Ramon Iglesias

ASL Publications

F. Rossi, R. Iglesias, M. Alizadeh, and M. Pavone, “On the Interaction Between Autonomous Mobility-on-Demand Systems and the Power Network: Models and Coordination Algorithms,” IEEE Transactions on Control of Network Systems, vol. 7, no. 1, pp. 384–397, 2020.
[BibTeX] [Abstract]

Abstract: 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, systems) and the electric power network. We propose a joint model that captures the coupling between the two systems stemming from the vehicles’ charging requirements, capturing time-varying customer demand, battery depreciation, and power transmission constraints. First, we show that the model is amenable to efficient optimization. Then, we prove that the socially optimal solution to the joint problem is a general equilibrium if locational marginal pricing is used for electricity. Finally, we show that the equilibrium can be computed by selfish transportation and generator operators (aided by a nonprofit independent system operator) without sharing private information. We assess the performance of the approach and its robustness to stochastic fluctuations in demand through case studies and agent-based simulations. Collectively, these results provide a first-of-a-kind characterization of the interaction between AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.
```
@article{RossiIglesiasEtAl2018b,
  author = {Rossi, F. and Iglesias, R. and Alizadeh, M. and Pavone, M.},
  title = {On the Interaction Between {Autonomous Mobility-on-Demand} Systems and the Power Network: Models and Coordination Algorithms},
  journal = {{IEEE Transactions on Control of Network Systems}},
  year = {2020},
  volume = {7},
  number = {1},
  pages = {384--397},
  doi = {10.1109/TCNS.2019.2923384},
  owner = {jthluke},
  timestamp = {2024-10-28},
  url = {https://arxiv.org/abs/1709.04906}
}
```
R. D. Iglesias, “Stochastic Modeling and Control of Autonomous Mobility-on-Demand Systems,” PhD thesis, Stanford University, Inst. for Civil and Environmental Engineering, Stanford, California, 2019.
[BibTeX] [Abstract]

Abstract: The last decade saw the rapid development of two major mobility paradigms: Mobility-on-Demand (MoD) systems (e.g. ridesharing, carsharing) and self-driving vehicles. While individually impactful, together they present a major paradigm shift in modern mobility. Autonomous Mobility-on-Demand (AMoD) systems, wherein a fleet of self-driving vehicles serve on-demand travel requests, present a unique opportunity to alleviate many of our transportation woes. Specifically, by combining fully-compliant vehicles with central coordination, AMoD systems can achieve system-level optimal strategies via, e.g., coordinated routing and preemptive dispatch. This thesis presents methods to model, analyze and control AMoD systems. In particular, special emphasis is given to develop stochastic algorithms that can cope with the uncertainty inherent to travel demand. In the first part, we present a steady-state modeling framework built on queueing networks and network flow theory. By casting the system as a multi-class BCMP network, the framework provides analysis tools that allow the characterization of performance metrics for a given routing policy, in terms, e.g., of vehicle availabilities, and first and second order moments of vehicle throughput. Moreover, we present a scalable method for the synthesis of routing policies, with performance guarantees in the limit of large fleet sizes. The framework provides a large set of modeling options, and specifically address cases where the operational concerns of congestion and battery charge level are considered. We validate our theoretical results on a case study of New York City. In the second part, we leverage the insights provided by the steady-state models to present real-time control algorithms. Specifically, we cast the real-time control problem within a stochastic model predictive control framework. The control loop consists of a forecasting generative model and a stochastic optimization subproblem. At each time step, the generative model first forecasts a finite number of travel demand for a finite horizon and then we solve the stochastic subproblem via Sample Average Approximation. We show via simulation that this approach is more robust to uncertain demand and vastly outperforms state-of-the-art fleet-level control algorithms. Finally, we validate the presented frameworks by deploying a fleet control application in a carsharing system in Japan. The application uses the aforementioned algorithms to provide, in real-time, tasks to the carsharing employees regarding actions to be taken to better meet customer demand. Results show significant improvement over human based decision making.
```
@phdthesis{Iglesias2019,
  author = {Iglesias, R. D.},
  title = {Stochastic Modeling and Control of Autonomous Mobility-on-Demand Systems},
  school = {{Stanford University, Inst. for Civil and Environmental Engineering}},
  year = {2019},
  address = {Stanford, California},
  month = aug,
  url = {https://stacks.stanford.edu/file/druid:mm997fz9077/IglesiasPhD-augmented.pdf},
  owner = {bylard},
  timestamp = {2021-12-06}
}
```
R. Iglesias, F. Rossi, R. Zhang, and M. Pavone, “A BCMP Network Approach to Modeling and Controlling Autonomous Mobility-on-Demand Systems,” Int. Journal of Robotics Research, vol. 38, no. 2–3, pp. 357–374, 2019.
[BibTeX] [Abstract]

Abstract: In this paper we present a queuing network approach to the problem of routing and rebalancing a fleet of self-driving vehicles providing on- demand mobility within a capacitated road network. We refer to such systems as autonomous mobility-on-demand systems, or AMoD. We first cast an AMoD system into a closed, multi-class BCMP queuing network model capable of capturing the passenger arrival process, traffic, the state- of-charge of electric vehicles, and the availability of vehicles at the stations. Second, we propose a scalable method for the synthesis of routing and charging policies, with performance guarantees in the limit of large fleet sizes. Third, we validate the theoretical results on a case study of New York City. Collectively, this paper provides a unifying framework for the analysis and control of AMoD systems, which provides a large set of modeling options (e.g., the inclusion of road capacities and charging constraints), and subsumes earlier Jackson and network flow models.
```
@article{IglesiasRossiEtAl2017,
  author = {Iglesias, R. and Rossi, F. and Zhang, R. and Pavone, M.},
  title = {A {BCMP} Network Approach to Modeling and Controlling Autonomous Mobility-on-Demand Systems},
  journal = {{Int. Journal of Robotics Research}},
  year = {2019},
  volume = {38},
  number = {2--3},
  pages = {357--374},
  url = {/wp-content/papercite-data/pdf/Iglesias.Rossi.Zhang.Pavone.IJRR18.pdf},
  owner = {rdit},
  timestamp = {2018-05-06}
}
```
M. Tsao, R. Iglesias, and M. Pavone, “Stochastic Model Predictive Control for Autonomous Mobility on Demand,” in Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Maui, Hawaii, 2018.
[BibTeX] [Abstract]

Abstract: This paper presents a stochastic, model predictive control (MPC) algorithm that leverages short-term proba- bilistic 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 guaran- tees. 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 non- stochastic algorithms.
```
@inproceedings{TsaoIglesiasEtAl2018,
  author = {Tsao, M. and Iglesias, R. and Pavone, M.},
  title = {Stochastic Model Predictive Control for Autonomous Mobility on Demand},
  booktitle = {{Proc. IEEE Int. Conf. on Intelligent Transportation Systems}},
  year = {2018},
  note = {{Extended version available} at \url{https://arxiv.org/pdf/1804.11074}},
  address = {Maui, Hawaii},
  month = nov,
  url = {https://arxiv.org/pdf/1804.11074.pdf},
  owner = {rdit},
  timestamp = {2018-07-12}
}
```
F. Rossi, R. Iglesias, M. Alizadeh, and M. Pavone, “On the Interaction Between Autonomous Mobility-on-Demand Systems and the Power Network: Models and Coordination Algorithms,” in Robotics: Science and Systems, Pittsburgh, Pennsylvania, 2018.
[BibTeX] [Abstract]

Abstract: 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 joint linear model that captures the coupling between the two systems stemming from the vehicles’ charging requirements. The model subsumes existing network flow models for AMoD systems and linear models for the power network, and it captures time-varying customer demand and power generation costs, road congestion, and power transmission and distribution constraints. We then leverage the linear model to jointly optimize the operation of both systems. We propose an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by state-of-the-art linear programming solvers. Finally, we study the implementation of a hypothetical electric-powered AMoD system in Dallas-Fort Worth, and its impact on the Texas power network. We show that coordination between the AMoD system and the power network can reduce the overall energy expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of $ 78M per year compared to an uncoordinated scenario. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the electric power network, and shed additional light on the economic and societal value of AMoD.
```
@inproceedings{RossiIglesiasEtAl2018,
  author = {Rossi, F. and Iglesias, R. and Alizadeh, M. and Pavone, M.},
  title = {On the Interaction Between {Autonomous Mobility-on-Demand} Systems and the Power Network: Models and Coordination Algorithms},
  booktitle = {{Robotics: Science and Systems}},
  year = {2018},
  address = {Pittsburgh, Pennsylvania},
  month = jun,
  note = {{Extended version available at }\url{https://arxiv.org/abs/1709.04906}},
  owner = {frossi2},
  timestamp = {2018-06-30},
  url = {/wp-content/papercite-data/pdf/Rossi.Iglesias.Alizadeh.Pavone.RSS18.pdf}
}
```
R. Iglesias, F. Rossi, K. Wang, D. Hallac, J. Leskovec, and M. Pavone, “Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems,” in Proc. IEEE Conf. on Robotics and Automation, Brisbane, Australia, 2018.
[BibTeX] [Abstract]

Abstract: The goal of this paper is to present an end-to-end, data-driven framework to control Autonomous Mobility-on-Demand systems (AMoD, i.e. fleets of self-driving vehicles). We first model the AMoD system using a time-expanded network, and present a formulation that computes the optimal rebalancing strategy (i.e., preemptive repositioning) and the minimum feasible fleet size for a given travel demand. Then, we adapt this formulation to devise a Model Predictive Control (MPC) algorithm that leverages short-term demand forecasts based on historical data to compute rebalancing strategies. We test the end-to-end performance of this controller with a state-of-the-art LSTM neural network to predict customer demand and real customer data from DiDi Chuxing: we show that this approach scales very well for large systems (indeed, the computational complexity of the MPC algorithm does not depend on the number of customers and of vehicles in the system) and outperforms state-of-the-art rebalancing strategies by reducing the mean customer wait time by up to to 89.6%.
```
@inproceedings{IglesiasRossiEtAl2018,
  author = {Iglesias, R. and Rossi, F. and Wang, K. and Hallac, D. and Leskovec, J. and Pavone, M.},
  title = {Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems},
  booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
  year = {2018},
  address = {Brisbane, Australia},
  month = may,
  owner = {frossi2},
  timestamp = {2018-01-14},
  url = {/wp-content/papercite-data/pdf/Iglesias.Rossi.Wang.ea.ICRA18.pdf}
}
```
R. Iglesias, F. Rossi, R. Zhang, and M. Pavone, “A BCMP Network Approach to Modeling and Controlling Autonomous Mobility-on-Demand Systems,” in Workshop on Algorithmic Foundations of Robotics, San Francisco, California, 2016.
[BibTeX] [Abstract]

Abstract: In this paper, we present a queueing network approach to the problem of routing and rebalancing a fleet of self-driving vehicles providing on-demand mobility within a capacitated road network. We refer to such systems as autonomous mobility-on-demand systems, or AMoD. We first cast an AMoD system into a closed, multi-class BCMP queueing network model. Second, we present analysis tools that allow the characterization of performance metrics for a given routing policy, in terms, e.g., of vehicle availabilities and second-order moments of vehicle throughput. Third, we propose a scalable method for the synthesis of routing policies, with performance guarantees in the limit of large fleet sizes. Finally, we validate our theoretical results on a case study of New York City. Collectively, this paper provides a unifying framework for the analysis and control of AMoD systems, which subsumes earlier Jackson and flow network models, provides a quite large set of modeling options (e.g., the inclusion of road capacities and general travel time distributions), and allows the analysis of second and higher-order moments for the performance metrics.
```
@inproceedings{IglesiasRossiEtAl2016,
  author = {Iglesias, R. and Rossi, F. and Zhang, R. and Pavone, M.},
  title = {A {BCMP} Network Approach to Modeling and Controlling {Autonomous} {Mobility-on-Demand} Systems},
  booktitle = {{Workshop on Algorithmic Foundations of Robotics}},
  year = {2016},
  address = {San Francisco, California},
  url = {/wp-content/papercite-data/pdf/Iglesias.Rossi.Zhang.Pavone.WAFR16.pdf},
  owner = {bylard},
  timestamp = {2017-03-07}
}
```
M. Tsao, R. Iglesias, and M. Pavone, “Stochastic Model Predictive Control for Autonomous Mobility on Demand.”
[BibTeX] [Abstract]

Abstract: 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 characterize the 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 non-stochastic algorithms.
```
@unpublished{TsaoIglesiasEtAl,
  author = {Tsao, M. and Iglesias, R. and Pavone, M.},
  title = {Stochastic Model Predictive Control for Autonomous Mobility on Demand},
  note = {{{Extended version of ITSC 2018 paper. Available at }\url{https://arxiv.org/pdf/1804.11074.pdf}}},
  howpublished = {Available at: \url{https://arxiv.org/pdf/1804.11074.pdf}},
  owner = {mwtsao},
  timestamp = {2018-07-24}
}
```