Abstract
In this paper, we present a suite of visualization techniques for sensor-based transportation system data at different scales to facilitate the exploration of interconnected traffic dynamics at intersections and highways. These techniques are designed for analyzing multivariate traffic data from radar-based highway sensors and camera-based intersection sensors recording turn movements and vehicle speed, in the Chattanooga Metropolitan Area, with the capability of (a) revealing multiscale mobility patterns using different levels of data aggregation (e.g., individual sensor for microscale, multiple sensors along a corridor for mesoscale, and a larger number of sensors across the region for macroscale visualization) at different intervals (e.g., 5-min intervals, time of day, full day, and day-of-the-week), and (b) exploring the spatial variation of multiple traffic-related variables (e.g., volumes, speeds, turn movements, and traffic light colors) provided by the sensors. We close with a case study to demonstrate the effectiveness of our multiscale and multivariate visualization techniques. At microscale, we focused on intersection data from a shopping district around Shallowford Road in East Chattanooga. For mesoscale visualization, we studied the Shallowford Road corridor and an adjacent stretch of I-75. At macroscale, we included highway data from the Chattanooga Metropolitan Area. All visualizations were integrated into a web-based situational awareness tool to promote user access and interaction. At a minimum, each visualization provides the option for selecting dates for real-time (depending on sensor availability) and historical data, and additional information on hovering, though most provide more detailed information, including different views of the selected data, or interactive highlights.
| Original language | English |
|---|---|
| Title of host publication | Transportation Research Record |
| Publisher | SAGE Publications Ltd |
| Pages | 23-37 |
| Number of pages | 15 |
| Volume | 2675 |
| Edition | 6 |
| DOIs | |
| State | Published - 2021 |
Funding
We thank the Chattanooga Department of Transportation for their guidance on choosing an arterial, and providing intersection sensor data, and the Tennessee Department of Transportation for providing radar data. Finally, we would like to thank the Department of Energy’s Vehicle Technologies Office and Oak Ridge National Laboratory for funding this work. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office; and Oak Ridge National Laboratory internal funding.