IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2019i1p268-d303019.html
   My bibliography  Save this article

Impact of Risk and Benefit on the Suppliers’ and Managers’ Intention of Shared Parking in Residential Areas

Author

Listed:
  • Jin Xie

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

  • Xiaofei Ye

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
    Ningbo Port Trade Cooperation and Development Collaborative Innovation Center, Ningbo University, Ningbo 315211, China)

  • Zhongzhen Yang

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

  • Xingchen Yan

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Lili Lu

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
    National Traffic Management Engineering & Technology Research Centre Ningbo University Sub-center, Ningbo University, Ningbo 315211, China)

  • Zhen Yang

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Tao Wang

    (School of Architecture and Transportation, Guilin University of Electronic Technology, Guilin 541004, China)

Abstract

Shared parking is not commonly applied in residential areas. The reason is that parking suppliers and managers believe that there are many uncertainties and conflicts in obtaining sharing benefits and taking sharing risks. To increase their acceptance of shared parking in residential areas, risk and benefit factors were identified by an influential analysis and a questionnaire survey. A research framework based on the structural equation model was developed to analyze the relationship between shared-parking risks, shared-parking benefits, management pressure, and intentions of parking suppliers and managers. The results showed that, to parking suppliers, the risks of shared parking have the largest effect on suppliers’ intention to apply shared parking by a standardized coefficient of −0.85, followed by the benefits of shared parking (0.29), and management pressures (−0.14). To the parking managers, management pressures have the largest effect on managers’ intention to apply shared parking by a standardized coefficient of −0.74, followed by the benefits of shared parking (0.52) and risks of shared parking (−0.46). These results can help in increasing parking suppliers’ and managers’ acceptance of shared parking in residential areas.

Suggested Citation

  • Jin Xie & Xiaofei Ye & Zhongzhen Yang & Xingchen Yan & Lili Lu & Zhen Yang & Tao Wang, 2019. "Impact of Risk and Benefit on the Suppliers’ and Managers’ Intention of Shared Parking in Residential Areas," Sustainability, MDPI, vol. 12(1), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2019:i:1:p:268-:d:303019
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/1/268/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/1/268/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Arnott, Richard, 2014. "On the optimal target curbside parking occupancy rate," Economics of Transportation, Elsevier, vol. 3(2), pages 133-144.
    2. Wang, Jian & Peeta, Srinivas & Lu, Lili & Li, Tao, 2019. "Multiclass information flow propagation control under vehicle-to-vehicle communication environments," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 96-121.
    3. Hensher, David A. & King, Jenny, 2001. "Parking demand and responsiveness to supply, pricing and location in the Sydney central business district," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(3), pages 177-196, March.
    4. Arnott, Richard & Williams, Parker, 2017. "Cruising for parking around a circle," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 357-375.
    5. Molenda, Inga & Sieg, Gernot, 2013. "Residential parking in vibrant city districts," Economics of Transportation, Elsevier, vol. 2(4), pages 131-139.
    6. Wang, Jian & Gong, Siyuan & Peeta, Srinivas & Lu, Lili, 2019. "A real-time deployable model predictive control-based cooperative platooning approach for connected and autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 271-301.
    7. Lambe, Thomas A., 1996. "Driver choice of parking in the city," Socio-Economic Planning Sciences, Elsevier, vol. 30(3), pages 207-219, September.
    8. Rye, Tom & Ison, Stephen, 2005. "Overcoming barriers to the implementation of car parking charges at UK workplaces," Transport Policy, Elsevier, vol. 12(1), pages 57-64, January.
    9. Tsai, Jyh-Fa & Chu, Chih-Peng, 2006. "Economic analysis of collecting parking fees by a private firm," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(8), pages 690-697, October.
    10. Wang, Jian & Peeta, Srinivas & He, Xiaozheng, 2019. "Multiclass traffic assignment model for mixed traffic flow of human-driven vehicles and connected and autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 139-168.
    11. Marsden, Greg, 2006. "The evidence base for parking policies--a review," Transport Policy, Elsevier, vol. 13(6), pages 447-457, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ning, Yu & Yan, Mian & Xu, Su Xiu & Li, Yina & Li, Lixu, 2021. "Shared parking acceptance under perceived network externality and risks: Theory and evidence," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 1-15.
    2. Zhiyuan Yu & Doudou Jin, 2021. "Determinants of Users’ Attitude and Intention to Intelligent Connected Vehicle Infotainment in the 5G-V2X Mobile Ecosystem," IJERPH, MDPI, vol. 18(19), pages 1-19, September.
    3. Sheng-Ming Wang & Wei-Min Cheng, 2023. "Fast Way to Predict Parking Lots Availability: For Shared Parking Lots Based on Dynamic Parking Fee System," Future Internet, MDPI, vol. 15(3), pages 1-22, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Inci, Eren, 2015. "A review of the economics of parking," Economics of Transportation, Elsevier, vol. 4(1), pages 50-63.
    2. Yating Zhu & Xiaofei Ye & Jun Chen & Xingchen Yan & Tao Wang, 2020. "Impact of Cruising for Parking on Travel Time of Traffic Flow," Sustainability, MDPI, vol. 12(8), pages 1-17, April.
    3. Wang, Jian & Lu, Lili & Peeta, Srinivas, 2022. "Real-time deployable and robust cooperative control strategy for a platoon of connected and autonomous vehicles by factoring uncertain vehicle dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 88-118.
    4. Semeneh Hunachew Bayih & Surafel Luleseged Tilahun, 2024. "Dynamic vehicle parking pricing. A review," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(1), pages 35-59.
    5. Geva, Sharon & Fulman, Nir & Ben-Elia, Eran, 2022. "Getting the prices right: Drivers' cruising choices in a serious parking game," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 54-75.
    6. Evangelinos, Christos & Tscharaktschiew, Stefan & Marcucci, Edoardo & Gatta, Valerio, 2018. "Pricing workplace parking via cash-out: Effects on modal choice and implications for transport policy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 369-380.
    7. Olus Inan, Murat & Inci, Eren & Robin Lindsey, C., 2019. "Spillover parking," Transportation Research Part B: Methodological, Elsevier, vol. 125(C), pages 197-228.
    8. Tao Wang & Sihong Xie & Xiaofei Ye & Xingchen Yan & Jun Chen & Wenyong Li, 2020. "Analyzing E-Bikers’ Risky Riding Behaviors, Safety Attitudes, Risk Perception, and Riding Confidence with the Structural Equation Model," IJERPH, MDPI, vol. 17(13), pages 1-18, July.
    9. Yan, Xiang & Levine, Jonathan & Marans, Robert, 2019. "The effectiveness of parking policies to reduce parking demand pressure and car use," Transport Policy, Elsevier, vol. 73(C), pages 41-50.
    10. Juan Li & Jing Ye & Qinglian He & Chunfu Shao, 2016. "A Novel Scheme to Relieve Parking Pressure at Tourist Attractions on Holidays," Sustainability, MDPI, vol. 8(2), pages 1-11, February.
    11. Gallo, Mariano & D'Acierno, Luca & Montella, Bruno, 2011. "A multilayer model to simulate cruising for parking in urban areas," Transport Policy, Elsevier, vol. 18(5), pages 735-744, September.
    12. Bergantino, Angela Stefania & De Carlo, Angela & Morone, Andrea, 2015. "Individuals’ behaviour with respect to parking alternatives: a laboratory experiment," MPRA Paper 63815, University Library of Munich, Germany.
    13. Tscharaktschiew, Stefan & Reimann, Felix, 2021. "On employer-paid parking and parking (cash-out) policy: A formal synthesis of different perspectives," Transport Policy, Elsevier, vol. 110(C), pages 499-516.
    14. Rotaris, Lucia & Danielis, Romeo, 2014. "The impact of transportation demand management policies on commuting to college facilities: A case study at the University of Trieste, Italy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 67(C), pages 127-140.
    15. Qian Liu & James Wang & Peng Chen & Zuopeng Xiao, 2017. "How does parking interplay with the built environment and affect automobile commuting in high-density cities? A case study in China," Urban Studies, Urban Studies Journal Limited, vol. 54(14), pages 3299-3317, November.
    16. Ibeas, Ángel & Cordera, Ruben & dell'Olio, Luigi & Moura, Jose Luis, 2011. "Modelling demand in restricted parking zones," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(6), pages 485-498, July.
    17. Lam, William H.K. & Li, Zhi-Chun & Huang, Hai-Jun & Wong, S.C., 2006. "Modeling time-dependent travel choice problems in road networks with multiple user classes and multiple parking facilities," Transportation Research Part B: Methodological, Elsevier, vol. 40(5), pages 368-395, June.
    18. Ottosson, Dadi Baldur & Chen, Cynthia & Wang, Tingting & Lin, Haiyun, 2013. "The sensitivity of on-street parking demand in response to price changes: A case study in Seattle, WA," Transport Policy, Elsevier, vol. 25(C), pages 222-232.
    19. Guo, Zhan, 2013. "Does residential parking supply affect household car ownership? The case of New York City," Journal of Transport Geography, Elsevier, vol. 26(C), pages 18-28.
    20. Milosavljević, Nada & Simićević, Jelena, 2016. "User response to parking policy change: A comparison of stated and revealed preference data," Transport Policy, Elsevier, vol. 46(C), pages 40-45.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2019:i:1:p:268-:d:303019. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.