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Vertical curves provide the transition between two roadway grades, ensuring safety, comfort, and functionality. Calculating elevations at specific stations along the curve involves several systematic steps based on the curve's geometry and provided design parameters.The vertical curve is defined by its length, grades, Point of Vertical Intersection (P.V.I.) location, and P.V.I. elevation. The stations of the Point of Vertical Curvature (P.V.C.), where the curve begins, and the Point of Vertical...
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Vertical curves are parabolic transitions that connect different grades on highways and railroads, ensuring a smooth alignment between back and forward tangents. The back tangent represents the initial grade, while the forward tangent defines the subsequent grade. These curves can be symmetrical, with equal tangent lengths, or nonsymmetrical, with varying lengths. The key points defining a vertical curve include the Point of Vertical Intersection (P.V.I.), where the tangents meet; the Point of...
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Vertical curves are essential in roadway design because they provide smooth transitions between varying roadway grades. Designing vertical curves involves calculating intermediate elevations and identifying the curve's highest or lowest point, which is essential for optimal roadway performance.Intermediate elevations on a vertical curve are determined using the tangent offset method. This method considers the initial elevation at the start of the curve, the grades, and the curve's geometry. The...
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Sight Distance in a Vertical Curve01:29

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Sight distance on vertical curves is critical in roadway design. It ensures drivers can see far enough ahead to identify and respond to hazards effectively. This directly impacts safety, driver comfort, and the overall efficiency of the transportation network.Vertical curves are classified into crest and sag curves based on their geometry. For crest curves, sight distance is determined by the line of sight between a driver's eye and a small object on the road's surface. Design parameters for...
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A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data.

Georgios Pipelidis1, Omid Reza Moslehi Rad2, Dorota Iwaszczuk3

  • 1Software and Systems Engineering Research Group, Technical University of Munich, Boltzmannstr. 3, 85748 Garching bei München, Germany. pipelidi@in.tum.de.

Sensors (Basel, Switzerland)
|February 9, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a crowdsourced method for mapping building heights using smartphone barometers. It accurately determines the number of floors and estimates altitude, even in varied environmental conditions.

Keywords:
CityGMLdynamic mappingindoor mappingoutdoor–indoor transitionvertical mapping

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Area of Science:

  • Geomatics Engineering
  • Sensor Fusion
  • Crowdsourcing

Background:

  • Accurate vertical mapping of buildings is crucial for urban planning and emergency services.
  • Existing methods for building height estimation can be costly and time-consuming.
  • Leveraging ubiquitous smartphone sensors offers a scalable solution.

Purpose of the Study:

  • To develop and evaluate a crowdsourced approach for dynamic vertical building mapping.
  • To utilize smartphone barometric sensors for altitude difference estimation.
  • To identify outdoor-indoor transitions for accurate reference pressure extraction.

Main Methods:

  • Fusion of four different sensors to detect outdoor-indoor transitions (OITransition).
  • Utilizing barometric pressure readings from smartphones to estimate altitude.
  • Crowdsourcing building data through widespread smartphone adoption.

Main Results:

  • Consistent and accurate prediction of the correct number of floors in buildings.
  • Precise altitude approximation with an average error of 0.5 meters.
  • Robust performance across diverse environmental conditions (humidity, temperature, cloud cover) and times of day.

Conclusions:

  • The proposed crowdsourced dynamic vertical mapping approach is effective and reliable.
  • Smartphone sensor fusion offers a practical solution for large-scale building data acquisition.
  • This method provides a cost-effective and efficient alternative for vertical building mapping.