Analysis and Control of Autonomous Mobility-on-Demand Systems

Zardini, Gioele; Lanzetti, Nicolas; Pavone, Marco; Frazzoli, Emilio

doi:10.1146/annurev-control-042920-012811

Cited by 67 publications

(24 citation statements)

References 162 publications

(218 reference statements)

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“…Problem (36) satisfies the conditions of Lemma 1 in [25], and the maximum expectation value of H r + H c for any possible r ∼ F r , c ∼ F c where F r ∈ F r , F c ∈ F c equals the optimal value of the problem min Qr,qr,vr,tr; Qc,qc,vc,tc…”

Section: E Non-robust Methods Used In Experimentsmentioning

confidence: 99%

“…Vehicle balancing is an important objective of EV AMoD systems [5], [38], [52]. By repositioning of customer-free EVs, it aims to minimizing the imbalance of the EV distribution caused by asymmetrical transportation demand [36]. Our method achieves lower balancing cost and higher mobility and charging fairness, thus contributes to congestion mitigation and transportation efficiency.…”

Section: E Performance Comparisonmentioning

confidence: 99%

See 1 more Smart Citation

Data-Driven Distributionally Robust Electric Vehicle Balancing for Mobility-on-Demand Systems under Demand and Supply Uncertainties

Pepin

Wang

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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As electric vehicle (EV) technologies become mature, EV has been rapidly adopted in modern transportation systems, and is expected to provide future autonomous mobility-on-demand (AMoD) service with economic and societal benefits. However, EVs require frequent recharges due to their limited and unpredictable cruising ranges, and they have to be managed efficiently given the dynamic charging process. It is urgent and challenging to investigate a computationally efficient algorithm that provide EV AMoD system performance guarantees under model uncertainties, instead of using heuristic demand or charging models. To accomplish this goal, this work designs a data-driven distributionally robust optimization approach for vehicle supply-demand ratio and charging station utilization balancing, while minimizing the worstcase expected cost considering both passenger mobility demand uncertainties and EV supply uncertainties. We then derive an equivalent computationally tractable form for solving the distributionally robust problem in a computationally efficient way under ellipsoid uncertainty sets constructed from data. Based on E-taxi system data of Shenzhen city, we show that the average total balancing cost is reduced by 14.49%, the average unfairness of supply-demand ratio and utilization is reduced by 15.78% and 34.51% respectively with the distributionally robust vehicle balancing method, compared with solutions which do not consider model uncertainties.

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Section: E Non-robust Methods Used In Experimentsmentioning

confidence: 99%

Section: E Performance Comparisonmentioning

confidence: 99%

Data-Driven Distributionally Robust Electric Vehicle Balancing for Mobility-on-Demand Systems under Demand and Supply Uncertainties

Pepin

Wang

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…The shared MoD system is composed of three key components, which are platform, customer and driver. The platform connects customer requests and driver-owned vehicles, dramatically changing the transportation condition in real time [5]. Passengers submit the travel demand to the platform.…”

Section: Introductionmentioning

confidence: 99%

Fleet Management and Charging Scheduling for Shared Mobility-on-Demand System: A Systematic Review

Tan

Sun

Ding

et al. 2022

IEEE Open J. Power Energy

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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 fleet management and charging scheduling of a shared mobility-ondemand system, whereby electric vehicle fleets are operated by a centralized platform to provide customers with mobility service. We provide a comprehensive review of system operation based on the operational objectives. The fleet scheduling strategies are categorized into four types: ⅰ) order dispatching, ⅱ) order-dispatching and rebalancing, ⅲ) orderdispatching, rebalancing and charging, and ⅳ) extended. Specifically, we first identify mathematical modeling techniques implemented in the transportation network, then analyze and summarize the solution approaches including mathematical programming, reinforcement learning, and hybrid methods. The advantages and disadvantages of different models and solution approaches are compared. Finally, we present research outlook in various directions. INDEX TERMS shared mobility-on-demand, electric vehicles, fleet management, operational objective, deep reinforcement learning.W

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“…Within existing literature, AMoD systems can be coordinated with simple rule-based heuristics [6,7], model predictive control (MPC) approaches [8,9], and RL approaches [10][11][12][13][14][15]. Interested readers can refer to [16] for a comprehensive survey of the analysis and control of AMoD systems and [17] for RL approaches for ridesharing systems. For brevity, we only review RL approaches to rebalance vehicles in a centralized manner.…”

Section: Introductionmentioning

confidence: 99%

Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand

Gammelli¹,

Yang²,

Harrison³

et al. 2022

Preprint

Self Cite

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Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to existing transportation paradigms, currently challenged by urbanization and increasing travel needs. By centrally controlling a fleet of self-driving vehicles, these systems provide mobility service to customers and are currently starting to be deployed in a number of cities around the world. Current learning-based approaches for controlling AMoD systems are limited to the single-city scenario, whereby the service operator is allowed to take an unlimited amount of operational decisions within the same transportation system. However, real-world system operators can hardly afford to fully re-train AMoD controllers for every city they operate in, as this could result in a high number of poorquality decisions during training, making the single-city strategy a potentially impractical solution. To address these limitations, we propose to formalize the multi-city AMoD problem through the lens of meta-reinforcement learning (meta-RL) and devise an actorcritic algorithm based on recurrent graph neural networks. In our approach, AMoD controllers are explicitly trained such that a small amount of experience within a new city will produce good system performance. Empirically, we show how control policies learned through meta-RL are able to achieve near-optimal performance on unseen cities by learning rapidly adaptable policies, thus making them more robust not only to novel environments, but also to distribution shifts common in real-world operations, such as special events, unexpected congestion, and dynamic pricing schemes. CCS CONCEPTS• Computing methodologies → Reinforcement learning; Multitask learning; Neural networks.

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Analysis and Control of Autonomous Mobility-on-Demand Systems

Cited by 67 publications

References 162 publications

Data-Driven Distributionally Robust Electric Vehicle Balancing for Mobility-on-Demand Systems under Demand and Supply Uncertainties

Data-Driven Distributionally Robust Electric Vehicle Balancing for Mobility-on-Demand Systems under Demand and Supply Uncertainties

Fleet Management and Charging Scheduling for Shared Mobility-on-Demand System: A Systematic Review

Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand

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