a Department of Technology, Management and Economics , Technical University of Denmark , Lyngby , Denmark.
Traffic Inj Prev. 2019;20(4):442-448. doi: 10.1080/15389588.2019.1580700. Epub 2019 May 10.
We combine data on roads and crash characteristics to identify patterns in road traffic crashes with regard to road characteristics. We illustrate how combined analysis of data regarding road maintenance, maintenance costs, road characteristics, crash characteristics, and geographical location can enrich road maintenance prioritization from a traffic safety perspective. The study is based on traffic crash data merged with road maintenance data and annual average daily traffic (AADT) collected in Denmark. We analyzed 3,964 crashes that occurred from 2010 to 2015. A latent class clustering (LCC) technique was used to identify crash clusters with different road and crash characteristics. The distribution of crash severity and estimated road maintenance costs for each cluster was found and cluster differences were compared using the chi-square test. Finally, a map matching procedure was used to identify the geographical distribution of the crashes in each cluster. Results showed that based on road maintenance levels there was no difference in the distribution of crash severity. The LCC technique revealed 11 crash clusters. Five clusters were characterized by crashes on roads with a poor maintenance level (levels 4 and 3). Only a few of these crashes included a vulnerable road user (VRU) but many occurred on roads without barriers. Four clusters included a large share of crashes on acceptably maintained roads (level 2). For these clusters only small variations in road characteristics were found, whereas the differences in crash characteristics were more dominant. The last 2 clusters included crashes that mainly occurred on new roads with no need for maintenance (level 1). Injury severity, estimated maintenance costs, and geographical location were found to be differently distributed for most of the clusters. We find that focusing solely on road maintenance and crash severity does not provide clear guidance of how to prioritize between road maintenance efforts from a traffic safety perspective. However, when combined with geographical location and crash characteristics, a more nuanced picture appears that allows consideration of different target groups and perspectives.
我们结合道路数据和事故特征，根据道路特征识别道路交通碰撞模式。我们说明了如何综合分析道路养护、养护成本、道路特征、事故特征和地理位置的数据，从交通安全的角度丰富道路养护的优先级。该研究基于丹麦合并的交通事故数据和道路养护数据以及年平均日交通量（AADT）。我们分析了 2010 年至 2015 年期间发生的 3964 起事故。使用潜在类别聚类（LCC）技术识别具有不同道路和事故特征的事故聚类。发现每个聚类的事故严重程度分布和估计的道路养护成本，并使用卡方检验比较聚类差异。最后，使用地图匹配程序识别每个聚类中事故的地理分布。结果表明，根据道路养护水平，事故严重程度的分布没有差异。LCC 技术揭示了 11 个事故聚类。五个聚类的特征是道路养护水平较差（水平 4 和 3）的道路上发生的事故。这些事故中只有少数涉及弱势道路使用者（VRU），但许多发生在没有障碍物的道路上。四个聚类包括相当一部分发生在可接受养护水平道路（水平 2）的事故。对于这些聚类，只发现道路特征的微小变化，而事故特征的差异更为突出。最后 2 个聚类包括主要发生在无需维护的新道路上的事故（水平 1）。发现大多数聚类的伤害严重程度、估计的养护成本和地理位置分布不同。我们发现，仅关注道路养护和事故严重程度并不能从交通安全的角度提供明确的指导，说明如何优先考虑道路养护工作。但是，当与地理位置和事故特征结合使用时，会出现更细致的情况，可以考虑不同的目标群体和观点。
