Data-Driven Geospatial-Enabled Transportation Platform for Freeway Performance Analysis

The burgeoning field of big data nowadays has motivated the development of innovative architecture for better exploiting and exploring huge amount of multidisciplinary data. Inspired by the concept of eScience, the on-line transportation platform Digital Roadway Interactive Visualization and Evaluation Network (DRIVE Net) is developed in this study for the purpose of transportation data sharing, integration, visualization, and analysis. The major research goal of the DRIVE Net system can be summarized in threefold. First, it provides the repository service to facilitate data sharing and integration. Second, the system is capable of visualizing large sets of transportation data, helping users to perceive and understand the data. Third, the interactive and computational functionalities built into the DRIVE Net allow users to perform a variety of statistical modeling and analysis on multiple data sources, assisting with users to draw meaningful inferences and to make informed decisions. This research thus developed such an eScience platform addressing the aforementioned challenges for transportation applications. To particularly demonstrate the analytical capability of DRIVE Net, a new approach that automates real-time freeway performance measurement is developed and implemented onto the system. The proposed method provides quantitative evaluation of network-wide freeway performance to facilitate decision making in transportation operations and management.