Planning and Operations of Mixed Fleets in Mobility-on-Demand Systems

Yang, Kaidi; Tsao, Matthew W.; Xu, Xin; Pavone, Marco

doi:10.48550/arxiv.2008.08131

Cited by 3 publications

(3 citation statements)

References 34 publications

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“…Third, fleet size can be imposed via hard constraints, especially in time-invariant network flow models, where the number of AVs can be estimated by multiplying the sum of the customer and rebalancing flows with travel time (45). Finally, we remark on two aspects of these constraints: First, each type of constraint can be readily generalized to mixed fleets by imposing limitations on each vehicle class (95), and second, fleet sizing represents not only an operational constraint but also a design parameter for planning studies (43,45).…”

Section: Operational Constraintsmentioning

confidence: 99%

Analysis and Control of Autonomous Mobility-on-Demand Systems

Zardini

Lanzetti

Pavone

et al. 2022

Annu. Rev. Control Robot. Auton. Syst.

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Challenged by urbanization and increasing travel needs, existing transportation systems need new mobility paradigms. In this article, we present the emerging concept of autonomous mobility-on-demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to autonomous mobility-on-demand systems. Specifically, we first identify problem settings for their analysis and control, from both operational and planning perspectives. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: Operational Constraintsmentioning

confidence: 99%

Analysis and Control of Autonomous Mobility-on-Demand Systems

Zardini

Lanzetti

Pavone

et al. 2022

Annu. Rev. Control Robot. Auton. Syst.

View full text Add to dashboard Cite

show abstract

“…Finally, we remark two aspects. First, each type of constraints can be readily generalized to mixed fleets, by imposing limitations on each vehicle class (95). Second, fleet sizing represents not only an operational constraint, but also a design parameter for planning studies (43,45).…”

Section: Operational Constraintsmentioning

confidence: 99%

Analysis and Control of Autonomous Mobility-on-Demand Systems

Zardini,

Lanzetti,

Pavone

et al. 2021

Preprint

Self Cite

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Challenged by urbanization and increasing travel needs, existing transportation systems call for new mobility paradigms. In this article, we present the emerging concept of Autonomous Mobility-on-Demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to Autonomous Mobility-on-Demand systems. Specifically, we first identify problem settings for their analysis and control, both from the operational and the planning perspective. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research.

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“…It showed that in some cases, there is a regime in which the platform chooses to mix AVs with human drivers to maximize its profit, while in other cases, the platform will use only human drivers or AVs depending on the relative cost of AVs. In [38], an AMoD system with a mixed fleet was modeled as a Stackelberg game where the platform serves as the leader and human-driven vehicles serve as the followers. A steady-state model was proposed to determine the planning variables, and a time-varying model was formulated to design a real-time coordination algorithm for AVs.…”

Section: Introductionmentioning

confidence: 99%

Ride-Sourcing Platforms with Mixed Autonomy: How will Autonomous Vehicles Affect Others on a Ride-Sourcing Network?

Ao¹,

Lai²,

Li³

2021

Preprint

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This paper investigates how autonomous vehicles (AV) affect passengers, for-hire human drivers, and the platform on a ride-sourcing network. We consider a ride-sourcing market where a mixture of AVs and human drivers is deployed by the platform to provide mobility-on-demand services under labor regulations. In this market, the ride-sourcing platform determines the spatial prices, fleet size, human driver payments, and vehicle relocation strategies to maximize its profit, while individual passengers choose between different transport modes to minimize their travel costs. A market equilibrium model is proposed to capture the interactions among passengers, human drivers, AVs, and the ride-sourcing platform over the network. The overall problem is formulated as a non-convex program with network constraints, and an algorithm is developed to derive its approximate solution with performance guarantee. Our study shows that ridesourcing platform prioritizes AV deployment in high-demand areas to make a higher profit. As AVs flood into these high-demand areas, they compete with human drivers in the urban core and push them to relocate to suburbs. This leads to reduced earning opportunities for human drivers and increased spatial inequity for passengers. We also show that placing a wage floor may protect drivers from the negative impact of AVs, and meanwhile the total vehicle supply and passenger demand are almost unaffected. However, there exists a threshold beyond which the minimum wage will trigger a paradigm shift of labor supply where the platform will completely replace all human drivers with AVs. This indicates that the minimum wage should be carefully designed in the mixed environment to avoid the loss of job opportunities for human drivers. These results are validated with realistic case studies for San Francisco.

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Planning and Operations of Mixed Fleets in Mobility-on-Demand Systems

Cited by 3 publications

References 34 publications

Analysis and Control of Autonomous Mobility-on-Demand Systems

Analysis and Control of Autonomous Mobility-on-Demand Systems

Analysis and Control of Autonomous Mobility-on-Demand Systems

Ride-Sourcing Platforms with Mixed Autonomy: How will Autonomous Vehicles Affect Others on a Ride-Sourcing Network?

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