Pande Anurag, Chand Sai, Saxena Neeraj, Dixit Vinayak, Loy James, Wolshon Brian, Kent Joshua D
Civil & Environmental Engineering, Cal Poly State University, San Luis Obispo, CA 93407, United States.
Research Centre for Integrated Transport Innovation, School of Civil & Environmental Engineering, University of New South Wales, Sydney, Australia.
Accid Anal Prev. 2017 Apr;101:107-116. doi: 10.1016/j.aap.2017.01.023. Epub 2017 Feb 16.
This paper describes a project that was undertaken using naturalistic driving data collected via Global Positioning System (GPS) devices to demonstrate a proof-of-concept for proactive safety assessments of crash-prone locations. The main hypothesis for the study is that the segments where drivers have to apply hard braking (higher jerks) more frequently might be the "unsafe" segments with more crashes over a long-term. The linear referencing methodology in ArcMap was used to link the GPS data with roadway characteristic data of US Highway 101 northbound (NB) and southbound (SB) in San Luis Obispo, California. The process used to merge GPS data with quarter-mile freeway segments for traditional crash frequency analysis is also discussed in the paper. A negative binomial regression analyses showed that proportion of high magnitude jerks while decelerating on freeway segments (from the driving data) was significantly related with the long-term crash frequency of those segments. A random parameter negative binomial model with uniformly distributed parameter for ADT and a fixed parameter for jerk provided a statistically significant estimate for quarter-mile segments. The results also indicated that roadway curvature and the presence of auxiliary lane are not significantly related with crash frequency for the highway segments under consideration. The results from this exploration are promising since the data used to derive the explanatory variable(s) can be collected using most off-the-shelf GPS devices, including many smartphones.
本文描述了一个项目，该项目利用通过全球定位系统（GPS）设备收集的自然驾驶数据，来证明对易发生撞车地点进行主动安全评估的概念验证。该研究的主要假设是，驾驶员必须更频繁地急刹车（更高的急动度）的路段，从长期来看可能是撞车事故更多的“不安全”路段。在ArcMap中使用线性参照方法，将GPS数据与加利福尼亚州圣路易斯奥比斯波101号美国高速公路北行（NB）和南行（SB）的道路特征数据相链接。本文还讨论了将GPS数据与四分之一英里高速公路路段合并以进行传统撞车频率分析的过程。负二项回归分析表明，高速公路路段减速时高幅度急动度的比例（来自驾驶数据）与这些路段的长期撞车频率显著相关。一个具有均匀分布的日均交通量（ADT）参数和固定的急动度参数的随机参数负二项模型，为四分之一英里路段提供了具有统计学意义的估计。结果还表明，对于所考虑的高速公路路段，道路曲率和辅助车道的存在与撞车频率没有显著关系。这项探索的结果很有前景，因为用于得出解释变量的数据可以使用大多数现成的GPS设备收集，包括许多智能手机。
