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Spatio-Temporal Crash Prediction: Effects of Negative Sampling on Understanding Network-Level Crash Occurrence
Transportation Research Record: Journal of the Transportation Research Board ( IF 1.7 ) Pub Date : 2021-02-09 , DOI: 10.1177/0361198121991836
Peter Way 1 , Jeremiah Roland 1 , Mina Sartipi 1 , Osama Osman 2
Affiliation  

In projects centered around rare event case data, the challenge of data comprehension is greatly increased because of insufficient data for deriving insight and analysis. This is particularly the case with traffic crash occurrence, where positive events (crashes) are rare and, in most cases, no data set exists for negative events (non-crashes). One method to increase available data is negative sampling, which is the process of creating a negative event based on the absence of a positive event. In this work, four negative sampling techniques are presented with varying ratios of negative to positive data. These types of techniques are based on spatial data, temporal data, and a mixture of the two, with the data ratios acting as class balancing tools. The best performing model found was with a negative sampling technique that shifted temporal information and had an even 50/50 data split, with an F-1 score, a formulaic combination of precision and recall, of 93.68. These results are promising for Inteligent Transportation Systems (ITS) applications to inform of potential crash locations in an entire area for proactive measures to be put in place.



中文翻译:

时空崩溃预测:负采样对理解网络级崩溃发生的影响

在以罕见事件案例数据为中心的项目中,由于缺乏足够的数据来获取洞察力和进行分析,因此数据理解的挑战大大增加。发生交通事故时尤其如此,在这种情况下,正面事件(崩溃)很少发生,并且在大多数情况下,不存在负面事件(非崩溃)的数据集。增加可用数据的一种方法是负采样,这是基于不存在正事件而创建负事件的过程。在这项工作中,提出了四种负采样技术,其中负数据与正数据的比率各不相同。这些类型的技术基于空间数据,时间数据以及两者的混合,其中数据比率用作类平衡工具。发现的最佳模型是使用负采样技术进行的,该技术转移了时间信息,并且将数据分割为50/50,F-1得分(精确度和召回率的公式化组合)为93.68。这些结果对于智能运输系统(ITS)应用程序来说很有希望,可以告知整个区域潜在的撞车位置,以便采取积极措施。

更新日期:2021-02-09
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