THESIS
2022
1 online resource (vii, 58 pages) : illustrations (some color)
Abstract
Online ride-hailing platforms have become an important part of the transportation
system in modern life, especially in large cities. Under the fierce market competition,
companies need to pay more attention to the diversified customer needs. Companies
provide various types of services to customers with different pricing strategies nowadays.
We consider the case when the supply/demand is unbalanced among different
services and propose a mix allocation threshold-based matching policy. We model the
complete flow of the ride-hailing platform with the stochastic model and simplify it with
fluid approximation. Then we analyze the equilibrium states under conditions of the
matching thresholds. We analyze the optimal condition of maximizing total revenue in
two simple scenarios and implement a...[
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Online ride-hailing platforms have become an important part of the transportation
system in modern life, especially in large cities. Under the fierce market competition,
companies need to pay more attention to the diversified customer needs. Companies
provide various types of services to customers with different pricing strategies nowadays.
We consider the case when the supply/demand is unbalanced among different
services and propose a mix allocation threshold-based matching policy. We model the
complete flow of the ride-hailing platform with the stochastic model and simplify it with
fluid approximation. Then we analyze the equilibrium states under conditions of the
matching thresholds. We analyze the optimal condition of maximizing total revenue in
two simple scenarios and implement a numerical example to show the advantage of mix
allocation in a specific case.
Keywords— ride-hailing platform; on-demand matching; resource allocation; fluid approximation.
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