Fleet Management for Autonomous Vehicles Using Multicommodity Coupled Flows in Time-Expanded Networks

VIPAFLEET is a framework to develop models and algorithms for managing a fleet of Individual Public Autonomous Vehicles (VIPA). We consider a homogeneous fleet of such vehicles distributed at specified stations in a closed site to supply internal transportation, where the vehicles can be used in different modes of circulation (tram mode, elevator mode, taxi mode). We treat in this paper a variant of the Online Pickup-and-Delivery Problem related to the taxi mode by means of multicommodity coupled flows in a time-expanded network and propose a corresponding integer linear programming formulation. This enables us to compute optimal offline solutions. However, to apply the well-known meta-strategy Replan to the online situation by solving a sequence of offline subproblems, the computation times turned out to be too long, so that we devise a heuristic approach h-Replan based on the flow formulation. Finally, we evaluate the performance of h-Replan in comparison with the optimal offline solution, both in terms of competitive analysis and computational experiments, showing that h-Replan computes reasonable solutions, so that it suits for the online situation

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV and accepted by TCNS. In Version 4, the body of the paper is largely rewritten for clarity and consistency, and new numerical simulations are presented. All source code is available (MIT) at https://dx.doi.org/10.5281/zenodo.324165

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV, in prep. for journal submission. In V3, we add a proof that the socially-optimal solution can be enforced as a general equilibrium, a privacy-preserving distributed optimization algorithm, a description of the receding-horizon implementation and additional numerical results, and proofs of all theorem

Gemischt-autonome Flotten in der urbanen Logistik

We consider a city logistics application in which a service provider seeks a repeatable plan to transport commodities from distribution centers to satellites. The service provider uses a mixed autonomous fleet that is composed of autonomous vehicles and manually operated vehicles. The autonomous vehicles are only able to travel independently on feasible streets of the heterogeneous infrastructure but elsewhere need to be pulled by manually operated vehicles in platoons. We introduce the service network design problem with mixed autonomous fleets to determine a tactical plan that minimizes the total costs over a medium-term time horizon. The tactical plan determines the size and mix of the fleet, schedules transportation services, and decides on the routing or outsourcing of commodities. We model this problem as an integer program on a time-expanded network and study the impact of different problem characteristics on the solutions. To precisely depict the synchronization requirements of the problem, the time-expanded networks need to consider narrow time intervals. Thus, we develop an exact solution approach based on the dynamic discretization discovery scheme that refines partially time-expanded networks containing only a fraction of the nodes and arcs of the fully time-expanded network. Further methodological contributions of this work include the introduction of valid inequalities, two enhancements that exploit linear relaxations, and a heuristic search space restriction. Computational experiments show that all evaluated variants of the solution approach outperform a commercial solver. For transferring a tactical plan to an operational solution that minimizes the transshipment effort on a given day, we present a post-processing technique that specifically assigns commodities to vehicles and vehicles to platoons. Finally, we solve a case study on a real-world based network resembling the city of Braunschweig, Germany. Analyzing the tactical and operational solutions, we assess the value of using a mixed autonomous fleet and derive practical implications.Wir betrachten eine Anwendung der urbanen Logistik, bei der ein Dienstleister einen wiederholbaren Plan für den Gütertransport von Distributionszentren zu Satelliten anstrebt. Dafür setzt der Dienstleister eine gemischt-autonome Flotte ein, die sich aus autonomen Fahrzeugen und manuell gesteuerten Fahrzeugen zusammensetzt. Die autonomen Fahrzeuge können nur auf bestimmten Straßen der heterogenen Infrastruktur selbstständig fahren, außerhalb dieser müssen sie von manuell gesteuerten Fahrzeugen mittels Platooning gezogen werden. Wir führen das „service network design problem with mixed autonomous fleets“ ein, um einen taktischen Plan zu ermitteln, der die Gesamtkosten über einen mittelfristigen Zeithorizont minimiert. Der taktische Plan bestimmt die Größe und Zusammensetzung der Flotte, legt die Transportdienste fest und entscheidet über das Routing oder das Outsourcing von Gütern. Wir modellieren dieses Problem als ganzzahliges Programm auf einem zeiterweiterten Netzwerk und untersuchen die Auswirkungen verschiedener Problemeigenschaften auf die Lösungen. Um die Synchronisationsanforderungen des Problems präzise darzustellen, müssen die zeiterweiterten Netzwerke kleine Zeitintervalle berücksichtigen. Daher entwickeln wir einen exakten Lösungsansatz, der auf dem Schema des „dynamic discretization discovery“ basiert und partiell zeiterweiterte Netzwerke entwickelt, die nur einen Teil der Knoten und Kanten des vollständig zeiterweiterten Netzwerks enthalten. Weitere methodische Beiträge dieser Dissertation umfassen die Einführung von Valid Inequalities, zweier Erweiterungen, die lineare Relaxationen verwenden, und einer heuristischen Suchraumbegrenzung. Experimente zeigen, dass alle evaluierten Varianten des Lösungsansatzes einen kommerziellen Solver übertreffen. Um einen taktischen Plan in eine operative Lösung zu überführen, die die Umladevorgänge an einem bestimmten Tag minimiert, stellen wir eine Post-Processing-Methode vor, mit der Güter zu Fahrzeugen und Fahrzeuge zu Platoons eindeutig zugeordnet werden. Schließlich lösen wir eine Fallstudie auf einem realitätsnahen Netzwerk, das der Stadt Braunschweig nachempfunden ist. Anhand der taktischen und operativen Lösungen bewerten wir den Nutzen einer gemischt-autonomen Flotte und leiten Implikationen für die Praxis ab

Real-time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning

Operators of Electric Autonomous Mobility-on-Demand (E-AMoD) fleets need to make several real-time decisions such as matching available cars to ride requests, rebalancing idle cars to areas of high demand, and charging vehicles to ensure sufficient range. While this problem can be posed as a linear program that optimizes flows over a space-charge-time graph, the size of the resulting optimization problem does not allow for real-time implementation in realistic settings. In this work, we present the E-AMoD control problem through the lens of reinforcement learning and propose a graph network-based framework to achieve drastically improved scalability and superior performance over heuristics. Specifically, we adopt a bi-level formulation where we (1) leverage a graph network-based RL agent to specify a desired next state in the space-charge graph, and (2) solve more tractable linear programs to best achieve the desired state while ensuring feasibility. Experiments using real-world data from San Francisco and New York City show that our approach achieves up to 89% of the profits of the theoretically-optimal solution while achieving more than a 100x speedup in computational time. Furthermore, our approach outperforms the best domain-specific heuristics with comparable runtimes, with an increase in profits by up to 3x. Finally, we highlight promising zero-shot transfer capabilities of our learned policy on tasks such as inter-city generalization and service area expansion, thus showing the utility, scalability, and flexibility of our framework.Comment: 9 page

Stochastic Model Predictive Control for Autonomous Mobility on Demand

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 (SAA), and characterize the performance guarantees. Second, we separate the controller into two separate parts to address the task of assigning vehicles to the outstanding customers separate from that of rebalancing. This enables the problem to be solved as two totally unimodular linear programs, and thus easily scalable 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 DiDi Chuxing, the algorithm presented here exhibits a 62.3 percent reduction in customer waiting time compared to state of the art non-stochastic algorithms.Comment: Submitting to the IEEE International Conference on Intelligent Transportation Systems 201

The Critical Role of Public Charging Infrastructure

Editors: Peter Fox-Penner, PhD, Z. Justin Ren, PhD, David O. JermainA decade after the launch of the contemporary global electric vehicle (EV) market, most cities face a major challenge preparing for rising EV demand. Some cities, and the leaders who shape them, are meeting and even leading demand for EV infrastructure. This book aggregates deep, groundbreaking research in the areas of urban EV deployment for city managers, private developers, urban planners, and utilities who want to understand and lead change