Spatial analysis of road crash frequency using Bayesian models with Integrated Nested Laplace Approximation (INLA)
Spatial analysis of road crash frequency using Bayesian models with Integrated Nested Laplace Approximation (INLA)
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DOI：
10.1080/19439962.2020.1726542
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发表时间：
2020-03
影响因子：
2.6
通讯作者：
Romi Satria;Jonathan Aguero-Valverde;M. Castro
中科院分区：
文献类型：
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作者：
Romi Satria;Jonathan Aguero-Valverde;M. Castro
摘要提高交通安全是世界上大多数交通机构的优先事项。作为交通安全管理战略的一部分，努力的重点是开发更准确的碰撞频率模型，并查明促成因素，以便采取更好的对策，改善交通安全。随着时间的推移，模型的复杂性和计算时间都在增加。贝叶斯模型使用MCMC方法已被普遍用于交通安全分析，因为他们能够处理复杂的模型。最近，INLA的方法已经出现作为MCMC方法的替代显着减少计算时间。在这项研究中，INLA-CAR模型的开发，以评估碰撞的严重程度，在段级的高速公路路段在印度尼西亚班达亚齐，并与贝叶斯非空间模型进行比较。DIC的结果显示了在模型中包括空间相关性的重要性。系数估计表明，AADT是最有影响力的两种模式，并在所有严重程度类型，但是，系数估计土地利用和水平对齐不同的严重程度类型。最后，为了评估DIC的一些局限性，其他三个拟合优度的措施被用来交叉验证DIC的结果。
Abstract Improving traffic safety is a priority of most transportation agencies around the world. As part of traffic safety management strategies, efforts have focused on developing more accurate crash-frequency models and on identifying contributing factors in order to implement better countermeasures to improve traffic safety. Over time, models have increased in complexity and computational time. Bayesian models using the MCMC method have been commonly used in traffic safety analyses because of their ability to deal with complex models. Recently, the INLA approach has appeared as an alternative to the MCMC method by significantly reducing the computing time. In this study, an INLA-CAR model is developed to assess crashes by severity at the segment level on a highway section in Banda Aceh, Indonesia and is compared with a Bayesian non-spatial model. Results of the DIC show the importance of including spatial correlation in the models. The coefficient estimates show that AADT is the most influential in both models and across all severity types; however, the coefficient estimates for land use and horizontal alignment vary across severity types. Finally, in order to assess some limitations of the DIC, three other goodness-of-fit measures are used to cross-validate the results of the DIC.