This paper presents the investigation about a problem situation that Electric Distributor Companies are facing in Chile resulting from transit accidents. The number of vehicle crashes to power distribution poles and street lighting has grown. This situation causes discomfort to citizen and mainly to the neighbors due to power cuts and even on occasion , losses of human lives because of the accident that have occurred. Based on previous research, the accidents are not random nor chance dependent, but the majority of transit accident follow parameters or variables from the scenery where it occurs. In order to analyze the variables and the degree this variables affect the accidents, a model of Perceptron and Multipercetron Artificial Neural Networks and a Multiple Nonlinear Regression model are proposed. An empirical study was made; collecting data from a distributor company and from Chilean National Traffic Safety Commission, where the more frequent variables involved in accidents were determined to develop the mentioned models. These variables were investigated and also their influence on the occurrence of vehicle crashes to power distribution poles could be confirmed. With this data, the prediction of post crashes was developed, where through the application of the neural network and multiple nonlinear regression, revealed 95.7% of acceptable predictions. This study will bring benefits to power distribution companies considering a risk index in the streets, based on the number of crashes of poles per street; this will allow optimal decisions in future electrical distribution projects avoiding critical areas.

中文翻译：

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人工神经网络和非线性回归模型预测电杆碰撞

本文介绍了有关智利的配电公司因运输事故而面临的问题情况的调查。撞到配电杆和路灯的车辆数量有所增加。这种情况会因断电而给居民，主要是对邻居造成不适，甚至由于事故而造成人员伤亡。根据先前的研究，事故不是随机的，也不是偶然的，而是大多数交通事故遵循发生事故的地点的参数或变量。为了分析这些变量及其对事故的影响程度，提出了感知器和Multipercetron人工神经网络模型以及多元非线性回归模型。进行了实证研究。从分销商公司和智利国家交通安全委员会收集数据，确定涉及事故的更频繁变量以开发上述模型。对这些变量进行了调查，并且可以确定它们对车辆撞车事故对配电杆的影响。有了这些数据，就可以开发出对撞车事故的预测，其中通过应用神经网络和多元非线性回归，可以得出95.7％的可接受预测。这项研究将为配电公司带来好处，这些配电公司根据每条街道的电线杆撞车次数来考虑街道上的风险指数；这样可以在未来的配电项目中做出最佳决策，避免出现关键区域。