University of Washington

Digital Roadway Interactive Visualization and Evaluation Network (DRIVE Net) is an online transportation platform aimed at data sharing, integration, visualization, and analysis. The system provides users with the capability to store, access, and manipulate data from anywhere as long as they have Internet connections. The goal of this study is to remove the barriers existing in the current datasets archived by Washington State Department of Transportation (WSDOT), and to achieve the integration and visualization of information needed for decision support. The research findings not only include data fusion techniques and database design details, but also are delivered in a functioning DRIVE Net archive service capable of collecting detector data from all WSDOT Regions and incorporating third party data from both the WITS, INRIX and weather databases. Roadway geometric data are properly stored in an open-sourced geospatial database, and seamlessly connect with the traditional transportation database (i.e. loop detector data, weather data, WITS data and INRIX data). The existing data archiving systems, CD Analyst and Flow, are successfully recoded and redesigned for better stability and reliability. A series of loop data quality control algorithms is automated in the backend. These processed data are used to generate WSDOT's Gray Notebook statistics, and available for WSDOT personnel to visualize and produce the annual and quarterly congestion report through the DRIVE Net system. Additionally, a HCM 2010 Level of Service (LOS) module and mobile sensing data analysis module are incorporated into DRIVE Net for freeway performance measures and pedestrian trajectory reconstruction respectively. With the fast development of networking, data storage, and data collection by new sensors, big data, representing a new era in data exploration and utilization, is now rapidly expanding into the transportation arena. Big data give more opportunities to better monitor traffic networks and to increase the accuracy of traffic predictions. Inspired by the concept of e-Science, while many new data sources, such as roadway geometric data, traffic sensor data and incident data, are being captured, the coverage breadth and analysis depth of DRIVE Net were increased. The latest version of DRIVE Net has been expanded from the previous version to integrate third party data sets for analysis and offer the functions needed for real-time performance monitoring, quick operational decision support, and system-wide analysis. Based on DIGITAL ROADWAY INTERACTIVE VISUALIZATION AND EVALUATION NETWORK (DRIVE NET) this platform, the data-driven tools can not only help WSDOT's decision making and operational practices, but also can act as a platform to assist the data retrieving, modeling and visualization in transportation research. Overall, DRIVE Net is a powerful data management, quality control, analysis, and visualization platform that has the ability to layer a diverse spectrum of spatial and temporal data sets on an online digital roadway map. The latest version of the platform offers the ability to handle more complex computational tasks, perform large-scale spatial processing, and support data sharing services. This system will keep supporting WSDOT's mission and STAR Lab's research in the future. Contact: Yinhai Wang, yinhai@uw.edu; Weibin Zhang, wbin.zhang@outlook.co • Objective: Develop process for transit operator and bus passenger feedback. Accomplishments: Three operator surveys were administered. • Objective: Provide data and expertise on barriers to this technology (i.e. operational acceptance and rejection issues). Accomplishments: Approximately 16,600 hours of video data was collected and processed, representing about 10,000 events captured by the Shield+ system. Contact: Yinhai Wang, yinhai@uw.edu; Ruimin Ke, ker27@ uw.edu 11 2014-2015 Annual Report

• Cover