IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v105y2017icp105-122.html
   My bibliography  Save this article

Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers

Author

Listed:
  • Devari, Aashwinikumar
  • Nikolaev, Alexander G.
  • He, Qing

Abstract

This paper demonstrates the potential benefits of crowdsourcing last mile delivery by exploiting a social network of the customers. The presented models and analysis are informed by the results of a survey to gauge people’s attitudes toward engaging in social network-reliant package delivery to and by friends or acquaintances. It is found that using friends in a social network to assist in last mile delivery greatly reduces delivery costs and total emissions while ensuring speedy and reliable delivery. The proposed new delivery method also mitigates the privacy concerns and not-at-home syndrome that widely exist in last mile delivery.

Suggested Citation

  • Devari, Aashwinikumar & Nikolaev, Alexander G. & He, Qing, 2017. "Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 105-122.
    • Handle: RePEc:eee:transe:v:105:y:2017:i:c:p:105-122
    DOI: 10.1016/j.tre.2017.06.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554516308341
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2017.06.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yoram Shiftan & Moshe Ben-Akiva, 2011. "A practical policy-sensitive, activity-based, travel-demand model," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 47(3), pages 517-541, December.
    2. Mehmann, Jens & Frehe, Volker & Teuteberg, Frank, 2015. "Crowd Logistics − A Literature Review and Maturity Model," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Innovations and Strategies for Logistics and Supply Chains: Technologies, Business Models and Risk Management. Proceedings of the Hamburg Internationa, volume 20, pages 117-145, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    3. Chao Chen & Shenle Pan, 2015. "Using the Crowd of Taxis to Last Mile Delivery in E-commerce: a Methodological Research," Post-Print hal-01226813, HAL.
    4. Gerhard G. Van De Bunt & Marijtje A.J. Van Duijn & Tom A.B. Snijders, 1999. "Friendship Networks Through Time: An Actor-Oriented Dynamic Statistical Network Model," Computational and Mathematical Organization Theory, Springer, vol. 5(2), pages 167-192, July.
    5. Bowman, J. L. & Ben-Akiva, M. E., 2001. "Activity-based disaggregate travel demand model system with activity schedules," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(1), pages 1-28, January.
    Full references (including those not matched with items on IDEAS)

    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. Naqavi, Fatemeh & Sundberg, Marcus & Västberg, Oskar Blom & Karlström, Anders & Hugosson, Muriel Beser, 2023. "Mobility constraints and accessibility to work: Application to Stockholm," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    2. Feixiong Liao & Theo Arentze & Eric Molin & Wendy Bothe & Harry Timmermans, 2017. "Effects of land-use transport scenarios on travel patterns: a multi-state supernetwork application," Transportation, Springer, vol. 44(1), pages 1-25, January.
    3. Badiola, Nicolás & Raveau, Sebastián & Galilea, Patricia, 2019. "Modelling preferences towards activities and their effect on departure time choices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 39-51.
    4. Liu, Peng & Liao, Feixiong & Huang, Hai-Jun & Timmermans, Harry, 2015. "Dynamic activity-travel assignment in multi-state supernetworks," Transportation Research Part B: Methodological, Elsevier, vol. 81(P3), pages 656-671.
    5. Akeb, Hakim & Moncef, Btissam & Durand, Bruno, 2018. "Building a collaborative solution in dense urban city settings to enhance parcel delivery: An effective crowd model in Paris," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 119(C), pages 223-233.
    6. Ermagun, Alireza & Stathopoulos, Amanda, 2018. "To bid or not to bid: An empirical study of the supply determinants of crowd-shipping," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 468-483.
    7. Joshua Lospinoso & Michael Schweinberger & Tom Snijders & Ruth Ripley, 2011. "Assessing and accounting for time heterogeneity in stochastic actor oriented models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(2), pages 147-176, July.
    8. Mohammad Hesam Hafezi & Lei Liu & Hugh Millward, 2019. "A time-use activity-pattern recognition model for activity-based travel demand modeling," Transportation, Springer, vol. 46(4), pages 1369-1394, August.
    9. Dong, Xiaojing & Ben-Akiva, Moshe E. & Bowman, John L. & Walker, Joan L., 2006. "Moving from trip-based to activity-based measures of accessibility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(2), pages 163-180, February.
    10. Frank Primerano & Michael Taylor & Ladda Pitaksringkarn & Peter Tisato, 2008. "Defining and understanding trip chaining behaviour," Transportation, Springer, vol. 35(1), pages 55-72, January.
    11. Yasmin, Farhana & Morency, Catherine & Roorda, Matthew J., 2015. "Assessment of spatial transferability of an activity-based model, TASHA," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 200-213.
    12. Fehn, Fabian & Engelhardt, Roman & Dandl, Florian & Bogenberger, Klaus & Busch, Fritz, 2023. "Integrating parcel deliveries into a ride-pooling service—An agent-based simulation study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    13. Tu, Wei & Cao, Rui & Yue, Yang & Zhou, Baoding & Li, Qiuping & Li, Qingquan, 2018. "Spatial variations in urban public ridership derived from GPS trajectories and smart card data," Journal of Transport Geography, Elsevier, vol. 69(C), pages 45-57.
    14. Moyano, Amparo & Martínez, Héctor S. & Coronado, José M., 2018. "From network to services: A comparative accessibility analysis of the Spanish high-speed rail system," Transport Policy, Elsevier, vol. 63(C), pages 51-60.
    15. Blom Västberg, Oskar & Karlström, Anders & Jonsson, Daniel & Sundberg, Marcus, 2016. "Including time in a travel demand model using dynamic discrete choice," MPRA Paper 75336, University Library of Munich, Germany, revised 11 Nov 2016.
    16. Mark Huisman & Tom A. B. Snijders, 2003. "Statistical Analysis of Longitudinal Network Data With Changing Composition," Sociological Methods & Research, , vol. 32(2), pages 253-287, November.
    17. Zhao, Juanjuan & Ren, Huan & Gu, Yan & Pan, Haojie, 2023. "Relationships between the residential environment, travel attitude and behaviour among knowledge workers: The role of job types," Journal of Transport Geography, Elsevier, vol. 106(C).
    18. Maurits C. de Klepper & Giuseppe (Joe) Labianca & Ed Sleebos & Filip Agneessens, 2017. "Sociometric Status and Peer Control Attempts: A Multiple Status Hierarchies Approach," Journal of Management Studies, Wiley Blackwell, vol. 54(1), pages 1-31, January.
    19. Auld, Joshua & Mohammadian, Abolfazl (Kouros) & Doherty, Sean T., 2009. "Modeling activity conflict resolution strategies using scheduling process data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 386-400, May.
    20. He, Brian Y. & Zhou, Jinkai & Ma, Ziyi & Chow, Joseph Y.J. & Ozbay, Kaan, 2020. "Evaluation of city-scale built environment policies in New York City with an emerging-mobility-accessible synthetic population," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 444-467.

    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:eee:transe:v:105:y:2017:i:c:p:105-122. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    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.