Harshal Chaudhari - "Fleet Management Strategies for Urban Mobility-on-Demand Systems" - PhD Final Defense

  • Starts: 11:00 am on Monday, August 9, 2021
  • Ends: 1:00 pm on Monday, August 9, 2021
In recent years, the paradigm of personal urban mobility has radically evolved as an increasing number of Mobility-on-Demand (MoD) systems continue to revolutionize urban transportation. Hailed as the future of sustainable transportation, with significant implications on urban planning, these systems typically utilize a fleet of shared vehicles such as bikes, electric scooters, cars, etc., and provide a centralized matching platform to deliver point-to-point mobility to passengers. In this dissertation, we study MoD systems along three operational directions – (1) modeling: developing analytical models that capture the rich stochasticity of passenger demand and its impact on the fleet distribution, (2) economics: devising strategies to maximize revenue, and, (3) control: developing coordination mechanisms aimed at optimizing platform throughput.

First, we focus on the metropolitan bike-sharing systems where platforms typically do not have access to real-time location data to ascertain the exact spatial distribution of their fleet. We formulate the problem of accurately predicting the fleet distribution as a Markov Chain monitoring problem on a graph representation of a city. Specifically, each monitor provides information on the exact number of bikes transitioning to a specific node or traversing a specific edge at a particular time. Under budget constraints on the number of such monitors, we design efficient algorithms to determine appropriate monitoring operations and demonstrate their efficacy over synthetic and real datasets.

Second, we focus on the revenue maximization strategies for individual strategic driving partners on ride-hailing platforms. Under the key assumption that large-scale platform dynamics are agnostic to the actions of an individual strategic driver, we propose a series of dynamic programming-based algorithms to devise contingency plans that maximize the expected earnings of a driver. Using robust optimization techniques, we rigorously reason about and analyze the sensitivity of such strategies to perturbations in passenger demand distributions.

Finally, we address the problem of large-scale fleet management. Recent approaches for the fleet management problem have leveraged model-free deep reinforcement learning (RL) based algorithms to tackle complex decision-making problems. However, such methods suffer from a lack of explainability and often fail to generalize well. We consider an explicit need-based coordination mechanism to propose a non-deep RL-based algorithm that augments tabular Q-learning with a combinatorial optimization problem. Empirically, a case study on the New York City taxi demand enables a rigorous assessment of the value, robustness, and generalizability of the proposed approaches.

Location:
via Zoom

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