1,088 research outputs found
Pricing, competition and market segmentation in ride hailing
We analyse a non-cooperative strategic game among two ride-hailing platforms,
each of which is modeled as a two-sided queueing system, where drivers (with a
certain patience level) are assumed to arrive according to a Poisson process at
a fixed rate, while the arrival process of passengers is split across the two
providers based on QoS considerations. We also consider two monopolistic
scenarios: (i) each platform has half the market share, and (ii) the platforms
merge into a single entity, serving the entire passenger base using their
combined driver resources. The key novelty of our formulation is that the total
market share is fixed across the platforms. The game thus captures the
competition among the platforms over market share, which is modeled using two
different Quality of Service (QoS) metrics: (i) probability of driver
availability, and (ii) probability that an arriving passenger takes a ride. The
objective of the platforms is to maximize the profit generated from matching
drivers and passengers.
In each of the above settings, we analyse the equilibria associated with the
game. Interestingly, under the second QoS metric, we show that for a certain
range of parameters, no Nash equilibrium exists. Instead, we demonstrate a new
solution concept called an equilibrium cycle. Our results highlight the
interplay between competition, cooperation, passenger-side price sensitivity,
and passenger/driver arrival rates.Comment: 13 page
Surge pricing on a service platform under spatial spillovers: evidence from Uber
Ride-sharing platforms employ surge pricing to match anticipated capacity spillover with
demand. We develop an optimization model to characterize the relationship between surge
price and spillover. We test predicted relationships using a spatial panel model on a dataset
from Ubers operation. Results reveal that Ubers pricing accounts for both capacity and price
spillover. There is a debate in the management community on the ecacy of labor welfare
mechanisms associated with shared capacity. We conduct counterfactual analysis to provide
guidance in regards to the debate, for managing congestion, while accounting for consumer
and labor welfare through this online platform.First author draf
Information Design for Congested Social Services: Optimal Need-Based Persuasion
We study the effectiveness of information design in reducing congestion in
social services catering to users with varied levels of need. In the absence of
price discrimination and centralized admission, the provider relies on sharing
information about wait times to improve welfare. We consider a stylized model
with heterogeneous users who differ in their private outside options: low-need
users have an acceptable outside option to the social service, whereas
high-need users have no viable outside option. Upon arrival, a user decides to
wait for the service by joining an unobservable first-come-first-serve queue,
or leave and seek her outside option. To reduce congestion and improve social
outcomes, the service provider seeks to persuade more low-need users to avail
their outside option, and thus better serve high-need users. We characterize
the Pareto-optimal signaling mechanisms and compare their welfare outcomes
against several benchmarks. We show that if either type is the overwhelming
majority of the population, information design does not provide improvement
over sharing full information or no information. On the other hand, when the
population is a mixture of the two types, information design not only Pareto
dominates full-information and no-information mechanisms, in some regimes it
also achieves the same welfare as the "first-best", i.e., the Pareto-optimal
centralized admission policy with knowledge of users' types.Comment: Accepted for publication in the 21st ACM Conference on Economics and
Computation (EC'20). 40 pages, 6 figure
Double Auctions in Markets for Multiple Kinds of Goods
Motivated by applications such as stock exchanges and spectrum auctions,
there is a growing interest in mechanisms for arranging trade in two-sided
markets. Existing mechanisms are either not truthful, or do not guarantee an
asymptotically-optimal gain-from-trade, or rely on a prior on the traders'
valuations, or operate in limited settings such as a single kind of good. We
extend the random market-halving technique used in earlier works to markets
with multiple kinds of goods, where traders have gross-substitute valuations.
We present MIDA: a Multi Item-kind Double-Auction mechanism. It is prior-free,
truthful, strongly-budget-balanced, and guarantees near-optimal gain from trade
when market sizes of all goods grow to at a similar rate.Comment: Full version of IJCAI-18 paper, with 2 figures. Previous names:
"MIDA: A Multi Item-type Double-Auction Mechanism", "A Random-Sampling
Double-Auction Mechanism". 10 page
Operational research and simulation methods for autonomous ride-sourcing
Ride-sourcing platforms provide on-demand shared transport services by solving decision problems related to ride-matching and pricing. The anticipated commercialisation of autonomous vehicles could transform these platforms to fleet operators and broaden their decision-making by introducing problems such as fleet sizing and empty vehicle redistribution. These problems have been frequently represented in research using aggregated mathematical programs, and alternative practises such as agent-based models. In this context, this study is set at the intersection between operational research and simulation methods to solve the multitude of autonomous ride-sourcing problems.
The study begins by providing a framework for building bespoke agent-based models for ride-sourcing fleets, derived from the principles of agent-based modelling theory, which is used to tackle the non-linear problem of minimum fleet size. The minimum fleet size problem is tackled by investigating the relationship of system parameters based on queuing theory principles and by deriving and validating a novel model for pickup wait times. Simulating the fleet function in different urban areas shows that ride-sourcing fleets operate queues with zero assignment times above the critical fleet size. The results also highlight that pickup wait times have a pivotal role in estimating the minimum fleet size in ride-sourcing operations, with agent-based modelling being a more reliable estimation method.
The focus is then shifted to empty vehicle redistribution, where the omission of market structure and underlying customer acumen, compromises the effectiveness of existing models. As a solution, the vehicle redistribution problem is formulated as a non-linear convex minimum cost flow problem that accounts for the relationship of supply and demand of rides by assuming a customer discrete choice model and a market structure. An edge splitting algorithm is then introduced to solve a transformed convex minimum cost flow problem for vehicle redistribution. Results of simulated tests show that the redistribution algorithm can significantly decrease wait times and increase profits with a moderate increase in vehicle mileage.
The study is concluded by considering the operational time-horizon decision problems of ride-matching and pricing at periods of peak travel demand. Combinatorial double auctions have been identified as a suitable alternative to surge pricing in research, as they maximise social welfare by relying on stated customer and driver valuations. However, a shortcoming of current models is the exclusion of trip detour effects in pricing estimates. The study formulates a shared-ride assignment and pricing algorithm using combinatorial double auctions to resolve the above problem. The model is reduced to the maximum weighted independent set problem, which is APX-hard. Therefore, a fast local search heuristic is proposed, producing solutions within 10\% of the exact approach for practical implementations.Open Acces
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