Tit le: Towards Adaptive Decision Support: A Perspective from Intelligent and Annotated Visual Analytics for Exploring Big Urban Mobility Data

Challenges: As the critical component of many emerging dataand model-driven applications, visual analytics empowers the synergy of computational techniques with human reasoning to improve solutions to complex urban science and engineering problems. These techniques are frequently integrated with Cyber Geographic Information Systems (Cyber-GIS) to create intuitive and accessible visual analytical dashboards, providing new opportunities and solutions for enhancing the understanding of complicated urban dynamics. They also facilitate collaborative urban management by engaging non-scientific users (e.g., policymakers, the public, and business partners) into complex decision-making processes. Despite their value and usefulness, the design and development of visualization and visual analytics applications for urban big data and large-scale urban simulations often face methodological challenges caused by the Big Data Tsunami, where the increased velocity, volume, variety, and veracity of data creates cognitive overload and noise. These challenges prevent effective interpretation for generating meaningful insights that benefit decision analyses. Although more recent visualization technologies (OpenGL and extended reality technologies) can render a tremendous number of geometries and graphics in a canvas or virtual environment, extracting useful knowledge and domain-related inferences for nonscientific users can still be very challenging. This situation is typical when the data patterns exist in multiple spatial (e.g., district, city, county, state, and nation) and temporal scales (e.g., hourly, daily, monthly, and yearly), or in other data-facets (e.g., multi-value and multivariate dimensions). An example would be identifying other urban and environmental variables and statistics parameters that may affect transportation planning at the corridor level using visual analytics dashboards. This white paper uses an existing ORNL project to demonstrate the methodological challenges through real-world scenarios for exploring urban big data. The Chattanooga Digital Twin (CTwin) project creates a big data cyberinfrastructure that enables a wide variety of coordinated graphs, charts, and visual analytical dashboards that allow its users (e.g., urban planners, urban scientists, and city managers) to visually inspect and analyze the spatiotemporal variability of urban mobility attributes (e.g., traffic volume, speed, and lane performance) in response to other urban and environmental variables. Many of these urban mobility attributes are collected through the Internet of Things (IoT)-connected sensors and have generated second-resolution observations over three years. Even though the visual dashboard is implemented with modern user-interaction and datatransformation techniques that allow users to filter data, alter temporal and spatial aggregations, and select different mobility attributes for comparison, it remains time-consuming and labor-intensive for a user to go through years of sensor observations and comprehensive user-flows to perform an adequate search for useful insights. An example use case might include understanding what weather scenario, time of day, and within what regions do lane occupancy positively correlate with traffic speed. Many past studies arrange multiple plots and charts in a matrix to allow the overview of large-scale datasets, which is limited by the number of graphics and details that can be displayed through a matrix’s layout and screen resolution. Even so, this technique requires repetitive and labor-intensive exploratory analysis and interpretation when the dataset has such extensive temporal and spatial coverage.