Dynamic pricing and fleet management for electric autonomous mobility on demand systems

Turan, Berkay; Pedarsani, Ramtin; Alizadeh, Mahnoosh

doi:10.1016/j.trc.2020.102829

Cited by 74 publications

(44 citation statements)

References 35 publications

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“…For the intermediary that possesses autonomous EVs, the optimal pricing problem can be transformed into a profit maximization problem under the constraints of charging scheduling, electricity price, and routing [166]. In this scenario, adopting static pricing can lead to a longer waiting time and a lower profit; correspondingly, Turan, Pedarsani [166] and Al-Kanj, Nascimento [167] proposed different real-time dynamic pricing policies.…”

Section: Intermediary Pricingmentioning

confidence: 99%

A Systematic Literature Review on Pricing Strategies in the Sharing Economy

Yang

Xia

2021

Sustainability

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As an emerging business model, the sharing economy has gained a large amount of academic attention; the pricing problem in the sharing economy has also been widely investigated. Aiming to capture the current state-of-the-art research on pricing strategies in the sharing economy and foreseeing directions for future research, this article conducts a systematic literature review and content analysis of 158 articles from the Scopus and Web of Science databases. As a result, first, this review proposes an extended definition covering B2C and C2C models and a model structure covering the entire supply chain, based on which 158 articles are categorized into nine sub-models covering 30 scenarios. Second, the general characteristics (i.e., research fields, time and journal distributions, research themes and scenarios) and technical details (i.e., theories, methodologies, approaches, models, and conclusions) of the 158 articles are reviewed and summarized by the pricing party, business mode, and scenario. Finally, this review proposes some future research directions of existing scenarios from the perspectives of information asymmetry, market competition, and empirical approaches, and discusses some extensions, including uninvestigated scenarios and COVID-19-related topics; correspondingly, this review suggests some analytical models and empirical approaches that can be employed to fill these gaps. The proposed research directions and corresponding approaches can be references for future research.

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Section: Intermediary Pricingmentioning

confidence: 99%

A Systematic Literature Review on Pricing Strategies in the Sharing Economy

Yang

Xia

2021

Sustainability

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“…As pointed out by Swaszek and Cassandras (2019) and Turan et al (2019), one way in which we can derive optimal strategies for this problem is to frame it as a Markov Decision Process and use Dynamic Programming to solve it. Unfortunately, they observe that the problem suffers from the curse of dimensionality and becomes intractable even for small instances of the problem.…”

Section: Optimal Strategiesmentioning

confidence: 99%

“…In the literature, the joint pricing and rebalancing problem has been addressed using two different perspectives: one-sided, and two-sided markets. One-sided markets assume full control over the fleet of vehicles (Turan et al, 2019;Wollenstein-Betech et al, 2020a) and have been proposed for prospective MoD systems that operate with robotic taxis known as Autonomous Mobilityon-Demand (AMoD) systems. In contrast, two-sided markets consider self-interested drivers, typically human, that reallocate themselves in order to maximize their own profit (Banerjee et al, 2015;Bimpikis et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…This is especially true when the system experiences perturbations or when the realization of the arrival process deviates with respect to the parameters assumed by the static policy. To account for such perturbations, Turan et al (2019) uses Reinforcement Learning (RL) in a microsimulator environment to maximize the revenue of an electric fleet of robotaxis. However, even though good solutions are found, RL converges slowly and its learned parameters are hard to transfer to a different setting.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Operations Management of Mobility-on-Demand Systems

Wollenstein-Betech

Paschalidis

Cassandras

2021

Front. Sustain. Cities

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The emergence of the sharing economy in urban transportation networks has enabled new fast, convenient and accessible mobility services referred to as Mobilty-on-Demand systems (e.g., Uber, Lyft, DiDi). These platforms have flourished in the last decade around the globe and face many operational challenges in order to be competitive and provide good quality of service. A crucial step in the effective operation of these systems is to reduce customers' waiting time while properly selecting the optimal fleet size and pricing policy. In this paper, we jointly tackle three operational decisions: (i) fleet size, (ii) pricing, and (iii) rebalancing, in order to maximize the platform's profit or its customers' welfare. To accomplish this, we first devise an optimization framework which gives rise to a static policy. Then, we elaborate and propose dynamic policies that are more responsive to perturbations such as unexpected increases in demand. We test this framework in a simulation environment using three case studies and leveraging traffic flow and taxi data from Eastern Massachusetts, New York City, and Chicago. Our results show that solving the problem jointly could increase profits between 1% and up to 50%, depending on the benchmark. Moreover, we observe that the proposed fleet size yield utilization of the vehicles in the fleet is around 75% compared to private vehicle utilization of 5%.

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“…Macroscopic studies have shown that AMoD systems contribute to more accessible, efficient, and sustainable transportation systems (Pavone, 2015;Fagnant & Kockelman, 2015). Further, researchers studied design and operation of AMoD systems (Turan et al, 2020;Zardini et al, 2020), possibly in cooperation with public transport (Salazar et al, 2020), their interactions with stakeholders of the mobility systems (Zardini et al, 2021a), and the economics of AMoD systems, focusing on the effects of carpooling (Ostrovsky & Schwarz, 2019). All approaches but Ostrovsky & Schwarz (2019) imposed a central decision maker and neglected game-theoretic dynamics, while Ostrovsky & Schwarz (2019) neglected MSP behavior, essentially replacing MSPs with a perfectly competitive, zero-profit market.…”

Section: Related Literaturementioning

confidence: 99%

On the Interplay between Self-Driving Cars and Public Transportation

Lanzetti,

Schiffer,

Ostrovsky

et al. 2021

Preprint

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Cities worldwide struggle with overloaded transportation systems and their externalities, such as traffic congestion and emissions. The emerging autonomous transportation technology has a potential to alleviate these issues. Yet, the decisions of profit-maximizing operators running large autonomous fleets could have a negative impact on other stakeholders, e.g., by disproportionately cannibalizing public transport, and therefore could make the transportation system even less efficient and sustainable. A careful analysis of these tradeoffs requires modeling the main modes of transportation, including public transport, within a unified framework. In this paper, we propose such a framework, which allows us to study the interplay among mobility service providers, public transport authorities, and customers. Our framework combines a graph-theoretic network model for the transportation system with a game-theoretic market model in which mobility service providers are profitmaximizers, while customers select individually-optimal transportation options. We apply our framework to data for the city of Berlin, Germany, and present sensitivity analyses to study parameters that mobility service providers or municipalities can influence to steer the system. We show that depending on market conditions and policy restrictions, autonomous ride-hailing systems may complement or cannibalize a public transportation system, serving between 7 % and 80 % of all customers. We discuss the main factors behind differences in these outcomes as well as strategic design options available to policymakers. Among others, we show that the monopolistic and the competitive cases yield similar modal shares, but differ in the profit outcome of each mobility service provider.

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Dynamic pricing and fleet management for electric autonomous mobility on demand systems

Cited by 74 publications

References 35 publications

A Systematic Literature Review on Pricing Strategies in the Sharing Economy

A Systematic Literature Review on Pricing Strategies in the Sharing Economy

Optimal Operations Management of Mobility-on-Demand Systems

On the Interplay between Self-Driving Cars and Public Transportation

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