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Table 3 Road accident data attributes

From: An improved approach for association rule mining using a multi-criteria decision support system: a case study in road safety

Attribute name

Attribute 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’s age

Driver_Sex

M, F

Driver’s sex

Driver_Experience

<1, [2–4], >5

Driver’s experience

Vehicle_Age

[1–2], [3–4], [5–6] > 7

Service year of the vehicle

Vehicle_Type

Car, Truck, Motorcycle, Other

Type of vehicle

Light_Condition

Daylight, Twilight, Public Lighting, Night

Light conditions

Weather_Condition

Normal Weather, Rain, Fog, Wind, Snow

Weather conditions

Road_Condition

Highway, Icy Road, Collapsed 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 the city where the accident occurred.

Particular_Area

School, Market, Shop...

Where the accident occurred: in a school or market area.

Season

Autumn, Spring, Summer, Winter

Season of the year

Day

Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday

Days of week

Accident_Causes

Effects of Alcohol, 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