Stochastic modeling and control of autonomous mobility-on-demand systems
- 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.
|Type of resource
|electronic resource; remote; computer; online resource
|1 online resource.
|Iglesias, Ramón Darío
|Pavone, Marco, 1980-
|Pavone, Marco, 1980-
|Degree committee member
|Stanford University, Civil & Environmental Engineering Department.
|Statement of responsibility
|Ramón Darío Iglesias.
|Submitted to the Civil & Environmental Engineering Department.
|Thesis Ph.D. Stanford University 2019.
- © 2019 by Ramon Dario Iglesias
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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