (LinearSVC) and GaussianNB classifiers performs well for motorcycle ownership. LinearSVC and Logistic Regression classifiers performed well on the car ownership class. Also, the results indicated that travel mode choice, average monthly income, average travel distance to workplace, average monthly expenditure on transport, duration of travel to workplace, occupational rank, age, household size and marital status were significant in predicting vehicle ownership for most of the classifiers. These findings could help policies makers carve out strategies that would reduce vehicle ownership but improve personal mobility.

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Section: 0 Conclusionsupporting

confidence: 94%

Section: 0 Dataset and Modeling Frameworkmentioning

confidence: 99%

Section: 0 Introductionmentioning

confidence: 99%

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Modeling vehicle ownership with machine learning techniques in the Greater Tamale Area, Ghana

2021

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“…In [35], the authors propose an ensemble method-ensembling the results of separate NN regressors-to predict household electricity consumption based on a set of household characteristics including the number of rooms, the total floor space and the number of residents. In [36], car ownership in Thailand is predicted from independent variables relating to household socio-economic factors, activities and accessibility using both NNs and decision trees. By defining the problem as a classification, in which the number of cars per household is classified as either 0, 1 or 2+, it is shown that neural networks statistically outperform decision trees.…”

Section: Previous Work On Car Ownership Modellingmentioning

confidence: 99%

Spatially Disaggregated Car Ownership Prediction Using Deep Neural Networks

Dixon

Koukoura

Brand

et al. 2021

Future Transportation

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Predicting car ownership patterns at high spatial resolution is key to understanding pathways for decarbonisation—via electrification and demand reduction—of the private vehicle fleet. As the factors widely understood to influence car ownership are highly interdependent, linearised regression models, which dominate previous work on spatially explicit car ownership modelling in the UK, have shortcomings in accurately predicting the relationship. This paper presents predictions of spatially disaggregated car ownership—and change in car ownership over time—in Great Britain (GB) using deep neural networks (NNs) with hyperparameter tuning. The inputs to the models are demographic, socio-economic and geographic datasets compiled at the level of Census Lower Super Output Areas (LSOAs)—areas covering between 300 and 600 households. It was found that when optimal hyperparameters are selected, these neural networks can predict car ownership with a mean absolute error of up to 29% lower than when formulating the same problem as a linear regression; the results from NN regression are also shown to outperform three other artificial intelligence (AI)-based methods: random forest, stochastic gradient descent and support vector regression. The methods presented in this paper could enhance the capability of transport/energy modelling frameworks in predicting the spatial distribution of vehicle fleets, particularly as demographics, socio-economics and the built environment—such as public transport availability and the provision of local amenities—evolve over time. A particularly relevant contribution of this method is that by coupling it with a technology dissipation model, it could be used to explore the possible effects of changing policy, behaviour and socio-economics on uptake pathways for electric vehicles —cited as a vital technology for meeting Net Zero greenhouse gas emissions by 2050.

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“…This method predicts different data types, either present or future, such as travel demand predictions. Several minor models were used, including the household car ownership models, trip generation models, tour generation models, trip distribution models, travel time choice models, and travel route choice models [1], with either trip or tour used as the unit of analysis [2]. There are several techniques for data classification [3], e.g.…”

Section: Introductionmentioning

confidence: 99%

Multiclass Classification with Imbalanced Datasets for Car Ownership Demand Model – Cost-Sensitive Learning

Kaewwichian

2021

PROMET

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In terms of the travel demand prediction from the household car ownership model, if the imbalanced data were used to support the transportation policy via a machine learning model, it would negatively affect the algorithm training process. The data on household car ownership obtained from the study project for the expressway preparation in the Khon Kaen Province (2015) was an unbalanced dataset. In other words, the number of members of the minority class is lower than the rest of the answer classes. The result is a bias in data classification. Consequently, this research suggested balancing the datasets with cost-sensitive learning methods, including decision trees, k-nearest neighbors (kNN), and naive Bayes algorithms. Before creating the 3-class model, a k-folds cross-validation method was applied to classify the datasets to define true positive rate (TPR) for the model’s performance validation. The outcome indicated that the kNN algorithm demonstrated the best performance for the minority class data prediction compared to other algorithms. It provides TPR for rural and suburban area types, which are region types with very different imbalance ratios, before balancing the data of 46.9% and 46.4%. After balancing the data (MCN1), TPR values were 84.4% and 81.4%, respectively.

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Car Ownership Demand Modeling Using Machine Learning: Decision Trees and Neural Networks

Cited by 8 publications

References 0 publications

Modeling vehicle ownership with machine learning techniques in the Greater Tamale Area, Ghana

Modeling vehicle ownership with machine learning techniques in the Greater Tamale Area, Ghana

Spatially Disaggregated Car Ownership Prediction Using Deep Neural Networks

Multiclass Classification with Imbalanced Datasets for Car Ownership Demand Model – Cost-Sensitive Learning

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