Scalable and Congestion-Aware Routing for Autonomous Mobility-On-Demand Via Frank-Wolfe Optimization

Solovey, Kiril; Salazar, Mauro; Pavone, Marco

doi:10.15607/rss.2019.xv.066

Cited by 16 publications

(7 citation statements)

References 40 publications

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“…This work can be extended as follows. First, given the large computational time of the disjoint problem (NLP) we would like to propose a MSA-type method to solve the AMoD system-centric TAP considering exogenous flow, possibly leveraging computationally efficient algorithms such as in [24]. Second, we would like to generalize the approximation model to n line segments, and provide theoretical bounds on the model error.…”

Section: Discussionmentioning

confidence: 99%

Congestion-aware Routing and Rebalancing of Autonomous Mobility-on-Demand Systems in Mixed Traffic

Wollenstein-Betech¹,

Houshmand²,

Salazar³

et al. 2020

Preprint

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This paper studies congestion-aware routeplanning policies for Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides ondemand mobility under mixed traffic conditions. Specifically, we first devise a network flow model to optimize the AMoD routing and rebalancing strategies in a congestion-aware fashion by accounting for the endogenous impact of AMoD flows on travel time. Second, we capture reactive exogenous traffic consisting of private vehicles selfishly adapting to the AMoD flows in a user-centric fashion by leveraging an iterative approach. Finally, we showcase the effectiveness of our framework with two case-studies considering the transportation sub-networks in Eastern Massachusetts and New York City. Our results suggest that for high levels of demand, pure AMoD travel can be detrimental due to the additional traffic stemming from its rebalancing flows, while the combination of AMoD with walking or micromobility options can significantly improve the overall system performance.

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Section: Discussionmentioning

confidence: 99%

Congestion-aware Routing and Rebalancing of Autonomous Mobility-on-Demand Systems in Mixed Traffic

Wollenstein-Betech¹,

Houshmand²,

Salazar³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our problem is similar to routing a fleet of autonomous vehicles for on-demand mobility services (Solovey et al, 2019;Iglesias et al, 2019;Wallar et al, 2018). These approaches compute routes for vehicles (customer-carrying or empty) to fulfill travel demand and minimize total operational cost.…”

Section: Autonomous Mobility-on-demand Servicesmentioning

confidence: 99%

Efficient Large-Scale Multi-Drone Delivery using Transit Networks

Choudhury

Solovey²,

Kochenderfer³

et al. 2021

jair

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We consider the problem of routing a large fleet of drones to deliver packages simultaneously across broad urban areas. Besides flying directly, drones can use public transit vehicles such as buses and trams as temporary modes of transportation to conserve energy. Adding this capability to our formulation augments effective drone travel range and the space of possible deliveries but also increases problem input size due to the large transit networks. We present a comprehensive algorithmic framework that strives to minimize the maximum time to complete any delivery and addresses the multifaceted computational challenges of our problem through a two-layer approach. First, the upper layer assigns drones to package delivery sequences with an approximately optimal polynomial time allocation algorithm. Then, the lower layer executes the allocation by periodically routing the fleet over the transit network, using efficient, bounded suboptimal multi-agent pathfinding techniques tailored to our setting. We demonstrate the efficiency of our approach on simulations with up to 200 drones, 5000 packages, and transit networks with up to 8000 stops in San Francisco and the Washington DC Metropolitan Area. Our framework computes solutions for most settings within a few seconds on commodity hardware and enables drones to extend their effective range by a factor of nearly four using transit.

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“…Applications of homogeneous task allocation have been extensively explored recently within the setting of transportation and logistics. For instance, the operation of an autonomous mobility-on-demand system requires to assign ground vehicles to routes in order to fulfill passenger demand, while potentially accounting for road congestion [26][27][28]. A recent work develops efficient package delivery framework consisting of multiple drones in which drones are assigned to packages and delivery routes [29].…”

Section: A Related Workmentioning

confidence: 99%

Fast Near-Optimal Heterogeneous Task Allocation via Flow Decomposition

Solovey¹,

Bandyopadhyay²,

Rossi³

et al. 2020

Preprint

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Multi-robot systems are uniquely well-suited to performing complex tasks such as patrolling and tracking, information gathering, and pick-up and delivery problems, offering significantly higher performance than single-robot systems. A fundamental building block in most multi-robot systems is task allocation: assigning robots to tasks (e.g., patrolling an area, or servicing a transportation request) as they appear based on the robots' states to maximize reward. In many practical situations, the allocation must account for heterogeneous capabilities (e.g., availability of appropriate sensors or actuators) to ensure the feasibility of execution, and to promote a higher reward, over a long time horizon. To this end, we present the FLOWDEC algorithm for efficient heterogeneous task-allocation achieving an approximation factor of at least 1/2 of the optimal reward. Our approach decomposes the heterogeneous problem into several homogeneous subproblems that can be solved efficiently using min-cost flow. Through simulation experiments, we show that our algorithm is faster by several orders of magnitude than a MILP approach.

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Scalable and Congestion-Aware Routing for Autonomous Mobility-On-Demand Via Frank-Wolfe Optimization

Cited by 16 publications

References 40 publications

Congestion-aware Routing and Rebalancing of Autonomous Mobility-on-Demand Systems in Mixed Traffic

Congestion-aware Routing and Rebalancing of Autonomous Mobility-on-Demand Systems in Mixed Traffic

Efficient Large-Scale Multi-Drone Delivery using Transit Networks

Fast Near-Optimal Heterogeneous Task Allocation via Flow Decomposition

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