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Accid Anal Prev. 2010 Mar;42(2):676-88. doi: 10.1016/j.aap.2009.10.016. Epub 2009 Nov 22.
A common technique used for the calibration of collision prediction models is the Generalized Linear Modeling (GLM) procedure with the assumption of Negative Binomial or Poisson error distribution. In this technique, fixed coefficients that represent the average relationship between the dependent variable and each explanatory variable are estimated. However, the stationary relationship assumed may hide some important spatial factors of the number of collisions at a particular traffic analysis zone. Consequently, the accuracy of such models for explaining the relationship between the dependent variable and the explanatory variables may be suspected since collision frequency is likely influenced by many spatially defined factors such as land use, demographic characteristics, and traffic volume patterns. The primary objective of this study is to investigate the spatial variations in the relationship between the number of zonal collisions and potential transportation planning predictors, using the Geographically Weighted Poisson Regression modeling technique. The secondary objective is to build on knowledge comparing the accuracy of Geographically Weighted Poisson Regression models to that of Generalized Linear Models. The results show that the Geographically Weighted Poisson Regression models are useful for capturing spatially dependent relationships and generally perform better than the conventional Generalized Linear Models.
一种常用于碰撞预测模型校准的常用技术是广义线性建模 (GLM) 过程，并假设负二项式或泊松误差分布。在该技术中，估计了代表因变量与每个解释变量之间平均关系的固定系数。然而，假设的平稳关系可能隐藏了特定交通分析区域中碰撞次数的一些重要空间因素。因此，由于碰撞频率可能受到许多空间定义因素（如土地利用、人口特征和交通量模式）的影响，因此此类模型解释因变量与解释变量之间关系的准确性可能受到怀疑。本研究的主要目的是使用地理加权泊松回归建模技术研究特定交通分析区域中碰撞次数与潜在交通规划预测因子之间关系的空间变化。次要目标是在比较地理加权泊松回归模型和广义线性模型的准确性方面扩展知识。结果表明，地理加权泊松回归模型可用于捕获空间相关关系，并且通常比传统的广义线性模型表现更好。
