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

Zambang, Mohammed Abdul Muhsin; Jiang, Haobin; Wahab, Lukuman

doi:10.1371/journal.pone.0246044

Cited by 7 publications

(8 citation statements)

References 37 publications

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“…Public management influences car ownership, as in the case of public transportation. Depending on the quality of public transport and access to critical services, the need to own a car intensifies ( 41 , 49 ). In Ghana, for example, public transportation is characterized by low-quality services, thus reinforcing car ownership ( 49 ).…”

Section: Resultsmentioning

confidence: 99%

“…Depending on the quality of public transport and access to critical services, the need to own a car intensifies ( 41 , 49 ). In Ghana, for example, public transportation is characterized by low-quality services, thus reinforcing car ownership ( 49 ). Accessibility to public transport is low in Australia because of its low population density, which reinforces the dependence on private cars ( 50 ).…”

Section: Resultsmentioning

confidence: 99%

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Trends, Enablers, and Barriers for Car Ownership

Fronteli

Paladini

2022

Transportation Research Record: Journal of the Transportation R

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Car ownership has been the object of interest in various research areas. Studies on the topic focus especially on the influence of factors such as environment, life, family events, use, and availability, usually restricted to a given city or region. This paper focuses on recent studies that identified enablers, barriers, and trends related to car ownership in the automotive sector. We use an exploratory-qualitative approach based on systematic mapping of the literature to select papers published between 2019 and March 2021 on the Scopus database. As a result, we first classified the factors that act as enablers or barriers to the acquisition and maintenance of cars. These factors were divided into the following six groups: personal, cultural, political, economic, technological, and environmental. Next, we identified car-ownership-related trends for the automotive sector: purchase replaced by effective use; restrictions on car ownership; autonomous driving; sustainable mobility; new mobility models; customized customer service; real-time integrated modal communication, systematic global traffic, and integrated urban planning. We consider our findings to be relevant contributions to the industry in relation to establishing strategies and public administrators acting in the transportation planning field.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Trends, Enablers, and Barriers for Car Ownership

Fronteli

Paladini

2022

Transportation Research Record: Journal of the Transportation R

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“…To date, several studies have employed ML algorithms to predict the HVO (e.g., [6,36,[41][42][43][44][45]). Table 1 shows a summary of these studies.…”

Section: Methods Used For Hvo Predictionmentioning

confidence: 99%

“…The support vector machine (SVM) algorithm was employed in several studies to analyze vehicle ownership and showed promising performance [41,42,46]. Abdul Muhsin Zambang, Jiang, and Wahab [42] compared the performance of SVM with other ML techniques in Greater Tamale. However, their study did not employ any algorithm to optimize the SVM or other techniques' hyperparameters.…”

Section: Methods Used For Hvo Predictionmentioning

confidence: 99%

Targeting Sustainable Transportation Development: The Support Vector Machine and the Bayesian Optimization Algorithm for Classifying Household Vehicle Ownership

Xu¹,

Aghaabbasi

Ali

et al. 2022

Sustainability

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Predicting household vehicle ownership (HVO) is a crucial component of travel demand forecasting. Furthermore, reliable HVO prediction is critical for achieving sustainable transportation development objectives in an era of rapid urbanization. This research predicted the HVO using a support vector machine (SVM) model optimized using the Bayesian Optimization (BO) algorithm. BO is used to determine the optimal SVM parameter values. This hybrid model was applied to two datasets derived from the US National Household Travel Survey dataset. Thus, two optimized SVM models were developed, namely SVMBO#1 and SVMBO#2. Using the confusion matrix, accuracy, receiver operating characteristic (ROC), and area under the ROC, the outcomes of these two hybrid models were examined. Additionally, the results of hybrid SVM models were compared with those of other machine learning models. The results demonstrated that the BO algorithm enhanced the performance of the standard SVM model for predicting the HVO. The BO method determined the Gaussian kernel to be the optimal kernel function for both datasets. The performance of the SVM#1 model was improved by 4.27% and 5.16% for the training and testing phases, respectively. For SVM#2 model, the performance of this model was improved by 1.20% and 2.14% for the training and testing phases, respectively. Moreover, the BO method enhanced the AUC of the SVM models used to predict the HVO. The hybrid SVM models also outperformed other machine learning models developed in this study. The findings of this study showed that SVM models hybridized with the BO algorithm can effectively predict the HVO and can be employed in the process of travel demand forecasting.

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“…The work in [396] models vehicle ownership in the Greater Tamale Area, Ghana, utilizing machine learning techniques.…”

Section: Côte D'ivoirementioning

confidence: 99%