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Spatial heterogeneity analysis of macro-level crashes using geographically weighted Poisson quantile regression

机译：使用地理加权泊松分位数回归宏观撞击的空间异质性分析

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摘要

In recent years, globally quantile-based model (e.g. quantile regression) and spatially conditional mean models (e.g. geographically weighted regression) have been widely and commonly employed in macro-level safety analysis. The former ones assume that the model coefficients are fixed over space, while the latter ones only represent the entire distribution of variable effects by a single concentrated trend. However, the influence of crash related factors on the distribution of crash frequency is observed to vary over space and across different quantiles. Therefore, a geographically weighted Poisson quantile regression (GWPQR) model is employed to investigate the spatial heterogeneity of variable effects crossing different quantiles. Five categories, including exposure, socio-economic, transportation, network and land use were selected to estimate the spatial effects on crash frequency. In the case study, vehicle related crashes collected in New York City were used to validate the predicted performance of the proposed models. The results show that the GWPQR outperforms the NB, QR and GWNBR for modeling the skewed distribution, reconstructing the crash distribution and capturing the unobserved spatial heterogeneity. Additionally, the significant coefficients are further used to classify all 21 variables into key, important and general parts. Then we discuss how these factors affects the regional crashes over space and distribution of crash frequency. This study confirms that the influencing factors have varying effects on different quantiles of distribution and on different regions, which could be helpful to provide support for making safety countermeasures and policies at urban regional level.
机译：近年来，全球分位式的模型（例如，分量回归）和空间有条件的均值模型（例如，地理加权回归）已经广泛且通常用于宏观安全分析。前者假设模型系数在空间上固定，而后者仅代表单个集中趋势仅代表整个变量效应的分布。然而，观察到崩溃相关因素对碰撞频率分布的影响，以改变空间和跨越不同的量级。因此，采用地理加权泊松分量回归（GWPQR）模型来研究交叉量不同定量的可变效应的空间异质性。选择了五个类别，包括曝光，社会经济，运输，网络和土地使用，以估算对碰撞频率的空间影响。在案例研究中，纽约市收集的车辆相关崩溃用于验证所提出的模型的预测性能。结果表明，GWPQR优于Nb，QR和GWNBR，用于建模偏斜分布，重建碰撞分布并捕获未观察到的空间异质性。另外，重要的系数还用于将所有21个变量分类为密钥，重要和一般部分。然后我们讨论这些因素如何影响区域崩溃的空间和碰撞频率分配。本研究证实，影响因素对不同分量和不同地区的不同量数具有不同的影响，这有助于为在城市区域一级提供安全对策和政策。

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