Estimation of optimum supply of shared cars based on personal travel behaviors in condition of minimum energy consumption

Yin, Yong; Wang, Han; Xiong, Jimin; Yu-feng, Zhu; Tang, Zhanfeng

doi:10.1007/s10668-020-01213-y

Cited by 2 publications

(3 citation statements)

References 24 publications

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“…Gender has significant effects on PBC, SN, HAB, PCT and INT, indicating that women have a more accurate understanding of whether they could complete travel, which is in accordance with previous findings that women express greater perceived value [41]. Meanwhile, their behavior is easily influenced by social stress, travel habits and PCT policies.…”

Section: The Mimic Modelsupporting

confidence: 89%

“…The second defect is that the fixed value of β indicates that different individuals have the same preference for travel modes, failing to consider the heterogeneity among different individuals, which is obviously not in line with the actual situation. Therefore, the mixed logit (ML) model, which effectively considers individual heterogeneity, has gradually replaced the MNL model for analyzing travel choice behavior [40,41]. The utility random term of the ML model can be neither independent nor distributed differently, and the vector β to be estimated is considered to be a random variable, which can follow a normal distribution, lognormal distribution, uniform distribution, etc., and thus, the ML model clearly express the preference degree of different individuals.…”

Section: Hybrid Choice Modelmentioning

confidence: 99%

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A Study on the Formation and Distribution Mechanisms of the Demand for Shared Electric Vehicles

Sun,

Fu,

Wang

2023

WEVJ

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With the decarbonization of the transportation sector and the diversification of travel demand, the development of shared electric vehicles has become crucial. Based on survey data of travel mode and destination of shared electric vehicles in Beijing, this paper aims to explore the formation and distribution mechanisms of the demand for shared electric vehicles. First of all, a multi-index and multi-cause (MIMIC) model was established to quantify the psychological latent variables that cannot be directly observed and to analyze the mechanisms between individual socio-demographic attributes and latent variables. Secondly, these psychological latent variables were added to mixed logit (ML) models as explanatory variables to form hybrid choice models to explore the travel mode choice behavior and travel destination choice behavior, respectively, when using shared electric vehicles for leisure travel. The results show that potential users of shared electric vehicles are characterized by higher education, employees of enterprises, no car availability and high driving years, and most of them travel for the purpose of connecting to transport hubs. Latent variables such as individual carbon trading, subjective norms, risks and behavioral intentions all affect the demand for shared electric vehicles; in-car time, out-of-car time, travel cost and the number of subway stations have negative impacts on the demand, while mall properties and the number of parking lots have positive impacts on the demand. Furthermore, the use of shared electric vehicles is highly correlated with the use of cars and subways, and part of the travel demand could be transferred to shared electric vehicles by taking certain measures.

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Section: The Mimic Modelsupporting

confidence: 89%

Section: Hybrid Choice Modelmentioning

confidence: 99%

A Study on the Formation and Distribution Mechanisms of the Demand for Shared Electric Vehicles

Sun,

Fu,

Wang

2023

WEVJ

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“…This is one important obstacle to attracting and retaining customers for car-sharing, which reduces the car-sharing provider's revenue and profitability [13,14]. More importantly, insufficient vehicle supply and demand management counteract the environmental benefits of carsharing [15,16] due to the high number of vehicles required to serve all customers [17,18]. Therefore, vehicle supply and demand management are central processes in car-sharing.…”

Section: Introductionmentioning

confidence: 99%

Designing a Crowd-Based Relocation System—The Case of Car-Sharing

et al. 2022

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Car-sharing services promise environmentally sustainable and cost-efficient alternatives to private car ownership, contributing to more environmentally sustainable mobility. However, the challenge of balancing vehicle supply and demand needs to be addressed for further improvement of the service. Currently, employees must relocate vehicles from low-demand to high-demand areas, which generates extra personnel costs, driven kilometers, and emissions. This study takes a Design Science Research (DSR) approach to develop a new way of balancing the supply and demand of vehicles in car-sharing, namely crowd-based relocation. We base our approach on crowdsourcing, a concept by which customers are requested to perform vehicle relocations. This paper reports on our comprehensive DSR project on designing and instantiating a crowd-based relocation information system (CRIS). We assessed the resulting artifact in a car-sharing simulation and conducted a real-world car-sharing service system field test. The evaluation reveals that CRIS has the potential for improving vehicle availability, increasing environmental sustainability, and reducing operational costs. Further, the prescriptive knowledge derived in our DSR project can be used as a starting point to improve individual parts of the CRIS and to extend its application beyond car-sharing into other sharing services, such as power bank- or e-scooter-sharing.

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Estimation of optimum supply of shared cars based on personal travel behaviors in condition of minimum energy consumption

Cited by 2 publications

References 24 publications

A Study on the Formation and Distribution Mechanisms of the Demand for Shared Electric Vehicles

A Study on the Formation and Distribution Mechanisms of the Demand for Shared Electric Vehicles

Designing a Crowd-Based Relocation System—The Case of Car-Sharing

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