THESIS
2023
1 online resource (xvii, 168 pages) : color illustrations
Abstract
Rapid advancements in mobile and wireless communication, cloud computing, and
sophisticated artificial intelligence have precipitated technological paradigm shifts in the
transportation sector. As a typical example, globally expanded app-based on-demand
ride-sourcing services are revolutionizing the way of our daily mobility. In particular,
ride-pooling (a common ride-sourcing service) is generally deemed to be an efficient and
cost-effective way to ameliorate traffic congestion by improving vehicle utilization. Given
the highly stochastic nature of traffic conditions in both space and time, we confront the
challenges of precisely understanding relationships between average detour distance, average
driver’s routing distance, peer-to-peer matching rate, and the different ride-pooling
dem...[
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Rapid advancements in mobile and wireless communication, cloud computing, and
sophisticated artificial intelligence have precipitated technological paradigm shifts in the
transportation sector. As a typical example, globally expanded app-based on-demand
ride-sourcing services are revolutionizing the way of our daily mobility. In particular,
ride-pooling (a common ride-sourcing service) is generally deemed to be an efficient and
cost-effective way to ameliorate traffic congestion by improving vehicle utilization. Given
the highly stochastic nature of traffic conditions in both space and time, we confront the
challenges of precisely understanding relationships between average detour distance, average
driver’s routing distance, peer-to-peer matching rate, and the different ride-pooling
demand levels. To tackle these challenges, this dissertation is primarily dedicated to deepening
our understanding of the intertwining relations how the aforementioned three key
measurements vary with demand for shared rides in ride-sourcing markets by leveraging
data-driven analytics.
We first obtain specific quantitative relations (empirical laws) calibrated from extensive
numerical experiments on ride-pooling services. These experiments are underpinned
by publicly available real mobility data from DiDi and New York Taxi and Limousine
Commission. In addition to the above data-driven study, we then develop aggregate
theoretical models by incorporating the obtained empirical laws and traffic congestion
externalities to analyze the impact of ride-sourcing markets with or without ride-pooling
services on average network traffic speed. The model-driven analysis allows us to figure out
the optimal operating strategies of ride-sourcing platforms reacting to different congestion
levels, matching time windows, and maximum matching distances for pooled rides. Our
methodology and conclusions provide invaluable operational acumen for the ride-sourcing
service operator and policy insights for the government/regulators. Another large chunk
of this dissertation mainly investigates the impact of a newly introduced ride-sourcing
service option, i.e., bundled option, which combines ride-pooling and solo ride service.
Specifically, passengers who choose the bundled service option wait in the queue for both
non-pooling and ride-pooling service simultaneously, and the platform assigns them either service depending on the demand, supply level, successful matching rate, inconvenience
cost due to pooling, etc. Based on our models, we seek out the monopoly optimum strategy
that maximizes the platform profit for heterogeneous passengers in terms of different
values of time and then propose countermeasures to boost system performance.
Although this dissertation scopes out the ride-sourcing market with an emphasis on
ride-pooling services, the methods, analysis, and solutions can be readily extended to
address relevant research topics arising from other types of shared mobility services, e.g.,
multi-modal transportation or similar two-sided markets, such as bike-sharing, taxi market.
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