Integrated management of travel corridors comprising of freeways and adjacent arterial streets can potentially improve the performance of the highway facilities. However, several research gaps exist in data collection and performance measurement, analysis tools and control strategies. In this project first we analyzed high resolution data consisting of time-stamped records of every event involving vehicles, together with the signal phase at real-world signalized intersections and developed procedures for estimating performance measures. Next, we assessed the performance of a new microscopic simulator for signalized arterials. The model predictions were in close agreement with the predictions from widely used models in practice. We also developed and applied control strategies for freeway-arterial coordinated control to avoid queue override and developed a methodology to provide estimates of the amount and impacts of freeway diverted traffic in case of no-recurrent (incident related) congestion.

Three-dimensional integrated circuit (3D IC) technologies have been receiving much attention recently due to the near-ending of Moore's law of minimization in 2D IC. However, the reliability of 3D IC, which is greatly influenced by voids and failure in interconnects during the fabrication processes, typically requires slow testing and relies on human's judgement. Thus, the growing demand for 3D IC has generated considerable attention on the importance of reliability analysis and failure prediction. This research conducts 3D X-ray tomographic images combining with AI deep learning based on a convolutional neural network (CNN) for non-destructive analysis of solder interconnects. By training the AI machine using a reliable database of collected images, the AI can quickly detect and predict the interconnect operational faults of solder joints with an accuracy of up to 89.9% based on non-destructive 3D X-ray tomographic images. The important features which determine the "Good" or "Failure" condition for a reflowed microbump, such as area loss percentage at the middle cross-section, are also revealed.