10. Learning Enabled Control and Coordination for Societally-Aware Transportation Systems

Organizers: Alexandre Bayen, Karthik Gopalakrishnan, Devansh Jalota, Jessica Lazarus, Marco Pavone

Location: Melati Junior 4011

Website: https://sites.google.com/stanford.edu/l4sat2023

Lecture Scheduleshttps://sites.google.com/stanford.edu/l4sat2023/schedule

Abstract: In recent years, there have been significant advancements in using data-driven techniques to control and coordinate large-scale transportation systems. Typically, these techniques are designed to maximize the efficiency of the system, by minimizing delays and transportation costs. Future transportation systems however should ideally be designed not only to maximize efficiency but also to address societal objectives such as fairness, robustness, privacy, and sustainability. These socially-oriented desiderata introduce several challenges, including (i) requiring the consensus and coordination among agents on acceptable definitions of and tradeoffs between these desiderata, (ii) the computational intractability of traditional approaches, and (iii) the consideration of data-availability issues due to privacy concerns.

Learning-based approaches have the potential to address these issues and help extend the capabilities of traditional control algorithms for transportation applications; however, the use of data-driven techniques for the societally aware operation of emerging mobility systems is largely untapped. The objectives of the workshop are four-fold:

  1. Emphasize data-driven machine learning techniques in the context of transportation applications. The discussion of data-driven modeling, analysis, and control tools in applications such as road traffic management will help foster methodological developments to design societally aware algorithmic decision-making systems for emerging mobility systems.
  2. Provide a platform to explore new research directions geared towards tapping the full potential of data in achieving societal objectives in transportation systems.
  3. Foster the development of novel control mechanisms essential for advancing societally aware transportation systems and enable discussions on the appropriate measures for societal considerations such as fairness, equity, robustness, privacy, etc.
  4. Introduce the controls community to transportation-specific models for societal objectives, highlight limitations of the current models, and challenges faced in implementing these objectives using traditional approaches.

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