ABSTRACT

Crash data are often characterized with numerous zero observations. Sometimes, the number of zero observations in the compiled dataset is directly correlated with the selected spatial and/or temporal scales. By adjusting the time and spatial scales, the number of zero responses observed in the dataset can increase or decrease. Finding a balance in aggregation is a critical task in data preparation. On the one hand, using the disaggregated data may result in having excessive zero observations, in which the popular negative binomial model may not be adequate for the safety analysis. On the other hand, too much aggregation may result in loss of information. This paper documents a simulation study that aimed at determining criteria for deciding when data aggregation is needed. The simulation study explores the information loss due to aggregation as a function of the precision or accuracy in the estimation of model coefficients. The simulation results indicate that the reduction in variability, i.e., coefficient of variation, of the independent variables after aggregation, is important criteria to decide on the aggregation level.

摘要

崩溃数据通常具有大量零观测值。有时，已编译数据集中的零观测值数量与所选的空间和/或时间范围直接相关。通过调整时间和空间比例，可以增加或减少数据集中观测到的零响应数量。在聚合中找到平衡是数据准备中的关键任务。一方面，使用分类数据可能会导致观测值过大，而流行的负二项式模型可能不足以进行安全性分析。另一方面，过多的聚合可能会导致信息丢失。本文记录了一项模拟研究，旨在确定确定何时需要数据聚合的标准。仿真研究探讨了由于聚集引起的信息损失，这些损失是模型系数估计中的精度或准确性的函数。仿真结果表明，聚合后自变量的变异性（即变异系数）的降低是决定聚合水平的重要标准。