Safety & Behavior Dataset

**Overview**

Unlock the power of high-demand automotive curated dataset, designed to effortlessly provide access and uncover profound insights into vehicle safety, driver behavior, and Auto OEM competitor analysis.

Safety & Behavior dataset is enriched with historical data from reputable sources such as The National Highway Traffic Safety Administration (NHTSA), the National Center for Statistics and Analysis (NCSA), and the Bureau of Economic Analysis (BEA).

**Use cases**

**Insurance Industry**

- Predictive Modeling - Analyze historical accident data to predict future accident risks and adjust insurance premiums accordingly. 
- Product Development - Develop new insurance products and services tailored to specific demographics or driving behaviors based on accident data insights.
- Community Engagement - Develop the insights that can be shared with local governments to spot dangerous zones or road conditions that affect safety.  Advocate solutions to road and/or human behavior as a partner with local government and enforcement.

**Automotive**

- Product Safety and Improvement - Analyze accident data to identify common causes of accidents and flaws in vehicle design. This information can be used to improve vehicle safety features and prevent future accidents.
- Autonomous Vehicle Development - Test and refine the performance of autonomous driving systems using real-world accident data. This data can help ensure the safety and reliability of self-driving cars.
- Targeted Marketing - Identify potential car buyers based on their driving habits and geographic location, gleaned from accident data. This information can be used for targeted marketing campaigns.

**Transportation and Logistics**

- Route Optimization - Optimize delivery routes to avoid accident-prone areas, improving efficiency and reducing fuel costs.
- Infrastructure Planning - Identify high-risk road sections and invest in safety improvements based on accident data analysis.
- Autonomous Logistics Vehicle - Identify the unique aspects of autonomous logistic vehicles using real world logistic accident data.  Advocate for areas where autonomous solutions exceed human limits in delivering products safely to markets.

**Key Metrics Covered**

Geo & Demographics
-Avg. accidents per year by state (map)
-Road type analysis – interstate, intersection, junction
-Urban vs. Rural analysis
-Crashes by age group
-Crashes by gender

Driver Behavior
-Ratio of normal driver vs. drowsy driver (pie chart)
-Ratio of normal driver vs. distracted driver (pie chart)
-Percentage of accidents by Motorcycle, Pedestrian, Pedal-cyclist, Police Pursuit (bubble graph)
-Percentage of accidents by hit and run.
-Percentage of drivers with alcohol use, split by state.
-Helmet usage ratio

Crash Analysis
-Accidents by year for the past 20 years
-Accidents by crash type
-Accidents by crash
-Accidents by time of the day
-Accidents by vehicle type

Tables
- cevent
- damage
- distract
- drugs
- factor
- maneuver
- nmcrash
- nmimpair
- nmprior
- person
- recall
- safetyeq
- vehicle
- vevent
- violtn
- vision
- vsoe

All tables in “Traffic and Safety” dataset are partitioned on “L_YEAR” column. This will help in improving the query performance.