A Congestion-aware Routing Scheme for Autonomous Mobility-on-Demand Systems

Salazar, Mauro; Tsao, Matthew; Aguiar, Izabel; Schiffer, Maximilian; Pavone, Marco

doi:10.23919/ecc.2019.8795897

Cited by 46 publications

(48 citation statements)

References 24 publications

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“…the results in around 15 seconds, whereas the reported running times of [36] are below 4 minutes for similar hardware. We wish to point out that [36] do not minimize travel time for rebalancing vehicles, and also include walking times in their analysis.…”

Section: Resultsmentioning

confidence: 85%

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

Solovey

Salazar²,

Pavone³

2019

Robotics: Science and Systems XV

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We consider the problem of vehicle routing for Autonomous Mobility-on-Demand (AMoD) systems, wherein a fleet of self-driving vehicles provides on-demand mobility in a given environment. Specifically, the task it to compute routes for the vehicles (both customer-carrying and empty travelling) so that travel demand is fulfilled and operational cost is minimized. The routing process must account for congestion effects affecting travel times, as modeled via a volume-delay function (VDF). Route planning with VDF constraints is notoriously challenging, as such constraints compound the combinatorial complexity of the routing optimization process. Thus, current solutions for AMoD routing resort to relaxations of the congestion constraints, thereby trading optimality with computational efficiency. In this paper, we present the first computationally-efficient approach for AMoD routing where VDF constraints are explicitly accounted for. We demonstrate that our approach is faster by at least one order of magnitude with respect to the state of the art, while providing higher quality solutions. From a methodological standpoint, the key technical insight is to establish a mathematical reduction of the AMoD routing problem to the classical traffic assignment problem (a related vehicle-routing problem where empty traveling vehicles are not present). Such a reduction allows us to extend powerful algorithmic tools for traffic assignment, which combine the classic Frank-Wolfe algorithm with modern techniques for pathfinding, to the AMoD routing problem. We provide strong theoretical guarantees for our approach in terms of near-optimality of the returned solution.

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Section: Resultsmentioning

confidence: 85%

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

Solovey

Salazar²,

Pavone³

2019

Robotics: Science and Systems XV

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, future studies should investigate methods to proactively manage traffic flow congestion by incorporating strategic flow control [94] or an individual metering strategy to regulate traffic flow [33]. By gaining inspiration from autonomous vehicle research, future work should also study the notion of congestion-aware routing schemes by including the current traffic density of a given route in the cost function in an effort to circumvent the onset of congestion on typical busy streets [95]. Alternatively, further studies should explore a variety of different routing algorithms, such as simplest paths or minimum travel time paths [74,96] as a way to decrease the number of turns, which would reduce the number of merging flights and thus mitigate the probability of conflicts.…”

Section: Discussionmentioning

confidence: 99%

Investigation of Merge Assist Policies to Improve Safety of Drone Traffic in a Constrained Urban Airspace

Doole

Ellerbroek

2022

Aerospace

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Package delivery via autonomous drones is often presumed to hold commercial and societal value when applied to urban environments. However, to realise the benefits, the challenge of safely managing high traffic densities of drones in heavily constrained urban spaces needs to be addressed. This paper applies the principles of traffic segmentation and alignment to a constrained airspace in efforts to mitigate the probability of conflict. The study proposes an en-route airspace concept in which drone flights are directly guided along a one-way street network. This one-way airspace concept uses heading-altitude rules to vertically segment cruising traffic as well as transitioning flights with respect to their travel direction. However, transition flights trigger a substantial number of merging conflicts, thus negating a large part of the benefits gained from airspace structuring. In this paper, we aim to reduce the occurrence of merging conflicts and intrusions by using a delay-based and speed-based merge-assist strategy, both well-established methods from road traffic research. We apply these merge assistance strategies to the one-way airspace design and perform simulations for three traffic densities for the experiment area of Manhattan, New York. The results indicate, at most, a 9–16% decrease in total number of intrusions with the use of merge assistance. By investigating mesoscopic features of the urban street network, the data suggest that the relatively low efficacy of the merge strategies is mainly caused by insufficient space for safe manoeuvrability and the inability for the strategies to fully respond and thus resolve conflicts on short-distance streets.

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“…They modeled the routing problem between multiple MoD stations with in-route charging as a multi-server queuing system, and they formulated their objective as a stochastic convex optimization problem that minimizes the average overall trip time for all customers in comparison to their actual trip time without in-route charging. Additionally, Salazar et al [28] studied route-planning for autonomous MoD systems where they consider the impact of road traffic on travel time.…”

Section: Related Workmentioning

confidence: 99%

Algorithms to Manage Congestion in Large-Scale Mobility-on-Demand Schemes that Use Electric Vehicles

Rigas

Tsompanidis

2021

SN COMPUT. SCI.

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This paper studies the use of Electric Vehicles (EVs) in a Mobility-on-Demand (MoD) scheme. In this scheme, customers that act as cooperative agents request a set of alternative trips, where each trip provides a utility to the agent. The EVs are distributed across a number of stations. We propose congestion management algorithms which take as input the trip requests and calculate the EV-to-customer assignment, considering the number of executed trips and the utility obtained by the agents. Initially, we solve the problem offline and optimally using Mixed-Integer-Programming (MIP) techniques and then we solve it online using an equivalent greedy algorithm. The online algorithm uses three alternative heuristic functions to decide on the execution of a customer request: (a) The sum of squares of all EVs in all stations, (b) the percentage of trips' destination location fullness and (c) a random choice of trip execution. To further improve the performance of the online algorithms, we also propose an agent-based negotiation scheme where alternative offers in terms of trip starting time are made to the agents when the initial EV assignment problem is unsolvable due to insufficient resources. Through a detailed evaluation, we observe that (a) provides an increase of up to 3.2% compared to (b) and up to 3.7% compared to (c) in terms of average trip execution, while giving the higher utility to the agents. All variations achieve close to the optimal performance. At the same time, the negotiation scheme improves the performance of the algorithms by up to 15.7%.

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A Congestion-aware Routing Scheme for Autonomous Mobility-on-Demand Systems

Cited by 46 publications

References 24 publications

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

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

Investigation of Merge Assist Policies to Improve Safety of Drone Traffic in a Constrained Urban Airspace

Algorithms to Manage Congestion in Large-Scale Mobility-on-Demand Schemes that Use Electric Vehicles

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