Analysis of Road Accidents

Embed Size (px)

Citation preview

  • 7/29/2019 Analysis of Road Accidents

    1/10

    ANALYSIS OF ROADACCIDENTS

  • 7/29/2019 Analysis of Road Accidents

    2/10

    Introduction

    Road Accidents: when a vehicle collides with anothervehicle, pedestrian, animal, road debris, or otherstationary obstruction, such as a tree or utility pole.

    Factors that can contribute to road accidents,

    includes vehicle design/make

    speed of operation

    road design

    environment/ daytime

    driver skill and/or impairment

    age, sex

    and driver behaviour etc.

  • 7/29/2019 Analysis of Road Accidents

    3/10

    Data Collection & Pre-

    processing

    Data Collection: Large data files of Police accidentreports and Transport Accident Commission claimswere obtained

    Data Pre-processing:Data Cleaning: to clean the data by filling in

    missing values, smoothening noisy data,identifying and removing outliers ex. railaccidents removed

    Data Integration: Integrated multiple files - roadaccident details, people involved in the accidentand demographic details

    Data Reduction: Only fatality cases considered;

  • 7/29/2019 Analysis of Road Accidents

    4/10

    Data Analysis

    Age group of 16-25yrs is mostoften involved in roadaccidents

    The number of crashesincreased on weekends

    0

    2000

    4000

    6000

    8000

    10000

    12000

  • 7/29/2019 Analysis of Road Accidents

    5/10

    Data Analysis

    No. of accidents do notincrease with the speed of thevehicle

    Factors affecting Accident

    0

    1000

    2000

    3000

    4000

    5000

    6000

    7000

    8000

    9000

    0.00%

    10.00%

    20.00%

    30.00%

    40.00%

    50.00%

    60.00%

  • 7/29/2019 Analysis of Road Accidents

    6/10

    Time Series Analysis

    No. of accidents increasesduring late evening and latenight

    No. of accidents increasesduring mid of the year

    0

    500

    1000

    1500

    2000

    2500

    3000

    3500

    4000

    5 7 9 11 13 15 17 19 21 23 1 3

    0

    500

    1000

    1500

    2000

    2500

    3000

    3500

  • 7/29/2019 Analysis of Road Accidents

    7/10

    Dependency Network

    Drugs and Drinking directly predicts the fatalities

  • 7/29/2019 Analysis of Road Accidents

    8/10

    Dependency Network

    Drugs predicts fatalitiesDrinking and drugs predictsInjury severity

  • 7/29/2019 Analysis of Road Accidents

    9/10

    Cluster Analysis

    Six different clusters

    Variables States

    Population

    (All) Cluster 1 Cluster 2

    Size 61787 26730 14705AGE Mean 57.85 44.57 67.42

    AGE

    Deviatio

    n 22.42 28.57 16.21

    CITY

    Not

    Applicab

    le 32905 0.558 0.458

    CITY missing 17317 0.273 0.312

    CITY 1980 512 0.006 0.013CITY 4170 452 0.005 0.012

    CITY 3280 434 0.006 0.008

    CITY 1730 372 0.005 0.007

    CITY 370 344 0.004 0.008

    CITY 1670 314 0.005 0.006

  • 7/29/2019 Analysis of Road Accidents

    10/10

    Conclusion

    Texas, California and Florida are the threemost unsafe places

    Cluster1 has highest number of accidents

    where as cluster 4 has the lowest number ofaccidents

    Although drugs and drinking was not a majorfactor of accident on weekdays but the no. ofaccidents involving drinking and drugsincreased during the weekends.

    Majority of the accidents occur on the

    highways