11/29/2021
By Sokny Long

The Francis College of Engineering, Department of Mechanical, invites you to attend a doctoral proposal defense by Zhaohui (Brandon) Yang on “Macroscopic Observability of Emergent Behaviors in Multi-scale Models of Traffic Flow."

Ph.D. Candidate Name: Zhaohui (Brandon) Yang
Defense Date: Tuesday, Dec. 7, 2021
Time: 1 to 3 p.m. EST
Location: This will be a virtual defense via Zoom. Those interested in attending should contact zhaohui_yang@student.uml.edu and kshitij_jerath@uml.edu at least 24 hours prior to the defense to request access to the meeting.

Committee Chair (Advisor): Kshitij Jerath, Ph.D., Department of Mechanical Engineering, University of Massachusetts Lowell

Committee Members:

  • Juan Pablo Trelles, Ph.D., Department of Mechanical Engineering, University of Massachusetts Lowell
  • Danjue Chen, Ph.D., Department of Civil Engineering, University of Massachusetts Lowell
  • Carlos Gershenson, Ph.D., Department of Computer Science, National Autonomous University of Mexico

Brief Abstract:
Emergent behaviors in complex systems arise due to the numerous local  interactions that occur between large numbers of individual agents. The observation and quantification of such emergent behaviors remains a challenging task as information from all agents in the system is not always readily available. These challenges are also evident in the context of large-scale traffic systems, where the task of using measurements from a few individual vehicles to effectively observe and predict emergent congestion patterns (such as backward moving waves, and self-organizing or `phantom' traffic jams) remains currently unresolved. Attempts to understand the macroscopic observability of emergent traffic flows could benefit from a single framework that can seamlessly model traffic across different spatial scales, enabling the use of microscopic measurements made by vehicles to evaluate observability at another modeling scale. The presented work demonstrates how trends in observability of emergent behaviors can be determined as a function of the order of the system model. Specifically, it is shown that a trade-off exists between accuracy of the model and its observability, which may enable us to choose a `favorable' modeling scale that balances the advantages offered by increased observability versus increased accuracy. However, the control-theoretic model order reduction technique was found to be difficult to scale to larger traffic systems, so an alternative statistical mechanics-based approach using renormalization group (RG) theory is also presented and examined. The study indicates that this approach enables us to generate models of traffic flow dynamics at several coarser spatial scales in a manner such that the parameters of the models at different scales can be analytically related. The Renormalization Group-theoretic approach was further built upon to create a mutual information-based macroscopic observability metric that quantified the ability to relate the measurements made by an individual agent at the microscopic scale to the likelihood of occurrence of specific macroscopic states (i.e. emergent behaviors) of the traffic flow system. This approach could enable researchers and practitioners in transportation engineering and complex systems science to better understand macroscopic scale dynamics using microscopic scale information, potentially spurring advances in prediction and control of emergent behaviors.


All interested students and faculty members are invited to attend the online defense via remote access.