Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, Tennessee, USA.
Traffic Inj Prev. 2013;14(7):766-75. doi: 10.1080/15389588.2012.756582.
Previous studies of pavement management factors that relate to the occurrence of traffic-related crashes are rare. Traditional research has mostly employed summary statistics of bidirectional pavement quality measurements in extended longitudinal road segments over a long time period, which may cause a loss of important information and result in biased parameter estimates. The research presented in this article focuses on crash risk of roadways with overall fair to good pavement quality. Real-time and location-specific data were employed to estimate the effects of pavement management factors on the occurrence of crashes.
This research is based on the crash data and corresponding pavement quality data for the Tennessee state route highways from 2004 to 2009. The potential temporal and spatial correlations among observations caused by unobserved factors were considered. Overall 6 models were built accounting for no correlation, temporal correlation only, and both the temporal and spatial correlations. These models included Poisson, negative binomial (NB), one random effect Poisson and negative binomial (OREP, ORENB), and two random effect Poisson and negative binomial (TREP, TRENB) models. The Bayesian method was employed to construct these models. The inference is based on the posterior distribution from the Markov chain Monte Carlo (MCMC) simulation. These models were compared using the deviance information criterion.
Analysis of the posterior distribution of parameter coefficients indicates that the pavement management factors indexed by Present Serviceability Index (PSI) and Pavement Distress Index (PDI) had significant impacts on the occurrence of crashes, whereas the variable rutting depth was not significant. Among other factors, lane width, median width, type of terrain, and posted speed limit were significant in affecting crash frequency.
The findings of this study indicate that a reduction in pavement roughness would reduce the likelihood of traffic-related crashes. Hence, maintaining a low level of pavement roughness is strongly suggested. In addition, the results suggested that the temporal correlation among observations was significant and that the ORENB model outperformed all other models.
先前研究与交通相关碰撞发生有关的路面管理因素的研究很少。传统的研究大多采用在长时间内对延伸的纵向道路段中的双向路面质量测量进行汇总统计，这可能会导致重要信息的丢失，并导致参数估计出现偏差。本文中的研究重点是路面质量总体良好的道路的碰撞风险。使用实时和特定位置的数据来估计路面管理因素对碰撞发生的影响。
本研究基于 2004 年至 2009 年田纳西州公路的碰撞数据和相应的路面质量数据。考虑了未观察到的因素引起的观察值之间潜在的时间和空间相关性。总共建立了 6 个模型，分别考虑了无相关性、仅时间相关性以及时间和空间相关性。这些模型包括泊松、负二项式（NB）、一个随机效应泊松和负二项式（OREP、ORENB）和两个随机效应泊松和负二项式（TREP、TRENB）模型。使用贝叶斯方法构建了这些模型。推理基于马尔可夫链蒙特卡罗（MCMC）模拟的后验分布。使用偏差信息准则比较了这些模型。
参数系数的后验分布分析表明，以现行服务能力指数（PSI）和路面损坏指数（PDI）为指标的路面管理因素对碰撞发生有显著影响，而车辙深度变量则不显著。在其他因素中，车道宽度、中央分隔带宽度、地形类型和限速对碰撞频率有显著影响。
本研究的结果表明，减少路面粗糙度会降低与交通相关的碰撞的可能性。因此，强烈建议保持低水平的路面粗糙度。此外，结果表明观察值之间的时间相关性很重要，ORENB 模型优于所有其他模型。
