Car-sharing aims to reduce energy consumption by making the best use of carrying capacity of a car. Nevertheless, like any new things, it does not go on quite smoothly. Passengers may not be willing to participate in a car-sharing system (CSS), with consideration of safety, convenience, privacy, and so on. To solve these problems, key elements in CSS are identified, and a comprehensive car-sharing model is established. Finally, the design process of CSS is proposed with four steps: identification of user requirements, service development, optimization & feedback, and verification.
MacArthurE., ZumwinkelK. and StuchteyM.R., Growth within: A circular economy vision for a competitive europe, Ellen MacArthur Foundation (2015).
2.
NijlandH. and van MeerkerkJ., Mobility and environmental impacts of car sharing in the Netherlands, Environmental Innovation and Societal Transitions23 (2017), 84–91.
3.
QuM., YuS. and YuM., An improved approach to evaluate car sharing options, Ecological Indicators72 (2017), 686–702.
4.
StröhleP., FlathC.M. and GärttnerJ., Leveraging customer flexibility for car-sharing fleet optimization, Transportation Science53(1) (2018), 42–61.
5.
AjzenI., The theory of planned behaviour: Reactions and reflections (2011), 1113–1127.
6.
ChenC-F. and Fu-ShianC., Experience quality, perceived value, satisfaction and behavioral intentions for heritage tourists, Tourism Management31(1) (2010), 29–35.
7.
KeH., ChaiS. and ChengR., Selling or sharing: Business model selection problem for an automobile manufacturer with uncertain information, Journal of Intelligent & Fuzzy Systems Preprint (2019), 1–16.
8.
MugionR.G., et al., Does the service quality of urban public transport enhance sustainable mobility?Journal of Cleaner Production174 (2018), 1566–1587.
9.
SeverisR.M., et al., Sustainable consumption in mobility from a life cycle assessment perspective, Journal of Cleaner Production234 (2019), 579–587.
10.
MeelenT., FrenkenK. and HobrinkS., Weak spots for car-sharing in The Netherlands? The geography of socio-technical regimes and the adoption of niche innovations, Energy Research & Social Science52 (2019), 132–143.
11.
BeckerH., CiariF. and AxhausenK.W., Comparing car-sharing schemes in Switzerland: User groups and usage patterns, Transportation Research Part A: Policy and Practice97 (2017), 17–29.
12.
GongH., WangM.Q. and WangH., New energy vehicles in China: Policies, demonstration, and progress, Mitigation and Adaptation Strategies for Global Change18(2) (2013), 207–228.
13.
TeubnerT., et al., Understanding resource sharing in C2C platforms: The role of picture humanization, ACIS (2014).
14.
SimićevićJ., SmiljanV. and MilosavljevićN., The effect of parking charges and time limit to car usage and parking behaviour, Transport Policy30 (2013), 125–131.
15.
Mendoza-SilvaG.M., et al., An occupancy simulator for a smart parking system: Developmental design and experimental considerations, ISPRS International Journal of Geo-Information8(5) (2019), 212.
16.
HamariJ., SjöklintM. and UkkonenA., The sharing economy: Why people participate in collaborative consumption, Journal of The Association for Information Science and Technology67(9) (2016), 2047–2059.
17.
ZhaoD. and ChenM., Ex-ante versus ex-post destination information model for on-demand service ride-sharing platform, Annals of Operations Research279(1-2) (2019), 301–341.
18.
KimJ., RasouliS. and TimmermansH., Satisfaction and uncertainty in car-sharing decisions: An integration of hybrid choice and random regret-based models, Transportation Research Part A: Policy and Practice95 (2017), 13–33.
19.
NordJ.H., KoohangA. and PaliszkiewiczJ., The internet of things: Review and theoretical framework, Expert Systems with Applications133 (2019), 97–108.
20.
HossainM.S. and MuhammadG., Emotion recognition using secure edge and cloud computing, Information Sciences504 (2019), 589–601.
21.
LiJ., et al., A trustworthiness-enhanced reliable forwarding scheme in mobile Internet of Things, Journal of Network and Computer Applications140 (2019), 40–53.
22.
SwanM., The quantified self: Fundamental disruption in big data science and biological discovery, Big Data1(2) (2013), 85–99.
23.
AlkafH., et al., An automated change impact analysis approach for user requirements notation models, Journal of Systems and Software157 (2019), 110397.
24.
LiuJ., YinY. and YanS., Research on clean energy power generation-energy storage-energy using virtual enterprise risk assessment based on fuzzy analytic hierarchy process in China, Journal of Cleaner Production236 (2019), 117471.
