User satisfaction with battery electric vehicles in South Korea

Kwon, Yeongmin; Son, Sanghoon; Jang, Kitae

doi:10.1016/j.trd.2020.102306

Cited by 39 publications

(11 citation statements)

References 68 publications

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“…e survey is comprised of three parts. e first part is a respondent socio-demographic characteristic (e.g., gender, age, job, household size, household income, and residential area) [28]. e second part is about the current and recent past use of conventional transportation modes and existing shared mobility services in the province.…”

Section: Survey Overview and Questionnairementioning

confidence: 99%

“…Second, the shared mobility services and their penetration rates are rapidly changing and still at a pilot phase, so the current perception of shared mobility services could not be highly influenced by old experiences obtained when the service environments were different from now. Since satisfaction is difficult to observe and measure explicitly, it is collected using the Likert scale [28,29]. e third part is a future preference survey assuming that shared mobility services will be provided.…”

Section: Survey Overview and Questionnairementioning

confidence: 99%

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Survey Data Analysis on Intention to Use Shared Mobility Services

Kim

Lee

2021

Journal of Advanced Transportation

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Shared mobility is a service that allows users to share various transportation modes and use them with reservations when necessary. It started with private automotive car-sharing and ride-sharing services. Currently, it operates on a wider range, including personal mobility devices such as electric bicycles and scooters. The purpose of this study is to derive a direction for providing future shared mobility services through analysis of factors affecting the usage intention of both current and prospective users. The survey targets 753 citizens living in Gyeonggi Province, Korea. The survey period is from February 12, 2020, to February 26, 2020. In this study, a logistic regression analysis is conducted to investigate the factors affecting the use intention of shared mobility. The analysis results show that gender, car ownership, and education, among variables reflecting socio-demographic characteristics, have significant effects on intention to use shared mobility. Moreover, we find that experience factors, including mainly used transportation modes, ownership of shared mobility device, past experience in similar services, satisfaction of existing shared mobility services, and distance from the home to the nearest bus stop, are also statistically influential. The analysis results are expected to lay the foundation for the introduction of shared mobility services and can be used as data for planning smart mobility services in the future.

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Section: Survey Overview and Questionnairementioning

confidence: 99%

Section: Survey Overview and Questionnairementioning

confidence: 99%

Survey Data Analysis on Intention to Use Shared Mobility Services

Kim

Lee

2021

Journal of Advanced Transportation

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“…The research topics on mobility services are divided into two types: mobility service platforms [7,8,17,18] and mobility [9,[19][20][21][22][23][24]. As mentioned earlier, the studies conducted on mobility have contributed to the invention of various means of transportation (e.g., electric kickboards and electric bicycles), which shorten the necessary duration to arrive at a certain destination.…”

Section: Mobility Servicementioning

confidence: 99%

Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea

et al. 2020

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Mobility services facilitate various tasks related to transportation and passenger movements. Because of the Fourth Industrial Revolution, the importance of mobility services has been recognized by many countries. Thus, research is ongoing to provide more convenience to passengers and to obtain more efficient transportation systems. In the Republic of Korea, the officials of Gyeonggi Province are interested in providing an advanced mobility service to its residents; however, they still do not have any specific or detailed policies. This study aimed at deriving the key issues facing mobility services, especially in the case of Gyeonggi Province, by using a text mining technique and a clustering algorithm. First, a survey was taken by traffic and urban experts to collect reasonable plans for Gyeonggi-Province-type mobility service, and a morpheme analysis was then used for text mining. Second, the results reveal that the term frequency–inverse document frequency (TF-IDF) algorithm has better performance than frequency analysis. Third, the K-means application results in six clusters and six mobility service policy issues were determined by combining the words in each cluster. Finally, the methodology confirmed the validity and effectiveness of the proposed method by showing that the results reflect the current situation in the province.

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“…EVs were initially limited by factors such as reliability and battery range, which have significantly improved in recent years and led to an increase in EV popularity [5]. As a result, the trust in EV reliability has grown and satisfaction among EV owners are higher [6]. The driver flexibility has also increased with the addition of charging stations in many parts of the world, often lead by various government initiatives encouraging further adoption of EVs.…”

Section: Introductionmentioning

confidence: 99%

Prediction of EV Charging Behavior Using Machine Learning

et al. 2021

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As a key pillar of smart transportation in smart city applications, electric vehicles (EVs) are becoming increasingly popular for their contribution in reducing greenhouse gas emissions. One of the key challenges, however, is the strain on power grid infrastructure that comes with large-scale EV deployment. The solution to this lies in utilization of smart scheduling algorithms to manage the growing public charging demand. Using data-driven tools and machine learning algorithms to learn the EV charging behavior can improve scheduling algorithms. Researchers have focused on using historical charging data for predictions of behavior such as departure time and energy needs. However, variables such as weather, traffic, and nearby events, which have been neglected to a large extent, can perhaps add meaningful representations, and provide better predictions. Therefore, in this paper we propose the usage of historical charging data in conjunction with weather, traffic, and events data to predict EV session duration and energy consumption using popular machine learning algorithms including random forest, SVM, XGBoost and deep neural networks. The best predictive performance is achieved by an ensemble learning model, with SMAPE scores of 9.9% and 11.6% for session duration and energy consumptions, respectively, which improves upon the existing works in the literature. In both predictions, we demonstrate a significant improvement compared to previous work on the same dataset and we highlight the importance of traffic and weather information for charging behavior predictions.

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User satisfaction with battery electric vehicles in South Korea

Cited by 39 publications

References 68 publications

Survey Data Analysis on Intention to Use Shared Mobility Services

Survey Data Analysis on Intention to Use Shared Mobility Services

Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea

Prediction of EV Charging Behavior Using Machine Learning

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