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Related Concept Videos

Bus Impedance Matrix01:24

Bus Impedance Matrix

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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.
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,...
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Short-distance Transport of Resources02:12

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Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
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Probability Histograms01:17

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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What is a Frequency Distribution00:51

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A frequency is the number of times a value of the data occurs. The sum of all the frequency values represents the total number of students included in the sample. It is commonly used to group data of quantitative types. Frequency distributions can be displayed in a table, histogram, line graph, dot plot, or pie chart, just to name a few. A histogram is a graphical representation of tabulated frequencies, shown as adjacent rectangles, erected over discrete intervals (bins), with an area equal to...
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Construction of Frequency Distribution01:15

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A frequency distribution table can be constructed using the steps given below.
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is...
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Relative Frequency Distribution00:55

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A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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On bus ridership and frequency.

Simon J Berrebi1, Sanskruti Joshi1, Kari E Watkins1

  • 1Georgia Institute of Technology, School of Civil and Environmental Engineering, United States.

Transportation Research. Part A, Policy and Practice
|December 26, 2022
PubMed
Summary
This summary is machine-generated.

Bus route frequency significantly impacts ridership. While high-frequency routes are most productive, low-frequency routes show greater sensitivity to service changes, offering insights for transit agencies seeking to boost ridership.

Keywords:
BusElasticityFixed-effectsHeadwayPublic transitReliabilityService allocation

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Area of Science:

  • Urban Planning
  • Transportation Science
  • Public Transit Operations

Background:

  • US bus ridership reached a multi-decade low before the COVID-19 pandemic.
  • Understanding service allocation's impact on ridership is crucial for transit agencies.
  • Hyper-local, time-series modeling of ridership trends is an emerging area of research.

Purpose of the Study:

  • To model bus ridership elasticity to service frequency on a hyper-local level.
  • To analyze how variations in bus route frequency affect passenger counts over time and across different route segments.
  • To provide data-driven insights for transit agencies to optimize service allocation and improve ridership.

Main Methods:

  • Utilized a Poisson fixed-effects model to analyze weekday passenger data from four US cities (Portland, Miami, Minneapolis/St-Paul, Atlanta) between 2012 and 2018.
  • Examined ridership elasticity concerning frequency at two levels: between individual route-segments at a single point in time, and within each route-segment over time.
  • Assessed the relationship between prior frequency and elasticity to understand sensitivity to changes.

Main Results:

  • Ridership is elastic to frequency when comparing different route-segments at a given time; more frequent routes are more productive per vehicle-trip.
  • Ridership is inelastic when analyzing changes within a single route-segment over time; additional trips yield diminishing returns.
  • In three of four cities, elasticity decreased with prior frequency, indicating low-frequency routes are more responsive to frequency increases.

Conclusions:

  • Transit agencies can use these findings to predict the impact of reallocating bus service across their networks.
  • The study highlights the differential impact of frequency changes depending on the route's existing service level.
  • This methodological approach can be expanded for future, more detailed analyses of bus ridership dynamics as data quality improves.