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Related Concept Videos

Bus Impedance Matrix01:24

Bus Impedance Matrix

194
Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
194
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

378
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
378
Friction: Problem Solving01:21

Friction: Problem Solving

285
Friction is an essential force that influences the motion of objects in daily life. Depending on the situation, it can be either beneficial or problematic. Consider a bus with a mass of three megagrams and its center of mass at a specific point, moving along a banked road at a constant speed. The coefficient of static friction between the tires and the road is 0.5. Find the maximum angle of the banked road at which the bus would not slip or tip.
Initially, a visual representation of the...
285
Multimachine Stability01:25

Multimachine Stability

247
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
247
Bewley Lattice Diagram01:12

Bewley Lattice Diagram

915
The Bewley lattice diagram, developed by L. V. Bewley, effectively organizes the reflections occurring during transmission-line transients. It visually represents how voltage waves propagate and reflect within a transmission line, making it easier to understand the complex interactions that occur.
915
Root-Locus Method01:19

Root-Locus Method

231
A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block...
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Estimating Congestion in a Fixed-Route Bus by Using BLE Signals.

Yuji Kanamitsu1,2, Eigo Taya1,2, Koki Tachibana1

  • 1Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara 630-0192, Japan.

Sensors (Basel, Switzerland)
|February 15, 2022
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Summary

This study introduces a novel system for automatically monitoring bus congestion using Bluetooth low-energy (BLE) signals. The developed system accurately estimates passenger numbers while preserving privacy, offering a cost-effective solution for public transport management.

Keywords:
BLEcrowd densitymachine learningpeople countingroute bus

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Area of Science:

  • Transportation Engineering
  • Computer Science
  • Public Health

Background:

  • Public transportation systems require real-time congestion data for efficient operation, especially during health crises like COVID-19.
  • Manual monitoring of bus occupancy is impractical and resource-intensive.
  • Existing systems may raise privacy concerns or incur high installation costs.

Purpose of the Study:

  • To develop an automated system for estimating bus congestion levels with acceptable accuracy.
  • To design a privacy-preserving and cost-effective solution for monitoring passenger density on buses.
  • To leverage Bluetooth low-energy (BLE) signals for non-intrusive sensing.

Main Methods:

  • Proposed a system utilizing BLE signals emitted from passengers' mobile devices as sensing data.
  • Implemented a sensing mechanism to acquire BLE signals within the bus.
  • Developed a mechanism to estimate congestion levels based on the acquired BLE signal data.

Main Results:

  • The system demonstrated the ability to estimate the number of passengers on a bus route.
  • Achieved a mean absolute error of 2.49 passengers, corresponding to an error rate of 38.8%.
  • Data collection and validation were conducted on an actual bus route in collaboration with Nara Kotsu Co., Ltd.

Conclusions:

  • The proposed BLE-based system offers a viable method for automatic bus congestion estimation.
  • The system effectively balances the need for occupancy data with passenger privacy and cost considerations.
  • Further refinement could improve accuracy for enhanced public transport management.