Multiscale and Multivariate Transportation System Visualization for Shopping District Traffic and Regional Traffic

Journal Article · Sun Dec 20 00:00:00 EST 2020 · Transportation Research Record: Journal of the Transportation Research Board

DOI:https://doi.org/10.1177/0361198120970526· OSTI ID:1782056

Berres, Anne ^[1]; Laclair, Tim ^[1]; Wang, Ross ^[1]; Xu, Haowen ^[1]; Ravulaparthy, Srinath ^[2]; Todd, Austin ^[3]; Tennille, Sarah ^[1]; Sanyal, Jibonananda ^[1]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
3. National Renewable Energy Lab. (NREL), Golden, CO (United States)

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. Additionally, 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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office
• Grant/Contract Number: AC05-00OR22725; AC36-08GO28308
• OSTI ID: 1782056
• Alternate ID(s): OSTI ID: 1818462
• Report Number(s): NREL/JA-2C00-80830
• Journal Information: Transportation Research Record: Journal of the Transportation Research Board, Journal Name: Transportation Research Record: Journal of the Transportation Research Board Journal Issue: 6 Vol. 2675; ISSN 0361-1981
• Publisher: SAGE
• Country of Publication: United States
• Language: English

Related Subjects

33 ADVANCED PROPULSION SYSTEMS
99 GENERAL AND MISCELLANEOUS
corridor
intersection
metropolitan area
mobility pattern
multiple traffics
multiscale visualization
multivariate visualization
shopping district
situational awareness
spatial variations
traffic flow
transportation system
visualization technique