Home
Resource
Mechanism design for first-mile ridesharing
PDF
PDF format is widely accepted and good for printing.
Plug-in required
PDF-1(4.18 MB)
Citation & Export
View Usage Statistics
Staff View

Citation & Export
Hide

Simple citation

Bian, Zheyong. Mechanism design for first-mile ridesharing. Retrieved from https://doi.org/doi:10.7282/t3-vt3d-3955

Export

Click here for information about Citation Management Tools at Rutgers.

Statistics
Hide

Description

TitleMechanism design for first-mile ridesharing
NameBian, Zheyong (author); Liu, Xiang (chair); Jin, Jing (internal member); Gong, Jie (internal member); Yu, Jingjin (outside member); Rutgers University; School of Graduate Studies
Date Created2020
Other Date2020-10 (degree)
SubjectCivil and Environmental Engineering
Extent1 online resource (xiii, 225 pages)
DescriptionAmericans take 11 billion trips annually on public transportation, a 40 percent increase since 1995 (American Public Transportation Association 2016). The $61 billion American public transportation industry faces an ongoing challenge of transit hub accessibility – how travelers get to nearby transit hubs. This challenge is also known as the “first-mile” bottleneck. In the United States, many transit riders either drive their own vehicles or take taxis or other emerging mobility services (e.g. Uber and Lyft) to nearby transit hubs. However, uncoordinated traveling does not fully utilize the empty seats in a car. This increases traffic congestion, fuel consumption, emissions, and parking demands. Ridesharing is an effective transportation mode to provide first-mile accessibility to public transit and low-cost, environment-friendly and sustainable mobility service. A key issue is to incentivize passengers for ridesharing participation. This dissertation addresses this problem using Mechanism Design Theory. “Mechanism design” is a field in economics and game theory that designs economic incentives toward desired states by reconciling players’ objectives and has been applied in transportation research fields recently.

This dissertation accounts for passengers’ personalized requirements for inconvenience attributes in optimizing the vehicle-passenger matching and vehicle routing as well as designing incentive prices for both scheduled and on-demand first-mile ridesharing services. The basic problem studied in the dissertation is that if the designed incentive is able to compensate for the inconvenience cost caused by ridesharing considering passengers’ personalized requirements. This dissertation considers multiple incentive objectives to achieve the ultimate goal of maximizing the total social welfare. These incentive objective includes 1) promoting passengers’ collaboration to participate in the service (i.e. individual rationality), 2) incentivizing passengers to truthfully report their personalized information (e.g. the maximum willing-to-pay price bidden for the service and personalized requirements on inconvenience attributes) (i.e. incentive compatibility), and 3) incentivizing the service provider to be financially sustainable. In order to obtain the mechanism results for large-scale problems for both scheduled and on-demand service, I develop a novel heuristic algorithm called Solution Pooling Approach (SPA) to optimize the vehicle-passenger matching and vehicle routing plan as well as to calculate the prices. It is proved that SPA is able to sustain the properties of “individual rationality” and “incentive compatibility”. Based on the experimental results, I find that SPA is much more efficient in solving large-scale problems compared with the commercial solver (e.g. Branch and Bound) and traditional heuristic algorithms (e.g. hybrid simulated annealing and tabu search) from the literature.
NotePh.D.
NoteIncludes bibliographical references
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/t3-vt3d-3955
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
Version 8.5.1
Rutgers University Libraries - Copyright ©2023