On the Interaction between Autonomous Mobility-on-Demand and Public Transportation Systems

Salazar, Mauro; Rossi, Federico; Schiffer, Maximilian; Onder, Christopher H.; Pavone, Marco

doi:10.1109/itsc.2018.8569381

Cited by 83 publications

(59 citation statements)

References 24 publications

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“…To this end, it might be necessary to employ a time-expansion of the road-graph and account for stochastic effects, as it was done in [18,45]. In addition, it is of interest to extend this framework to capture the interaction with public transit [35] and the power grid [33], and account for the interaction of self-driving vehicles with the urban infrastructure.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…There exist several approaches to study AMOD systems, spanning from simulation models [16,21,23] and queuingtheoretical models [53,18] to network-flow models [29,34,44]. On the algorithmic side, the control of AMOD systems has been mostly based on network flow models employed in a receding-horizon fashion [19,45,46], and thresholded approximations of congestion effects [34], also accounting for the interaction with public transit [35] and the powergrid [33]. In such a framework, cars can travel through a road at free-flow speed until a fixed capacity of the road is reached.…”

Section: Related Workmentioning

confidence: 99%

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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: Discussion and Future Workmentioning

confidence: 99%

Section: Related Workmentioning

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

View full text Add to dashboard Cite

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“…Future work will incorporate traffic congestion by modeling a detailed road network with finite capacities as is done in [15]. This, in turn, can be coupled with models for public transit to provide a multi-modal [16], real-time stochastic control of AMoD systems. Similarly, ongoing research is studying coordination between the power network and electric AMoD systems [17].…”

Section: Discussionmentioning

confidence: 99%

Stochastic Model Predictive Control for Autonomous Mobility on Demand

Tsao

Iglesias

Pavone

2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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This paper presents a stochastic, model predictive control (MPC) algorithm that leverages short-term probabilistic forecasts for dispatching and rebalancing Autonomous Mobility-on-Demand systems (AMoD), i.e. fleets of self-driving vehicles. We first present the core stochastic optimization problem in terms of a time-expanded network flow model. Then, to ameliorate its tractability, we present two key relaxations. First, we replace the original stochastic problem with a Sample Average Approximation, and provide its performance guarantees. Second, we divide the controller into two submodules. The first submodule assigns vehicles to existing customers and the second redistributes vacant vehicles throughout the city. This enables the problem to be solved as two totally unimodular linear programs, allowing the controller to scale to large problem sizes. Finally, we test the proposed algorithm in two scenarios based on real data and show that it outperforms prior state-of-the-art algorithms. In particular, in a simulation using customer data from the ridesharing company DiDi Chuxing, the algorithm presented here exhibits a 62.3 percent reduction in customer waiting time compared to state of the art nonstochastic algorithms.

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“…Contardo et al solved a deterministic bike-share balancing problem using a flow-based formulation, employing Benders' decomposition and column generation to solve it [12]. Very recently, flow formulations have been used to address broader design questions for (autonomous) mobility systems, but these differ from the examples above in that they view all transport flows as continuous variables [31,32].…”

Section: Introductionmentioning

confidence: 99%

Two-stage stochastic approximation for dynamic rebalancing of shared mobility systems

Warrington

Ruchti

2019

Transportation Research Part C: Emerging Technologies

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Mobility systems featuring shared vehicles are often unable to serve all potential customers, as the distribution of demand does not coincide with the positions of vehicles at any given time. System operators often choose to reposition these shared vehicles (such as bikes, cars, or scooters) actively during the course of the day to improve service rate. They face a complex dynamic optimization problem in which many integer-valued decisions must be made, using real-time state and forecast information, and within the tight computation time constraints inherent to real-time decision-making. We first present a novel nested-flow formulation of the problem, and demonstrate that its linear relaxation is significantly tighter than one from existing literature. We then adapt a two-stage stochastic approximation scheme from the generic SPAR algorithm due to Powell et al., in which rebalancing plans are optimized against a value function representing the expected cost (in terms of fulfilled and unfulfilled customer demand) of the future evolution of the system. The true value function is approximated by a separable function of contributions due to the rebalancing actions carried out at each station and each time step of the planning horizon. The new algorithm requires surprisingly few iterations to yield high-quality solutions,and is suited to real-time use as it can be terminated early if required. We provide insight into this good performance by examining the mathematical properties of our new flow formulation, and perform rigorous tests on standardized benchmark networks to explore the effect of system size. We then use data from Philadelphia's public bike sharing scheme to demonstrate that the approach also yields performance gains for real systems.

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On the Interaction between Autonomous Mobility-on-Demand and Public Transportation Systems

Cited by 83 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

Stochastic Model Predictive Control for Autonomous Mobility on Demand

Two-stage stochastic approximation for dynamic rebalancing of shared mobility systems

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