Using Geographically Weighted Poisson Regression for county-level crash modeling in California
Using Geographically Weighted Poisson Regression for county-level crash modeling in California
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DOI：
10.1016/j.ssci.2013.04.005
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发表时间：
2013-10-01
期刊：
影响因子：
6.1
通讯作者：
Ragland, David R.
中科院分区：
文献类型：
--
作者：
Li, Zhibin;Wang, Wei;Ragland, David R.
县级交通事故预测模型的发展已经引起了国家机构的兴趣，根据一系列县域特征来预测正常的交通安全水平。用于县级碰撞建模的一种常见技术是广义线性建模(GLM)过程。然而，GLM未能捕捉到存在于各县的碰撞次数和解释变量之间关系中的空间异质性。这项研究旨在评估地理加权泊松回归(GWPR)在县级碰撞数据中捕捉这些空间变化关系的使用。将GWPR的性能与传统GLM进行了比较。从加利福尼亚州的58个县收集了致命的撞车事故和全县的因素，包括交通模式、道路网络属性和社会人口特征。结果表明，GWPR可以有效地捕捉事故与县级预测因子之间的空间非平稳关系。通过捕捉空间异质性，GWPR在预测个别县的致命撞车事故方面优于GLM。GWPR显著降低了县上空致命撞车预测残差的空间相关性。(C)2013爱思唯尔有限公司。保留所有权利。
Development of crash prediction models at the county-level has drawn the interests of state agencies for forecasting the normal level of traffic safety according to a series of countywide characteristics. A common technique for the county-level crash modeling is the generalized linear modeling (GLM) procedure. However, the GLM fails to capture the spatial heterogeneity that exists in the relationship between crash counts and explanatory variables over counties. This study aims to evaluate the use of a Geographically Weighted Poisson Regression (GWPR) to capture these spatially varying relationships in the county-level crash data. The performance of a GWPR was compared to a traditional GLM. Fatal crashes and countywide factors including traffic patterns, road network attributes, and socio-demographic characteristics were collected from the 58 counties in California. Results showed that the GWPR was useful in capturing the spatially non-stationary relationships between crashes and predicting factors at the county level. By capturing the spatial heterogeneity, the GWPR outperformed the GLM in predicting the fatal crashes in individual counties. The GWPR remarkably reduced the spatial correlation in the residuals of predictions of fatal crashes over counties. (C) 2013 Elsevier Ltd. All rights reserved.