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Table 3 Attributes and factors of traffic accident

From: Mining and prioritization of association rules for big data: multi-criteria decision analysis approach

Attribute name Values Description
Accident_ID Integer Identification of accident
Accident_Type Fatal, Injury, Property damage Accident type
Driver_Age < 20, [21–27], [28–60], > 61 Driver age
Driver_Sex M, F Driver sex
Driver_Experience < 1, [2–4], > 5 Driver experience
Vehicle_Age [1–2], [3–4], [5–6], > 7 Service year of the vehicle
Vehicle_Type Car, Trucks, Motorcycles, Other Type of the vehicle
Light_Condition Daylight, Twilight, Public lighting, Night Light conditions
Weather_Condition Normal weather, Rain, Fog, Wind, Snow Weather conditions
Road_Condition Highway, Ice Road, Collapse Road, Unpaved Road Road conditions
Road_Geometry Horizontal, Alignment, Bridge, Tunnel Road geometry
Road_Age [1–2], [3–5], [6–10], [11–20], > 20 The age of road
Time [00–6], [6–12], [12–18], [18–00] Accident time
City Marrakesh, Casablanca, Rabat… Name of city where accident occurred
Particular_Area School, Market, Shops… Where the accident occurred
Season Autumn, Spring, Summer, Winter Seasons of year
Day Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday Days of week
Accident_Causes Alcohol effects, Fatigue, Loss of control, Speed, Pushed by another vehicle, Brake failure Causes of accident
Number_of_injuries 1, [2–5], [6–10], > 10 Number of injuries
Number_of_deaths 1, [2–5], [6–10], > 10 Number of deaths
Victim_Age < 1, [1–2], [3–5], > 5 Victim age