STATISTICS COLLOQUIUM
Fei Miao
Department of Computer Science & Engineering
University of Connecticut
Data-Driven Dynamic Robust Resource Allocation for Efficient Transportation
Abstract
Ubiquitous sensing in smart cities enables large-scale multi-source data collected in real-time, poses several challenges and requires a paradigm-shift to capture the complexity and dynamics of systems. Data-driven cyber-physical systems (CPSs) integrating machine learning, optimization, and control are highly desirable for this paradigm-shift, since existing model-based techniques of CPSs become inadequate. For instance, how to identify, analyze the dynamical interplay between urban-scale phenomena (such as mobility demand and supply) from data, and take actions to improve system-level service efficiency is still a challenging problem in transportation systems. In this talk, we present a data-driven dynamic robust resource allocation framework to match supply towards spatial-temporally uncertain demand, while seeking to reduce total resource allocation cost. First, we present a receding horizon control framework that incorporates large-scale historical and real-time sensing data in demand prediction and dispatch decisions under practical constraints. However, demand prediction error is not negligible and affects the system’s performance. Therefore, with spatial-temporal demand uncertainty models constructed from data, we then develop two computationally tractable robust resource allocation methods to provide probabilistic guarantees for the system’s worst-case and expected performances. As a case study, we evaluated the proposed framework using real taxi operational data. Lastly, I will provide an overview of my research that uses the knowledge of the system dynamics to guarantee security and resiliency properties of CPSs. I will introduce my research of coding schemes for stealthy data injection attacks detection, and stochastic game schemes for resilient control strategies.
DATE: Wednesday, February 14, 2018
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 108
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu