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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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The study of external flow is essential for creating structures and objects that interact efficiently and safely with moving fluids, such as air or water. When a body is immersed in a flowing fluid, it experiences two primary forces: drag, which opposes motion along the flow direction, and lift, which acts perpendicular to the flow. The shape, size, and orientation of the object influence these forces.Streamlined and Blunt Bodies in External FlowObjects in fluid flow are classified as...
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Pipe Flowrate Measurement: Problem Solving01:28

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A spray tank system is engineered to uniformly distribute a pest-control liquid across plants by using a pressurized mechanism. The tank, pressurized to 150 kPa, holds the pesticide at a height of 0.80 meters. Liquid flows from the tank through a 1.9 meter pipe with a diameter of 0.015 meters, angled at 0.698 radians, ultimately reaching a 0.007 meter nozzle that sprays the pesticide. Accurate calculation of the system's flow rate is crucial to ensure uniform application, and this is achieved...
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Uniform Depth Channel Flow01:27

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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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Related Experiment Video

Updated: Dec 8, 2025

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
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Origin-Destination Flow Estimation from Link Count Data Only.

Subhrasankha Dey1, Stephan Winter1, Martin Tomko1

  • 1Department of Infrastructure Engineering, University of Melbourne, 3010 Parkville, Victoria, Australia.

Sensors (Basel, Switzerland)
|September 16, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel origin-destination flow estimation model using only traffic counts, eliminating the need for historical data. The model accurately estimates traffic flows and individual vehicle travel times, enhancing transportation engineering.

Keywords:
OD flow predictionmicroscopic simulationtraffic count datatravel time estimationurban road network

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Area of Science:

  • Transportation Engineering
  • Traffic Flow Analysis
  • Statistical Modeling

Background:

  • Established origin-destination (OD) flow models rely on a priori traffic knowledge.
  • Existing methods often require historical OD trip data or assumed flow distributions.
  • These conventional approaches have conceptual weaknesses in accurately reflecting real-world traffic dynamics.

Purpose of the Study:

  • To develop a novel OD flow estimation model using solely observed vehicle counts from traffic sensors.
  • To eliminate the dependency on historical OD trip data and assumed flow distributions.
  • To enhance the accuracy and stochastic nature of traffic flow estimation and incorporate travel time estimation.

Main Methods:

  • Adapted statistical OD flow estimation methods from computer networks.
  • Applied transport-geographic constraints to integrate traffic flow within physical road networks.
  • Developed a purely stochastic model for OD flow and individual vehicle travel time estimation.
  • Implemented and validated the model on a real-world road network in Melbourne, Australia.

Main Results:

  • The model successfully estimated origin-destination flows using only link count data with high accuracy.
  • Validation against simulated and real-world data confirmed the model's effectiveness.
  • Estimated individual vehicle travel times closely approximated observed travel times.

Conclusions:

  • The novel stochastic model provides a robust alternative for origin-destination flow estimation in transportation engineering.
  • The model's ability to utilize only traffic counts simplifies data requirements and enhances applicability.
  • Accurate estimation of both traffic flows and travel times offers significant advancements for traffic management and planning.