Current Research Areas

Data-Driven Intelligent Transportation Networks

The advent of ubiquitous traffic sensing provides unprecedented real-time, high-resolution data that elucidate historical trends and current traffic conditions, yet these data are currently under-utilized. Moreover, the extensive connectivity of traffic infrastructure allows access to significant computational resources and allows network-level control. This research investigates new traffic control and optimization approaches that leverage high-resolution measurements of flow in traffic networks.

For example, in collaboration with Sensys Networks Inc. in Berkeley, CA, we are developing a traffic predictive control strategy that learns statistical trends from collected historical data. Then, real-time measurements are compared against historical trends to predict traffic flow minutes or hours into the future. Based on this prediction, preemptive control strategies accommodate deviations in traffic conditions before they occur. As another example, we consider the problem of coordinating traffic signals in a network of signalized intersections to reduce queues throughout the network. We leverage results from convex, semidefinite relaxations of quadratic programs to propose algorithms that scale to large networks.

Scalable Abstraction and Formal Methods For Control

Networked systems often consist of physical components coupled through the tight integration of digital computation and communication. These networked, cyber-physical systems pose unique challenges for control and verification. For example, such systems exhibit complex global behavior induced by the interaction of comparatively simpler constituent components. Furthermore, the performance requirements are increasingly demanding while the costs of failure rise.

To overcome these issues, we are developing scalable algorithms for verifying and synthesizing networked systems by exploiting structure inherent in the physical components and their interconnection. For example, we aim to bring the tools of formal methods to control theory so that a control system's closed-loop behavior is guaranteed to satisfy diverse objectives. Algorithmic formal methods in computer science were originally developed for specifying and verifying the correct behavior of software and hardware systems, and an important research task now is to ensure these approaches are scalable, adaptable, and reliable when applied to networked control systems.

Developing formal methods for control systems requires new techniques for abstracting dynamical, continuous-state systems to finite models amenable to formal verification and synthesis algorithms. A primary focus of this work is to develop the necessary theory and practical algorithms to ensure that these techniques scale well with system size.

Dynamical Behavior, Control, and Optimization of Complex Networks

We are investigating fundamental results and domain-driven theory for studying the behavior of complex networks. For example, physical flow networks, which model the flow of physical material among interconnected components, capture the essential features of large-scale cyber-physical systems including transportation networks, air traffic networks and civil infrastructures such as irrigation and natural gas networks.

In these examples and others, there exists a number of unifying abstract qualities to the underlying network dynamics. Such networks consist of interconnected components whereby material flows from component to component, and thus the contents of the network change over time, that is, such networks are time-varying. We study the underlying structure of appropriate flow models to develop generalizable theory and apply our results to domain specific problems. For example, we show that a large class of physical flow models are mixed monotone which generalizes the class of monotone dynamical systems. Mixed monotonicity enables new approaches for analysis and control of these networks.

Current Sponsored Projects

Data-Driven Sustainable EV Infrastructure for Los Angeles

Sponsor: UCLA Sustainable LA Grand Challenge Research Grants Program
Duration: Jan. 2017—Dec. 2017

Project Description: Widespread adoption of EVs requires the development of a reliable and efficient charging infrastructure that is cognizant of the interplay between peak system loads in the electricity grid and EV charging behavior. This collaborative project will establish data-driven models of EV charging behavior and will use these models to develop theory and algorithms for reducing costs and optimizing the efficiency of EV charging. The project will also seek to provide the first large-scale evidence on EV charging behavior using data from nearly 35,000 charging stations via PlugShare, the world's largest and most widely used EV charge station locator app.

Control and Management of Urban Traffic Networks with Mixed Autonomy

Sponsor: California Department of Transportation (Caltrans)
Duration: Feb. 2017—Jan. 2018

Project Description: Autonomous vehicles promise to transform transportation and mobility. While the most dramatic changes likely remain decades away, semiautonomous capabilities such as adaptive cruise control are already available. These technologies will coexist and interact with traditional, manually driven vehicles into the foreseeable future. As a result, transportation infrastructure is entering a stage of mixed use whereby vehicles are capable of varying levels of autonomy. Understanding and harnessing the potential benefits of this mixed infrastructure is a critical step to fully realizing the mobility benefits of autonomy. This project will develop models and algorithms for controlling mixed traffic flow where some fraction of vehicles are equipped with varying levels of autonomy and the remaining are manually driven. In particular, we will develop models of link and network capacity for mixed traffic. These models will be developed from first principles based on the fundamental behavior of autonomous technologies such as adaptive cruise control and validated with simulation data. We will then utilize these models to develop algorithms for management and control of mixed traffic. The overarching goal of the project is to develop theory and techniques that fully leverage the mixed traffic flow to improve mobility at the network scale.

Past Sponsored Projects

Traffic Predictive Control

Sponsor: California Department of Transportation (Caltrans)
Duration: May 2016—Apr. 2017

Project Description: The advent of ubiquitous traffic sensing provides unprecedented real-time, high-resolution data of traffic conditions that elucidate historical trends and current traffic conditions, yet traditional signal control approaches are designed to operate with limited or no real-time and/or historical data. Adaptive control schemes adjust to accommodate current traffic demands yet have been observed to react slowly to changing conditions. In contrast, nonadaptive signal timing schemes are designed based on limited and often outdated historical measurements. This project seeks to fully leverage historical and real-time traffic data and proposes traffic predictive control for improved efficiency on arterial corridors. The proposed approach learns statistical trends from collected historical data using principle component-based decomposition techniques. Then, real-time measurements are compared against historical trends to predict traffic flow minutes or hours into the future. Based on this prediction, preemptive control strategies accommodate deviations in traffic conditions before they occur. Additionally, these historical trends are used to identify when anomalous traffic conditions have occurred or are likely to occur soon. This anomaly detection serves as a component of decision support systems to notify when traffic conditions are likely to require additional resources.