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theses

Americans 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.

ETD_11072

Ph.D.

Includes bibliographical references

ETD doctoral

doi:10.7282/t3-vt3d-3955

The author owns the copyright to this work.