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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