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相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
152
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

414
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
414
Causality in Epidemiology01:21

Causality in Epidemiology

482
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
482
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
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Bus Impedance Matrix01:24

Bus Impedance Matrix

149
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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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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相关实验视频

Updated: Jul 21, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Published on: January 20, 2023

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数据集用于在流行病爆发期间制定最佳的基于路线的公共汽车调度策略.

Yan Huang1, Zongzhi Li2, Shengrui Zhang1

  • 1College of Transportation Engineering, Chang'an University, Xi'an, Shaanxi 710064, China.

Data in brief
|July 28, 2023
PubMed
概括
此摘要是机器生成的。

这项研究提供了数据,用于优化公共汽车调度策略在西安的CBD的流行病期间. 该数据集涵盖了网络细节,乘客流量和旅行时间,以加强城市交通规划.

关键词:
登机和下车的计数时间.公共汽车运输服务 公共汽车运输服务在发送前进的路上.这是一场流行性流行病.网络 网络 网络 网络 网络 网络

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相关实验视频

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科学领域:

  • 城市规划和交通科学.
  • 对公共交通的流行病学影响分析.
  • 数据科学和地理空间分析.

背景情况:

  • 公共交通系统在流行病期间面临挑战,需要优化运营.
  • 有效的城市交通规划需要对网络基础设施和乘客行为的全面数据.
  • 西安小商务区 (CBD) 作为城市大众交通分析的案例研究.

研究的目的:

  • 为在流行病条件下确定最佳公共汽车调度策略提供数据集.
  • 为分析综合城市客运服务提供基础.
  • 支持研究公共交通系统的弹性.

主要方法:

  • 从政府记录,Amap平台和现场调查中收集数据.
  • 对十字路口和巴士站的地理空间数据的分析.
  • 包括信号定时,操作属性和乘客O-D数据.

主要成果:

  • 该数据集详细介绍了33个交叉路口,112个巴士站和12条巴士路线.
  • 包括乘客计数和高峰期和低峰期的起点-目的地数据.
  • 捕获交叉路口设计,信号定时和总线运行参数.

结论:

  • 该数据集对于在正常和流行情景下研究综合城市交通运营至关重要.
  • 能够研究多式联运客运服务,包括运输,共享乘车和主动运输.
  • 为增强公共汽车调度策略和城市移动性弹性提供资源.