Articlegold2022

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

Zhiqiang Xu · Mahdi Aghaabbasi · Mujahid Ali · Elżbieta Macioszek

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Transportation Planning and Optimization Data Mining and Machine Learning Applications Traffic Prediction and Management Techniques

Abstract

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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2012Practical Bayesian Optimization of Machine Learning Algorithms 2002How the built environment affects physical activity 2020Bayesian Optimization for Adaptive Experimental Design: A Review 2004Comparison of car ownership models 2007Volatility of car ownership, commuting mode and time in the UK 2012Factors affecting modal choice in urban mobility 2017Exploring car ownership and car use in neighborhoods near metro stations in Beijing: Does the neighborhood built environment matter?2010Modeling Private Car Ownership in China

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