Destination Area: Intelligent Infrastructure for Human-Centered Communities (IIHCC)
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IIHCC focuses its attention on the ways that people interact with one another and with their environment. Interest areas in this DA include smart, healthy, and sustainable cities and communities; transportation systems; human safety, health, and wellness; integrated energy systems; network science and engineering; public policy; and cyber-physical systems. The initial focus for IIHCC will be on four themes:
Ubiquitous Mobility: The location-agnostic promise of new communication and information technologies
Automated Vehicle Systems: vehicles that can transit safely and efficiently through our communities independent of a human operator
Smart Design and Construction: an intelligent, integrated, adaptable, responsive, and sustainable human-centric built environment
Energy: the underlying innovations that will be required in the production, distribution, and consumption of energy to realize such a system
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Browsing Destination Area: Intelligent Infrastructure for Human-Centered Communities (IIHCC) by Department "Civil and Environmental Engineering"
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- Dynamic Travel Time Prediction using Pattern RecognitionChen, Hao; Rakha, Hesham A.; McGhee, Catherine C. (TU Delft, 2013)Travel-time information is an essential part of Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of travel times. From the perspective of travelers such information may assist in making better route choice and departure time decisions. For transportation agencies these data provide criteria with which to better manage and control traffic to reduce congestion. This study proposes a dynamic travel time prediction algorithm that matches current traffic patterns to historical data. Unlike previous approaches that use travel time as the control variable, the approach uses the temporal-spatial traffic state evolution to match traffic states and predict travel times. The approach first identifies candidate historical time intervals by matching real-time traffic state data against historical data for use in prediction purposes. Subsequently, the selected candidates are used to predict the temporal-spatial evolution of traffic. Lastly, dynamic travel times are constructed using the identified candidate historical data. The proposed algorithm is tested on a 37-mile freeway segment from Newport News to Virginia Beach along the I-64 and I-264 freeways using historical INRIX data. The prediction results indicate that the proposed method produces predictions that are more accurate than the state-of-the-art K-Nearest Neighbor methods reducing the prediction error by 15 percent to less than 3 minutes on a 50-minute trip.
- Evaluation of Resiliency of Transportation Networks After DisastersFreckleton, Derek; Heaslip, Kevin Patrick; Louisell, William; Collura, John (The National Academies of Sciences, Engineering, and Medicine, 2012)The resiliency of infrastructure, particularly as related to transportation networks, is essential to any society. This resiliency is especially vital in the aftermath of disasters. Recent events around the globe, including Hurricane Katrina and significant seismic events in Haiti, Chile, and Japan, have increased the awareness and the importance of resiliency. Transportation systems are key to response and recovery. These systems must withstand stress, maintain baseline service levels, and be stout enough in physical design and operational concept to provide restoration to the system. Analysis of a transportation network’s resiliency before a disruptive event will help decision makers identify specific weaknesses within the network so that investments and improvement projects are prioritized appropriately. Previous research in quantification of network resiliency was expanded into a proposed methodology, through which understanding and applying concepts of network resiliency could preclude many devastating effects of destabilizing events and preserve the quality of life and economic stability.
- Intercomparison of Small Unmanned Aircraft System (sUAS) Measurements for Atmospheric Science during the LAPSE-RATE CampaignBarbieri, Lindsay; Kral, Stephan T.; Bailey, Sean C. C.; Frazier, Amy E.; Jacob, Jamey D.; Reuder, Joachim; Brus, David; Chilson, Phillip B.; Crick, Christopher; Detweiler, Carrick; Doddi, Abhiram; Elston, Jack; Foroutan, Hosein; González-Rocha, Javier; Greene, Brian R.; Guzman, Marcelo I.; Houston, Adam L.; Islam, Ashraful; Kemppinen, Osku; Lawrence, Dale; Pillar-Little, Elizabeth A.; Ross, Shane D.; Sama, Michael P.; Schmale, David G. III; Schuyler, Travis J.; Shankar, Ajay; Smith, Suzanne W.; Waugh, Sean; Dixon, Cory; Borenstein, Steve; de Boer, Gijs (MDPI, 2019-05-10)Small unmanned aircraft systems (sUAS) are rapidly transforming atmospheric research. With the advancement of the development and application of these systems, improving knowledge of best practices for accurate measurement is critical for achieving scientific goals. We present results from an intercomparison of atmospheric measurement data from the Lower Atmospheric Process Studies at Elevation—a Remotely piloted Aircraft Team Experiment (LAPSE-RATE) field campaign. We evaluate a total of 38 individual sUAS with 23 unique sensor and platform configurations using a meteorological tower for reference measurements. We assess precision, bias, and time response of sUAS measurements of temperature, humidity, pressure, wind speed, and wind direction. Most sUAS measurements show broad agreement with the reference, particularly temperature and wind speed, with mean value differences of 1.6 ± 2.6 ∘ C and 0.22 ± 0.59 m/s for all sUAS, respectively. sUAS platform and sensor configurations were found to contribute significantly to measurement accuracy. Sensor configurations, which included proper aspiration and radiation shielding of sensors, were found to provide the most accurate thermodynamic measurements (temperature and relative humidity), whereas sonic anemometers on multirotor platforms provided the most accurate wind measurements (horizontal speed and direction). We contribute both a characterization and assessment of sUAS for measuring atmospheric parameters, and identify important challenges and opportunities for improving scientific measurements with sUAS.
- Remote collection of microorganisms at two depths in a freshwater lake using an unmanned surface vehicle (USV)Powers, Craig W.; Hanlon, Regina; Schmale, David G. III (PeerJ, 2018-01-26)Microorganisms are ubiquitous in freshwater aquatic environments, but little is known about their abundance, diversity, and transport. We designed and deployed a remote-operated water-sampling system onboard an unmanned surface vehicle (USV, a remote-controlled boat) to collect and characterize microbes in a freshwater lake in Virginia, USA. The USV collected water samples simultaneously at 5 and 50 cm below the surface of the water at three separate locations over three days in October, 2016. These samples were plated on a non-selective medium (TSA) and on a medium selective for the genus Pseudomonas (KBC) to estimate concentrations of culturable bacteria in the lake. Mean concentrations ranged from 134 to 407 CFU/mL for microbes cultured on TSA, and from 2 to 8 CFU/mL for microbes cultured on KBC. There was a significant difference in the concentration of microbes cultured on KBC across three sampling locations in the lake (P = 0.027), suggesting an uneven distribution of Pseudomonas across the locations sampled. There was also a significant difference in concentrations of microbes cultured on TSA across the three sampling days (P = 0.038), demonstrating daily fluctuations in concentrations of culturable bacteria. There was no significant difference in concentrations of microbes cultured on TSA (P = 0.707) and KBC (P = 0.641) across the two depths sampled, suggesting microorganisms were well-mixed between 5 and 50 cm below the surface of the water. About 1 percent (7/720) of the colonies recovered across all four sampling missions were ice nucleation active (ice+) at temperatures warmer than — 10 °C. Our work extends traditional manned observations of aquatic environments to unmanned systems, and highlights the potential for USVs to understand the distribution and diversity of microbes within and above freshwater aquatic environments.