We addressed the problem of optimally controlling connected automated vehicles (CAVs) crossing an urban intersection without any explicit traffic signaling. A decentralized optimal control framework is established whereby, under proper coordination among CAVs, each CAV can jointly minimize its energy consumption and travel time subject to hard safety constraints. The analytical solution of each such problem, when it exists, yields the optimal CAV acceleration/deceleration.

The framework is capable of accommodating for turns and ensure the absence of collision. In the meantime, a measurement of passenger comfort is considered while the vehicles make turns. In addition to the first-in-first-out ordering structure, the concept of dynamic resequencing is introduced which aims at further increasing the traffic throughput. This dissertation also studies the impact of CAVs and shows the benefit that can be achieved by incorporating CAVs to conventional traffic.

We develop an anomaly detection and decision support system based on data collected through the Street Bump smartphone application. The system is capable of effectively classifying roadway obstacles into predefined categories using machine learning algorithms, as well as identifying actionable ones in need of immediate attention based on a proposed “anomaly index.”  Results on an actual data set provided by the City of Boston illustrate the feasibility and effectiveness of our system in practice.

To validate the effectiveness of the proposed solution, she proposed a discrete-event and hybrid simulation framework based on SimEvents, which facilitates safety and performance evaluation of an intelligent transportation system. The traffic simulation model enables traffic study at microscopic level, including new control algorithms for CAVs under different traffic scenarios, the event-driven aspects of transportation systems, and the effects of communication delays.