School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China; National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China; Fujian University of Technology, Fuzhou 350118, China.
Accid Anal Prev. 2022 Sep;174:106756. doi: 10.1016/j.aap.2022.106756. Epub 2022 Jun 18.
Analyzing speed mean and variance is vital to safety management in urban roadway networks. However, modeling speed mean and variance on structured roads could be influenced by the spatial effects, which are rarely addressed in the existing studies. The inadequacy may lead to biased conclusions when considering vehicle speed as a surrogate safety measure. The current study focuses on developing a Bayesian modeling approach with three types of spatial effects, i.e., spatial correlation, spatial heterogeneity, and spillover effect. To capture the spatial correlation, the study employs the intrinsic conditional autoregressive (ICAR) models, spatial lag models (SLM), and spatial error models (SEM). Spatial heterogeneity and spillover effect are considered by the random parameters approach and spatially lagged covariates (SLCs). Speed data are collected from the float cars running on 134 urban arterials in Chengdu, China. The results indicate that the random parameters ICAR model with SLCs (RPICAR-SLC) outperforms others in terms of goodness-of-fit, accuracy, and efficiency for modeling speed mean, while the random parameters ICAR model (RPICAR) is the best for modeling speed variance. Moreover, RPICAR-SLC and RPICAR models are beneficial to address spatial correlation of residuals, explaining the unobserved influence among the observations, and are less likely to cause biased or overestimated parameters. The study also discusses how traffic conditions, road characteristics, traffic management strategies, and facilities on roadway networks influence speed mean and variance. The findings highlight the importance of multi-type spatial effects on modeling speed mean and variance along the structured roadways.
分析速度均值和方差对于城市道路网络的安全管理至关重要。然而，在结构化道路上建模速度均值和方差可能会受到空间效应的影响，而现有研究很少涉及到这些影响。这种不足可能会导致在将车速作为替代安全措施进行考虑时产生有偏差的结论。本研究专注于开发一种具有三种空间效应（即空间相关性、空间异质性和溢出效应）的贝叶斯建模方法。为了捕捉空间相关性，研究采用了内蕴条件自回归（ICAR）模型、空间滞后模型（SLM）和空间误差模型（SEM）。空间异质性和溢出效应则通过随机参数方法和空间滞后协变量（SLC）来考虑。速度数据是从在中国成都的 134 条城市干道上行驶的浮动车收集得到的。结果表明，在拟合优度、准确性和效率方面，具有 SLC 的随机参数 ICAR 模型（RPICAR-SLC）优于其他模型，适用于建模速度均值，而随机参数 ICAR 模型（RPICAR）则是建模速度方差的最佳模型。此外，RPICAR-SLC 和 RPICAR 模型有助于解决残差的空间相关性，解释观测值之间未被观察到的影响，并且不太可能导致参数有偏差或被高估。本研究还讨论了交通条件、道路特征、交通管理策略以及道路网络设施如何影响速度均值和方差。研究结果强调了在建模结构化道路上的速度均值和方差时考虑多种类型空间效应的重要性。
