Search

The stop-and-go traffic pattern at urban intersections leads to excessive energy use due to frequent vehicle braking, idling, and acceleration. This pattern, amplified by the growing use of automobiles, adversely affects city sustainability, causing delays, pollution, and increased carbon emissions. Addressing this, we introduce a mobile edge computing framework utilizing Internet of Things (IoT) and edge computing. This framework incorporates real-time vehicle-to-infrastructure communication and intelligent speed control algorithms into a mobile app, targeting speed optimization at signalized intersections to alleviate the negative impacts of stop-and-go traffic at urban intersections. The framework comprises three components: a cyberinfrastructure-based messaging system for real-time traffic and signal data from IoT-connected signal controllers and traffic sensors; a speed optimization algorithm generating speed advisories using phone-based sensor data (like GPS and driving directions), signal phase and timing information, and roadside vehicle detector data; and an ad-hoc mobile computing environment turning smartphones into edge devices for hosting the algorithm. This enables intelligent speed advisory along signalized corridors. The paper presents the detailed design and implementation of the proposed framework, highlighting its practicality, utility, and energy-saving potential. Our studies, including traffic simulations, real-vehicle lab experiments, evaluation surveys, and field tests, demonstrate its robustness and effectiveness. Specifically, simulations indicate that using the mobile app universally could lead to a 24% energy reduction in urban transportation systems.

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.

AbstractGlobal research activities have been driven by the quest to develop and characterize novel materials for technological advancements. The total scattering pair distribution function (TSPDF) is a powerful and versatile characterization technique for examining the structural details of diverse complex materials including liquid, amorphous, disordered crystalline, and nanostructured materials. Thus, it is critical to keep track of research progress, identify research gaps, and future research directions of the application of the TSPDF technique in materials development and discovery. In this work, a bibliometric analysis of literature regarding the TSPDF technique between 2000 and 2021 was conducted using datasets retrieved from the Web of Science database. The research trends based on publication outputs, research subject distribution, co-authorships among institutions, countries/regions, co-citation of referenced sources, and keyword co-occurrence are evaluated and discussed herein. The impact of the TSPDF technique is projected to increase due to its importance in probing emerging functional materials, and the advances in specialized facilities and instrumentation among the scientific communities engaged with it. Finally, current and emerging research hotspots related to TSPDF technique such as catalysis, computer modeling and simulation, pharmaceutics, machine learning, hydrogen storage, battery materials, and layered structured materials are also identified and discussed.