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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.
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Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
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No-boarding buses: Synchronisation for efficiency.

Vee-Liem Saw1,2, Lock Yue Chew1,2,3

  • 1Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore.

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|March 24, 2020
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Summary
This summary is machine-generated.

Implementing a no-boarding policy for buses significantly reduces passenger average waiting times. This strategy synchronizes bus schedules, improving public transit efficiency and passenger experience.

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Area of Science:

  • Operations Research
  • Transportation Science
  • Queueing Theory

Background:

  • Public transportation systems face challenges with bus synchronization and passenger waiting times.
  • Current bus systems often lack mechanisms to actively manage passenger flow and schedule adherence.
  • Bus bunching and irregular headways negatively impact passenger experience and system efficiency.

Purpose of the Study:

  • To investigate the effectiveness of a no-boarding policy as an entrainment mechanism for bus synchronization.
  • To analytically and computationally assess the impact of a no-boarding policy on average passenger waiting times.
  • To develop and test an adaptive algorithm for dynamic no-boarding implementation in real-world transit scenarios.

Main Methods:

  • Developed an analytical model for a bus system with a no-boarding policy, assuming uniform bus speeds.
  • Calculated average passenger waiting times under the no-boarding policy and compared it to a baseline scenario.
  • Conducted simulations to validate analytical results and extend the analysis to heterogeneous bus speeds (human-driven buses).
  • Implemented and tested a general adaptive algorithm for dynamic no-boarding control.

Main Results:

  • The no-boarding policy was shown to dramatically reduce average passenger waiting times compared to systems without the policy.
  • Simulations confirmed the theoretical findings and demonstrated the policy's effectiveness even with varying bus speeds.
  • The adaptive algorithm showed promise for dynamic, real-time implementation in transit operations.

Conclusions:

  • A no-boarding policy is a highly effective strategy for improving bus synchronization and reducing passenger waiting times.
  • The policy is particularly beneficial for programmable self-driving buses but also shows advantages for human-driven systems.
  • Adaptive control algorithms can enable dynamic implementation of no-boarding, optimizing transit operations in real-time.