Studying the effects of weather and roadway geometrics on daily accident occurrence using a multilayer perceptron model

Proceedings of the Fourth Workshop on International Science of Smart City Operations and Platforms Engineering Pub Date : 2019-04-15 DOI:10.1145/3313237.3313304

Jeremiah Roland, Peter Way, Mina Sartipi

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Abstract

One of the most common, yet dangerous, events that people face each day is driving. From unpredictable weather to hazardous roadways, there is a seemingly endless number of factors at play that can lead to vehicular accidents. Therefore, attempting to predict these accidents is a timely topic in today's research spectrum. The data used in this research consists of historical accident records from Hamilton County, Tennessee beginning in 2016 and continues to be updated daily, as well as the associated weather occurrences and roadway geometrics. To enhance heterogeneity a procedure was performed that generated non-accident traffic data based on our actual traffic accident data. This procedure is called negative sampling. These different data sets were combined and placed through a Multilayer Perceptron (MLP) machine learning model. The end results displayed a high collective correlation between accident occurrence and the various features considered in our proposed model, allowing us to predict with 77.5% accuracy where and when an accident will occur.

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使用多层感知器模型研究天气和道路几何对日常事故发生率的影响

开车是人们每天面临的最常见但也是最危险的事情之一。从不可预测的天气到危险的道路，似乎有无数的因素可以导致交通事故。因此，试图预测这些事故是当今研究领域的一个及时课题。本研究中使用的数据包括从2016年开始的田纳西州汉密尔顿县的历史事故记录，以及相关的天气情况和道路几何形状，这些记录每天都会更新。为了增强异质性，我们执行了一个基于实际交通事故数据生成非事故交通数据的程序。这个过程称为负抽样。这些不同的数据集通过多层感知器(MLP)机器学习模型进行组合和放置。最终结果显示，事故发生与我们提出的模型中考虑的各种特征之间存在高度的集体相关性，使我们能够以77.5%的准确率预测事故发生的地点和时间。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the Fourth Workshop on International Science of Smart City Operations and Platforms Engineering

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