A prototype system for visualizing time-dependent volume data

2001 ◽  
Author(s):  
Lutz Kettner ◽  
Jack Snoeyink
Keyword(s):  
Time Dependent ◽  
Prototype System ◽  
Volume Data

scholarly journals The Safari interface for visualizing time-dependent volume data using iso-surfaces and contour spectra

2003 ◽  
Vol 25 (1-2) ◽  
pp. 97-116 ◽  
Author(s):  
Lutz Kettner ◽  
Jarek Rossignac ◽  
Jack Snoeyink
Keyword(s):  
Time Dependent ◽  
Volume Data

scholarly journals On the Clague–Mathews relation for jökulhlaups

2003 ◽  
Vol 49 (165) ◽  
pp. 161-172 ◽  
Author(s):  
Felix NG ◽  
Helgi Björnsson
Keyword(s):  
Field Data ◽  
Time Dependent ◽  
Water Volume ◽  
Volume Data ◽  
Physical Origin ◽  
Flood Hydrograph ◽  
Data Record ◽  
Flood Magnitude ◽  
Discharge Volume ◽  
Lake Systems

AbstractIn the empirical study of jökulhlaups, the peak discharge, Qmax, and water volume drained by the ice-dammed lake during the floods, Vt, appear to follow a power-law relation , where K are b are constants determined from field data. First identified by Clague and Mathews (1973), this relation is a useful reference for predicting flood magnitude, but its physical origin remains unclear. Here, we develop the theory that connects it to contemporary models for simulating the flood hydrograph. We explain how the function Qmax = f(Vt) arises from Nye’s (1976) theory of time-dependent water flow in a subglacial channel coupled to a lake, and we describe how discharge–volume data record the (monotonically increasing) form of this function so long as the lake is not emptied in the floods. The Grímsvötn jökulhlaups present an example where, because of partial draining of the lake, agreement between the model-derived f and data is excellent. It is documented that other lake systems drain completely, but we explain how the exponent b ≈ 2/3 observed for them collectively is due primarily to a scaling effect related to their size, modified by other factors such as the flood initiation process.

scholarly journals Using Emerging and Extraordinary Data Sources to Improve Traffic Safety

2021 ◽  
Author(s):  
Andrew P. Tarko ◽  
Qiming Guo ◽  
Raul Pineda-Mendez
Keyword(s):  
Traffic Safety ◽  
Traffic Control ◽  
Management Practices ◽  
Safety Management ◽  
Rapid Development ◽  
Management Program ◽  
Time Dependent ◽  
Data Sources ◽  
Dependent Data ◽  
Volume Data

The current safety management program in Indiana uses a method based on aggregate crash data for conditions averaged over several-year periods with consideration of only major roadway features. This approach does not analyze the risk of crashes potentially affected by time-dependent conditions such as traffic control, operations, weather and their interaction with road geometry. With the rapid development of data collection techniques, time-dependent data have emerged, some of which have become available for safety management. This project investigated the feasibility of using emerging and existing data sources to supplement the current safety management practices in Indiana and performed a comprehensive evaluation of the quality of the new data sources and their relevance to traffic safety analysis. In two case studies, time-dependent data were acquired and integrated to estimate their effects on the hourly probability of crash and its severity on two selected types of roads: (1) rural freeways and (2) signalized intersections. The results indicate a considerable connection between hourly traffic volume, average speeds, and weather conditions on the hourly probability of crash and its severity. Although some roadway geometric features were found to affect safety, the lack of turning volume data at intersections led to some counterintuitive results. Improvements have been identified to be implemented in the next phase of the project to eliminate these undesirable results.

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