Highlights A peer 2 peer dynamic crowd-shipping model is developed. The details of activity itineraries are used within a pricing & matching mechanism. Crowdshipping would result in significant shifts in the time use behavior in cities.

    Abstract Using a dynamic optimization model, we study the transaction of pickup/delivery activities between two groups of individuals: carriers and requesters, in a P2P crowdshipping model. Based on their value of time, requesters set maximum willingness to pay for their parcels to be picked up and delivered and carriers, make an offer depending on the changes that needs to be made to their original itinerary. The proposed model was tested for Los Angeles and Orange Counties, with 27% of successful matches, also demonstrating the impacts of crowdshipping on regional travel behavior and shifts in space-time distribution of the demand.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A dynamic crowdshipping model and daily travel behavior


    Contributors:


    Publication date :

    2019-06-04


    Size :

    16 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    A disaggregate model of passenger-freight matching in crowdshipping services

    Tapia, Rodrigo J. / Kourounioti, Ioanna / Thoen, Sebastian et al. | Elsevier | 2023


    Crowdshipping: An open VRP variant with stochastic destinations

    Torres, Fabian / Gendreau, Michel / Rei, Walter | Elsevier | 2022


    Deep reinforcement learning for stochastic last-mile delivery with crowdshipping

    Marco Silva / João Pedro Pedroso / Ana Viana | DOAJ | 2023

    Free access

    The effect of trust on the choice for crowdshipping services

    Cebeci, Merve Seher / Tapia, Rodrigo Javier / Kroesen, Maarten et al. | Elsevier | 2023


    Dynamic On-Demand Crowdshipping Using Constrained and Heuristics-Embedded Double Dueling Deep Q-Network

    Parvez Farazi, Nahid / Zou, Bo / Tulabandhula, Theja | Elsevier | 2022