Accidental Prone Area Detection in Bangladesh using Machine Learning Model

Khan Md Hasib, Md. Imran Hossain Showrov, Anik Das
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引用次数: 10

Abstract

Nowadays road accident in Bangladesh is a buzzword due to its lack of carefulness of the driver of the vehicle where some parameter exists. The traffic safety of the roadway is an essential concern not only for transportation governing agencies but also for citizens of our country. For safe driving suggestions, the important thing is to find the variables that are tensed to relate to the fatal accidents that are occurring often. In this paper, we create a model using a machine learning approach on the countrywide traffic accident dataset of Bangladesh as an aim to address this problem. The model also helps out to find the diversity of the data by grouping similar objects together to find the accident-prone areas in the country concerning different accident factors as well as detects the cooperation between these factors and causalities.
使用机器学习模型检测孟加拉国的意外易发区域
如今,孟加拉国的交通事故是一个流行语,因为它缺乏司机的细心,其中一些参数存在。道路交通安全不仅是交通管理部门关心的问题,也是我国公民关心的问题。对于安全驾驶的建议,重要的是找到与经常发生的致命事故有关的变量。在本文中,我们使用机器学习方法在孟加拉国的全国交通事故数据集上创建了一个模型,以解决这个问题。该模型还通过将相似的对象分组在一起,找到全国不同事故因素下的事故易发区域,并检测这些因素与伤亡之间的合作关系,从而发现数据的多样性。
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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