Addis Ababa City Road Traffic Accident Severity Dataset

This dataset contains traffic accident records from Addis Ababa City, Ethiopia, spanning the years 2016 to 2022. The dataset includes <b>13,064 rows</b> and <b>31 features</b> related to various factors influencing road traffic accident severity. The target variable is categorized into three severity levels: <b>slight</b>, <b>serious</b>, and <b>fatal injuries</b>.The dataset aims to facilitate the analysis and prediction of road traffic accident severity using machine learning algorithms. It was initially collected by the <b>Addis Ababa Police Department</b> and contains a rich set of variables, including <b>weather conditions, collision type, driver demographics, road conditions, and time of accident</b>, among others. This comprehensive dataset serves as a foundation for developing predictive models for accident severity, which can be valuable for urban planning, traffic safety research, and policy development.Key features in the dataset include:<b>Accident Severity</b> (Target Variable): Categorical variable indicating the severity of the accident (slight, serious, fatal).<b>Weather Conditions</b>: Describes the weather at the time of the accident (e.g., clear, rainy, foggy).<b>Collision Type</b>: The type of collision (e.g., rear-end, side-impact).<b>Driver Demographics</b>: Features like <b>driver age</b>, <b>driver sex</b>, and <b>experience</b> that may affect accident outcomes.<b>Location</b>: Various aspects of the accident location, including <b>junction type</b>, <b>road type</b>, and <b>alignment</b>.<b>Temporal Features</b>: Time-related variables such as <b>day of the week</b>, <b>time of day</b>, and <b>seasonal trends</b>.<b>Vehicle Information</b>: Includes <b>vehicle type</b>, <b>vehicle defect</b>, <b>vehicle movement</b>, and the relationship between the vehicle owner and the driver.<b>Casualty Information</b>: Includes <b>age</b>, <b>sex</b>, and <b>fitness of the casualty</b>.

本数据集收录埃塞俄比亚亚的斯亚贝巴市2016年至2022年间的交通事故记录。
数据集共包含<b>13064条数据样本</b>与<b>31项特征</b>，这些特征均与影响道路交通事故严重程度的各类因素相关。
目标变量被划分为三个严重程度等级：<b>轻微 (slight)</b>、<b>严重 (serious)</b>与<b>致命伤害 (fatal injuries)</b>。
本数据集旨在助力基于机器学习算法开展道路交通事故严重程度的分析与预测工作。
本数据集最初由<b>亚的斯亚贝巴警察局 (Addis Ababa Police Department)</b>收集，涵盖了丰富的变量维度，包括<b>天气状况、碰撞类型、驾驶员人口统计学特征、道路状况以及事故发生时间</b>等。
这套全面的数据集可为交通事故严重程度预测模型的开发提供核心支撑，对城市规划、交通安全研究以及政策制定均具有重要应用价值。
数据集的核心特征包括：
<b>事故严重程度 (Accident Severity)</b>（目标变量）：用于表征事故严重程度的分类变量，涵盖轻微、严重与致命三个等级。
<b>天气状况 (Weather Conditions)</b>：描述事故发生时的天气情况（例如晴朗、降雨、雾天等）。
<b>碰撞类型 (Collision Type)</b>：指事故的碰撞形式（例如追尾、侧面碰撞等）。
<b>驾驶员人口统计学特征 (Driver Demographics)</b>：包含<b>驾驶员年龄、驾驶员性别以及驾驶经验</b>等可能影响事故后果的特征。
<b>事故地点 (Location)</b>：涵盖事故地点的各类属性，例如<b>路口类型、道路类型以及道路线形 (alignment)</b>等。
<b>时间特征 (Temporal Features)</b>：与时间相关的变量，例如<b>星期几、当日时段以及季节趋势</b>等。
<b>车辆信息 (Vehicle Information)</b>：包括<b>车辆类型、车辆故障、车辆运行状态</b>以及车主与驾驶员的关系等。
<b>伤亡人员信息 (Casualty Information)</b>：涵盖<b>伤亡人员年龄、性别以及健康状况</b>等信息。

搜集汇总

背景与挑战

背景概述

该数据集包含2016年至2022年埃塞俄比亚亚的斯亚贝巴市的交通事故记录，共有13,064行数据和31个特征，目标变量为事故严重程度（轻微、严重、致命）。数据集旨在通过机器学习算法分析和预测交通事故严重程度，包含天气条件、碰撞类型、驾驶员人口统计、道路条件和事故时间等多种变量。