Learning-Guided Optimization for Intelligent Mobility Systems

Efficient and reliable mobility systems are essential to modern-day society, with broad impacts ranging from day-to-day commuting, public transportation, emergency response to last-mile package delivery and freight logistics. Autonomous vehicles have the potential to improve mobility efficiency and convenience but also raise questions about reliability and feasibility of deployment. The first contribution of this thesis is a set of novel, principled control-theoretical analyses that provide strong stability and reliability guarantees for autonomous vehicles and human-compatible driving, and they further covers emergent traffic behaviors in mixed-autonomy systems. While these theoretical guarantees offer valuable insights, mobility systems are inherently complex, and their overall performance often relies on solving difficult optimization problems, many of which are combinatorial, thus presenting significant scalability challenges. Overcoming these challenges requires innovative approaches that extend beyond traditional control techniques. This thesis further contributes a set of machine learning-guided optimization algorithms that significantly enhance the efficiency and scalability of solving combinatorial optimization problems. These algorithms have proven effective across a wide range of mobility-related applications. Compared to state-of-the-art solvers, they achieve 10× to 100× speed-up in large-scale vehicle routing problems, 35% to 70% solve-time improvement in various mixed-integer linear programming problems, and up to 54% acceleration in long-horizon scheduling problems. These advancements open new possibilities for efficient decision-making in large-scale transportation systems, enabling smarter, faster, and more adaptive mobility solutions. Combining learning, optimization, and control, this thesis demonstrates the potential of learning-guided optimization and principled control-theoretical analysis to address the increasing complexity of modern mobility systems.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Institute for Data, Systems, and Society

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Li, Sirui

Advisor dc:contributor.advisor

• Wu, Cathy

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/163568

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/163568