Crowdsourcing the Last Mile Delivery of Online Orders by Exploiting the Social Networks of Retail Store Customers
Most major retailers and organizations strive to provide speedy and efficient delivery of products and explore the opportunities for saving on their last mile delivery costs. Crowd logistics is a subject of high interest in such endeavors. However, at its current state of development and adoption, further research is required to control and improve upon crowdsourced delivery times, risks and costs. This paper demonstrates the potential benefits of crowdsourcing delivery operations exploiting a social network of the customers of a retail store in assisting with the last mile delivery. In this paper, the authors conceive of a social network that connects the customers who are co-workers and/or neighbors of each other. The presented models and analyses are informed by the results of a survey conducted with 101 participants to gauge people’s attitudes towards package delivery to and by friends or acquaintances. Relying on the survey responses, a logistic regression model is built to predict the probability of a package being delivered from a store to a customer by the customer’s friends. In order to study a potential large-scale impact of such delivery mechanism, the authors set up a simulation environment in TRANSIMS, an activity-based transportation modeling tool with the data collected from a real-world city. The results of the simulated experiments indicate that, by exploiting crowdsourcing, a retailer in a small city can reduce truck mileage by 57%, which is equivalent to reducing delivery costs by 8600USD per day. On average, each delivery adds extra 10 minutes to the regular trip of the party providing the delivery assistance. As a result of this assistance, the expected achieved reduction in pollutants, i.e., NOx, PM2.5 and PM10, emitted by delivery trucks amounts to nearly 55%.
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Supplemental Notes:
- This paper was sponsored by TRB committee AT025 Standing Committee on Urban Freight Transportation. Alternate title: Crowdsourcing Last-Mile Delivery of Online Orders by Exploiting Social Networks of Retail Store Customers.
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Corporate Authors:
500 Fifth Street, NW
Washington, DC United States 20001 -
Authors:
- Devari, Aashwinikumar
- Nikolaev, Alexander G
- He, Qing
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Conference:
- Transportation Research Board 96th Annual Meeting
- Location: Washington DC, United States
- Date: 2017-1-8 to 2017-1-12
- Date: 2017
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; Maps; References; Tables;
- Pagination: 20p
- Monograph Title: TRB 96th Annual Meeting Compendium of Papers
Subject/Index Terms
- TRT Terms: Attitudes; Cities; Costs; Crowdsourcing; Customers; Delivery service; Logistic regression analysis; Pollutants; Retail trade; Simulation; Social media; Surveys
- Subject Areas: Freight Transportation; Planning and Forecasting;
Filing Info
- Accession Number: 01626729
- Record Type: Publication
- Report/Paper Numbers: 17-01799
- Files: TRIS, TRB, ATRI
- Created Date: Feb 27 2017 9:26AM