基于贝叶斯网络的单个车辆/行人碰撞中超速影响的识别
Using Bayesian Networks to Identify the Causal Effect of Speeding in Individual Vehicle/Pedestrian Collisions
摘要：
在严重违反限速标志的道路上，衡量超速安全影响的一个指标是道路实际事故数与严格遵守限速标志时发生的事故数之间的差异。通过计算超速是每一组事故的必要条件的概率，可以估计这种事故减少。这是评估因果关系的个体概率的一个实例，如果没有因果结构的先验知识，这通常是不可能的。对于交通事故，这种先验知识往往是可用的，本文说明了如何，对于一个常见的一类车辆/行人事故，事故重建文献中出现的不确定性和因果分析的方法可以统一使用贝叶斯网络。测量的刹车痕，行人投掷距离，行人受伤的严重程度被视为证据，并使用吉布斯抽样例程BUGS，后验概率分布的外生变量，如车辆的初始速度，位置，和驾驶员的反应时间，计算。这个后验分布然后被用来计算超速的“必要性概率”。
英文摘要：
On roads showing significant violations of posted speed limits, one measure of the safety effect of speeding is the difference between the road's actual accident count and the count that would have occurred if the posted speed limit had been strictly obeyed. An estimate of this accident reduction can be had by computing the probability that speeding was a necessary condition for each of set of accidents. This is an instance of assessing individual probabilities of causation, which is generally not possible absent prior knowledge of causal structure. For traffic accidents such prior knowledge is often available and this paper illustrates how, for a commonly occurring class of vehicle/pedestrian accidents, approaches to uncertainty and causal analyses appearing in the accident reconstruction literature can be unified using Bayesian networks. Measured skidmarks, pedestrian throw distances, and pedestrian injury severity are treated as evidence, and using the Gibbs Sampling routine BUGS, the posterior probability distribution over exogenous variables, such as the vehicle's initial speed, location, and driver reaction time, is computed. This posterior distribution is then used to compute the "probability of necessity" for speeding.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
