Why are fairness concerns so important? Lessons from pricing a shared last-mile transportation system

The Last-Mile Problem refers to the provision of travel service from the nearest public transportation node to the final destination. The Last-Mile Transportation System (LMTS), which has recently emerged, provides on-demand shared last-mile transportation service. While it is natural that in the la...

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Bibliographic Details
Main Authors: CHEN, Yiwei, WANG, Hai
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2018
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/4029
https://ink.library.smu.edu.sg/context/sis_research/article/5031/viewcontent/SSRN_id3168324.pdf
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Institution: Singapore Management University
Language: English
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Summary:The Last-Mile Problem refers to the provision of travel service from the nearest public transportation node to the final destination. The Last-Mile Transportation System (LMTS), which has recently emerged, provides on-demand shared last-mile transportation service. While it is natural that in the last-mile supply chain, a high-value parcel should be charged a higher price and deserves service priority compared to a low-value parcel, it is not straightforward to identify an obvious pricing and service priority for an LMTS that serves passengers. In an LMTS, a special-type passenger who has a higher valuation of service usually has a lower waiting time disutility; i.e., the valuation of service and the waiting time disutility rate are negatively correlated. In this paper, we consider two fairness guarantees — price discount and service priority — applied to special-type passengers with higher service valuation but lower waiting time disutility. We propose models to analyze pricing and service priority policies. We prove that the LMTS is more profitable if a smaller price discount and no service priority are given to special-type passengers, and this is also the case for the social welfare maximization objective. We implement the models in a set of numerical experiments using real public transport data. Based on both the theoretical analysis and the numerical experiments, we find that enforcing fairness guarantees in the LMTS is critical.