The Impact of Land-Use Variables on Free-Floating Carsharing Vehicle Rental Choice and Parking Duration

Khan, Mubassira; Machemehl, Randy B.

doi:10.1007/978-3-319-40902-3_19

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…The current references mainly focus on RTC [11][12][13][14][15][16] and FFC [17][18][19][20][21][22], but the study on OWC is insufficient. In basic analysis unit, the user is always selected for the RTC model [11][12][13].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In basic analysis unit, the user is always selected for the RTC model [11][12][13]. The spatial scales of a single station, the station cluster to land parcel (including grid, census tracts, transportation analysis zone, and district), are in increasing order [14][15][16][17][18][19][20][21][22][23][24]. The smaller the spatial scale of the analysis unit is, the greater the model can reflect the demand characteristics of the local space.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Locating Station of One-Way Carsharing Based on Spatial Demand Characteristics

Chen

Cheng

et al. 2018

Journal of Advanced Transportation

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This study aims to explore factors that affect carsharing demand characteristics in different time periods based on EVCARD transaction data, which is the largest station-based one-way carsharing program in Shanghai, China. Monthly usage intensity and degree of usage imbalance are used as proxies of demand. This study uses three groups of independent variables: carsharing station attributes, built environment (density, diversity, design, and destination accessibility), and transportation facilities. The adaptive elastic net regression is developed to identify factors that influence carsharing usage intensity and degree of usage imbalance after factor selection using extra-randomized-tree algorithm. Finally, a station layout is proposed according to both usage intensity and degree of imbalance. The main results of this study are presented as follows: (1) different effects of built environment and transportation factors cause dynamic demand across different time periods; (2) factors with positive and negative effect on carsharing demand are divided clearly for guidance of the carsharing station layout; (3) public parking space leads to more personal vehicle trip compared to a carsharing trip; and (4) as public transportation, the relationship of the metro and carsharing is complementary. However, the bus stop and carsharing have a competitive relationship. This study provides a carsharing layout method based on both usage intensity and degree of imbalance. Furthermore, several policies concerning carsharing are proposed.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Locating Station of One-Way Carsharing Based on Spatial Demand Characteristics

Chen

Cheng

et al. 2018

Journal of Advanced Transportation

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“…In previous studies, carsharing preference is predicted based on the factors of carsharing attributes (e.g., price, type of vehicle, access distance, or availability of the vehicle) and users' socio-demographic characteristics [8][9][10][11]. Research has shown that price, parking convenience, and car type were the significant factors influencing individuals' preferences [5,9,12].…”

Section: Introductionmentioning

confidence: 99%

Travelers’ Acceptance of Electric Carsharing Systems in Developing Countries: The Case of China

et al. 2019

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Emerging electric carsharing (EC) systems have demonstrated their advantages and attracted public attention. The number of EC systems is growing throughout the world, especially in metropolitan areas in developing countries. For successful implementation, developers need to understand the public acceptance of EC services. In this study, we sought to determine the factors that affect EC acceptance in the context of developing countries. The study involved 437 individuals, aged between 18 and 65 years, who were randomly sampled from an EC service area in China. The determinants of EC acceptance were investigated based on an extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT) and tested by using Structural Equation Modeling. The results indicated that hedonic motivation (HM) has a powerful effect on behavioral intention (BI) to use the EC service in the future. Performance expectancy (PE), effort expectancy (EE), and familiarity with the carsharing concept (FM) also influenced EC’s acceptance. However, the impact of social influence (SI) did not emerge from this study. The results also revealed that gender moderates the effects of EE and FM on BI. Age moderated the effect of FM on BI and unexpectedly moderated the impact of HM on BI. The present study confirmed the validity of the UTAUT research model in predicting the intention to use an EC system in developing countries. Implications and recommendations for government and EC developers are also discussed.

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“…A selection model has been proposed to identify those variables that can significantly influence user willingness, such as family economic conditions, travel expenses, vehicle demands, and personal attributes. Several studies have attempted to analyze the behavior of car-sharing users by using order data [13][14][15][16][17][18][19][20]. The order data of a car-sharing company mainly refer to the actual car rental information of users that are renting cars from this company.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying and Predicting the Expenditure Level Characteristics of Car-Sharing Users Based on the Empirical Data

Sai

Xie

et al. 2019

Sustainability

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Car-sharing plays a positive role in reducing vehicle ownership and greenhouse gas emissions. However, the developmental contradictions between high investment and low revenues hinder the development of the car-sharing industry. Fully understanding car-sharing users can effectively ensure the healthy development of car-sharing companies and promote the development of the entire industry. To this end, this study attempts to develop a user management method that is based on user layering and prediction methods. By using order data from the Lan Zhou car-sharing company in China, this paper develops a clustering method for layering car-sharing users. A multi-layer perceptron model is also developed to categorize these users into different expenditure level categories while considering periodic features. Results show that new users can be divided into three categories according to their expenditures to car-sharing companies within 84 days. After 5 weeks of observation, the 84-day category of new users can be predicted with an accuracy of over 85%. These results provide scientific decision support for the user management and profitability of car-sharing companies.

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The Impact of Land-Use Variables on Free-Floating Carsharing Vehicle Rental Choice and Parking Duration

Cited by 8 publications

References 8 publications

Locating Station of One-Way Carsharing Based on Spatial Demand Characteristics

Locating Station of One-Way Carsharing Based on Spatial Demand Characteristics

Travelers’ Acceptance of Electric Carsharing Systems in Developing Countries: The Case of China

Identifying and Predicting the Expenditure Level Characteristics of Car-Sharing Users Based on the Empirical Data

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