Elmore K.L.,University of Oklahoma |
Flamig Z.L.,University of Oklahoma |
Lakshmanan V.,University of Oklahoma |
Kaney B.T.,University of Oklahoma |
And 3 more authors.
Bulletin of the American Meteorological Society | Year: 2014
The mobile Precipitation Identification Near the Ground project (MPING) is an app for smartphones that allows citizen scientists to provide observations about winter precipitation type at the surface at least equivalent in quality to human-augmented Automated Surface Observing System (ASOS) observations Among the key features of mPING are immediate feedback to users that their submission has been accepted and the ability to display and even download all submissions using a web-based display. Precipitation type choice is made via a drop-down list or menu. Users simply select the observed precipitation type, at which point the app returns to the submit page. Two taps of the screen are all that is needed to submit an observation once the app is opened. An extra tap is needed for hail because the user must select thehail size using a slider bar. Both the mobile apps and the web page submit information via HTTP to a common database that validates user input and provides persistent storage of the public reports. The app itself is not static, enhancements have already been made and additional mobile platforms may be considered in the future.
Liu K.,Indus Corporation |
Fei X.,Operations Engineering and Decision Support Division
Transportation Research Part C: Emerging Technologies | Year: 2010
It is essential for local traffic jurisdictions to systematically spot freeway bottlenecks and proactively deploy appropriate congestion mitigation strategies. However, diagnostic results may be influenced by unreliable measurements, analysts' subjective knowledge and day-to-day traffic pattern variations. In order to suitably address these uncertainties and imprecise data, this study proposes a fuzzy-logic-based approach for bottleneck severity diagnosis in urban sensor networks. A dynamic bottleneck identification model is first proposed to identify bottleneck locations, and a fuzzy inference approach is then proposed to systematically diagnose the severities of the identified recurring and non-recurring bottlenecks by incorporating expert knowledge of local traffic conditions. Sample data over a 1-month period on an urban freeway in Northern Virginia was used as a case study for the analysis. The results reveal that the proposed approach can reasonably determine bottleneck severities and critical links, accounting for both spatial and temporal factors in a sensor network. © 2009.