ABSTRACT

Modern cities accommodate more people than ever before, leading to transportation networks that operate at or near capacity. In addition, the next generation of transportation systems will include connected vehicles, connected infrastructure, and increased automation, and these advances must coexist with legacy technology into the foreseeable future. Accommodating these rapidly developing advancements requires smarter and more efficient use of existing infrastructure with guarantees of performance, safety, and interoperability. The goal of this project is to develop fundamental theory and domain-driven techniques for controlling traffic flow in large-scale transportation networks. Recent advances in inexpensive sensors, wireless technology, and the Internet of Things (IoT) enable real-time connectivity of vehicles and infrastructure that offers abundant data and unprecedented opportunities for efficient and optimized transportation systems. The main technical goal is to develop techniques and algorithms that are correct-by-design, ensuring that these transportation systems satisfy required operating specifications. In pursuit of this goal, the project will first develop models of traffic flow from rich data streams and then will leverage these models to enable scalable control approaches. In addition, this project will integrate an ambitious education plan that includes a redesigned introductory course in control theory for undergraduates. The course will be restructured to focus on modern challenges in control, culminating in a Control Grand Challenge design competition in which students will design a controller for an autonomous, scale-model car and then compete with their design.

To achieve systems that satisfy the rich design specifications demanded of traffic networks, the project will especially focus on bringing powerful techniques from formal methods for verification and synthesis to large-scale physical networks. These formal methods were originally developed for specifying and verifying the correct behavior of software and hardware systems, and an important research objective now is to ensure these approaches are scalable, adaptable, and reliable when applied to physical control systems. The project will focus on the following objectives: i) Develop theory and models for the dynamic behavior of traffic networks that captures domain-specific phenomena such as congestion propagation, ii) Determine how traffic flow dynamics will change as vehicles are increasingly equipped with autonomous capabilities, iii) Identify and exploit intrinsic structure in traffic flow networks to enable scalable formal methods for verification and synthesis, and iv) Use data available through industry collaborations to develop probabilistically correct control of traffic flow networks. These objectives address a growing need for systematic guarantees of performance in traffic networks as the increasing complexity and interdependence of transportation systems renders ad hoc approaches insufficient. The research activities of this project will use real traffic data available through ongoing collaborations with industry. An expected outcome of this project is a suite of scalable algorithms that will be tested on a pilot traffic network available through this collaboration. In addition, the project will establish foundational theory applicable outside the traffic domain.

Please report errors in award information by writing to: awardsearch@nsf.gov.