Ziyuan111/traffic_accident_data

---
license: apache-2.0
task_categories:
- table-question-answering
language:
- en
size_categories:
- 1K<n<10K
---
---

# Dataset Card for Car Crash Dataset

## Dataset Details

- **NetID:** zm83
- **Repository:** Access the Car Crash Dataset on [Kaggle](https://www.kaggle.com/)
- **License:** Apache 2.0
- **Expected Update Frequency:** Never (Last updated 22 days ago)

## Dataset Description

The Car Crash Dataset provides a comprehensive collection of detailed records on traffic accidents from 2003 to 2015 in Monroe County. It offers insights into various factors influencing road accidents, including collision severity, weather conditions, road types, and other contributing factors. This dataset is crucial for analyses aimed at improving road safety and implementing preventive measures.

### Collaborators

- Jackson Divakar R (Owner)

### Provenance

Data for the Car Crash Dataset come from:
- Official traffic incident reports
- Law enforcement records
- Insurance claims

These sources ensure a comprehensive and accurate representation of the factors contributing to road accidents.

### Collection Methodology

A consistent methodology was applied during the compilation of data from various sources to ensure the accuracy and reliability of the dataset.

## Dataset Summary

The dataset is a meticulous aggregation of data points that delve into the factors influencing road accidents, documenting various aspects of car crashes. It is designed to facilitate the development of predictive models, safety analytics, and enhanced traffic management systems.

## Dataset Uses

The Car Crash Dataset can be used for a variety of applications, including traffic safety analysis, urban planning, machine learning, policy development, public health, and insurance analysis. It supports in-depth research into the causes and consequences of road traffic accidents.

### Possible Topics for Analysis

The dataset supports a range of analysis topics, such as:
- The effect of weather conditions on road accidents
- Correlations between traffic volume and accident frequency
- The effectiveness of road safety laws and regulations
- Geographic analysis of accident hotspots
- Human factors versus environmental factors in accidents
- Vehicle type and collision severity

### Curation Motivation

The dataset was curated to provide a resource for stakeholders to understand and mitigate the factors behind road accidents, aiming to reduce their frequency and severity, and to support the development of road safety strategies. Researchers and analysts are invited to use this dataset to explore various topics and generate actionable insights for community safety and well-being.

### Data Instances

A typical entry in the dataset might look like the following (example in JSON format):

```json
{
"accident_id": "XYZ123",
"timestamp": "2015-08-21T14:30:00Z",
"location": {
"latitude": 43.1566,
"longitude": -77.6088
},
"severity": "Moderate",
"weather_condition": "Clear",
"road_type": "Highway",
"vehicles_involved": 2,
"contributing_factors": ["Speeding", "Distracted Driving"]
}
```
Additional fields in the dataset may include but are not limited to:
```jason
{
"injury_types": ["None", "Minor", "Severe"],
"involved_parties": {
"drivers": [
{
"age": 35,
"gender": "Female",
"driving_experience": "10 years"
},
{
"age": 22,
"gender": "Male",
"driving_experience": "2 years"
}
],
"pedestrians": []
},
"collision_type": "Rear-end",
"law_enforcement_response": {
"response_time": "5 minutes",
"actions_taken": ["Traffic control", "Medical assistance"]
}
}
```

---
license: apache-2.0
task_categories:
- 表格问答（table-question-answering）
language:
- en
size_categories:
- 1K<n<10K
---

# 汽车碰撞数据集卡片

## 数据集详情

- **网络标识（NetID）：zm83**
- **仓库：可在[Kaggle](https://www.kaggle.com/)平台获取本汽车碰撞数据集**
- **许可证：Apache 2.0**
- **预期更新频率：无更新（最近一次更新为22天前）**

## 数据集描述

本汽车碰撞数据集收录了门罗县2003年至2015年间的交通事故详细记录，内容全面详实。数据集涵盖了影响道路交通事故的多项核心因素，包括碰撞严重程度、天气状况、道路类型及其他致因，可为旨在提升道路安全水平、制定预防措施的相关分析提供关键支撑。

### 协作方

- Jackson Divakar R（数据集所有者）

### 数据溯源

本汽车碰撞数据集的数据源自：
- 官方交通事故报告
- 执法部门记录
- 保险理赔档案
上述数据源确保了道路交通事故致因的记录全面且准确。

### 数据采集方法

在多源数据整合过程中，本数据集采用统一的处理流程，以确保数据的准确性与可靠性。

## 数据集概述

本数据集对影响道路交通事故的各类因素进行了细致的数据点聚合，完整记录了汽车碰撞事故的多维度信息，旨在助力预测模型开发、安全分析及精细化交通管理系统的构建。

## 数据集用途

本汽车碰撞数据集可应用于多个领域，包括交通安全分析、城市规划、机器学习、政策制定、公共卫生及保险分析，可为道路交通事故的成因与影响的深入研究提供支撑。

### 可选分析主题

本数据集支持多类分析主题，例如：
- 天气状况对道路交通事故的影响
- 交通流量与事故发生频率的相关性
- 道路交通安全法律法规的实施效果
- 事故高发区域的地理空间分析
- 交通事故中的人为因素与环境因素对比
- 车辆类型与碰撞严重程度的关联

### 数据整理动机

本数据集的整理旨在为相关利益方提供一个可用于理解并缓解道路交通事故致因的资源，以降低事故发生频率与严重程度，助力道路安全策略的制定。本数据集欢迎研究人员与分析师使用，以探索各类分析主题，并为社区安全与福祉生成可落地的决策依据。

### 数据示例

本数据集的典型条目示例如下（JSON格式）：
json
{
"accident_id": "XYZ123",
"timestamp": "2015-08-21T14:30:00Z",
"location": {
"latitude": 43.1566,
"longitude": -77.6088
},
"severity": "中度",
"weather_condition": "晴朗",
"road_type": "高速公路",
"vehicles_involved": 2,
"contributing_factors": ["超速", "分心驾驶"]
}

数据集还包含但不限于以下额外字段：
json
{
"injury_types": ["无伤害", "轻微伤害", "严重伤害"],
"involved_parties": {
"drivers": [
{
"age": 35,
"gender": "Female",
"driving_experience": "10年驾龄"
},
{
"age": 22,
"gender": "Male",
"driving_experience": "2年驾龄"
}
],
"pedestrians": []
},
"collision_type": "追尾",
"law_enforcement_response": {
"response_time": "5分钟",
"actions_taken": ["交通管制", "医疗救助"]
}
}

原始信息汇总

数据集卡片 for Car Crash Dataset

数据集详情

• NetID: zm83
• 许可证: Apache 2.0
• 预期更新频率: 永不更新（最近更新于22天前）

数据集描述

Car Crash Dataset 提供了一个全面的详细记录，涵盖了2003年至2015年门罗县的交通事故。它提供了影响道路事故的各种因素的见解，包括碰撞严重性、天气条件、道路类型和其他相关因素。该数据集对于旨在提高道路安全和实施预防措施的分析至关重要。

合作者

• Jackson Divakar R (所有者)

数据来源

Car Crash Dataset 的数据来源包括：

• 官方交通事故报告
• 执法记录
• 保险索赔

这些来源确保了对影响道路事故因素的全面和准确表示。

收集方法

在从各种来源编译数据时，采用了一致的方法，以确保数据集的准确性和可靠性。

数据集摘要

该数据集是深入研究影响道路事故因素的数据点的细致聚合，记录了汽车碰撞的各个方面。它旨在促进预测模型、安全分析和增强交通管理系统的发展。

数据集用途

Car Crash Dataset 可用于多种应用，包括交通安全分析、城市规划、机器学习、政策制定、公共卫生和保险分析。它支持对道路交通事故的原因和后果进行深入研究。

可能的分析主题

该数据集支持一系列分析主题，例如：

• 天气条件对道路事故的影响
• 交通量与事故频率之间的相关性
• 道路安全法律和法规的有效性
• 事故热点地理分析
• 人为因素与环境因素在事故中的作用
• 车辆类型和碰撞严重性

数据集动机

该数据集是为了提供一个资源，让利益相关者了解并减轻道路事故背后的因素，旨在减少其频率和严重性，并支持道路安全战略的发展。研究人员和分析师被邀请使用此数据集探索各种主题，并为社区安全和福祉生成可操作的见解。

数据实例

数据集中的典型条目可能如下所示（JSON格式示例）：

json { "accident_id": "XYZ123", "timestamp": "2015-08-21T14:30:00Z", "location": { "latitude": 43.1566, "longitude": -77.6088 }, "severity": "Moderate", "weather_condition": "Clear", "road_type": "Highway", "vehicles_involved": 2, "contributing_factors": ["Speeding", "Distracted Driving"] }

其他字段可能包括但不限于：

json { "injury_types": ["None", "Minor", "Severe"], "involved_parties": { "drivers": [ { "age": 35, "gender": "Female", "driving_experience": "10 years" }, { "age": 22, "gender": "Male", "driving_experience": "2 years" } ], "pedestrians": [] }, "collision_type": "Rear-end", "law_enforcement_response": { "response_time": "5 minutes", "actions_taken": ["Traffic control", "Medical assistance"] } }