相关概念视频
01:09Censoring Survival Data
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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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01:08Determination of Expected Frequency
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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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01:11Hazard Rate
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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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01:14Parametric Survival Analysis: Weibull and Exponential Methods
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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
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01:15Assumptions of Survival Analysis
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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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01:03Prediction Intervals
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
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为高速公路特定时间安全性能函数的估计计算完整数据.
Jingwan Fu1, Mohamed Abdel-Aty1, Xin Yan1
1Department of Civil, Environmental, and Construction Engineering, Department of Statistics and Data Science, University of Central Florida (UCF), Orlando, FL 32816-2450, United States.
Accident; analysis and prevention
|June 26, 2023
概括
本研究引入了一种代归算方法,以填补缺少的交通数据,以开发精确的特定时间的安全性能函数 (SPF). 该方法成功地重建了交通模式,即使没有完整的数据,也能够实现可靠的撞车预测模型.
科学领域:
- 运输工程 运输工程
- 交通安全分析 交通安全分析
- 数据科学数据科学数据科学
背景情况:
- 准确的碰撞频率预测需要特定时间的安全性能功能 (SPF).
- 在许多州,高分辨率的交通数据 (体积和速度) 往往缺失或没有归档,阻碍了SPF的开发.
- 现有的方法缺乏强大的解决方案来归因完整的流量数据集.
研究的目的:
- 提出和验证一种新的代归算方法,用于重建缺失的交通量和速度数据.
- 允许在缺乏完整交通数据的州开发特定时间的SPF.
- 在交通建模中评估归算数据的准确性和有效性.
主要方法:
- 计算了18个州的撞车率,并使用单向ANOVA对类似撞车率的州进行分组.
- 开发并测试了一种使用佛罗里达州 (FL) 和弗吉尼亚州 (VA) 交通数据的代归算方法.
- 使用平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE) 与实际收集的数据对比,验证了归算数据.
主要成果:
- 代归算方法成功捕获了与真实数据可比的交通模式.
- 归算Ln体积的MAE为2.47辆/段/3小时;归算Ln平均速度的MAE在FL为1.36英里/小时.
- 归算Ln体积的MAPE为11.07%;归算Ln平均速度的MAPE为FL的7.40%.
- 使用归算数据开发的特定时间的SPF在早晨峰值模型中实现了87.1%的预测准确度.
结论:
- 提出的代归算方法对于重建缺失的交通数据是有效的.
- 导入的数据可以可靠地用于开发精确的特定时间的安全性能功能.
- 这种方法解决了数据缺口,并促进了数据稀缺地区的动态崩预测建模.