Spatial Equilibrium, Search Frictions, and Dynamic Efficiency in the Taxi Industry

Buchholz, Nicholas

doi:10.1093/restud/rdab050

Cited by 62 publications

(43 citation statements)

References 35 publications

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“…Since η(k) → η , then max{0, η(k) − η(k + 1)} → 0 as k → ∞. This, together with η(k) → η , implies that condition (28) is equivalent to the existence of θ > 0 such that for all k ≥ 0 there exists k ≥ k such that…”

Section: Since By Assumptionmentioning

confidence: 95%

The Nash Equilibrium with Inertia in Population Games

Gentile¹,

Paccagnan²,

Ogunsula³

et al. 2019

Preprint

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In the traditional game-theoretic set up, where agents select actions and experience corresponding utilities, an equilibrium is a configuration where no agent can improve their utility by unilaterally switching to a different action. In this work, we introduce the novel notion of inertial Nash equilibrium to account for the fact that, in many practical situations, action changes do not come for free. Specifically, we consider a population game and introduce the coefficients cij describing the cost an agent incurs by switching from action i to action j. We define an inertial Nash equilibrium as a distribution over the action space where no agent benefits in moving to a different action, while taking into account the cost of this change. First, we show that the set of inertial Nash equilibria contains all the Nash equilibria, but is in general not convex. Second, we argue that classical algorithms for computing Nash equilibria cannot be used in the presence of switching costs. We then propose a natural better-response dynamics and prove its convergence to an inertial Nash equilibrium. We apply our results to predict the drivers' distribution of an on-demand ride-hailing platform.

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Section: Since By Assumptionmentioning

confidence: 95%

The Nash Equilibrium with Inertia in Population Games

Gentile¹,

Paccagnan²,

Ogunsula³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Recent papers analyze the supply side in ride-hail markets. Buchholz (2018) quantifies the impact of uniform pricing regulation and search frictions on the spatial allocation of drivers and passengers in the NYC taxi market; Frechette et al ( 2018) assess the effect of entry restrictions and market thickness on efficiency in the NYC taxi market. 4 In these papers, the demand for taxis is estimated either as a function of prices (Buchholz (2018)) or waiting times (Frechette et al (2018)).…”

Section: Related Literaturementioning

confidence: 99%

The Value of Time: Evidence from Auctioned Cab Rides

Buchholz¹,

Doval²,

Kastl³

et al. 2020

SSRN Journal

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“…Matching probability, p m (t, s), estimates the probability that the vacant taxi is matched to a trip. According to other scholars' work (Buchholz 2018;Yu et al 2019), the matching probability from a taxi's view is:…”

Section: Single-agent Settingmentioning

confidence: 99%

Real-time and Large-scale Fleet Allocation of Autonomous Taxis: A Case Study in New York Manhattan Island

Yang,

Bao,

Ramezani

et al. 2020

Preprint

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Nowadays, autonomous taxis become a highly promising transportation mode, which helps relieve traffic congestion and avoid road accidents. However, it hinders the wide implementation of this service that traditional models fail to efficiently allocate the available fleet to deal with the imbalance of supply (autonomous taxis) and demand (trips), the poor cooperation of taxis, hardly satisfied resource constraints, and on-line platform's requirements. To figure out such urgent problems from a global and more farsighted view, we employ a Constrained Multi-agent Markov Decision Processes (CMMDP) to model fleet allocation decisions, which can be easily split into sub-problems formulated as a Dynamic assignment problem combining both immediate rewards and future gains. We also leverage a Column Generation algorithm to guarantee the efficiency and optimality in a large scale. Through extensive experiments, the proposed approach not only achieves remarkable improvements over the state-of-theart benchmarks in terms of the individuals efficiency (arriving at 12.40%, 6.54% rise of income and utilization, respectively) and the platforms profit (reaching 4.59% promotion) but also reveals a time-varying fleet adjustment policy to minimize the operation cost of the platform.

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Spatial Equilibrium, Search Frictions, and Dynamic Efficiency in the Taxi Industry

Cited by 62 publications

References 35 publications

The Nash Equilibrium with Inertia in Population Games

The Nash Equilibrium with Inertia in Population Games

The Value of Time: Evidence from Auctioned Cab Rides

Real-time and Large-scale Fleet Allocation of Autonomous Taxis: A Case Study in New York Manhattan Island

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