Model Predictive Control of Ride-sharing Autonomous Mobility-on-Demand Systems

Tsao, Matthew; Milojevic, Dejan; Ruch, Claudio; Salazar, Mauro; Pavone, Marco

doi:10.1109/icra.2019.8794194

Cited by 52 publications

(37 citation statements)

References 23 publications

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“…Fourth, we model the impact of road traffic on travel time with the BPR function [9], which is well established and, despite it does not account for microscopic traffic phenomena such as traffic lights, serves the purpose of route-planning on the mesoscopic level. Finally, we constrain the capacity of the vehicles to one single customer, which is in line with current trends, and leave the extension to ride-sharing to future research [10,46].…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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

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“…Topic Reference Optimization Fleet sizing [38], [73], [92], [93], [98], [115], [130] Vehicle routing / trip assignment [81], [98], [106], [120], [123], [125], [127], [131]- [136] Vehicle rebalancing / relocation…”

Section: Approachmentioning

confidence: 99%

Enhanced Mobility With Connectivity and Automation: A Review of Shared Autonomous Vehicle Systems

Zhao

Malikopoulos

2022

IEEE Intell. Transport. Syst. Mag.

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Shared mobility can provide access to transportation on a custom basis without vehicle ownership. The advent of connected and automated vehicle technologies can further enhance the potential benefits of shared mobility systems. Although the implications of a system with shared autonomous vehicles have been investigated, the research reported in the literature has exhibited contradictory outcomes. In this paper, we present a summary of the research efforts in shared autonomous vehicle systems that have been reported in the literature to date and discuss potential future research directions.

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“…However, such mobility systems are quite hard to operate, as they combine the principles of two classic NP-Hard problems: the Dynamic-Vehicle-Routing-Problem (as they are on-demand) and the Dial-A-Ride-Problem (due to the sharing aspect). Despite this complexity, some algorithms have been able to effectively decide how to match groups of users and how to assign them to vehicles (Ota et al 2016;Alonso-Mora et al 2017a;Tsao et al 2019;Simonetto et al 2019;Kucharski and Cats 2020), and their simulations have confirmed the potential of ridepooling.…”

Section: Introductionmentioning

confidence: 99%

Anticipatory routing methods for an on-demand ridepooling mobility system

2021

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On-demand mobility systems in which passengers use the same vehicle simultaneously are a promising transport mode, yet difficult to control. One of the most relevant challenges relates to the spatial imbalances of the demand, which induce a mismatch between the position of the vehicles and the origins of the emerging requests. Most ridepooling models face this problem through rebalancing methods only, i.e., moving idle vehicles towards areas with high rejections rate, which is done independently from routing and vehicle-to-orders assignments, so that vehicles serving passengers (a large portion of the total fleet) remain unaffected. This paper introduces two types of techniques for anticipatory routing that affect how vehicles are assigned to users and how to route vehicles to serve such users, so that the whole operation of the system is modified to reach more efficient states for future requests. Both techniques do not require any assumption or exogenous knowledge about the future demand, as they depend only on current and recent requests. Firstly, we introduce rewards that reduce the cost of an assignment between a vehicle and a group of passengers if the vehicle gets routed towards a high-demand zone. Secondly, we include a small set of artificial requests, whose request times are in the near future and whose origins are sampled from a probability distribution that mimics observed generation rates. These artificial requests are to be assigned together with the real requests. We propose, formally discuss and experimentally evaluate several formulations for both approaches. We test these techniques in combination with a state-of-the-art trip-vehicle assignment method, using a set of real rides from Manhattan. Introducing rewards can diminish the rejection rate to about nine-tenths of its original value. On the other hand, including future requests can reduce users’ traveling times by about one-fifth, but increasing rejections. Both methods increase the vehicles-hour-traveled by about 10%. Spatial analysis reveals that vehicles are indeed moved towards the most demanded areas, such that the reduction in rejections rate is achieved mostly there.

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Model Predictive Control of Ride-sharing Autonomous Mobility-on-Demand Systems

Cited by 52 publications

References 23 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

Enhanced Mobility With Connectivity and Automation: A Review of Shared Autonomous Vehicle Systems

Anticipatory routing methods for an on-demand ridepooling mobility system

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