Embedded Files
We extracted our data from a countrywide car accident dataset published on Kaggle (https://www.kaggle.com/sobhanmoosavi/us-accidents), which covers 49 states of the USA. In the original dataset, the accident data are collected from Feb 2016 to Dec 2020, using two APIs that offer streaming traffic incident or event data. These data were caught by a variety of entities including the US and state departments of transportation, traffic cameras, law enforcement agencies, and traffic sensors within the road-networks.
We extracted our data from a countrywide car accident dataset published on Kaggle (https://www.kaggle.com/sobhanmoosavi/us-accidents), which covers 49 states of the USA. In the original dataset, the accident data are collected from Feb 2016 to Dec 2020, using two APIs that offer streaming traffic incident or event data. These data were caught by a variety of entities including the US and state departments of transportation, traffic cameras, law enforcement agencies, and traffic sensors within the road-networks.
In our project, we only focus on the data in San Diego city from Jan 2017 to Dec 2020, consisting of 30528 rows and 49 columns. The data cleaning process involves transforming the data format, looking for entries that had missing values and duplicated values and dropping redundant columns. In total 8,254 rows and 16 columns were removed.
In our project, we only focus on the data in San Diego city from Jan 2017 to Dec 2020, consisting of 30528 rows and 49 columns. The data cleaning process involves transforming the data format, looking for entries that had missing values and duplicated values and dropping redundant columns. In total 8,254 rows and 16 columns were removed.
The Cleaning data set includes variables that are listed below:
The Cleaning data set includes variables that are listed below:
- Severity: Shows the severity of the accident, a number between 1 and 4, where 1 indicates the least impact on traffic (i.e., short delay as a result of the accident) and 4 indicates a significant impact on traffic (i.e., long delay).
- Start_Time: Shows the start time of the accident in the local time zone.
- End_Time: Shows end time of the accident in local time zone. End time here refers to when the impact of accident on traffic flow was dismissed.
- Start_Lat: Shows latitude in GPS coordinate of the start point.
- Start_Lng: Shows longitude in GPS coordinate of the start point.
- Distance(mi): The length of the road extent affected by the accident.
- Street: Shows the street name in address field.
- Zipcode: Shows the zipcode in address field.
- Weather_Timestamp: Shows the time-stamp of weather observation record (in local time).
- Temperature(F): Shows the temperature (in Fahrenheit).
- Humidity(%): Shows the humidity (in percentage).
- Pressure(in): Shows the air pressure (in inches).
- Visibility(mi): Shows visibility (in miles).
- Wind_Direction: Shows wind direction.
- Wind_Speed(mph): Shows wind speed (in miles per hour).
- Weather_Condition: Shows the weather condition (rain, snow, thunderstorm, fog, etc.)
- Bump: A POI annotation which indicates presence of speed bump or hump in a nearby location.
- Sunrise_Sunset: Shows the period of day (i.e. day or night) based on sunrise/sunset.
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