road safety final

ROAD SAFETY

ANALYTICS

Objectives:

Analysis of road safety data in TataSteel and to give prediction model and recommendations for reducing accidents and better management of road incidents.

Tools and Techniques-

Pareto charts Pie Charts Cluster Analysis Histograms Arena SAS PHP

Methodology Cause and Effect diagram

Pareto Charts – In general we can say that 20% of factors are responsible for 80% of the problems. For finding this we use pareto charts.

Clustering – We can group the data points in different clusters based on their location (co-ordinates) and risk score.

Distributions – We find the closest fit distribution to estimate the time between incidences(TBI).

Pie Charts – We can show contribution of factors with the help of pie charts.

Prescriptions.

Data Analysis

Injury Type Pareto

Injury

Injury Type frequency cum. percent

F/A 57 57 45.96774

NO 49 106 85.48387

LTI 15 121 97.58065

FATAL 2 123 99.19355

MTO 1 124 100

F/A NO LTI FATAL MTO0

10

20

30

40

50

60

0

20

40

60

80

100

Pareto Chart

frequency percent

injury

freq

uen

cy

Cu

mm

perc

en

t

Injury

Injury Type frequency cum. percent

NO 81 81 27.46479

F/A 39 120 84.50704

LTI 21 141 99.29577

FATAL 1 142 100

NO F/A LTI FATAL0

20406080

100

020406080100

Pareto Chart

Frequency Cummulative

injuryfr

equency

Cum

m p

erc

ent

2013-14 2012-13

Vehicle Type Pareto

Vehicle Type

Vehicle Type

Frequency prob cumm prob percentage

Two 60 0.3774 0.3774 37.7358

HV 55 0.3459 0.7233 72.3270

FW 26 0.1635 0.8868 88.6792

Cycle 18 0.1132 1.0000 100.0000

Two HV FW Cycle0

10

20

30

40

50

60

70

0

20

40

60

80

100

Pareto Chart

VEHICLE

FR

EQ

UEN

CY

CU

MM

PER

CEN

T

HV FW Two Cycle0

10

20

30

40

50

60

70

80

90

0

20

40

60

80

100

Pareto Chart

VEHICLEFR

EQ

UEN

CY

CU

MM

PER

CEN

T

Vehicle Type

Vehicle Type

Frequency prob cumm prob percentage

HV 81 0.5094 0.5094 50.9434

FW 37 0.2327 0.7421 74.2138

Two 27 0.1698 0.9119 91.1950

Cycle 14 0.0881 1.0000 100.0000

2013-14 2012-13

Control chart for no. of incidents per month

AprilJune

August

October

December

February

AprilJune

August

October

December

February

AprilJune

August

October

December

February

AprilJune

August

October

December

0

5

10

15

20

25

No. of Incidents per month UCL=17.633 LCL=4.367 Month

No.

of I

ncid

ents

Separation between 2012-13 and 2013-14 control chart

A B C D E

1 25 24 22 19 15

2 23 21 18 14 10

3 20 17 13 9 6

4 16 12 8 5 3

5 11 7 4 2 1

Risk Score CalculationConsequence

Pro

bab

ilit

y

Cluster Analysis combined for 2012-13 and 2013-14

Cluster Analysis for 2013-14Cluster Analysis for 2012-13

Plot of incidents based

on injury

Fatal

LTI

First Aid

No Injury

Plot of incidents based on property

damage risk score19 to 25

13 to 18

7 to 12

1 to 6

Plot of incidents based on both type risk score

19 to 25

13 to 18

7 to 12

1 to 6

Clustering of

incident locations

Represents cluster

Cluster Xcord Ycord count cum. percent Location

6 829.421 749.14 57 5721.5094

3East plant drop gate,LD#3 Traffic Signal,Near

LD#3 Office Turning

4 721.296 263.241 54 11141.8867

9L Town Gate, Diamond Crossing, G Blast Furncae

Ramp and crossing

8 1227.9 791.95 40 15156.9811

3HSM Gate, WRP Weigh Bridge, Canteen turning

5 342.892 194.757 37 188 70.9434 Security Office Traffic signal,Coke plant Drop Gate

7 451.419 1174.77 31 21982.6415

1Cabin#4 drop Gate,Near Merchant Mill Office

1 215 506.389 18 23789.4339

6West side peripheral Road,Near WGO

2 1109 298.471 17 25495.8490

6Slag Road Gate

3 154.455 964.545 11 265 100 Pellet Plant Turning, Near PH#3 Gate

6 4 8 5 7 1 2 30

10

20

30

40

50

60

0

20

40

60

80

100

count percent

CLUSTER NO.

frequency

CU

MM

. P

ER

CEN

T

Cluster Analysis

overlapped for

2012-13 and 2013-14

Represents cluster of 2012-13

Represents cluster of 2013-14

Clustering

based on risk scoreBelong to cluster 2

Belong to cluster 4

Belong to cluster 3

Belong to cluster 1

Distribution of Time between incidents

2013-14 2012-13

Distribution Parameters

Distribution Name: Weibull

Alpha: 3.64

Beta: 1.26

Expression: -0.5 + WEIB (3.64, 1.26)

Square Error: 0.007550

Sample Mean = 2.87

Sample Std. Dev = 2.78



Alpha: 3.33

Beta: 1.29

Expression: -0.5 + WEIB(3.33, 1.29)


Sample Mean = 2.57


Distribution of Time between incidents

LTIFirst AidNo Injury

Control Chart for Time between Incidents

2013-14 2012-13

4/1/2013 5/24/2013 7/16/2013 8/24/2013 10/1/2013 10/28/2013 12/5/2013 1/19/2014 2/17/20140

2

4

6

8

10

12

14

16

CONTROL CHART FOR TBI(2013-14)

TBI Linear (TBI) UCL=11.759 LCL=0.113

D A T E

T B

I

4/1/2012 5/21/2012 6/22/2012 7/23/2012 8/19/2012 9/29/2012 11/23/2012 1/10/2013 2/18/2013 3/20/20130

2

4

6

8

10

12

14



D A T ET

B I

Distribution of Time between injury



Alpha: 6.73

Beta: 1.13



Sample Mean = 5.92




Alpha: 5.32

Beta: 1.26



Sample Mean = 4.44


2013-14 2012-13

Control Chart for Injury2013-14

2012-13

4/18/2

013

5/2/2

013

5/24/2

013

6/13/2

013

6/29/2

013

7/10/2

013

7/26/2

013

8/11/2

013

8/24/2

013

9/6/2

013

9/11/2

013

9/27/2

013

10/3/2

013

10/7/2

013

10/14/2

013

10/22/2

013

11/17/2

013

12/7/2

013

12/23/2

013

12/29/2

013

1/15/2

014

1/26/2

014

2/6/2

014

2/15/2

014

2/19/2

0140

5

10

15

20

25


TBI Linear (TBI ) UCL=17.186 LCL=0.165

D A T E

T B

I

4/1/2

012

5/7/2

012

5/9/2

012

5/21/2

012

5/29/2

012

6/19/2

012

6/22/2

012

6/28/2

012

7/10/2

012

7/25/2

012

7/30/2

012

8/4/2

012

8/14/2

012

8/16/2

012

8/22/2

012

8/31/2

012

9/13/2

012

9/19/2

012

10/4/2

012

11/2/2

012

11/27/2

012

12/5/2

012

12/22/2

012

12/26/2

012

1/11/2

013

1/28/2

013

2/1/2

013

2/16/2

013

3/7/2

013

3/19/2

013

3/22/2

0130

5

10

15

20

25

30



D A T E

T B

I

Correlation between different Vehicles involved

HV HV-FW HV-TW HV-CYCLIST FW FW-TW FW-CYCLIST TW TW-CYCLIST CYCLIST0

5

10

15

20

25

4.75 4.31

7

15.5

8.8

4.67

19.5

10.29

7

14

8.24 7.75

6

0

9

4

00.875

0 0

Average Injury Risk score Average Property Damage Risk score

VEHICLE COMBINATION

RIS

K S

CO

RE

Correlation between different Vehicles involved

Combination

Incident count

HV 32

HV-FW 16

HV-TW 2

HV-CYCLIST 4

FW 5

FW-TW 3

FW-CYCLIST 2

TW 48

TW-CYCLIST 8

CYCLIST 4

HV26%

HV-FW13%

HV-TW2%

HV-CY-

CLIST3%

FW4%

FW-TW2%

FW-CYCLIST2%

TW39%

TW-CY-

CLIST6%

CYCLIST3%

Frequency Contribution

Contribution of different Vehicle combinations in risk

Combination

Cumulative Risk

Injurymateri

al

HV 152 263.68

HV-FW 68.96 124

HV-TW 14 12

HV-CYCLIS

T62 0

FW 44 45

FW-TW 14.01 12

FW-CYCLIS

T39 0

TW 493.92 42

TW-CYCLIS

T56 0

CYCLIST

56 0

HV15%

HV-FW7% HV-

TW1%

HV-CY-

CLIST6%

FW4%FW-TW1%FW-

CY-CLIST

4%

TW49%

TW-CY-

CLIST6%

CYCLIST6%

Injury Risk Contribution

HV53%

HV-FW25%

HV-TW2%

FW9%

FW-TW2%

TW8%

Property Damage risk Contribution

MASTER LOGIC DIAGRAM

Incident

VEHICULAR(5)

EXTERNAL(6)

PHYSICAL(1)

BEHAVIORAL(2)

SYSTEM(3)

INFRASTRUCTURE(4)

PHYSICAL(1)

OBSTRUCTION

DIVIDER SPILLAGE DROP GATE HEIGHT BARRIER ILLUMINATION

BEHAVIORAL(2)

INTENTIONAL

CARELESS DRIVING

TRAFFIC NORMS VIOLATION ALCOHOL OVERTAKING HIGH SPEED SLEEPY

NON INTENTIONAL

NOT KNOWLEDGEABLE

SYSTEM(3)

DESIGN

ZEBRA CROSSING TRAFFIC LIGHT CONVEX MIRROR SIGN BOARD

SUPERVISION

OTHERS(ROUTE SURVEY,SECURIT

Y

VEHICULAR ISSUES

(5)

BRAKE FAILURE

INDICATOR LIGHT TYRE BURST

FAIL SAFE BRAKE

FAILURE

STEERING JAMMED

HAND BRAKE NOT WORKING

EXTERNAL (6)

STREET DOGS

Contribution of Organizational

factors and Use of proactive data(FY-

14)

Issues Count cumulative count

Physical

Divider 2

34Spillage (material/water) 13

Environmental (Illumination) 11

Height Barrier 2

Drop Gate 6

Behavioral

Overtaking 28

192

Alcoholic 1Sleepy 0

Not knowledgeable 11Traffic norms violation 5

Careless Driving 107High Speed 40

SystemNo Zebra Crossing 1

32Traffic Light 11

Convex Mirror 3Others (supervision, route survey) 6

No Sign Board 11

InfrastructureBad Road Condition /intersection issue 6

53level crossing 17

Narrow Road (Congestion) 13Blind Turn 6

No separate cyclist/pedestrian pathway 11

Vehicular Issues

Brake Failure 4

16Steering problem 4

Indicator Light Failure 0Others (No reverse mirror) 6

Tyre Burst 2

External Issues Street dogs 6 6

Histogram of various Factor Contributions

Div

ider

Spill

age

(mat

eria

l/w

ater

)

Envi

ronm

enta

l (Il

lum

inati

on)

Hei

ght B

arri

er

Dro

p G

ate

Ove

rtak

ing

Alco

holic

Slee

py

Not

kno

wle

dgab

le

Traffi

c no

rms

viol

ation

Care

less

Dri

ving

Hig

h Sp

eed

No

Zebr

a Cr

ossi

ng

Traffi

c Li

ght

Conv

ex M

irro

r

Oth

ers

(sup

ervi

sion

, rou

te s

urve

y)

No

Sign

Boa

rd

Bad

Road

Con

ditio

n /i

nter

secti

on is

sue

leve

l cro

ssin

g

Nar

row

Roa

d (C

onge

stion

)

Blin

d Tu

rn

No

sepa

rate

cyc

list/

pade

stri

an p

athw

ay

Brak

e Fa

ilure

Stee

ring

pro

blem

Indi

cato

r Li

ght F

ailu

re

Oth

ers

(No

reve

rse

mir

ror)

Tyre

Bur

st

Stre

et d

ogs

Physical Behavioral System Infrastructure Vehicular Issues Ex-ter-nal Is-

sues

0

20

40

60

80

100

120

2 13 11 2 6 28 1 0 11 5 107 40 1 11 3 6 11 6 17 13 6 11 4 4 0 6 2 6

Frequency

PHYSICAL12%

BEHAV-IORAL48%

SYSTEM12%

IN-FRAS-TRUC-TURE18%

VEHICULAR ISSUES7%

EXTERNAL3%

Factor Contribution Based on Occurrence

Physical 28

Behavioral 109

System 27

Infrastructure 42

Vehicular Issues 16

External Issues 6

Factor Contribution Based on Frequency

Physical 34

Behavioral 192

System 32

Infrastructure 53

Vehicular Issues 16

External Issues 6

Physical10%

Behav-ioral58%

System10%

Infra-structure16%

Vehicular Issues5%

External Issues2%

Sub factor Contribution in Physical Factor

Count cumulative count

Physical

Divider 2

34

Spillage (material/water

)13

Environmental (Illumination)

11

Height Barrier 2

Drop Gate 6

Divider6%

Spillage (mate-rial/wa-

ter)38%

Envi-ron-

mental (Illu-mina-tion)32%

Height Barrier

6%

Drop Gate18%

PHYSICAL

Sub factor Contribution in Behavioral Factor


Behavioral

Overtaking 28

192

Alcoholic 1

Sleepy 0

Not knowledgeable 11

Traffic norms violation 5

Careless Driving

107

High Speed 40

Over-taking

15%

Al-co-

holic1%

Not knowledgabl

e6%

Traffic norms viola-tion3%

Careless Driving56%

High Speed21%

BEHAVIORAL

Sub factor Contribution in System Factor

Coun

tcumulative count

System

No Zebra Crossing 1

32

Traffic Light 11

Convex Mirror 3

Others (supervision, route survey)

6

No Sign Board 11

No Zebra Crossing3%

Traffic Light 34%

Convex Mirror

9%

Others (supervision, route survey)

19%

No Sign

Board34%

SYSTEM

Sub factor Contribution in Infrastructure Factor

Countcumulative count

Infrastructure

Bad Road Condition

/intersection issue6

53

level crossing 17

Narrow Road (Congestion) 13

Blind Turn 6

No separate cyclist/pedestrian

pathway11

Bad Road Condition /intersec-

tion issue11%

level cross-

ing32%

Narrow Road (Con-gestion)

25%

Blind Turn11%

No sepa-rate

cyclist/pades-trian path-way21%

INFRASTRUCTURE

Sub factor Contribution in Vehicular Issues Factor


Vehicula

r Issues

Brake Failure 4

16

Steering problem 4

Indicator Light Failure 0

Others (No reverse camera)

6

Tyre Burst 2

Brake Failure

25%

Steering problem

25%Others (No reverse mirror)

38%

Tyre Burst13%

VEHICULAR ISSUES

Factor Contribution in total Injury Score

PROCEDURE: Assumption : Each contributing sub factor is equally responsible for any

incident.

For every incident, Score is divided by no. of sub factors to get score/sub factor.

Finding no. of sub factors of each factor in each incident.

Multiply no. of sub factors to score/sub factor to get contributing score of each factor.

Add all incident’s score for each factor.

Factor Contribution in Total Injury Score

FactorsInjury Risk

Score

Physical 87.85

Behavioral 558.12

System 126.53

Infrastructure 130.50

Vehicular Issues

62.33

External Issues

25.67

physical9%

Behavioral56%

System13%

Infra-structure

13%

Vehicular Issues6%

External Issues

3%

Contribution in total risk(Injury)

Factor Contribution in Total Property Damage

FactorsProperty

damage Risk score

Physical 53.32

Behavioral 280.53

System 69.05Infrastructur

e28.27

Vehicular Issues

75.33

External Issues

0.50

physical11%

Behav-ioral55%

System14%

Infra-structure6%

Vehicu-lar Is-sues15%

External Issues0%

Contribution in total risk(Property Damage)

Comparison of contribution based on Frequency and Total

Risk

Physical10%

Behav-ioral58%

System10%

Infra-structure16%

Vehicular Issues5%

External Issues2%

FACTOR CONTRIBUTION BASED ON FREQUENCY

phys-ical9%

Behavioral56%

System13%

Infra-structure13%

Vehicular Issues6%

Ex-ternal Issues

3%

FACTOR CONTRIBUTION IN TOTAL RISK(INJURY)

Comparison of contribution based on Frequency and Total

Risk

Physical10%

Behav-ioral58%

System10%

Infra-structure16%

Vehicular Issues5%

External Issues2%

FACTOR CONTRIBUTION BASED ON FREQUENCY

physical11%

Behav-ioral55%

System14%

Infra-structure6%

Vehicu-lar Is-sues15%

External Issues0%

FACTOR CONTRIBUTION IN TOTAL RISK(PROPERTY DAM-

AGE)

Correlation between different organizational

measures and number of incidents

Effect of Speed violations on Total incidents due to high speed

MonthSpeed

Violations

No. of incidents(High speed)

April 334 2May 414 2June 326 0July 410 0

August 296 0September 321 1

October 330 14November 311 1December 231 7

January 215 5February 126 4

March 114 3

April

May

June Ju

ly

Augus

t

Sept

embe

r

Octob

er

Novem

ber

Decem

ber

Janu

ary

Febr

uary

Mar

ch0

50

100

150

200

250

300

350

400

450

334

414

326

410

296321 330

311

231215

126 114

2 2 0 0 0 1 14 1 7 5 4 3

Speed violations vs No. of in-cidents due to High Speed

Speed Violations No. of incidents(Highspeed)

MONTH

FR

EQ

UEN

CY

Effect of Speed violations on Total incidents

Effect of Heavy vehicle inspection on Total incidents due to vehicular issues

MONTHSNo. of

inspections

No. of incidents

due to vehicular

issuesApril 434 1

May 362 2

June 403 0

July 368 0

August 308 2

September 330 0

October 334 2

November 417 4

December 395 2

January 528 0

February 421 1

March 288 2

April

May

June Ju

ly

Augus

t

Sept

embe

r

Octob

er

Novem

ber

Decem

ber

Janu

ary

Febr

uary

Mar

ch0

100

200

300

400

500

600

1 2 0 0 2 0 2 4 2 0 1 2

434

362403

368

308 330 334

417 395

528

421

288

No. of HV inspection vs No. of incidents due to vehicular issues

No. of incidents due to vehicular issues No. of inspections

MONTH

FR

EQ

UEN

CY

Effect of HV inspection on Total incidents

Effect of R-SAP on Total incidents due to careless driving

MonthR-SAP

Conducted

No. of Incidents due

to careless driving

April 106 5

May 98 8

June 116 8

July 101 6

August 89 9

September 92 11

October 110 18

November 102 8

December 115 10

January 104 8

Apr'13 May'13 June'13 July'13 Aug'13 Sep'13 Oct'13 Nov'13 Dec'13 Jan'140

20

40

60

80

100

120

140

10698

116

101

89 92

110102

115

104

5 8 8 6 9 1118

8 10 8

R-SAP conducted Vs No. of incidents due to Careless driving

R-SAP Conducted Careless Driving

MONTH

FR

EQ

UEN

CY

Effect of R-SAP on Total incidents

Y MONTH

Speed Violation

Heavy Vehicle Checking

RSAP

6 1 334 434 10610 2 414 362 989 3 326 403 1167 4 410 368 10111 5 296 308 8911 6 321 330 9218 7 330 334 1109 8 311 417 10213 9 231 395 1158 10 215 528 10413 11 126 421 1038 12 114 288 98

Analysis of Variance

Source DF Sum of Squares

Mean Square

F Value Pr > F

Model 3 26.757535 8.919178 0.78 0.538

Error 8 91.492465 11.436558

Corrected Total 11 118.25

Model Fit Statistics

R-Square 0.226 Adj R-Sq. -0.0639

Analysis of Maximum Likelihood Estimates

Parameter DF EstimateStandard

ErrorF Value Pr > |t|

Intercept 1 0.4805 13.2828 0.34 0.745

HVC 1 -0.0235 0.0173 -1.36 0.212

RSAP 1 0.1562 0.1376 1.14 0.289

SV 1 -0.00463 0.0106 -0.44 0.674

Forecasting using Decomposition

Method

Month Forecast Actual Sum

Square Error

September 12.62 11.00 2.61

October 17.48 18.00 0.27

November 7.00 9.00 3.98

December 11.03 13.00 3.88

January 7.39 8.00 0.37

February 13.41 13.00 0.17

March 8.60 8.00 0.36

Validation for number of Incidents

MSE 1.67

Model Building Data: April2011-Aug2013 Validation Data: Sep2013-March2014

Forecast for number of incidentsMonth Polynomial Actual FY-15April 7.23 10May 10.61 10June 14.49 8July 10.00

August 13.53 September 6.77

October 10.29 November 7.52 December 11.46

January 8.39 February 7.87

March 11.51

Quarter wise

0 2 4 6 8 10 1202468

101214

Injury per month

2011-12 2012-13 2013-14

Month

No.

of I

njur

ies

Validation for number of injury

Month Forecast (2013-14)

Actual 2013-14 SS

September 7.74 9.00 1.5952

October 10.08 13.00 8.5452

November -0.75 3.00 14.065

December 4.92 8.00 9.4715

January 5.09 7.00 3.6291

February 6.27 8.00 3.0020

March -0.39 1.00 1.9424

MSE 6.0357

Model Building Data: April2011-Aug2013 Validation Data: Sep2013-March2014

Forecast for No. of Injury type IncidentsMonth Polynomial Actual FY-15

April 4.65 4May 8.83 4June 9.60 5July 8.64




March 8.46 Forecast for First Aid

Forecast for LTI

Prescriptions: In 2013-14, 6 incidents happened due to street dogs which mostly resulted in LTI.

Inside a steel plant, presence of street dogs should not be tolerated and street actions should be taken to eliminate this hazard.

In some incidents, illumination was also a major contributing factor. (E.g. Near pellet plant level crossing, illumination is poor as well as road condition is also poor.) These types of roads should be identified and proper action should be taken)

Over 3 years, there is an increasing trend of incidents near Diamond crossing; even then there is no traffic signal at the crossing. It should be implemented as soon as possible. Also at major crossings (Diamond, LD#2, LD#3, Cabin#4), most near misses are either due to crossing with high speed or carelessness of cyclists/pedestrians. To reduce this no. ,there must be a speed limit (say 20kmph) at all crossings and if possible separate pathways for cyclists/pedestrians.

In some areas, proper sign boards are not present at roads. This should be immediately identified and implemented.

Vehicle failure (mainly steering jammed and brake failure) may lead to a very serious accident inside plant. So these types of incidents should have separate investigation to take preventive actions.

In case of property damages, more than 50% incidents happened due to dashing in which a major contribution is, while reversing a heavy vehicle. So if we strictly implement the availability of reverse camera, these types of incidents can be reduced.

As far as behavioral related issues are concerned, careless driving and high speed are long term issues. But sleepiness and alcohol must not be tolerated. In such cases heavy penalty should be imposed to avoid any future serious accident.

Spillage prone roads should be identified (like near sinter and pellet plant) and there should be warning about spillage to avoid any skidding/slipping.

With the help of regression it is evident that heavy vehicle inspection and speed violation checking are helping in reducing road accidents but RSAP is not up to the mark. More needs to be done in that area.

Thank You

Rohit RajSonu Kumar

Backup Slides

Incidents/Accidents

on Road(124)

Incidents/Accidents

on Road(124)

Cause and Effect diagram for Road IncidentsCause and Effect diagram for Road Incidents

External Factors(17)External Factors(17) Human Factors(83)Human Factors(83)

Material /Water Spillage

Sudden appearance of Street Dogs/animals

Stones and other obstructions

Driver mentally stressed or tired Unskilled in

driving/driving a new Vehicle

Lack of sleep, carelessness (taking risks, overtaking)

Road conditions(15)Road conditions(15)

Sharp and blind turns

Slope (increasing or decreasing)

Uneven or damaged road

Steering jammed

Fail safe brake, blinker, wiper, hand brake not working

Tyre bursted

Vehicle Breakdown (9)Vehicle Breakdown (9)

Low Visibility due to fog or night time

Environmental Factors (N/A) Environmental Factors (N/A)

Rainy or windy conditions

(seasonality)

Cause type Pareto

Real Causes

CAUSES Frequency cum. percent

Human Factors 83 8366.935483

9

External Factor 17 10080.645161

3

Road Condition 15 11592.741935

5Vehicle

Breakdown9 124 100

Human Factors

External Factor

Road Condition

Vehicle Breakdown

0

10

20

30

40

50

60

70

80

90

0

20

40

60

80

100

Pareto Chart freque...

CAUSE

FR

EQ

UEN

CY

CU

MM

PER

CEN

T

Real Causes

CAUSES Frequency cum. percent

Human Factors 105 105 75

Road Condition 14 119 85

Vehicle Breakdown 12 131 93.5714286

External Factor 9 140 100

0

20

40

60

80

100

120

0

20

40

60

80

100

Pareto Chart Freque...

CAUSE

FR

EQ

UEN

CY

CU

MM

PER

CEN

T

2013-14 2012-13

Month type ParetoMonth Frequency Cum. Freq. percent

April,13 6 6 0.04878

May,13 10 16 0.130081

June,13 9 25 0.203252

July,13 7 32 0.260163

August,13 11 43 0.349593September,1

311 54 0.439024

October,13 18 72 0.585366

November,13 9 81 0.658537

December,13 13 94 0.764228

January,14 8 102 0.829268

February,14 13 115 0.934959

March,14 8 123 1

April,

13

May,1

3

June

,13

July

,13

Augus

t,13

Sept

embe

r,13

Octob

er,1

3

Novem

ber,1

3

Decem

ber,1

3

Janu

ary,

14

Febr

uary

,14

March

,14

0

4

8

12

16

Frequency

0 2 4 6 8 10 12 140

2

4

6

8

10

12

14

16

18

20

Month

Fre

qu

en

cy

2013-14

Month type ParetoMonth Frequency Cum. Freq. percent

April,12 8 8 5.633803

May,12 11 19 13.38028

June,12 21 40 28.16901

July,12 9 49 34.50704

August,12 21 70 49.29577

September,12 6 76 53.52113

October,12 8 84 59.15493

November,12 9 93 65.49296

December,12 10 103 72.53521

January,13 11 114 80.28169

February,13 10 124 87.32394

March,13 18 142 100

April

,12

May

,12

June

,12

July,1

2

Augu

st,1

2

Sept

embe

r,12

Octob

er,1

2

Novem

ber,1

2

Decem

ber,1

2

Janu

ary,

13

Febr

uary

,13

Mar

ch,1

30

5

10

15

20

25

811

21

9

21

68 9 10 11 10

18

No. of incidents

0 2 4 6 8 10 12 140

5

10

15

20

25Scatter Plot Frequency

2012-13

Month wise comparison

0 2 4 6 8 10 12 140

5

10

15

20

25

frequency 13 frequency 14

Cluster Analysis for 2013-2014


on injury

Fatal

LTI

First Aid

No Injury



13 to 18

7 to 12

1 to 6


19 to 25

13 to 18

7 to 12

1 to 6

Clustering of

incident location

sRepresents

cluster

6 4 8 5 7 2 1 30

5

10

15

20

25

30

35

0

20

40

60

80

100

120

frequency cum %

Cluster NO.

Fre

quency

Cum

m.

perc

ent

Cluster Means Cluster Xcord Ycord count cum percent Important locations

6 826.25 722.84 32 32 26.0163 LD 2 traffic signal, LD 3 traffic signal, level crossing near LD 3

4 709.9 254.6 30 62 50.4065 Diamond Crossing, L Town Gate, G Blast furnace Turning

8 1225.3 791.5 18 80 65.0407 HSM Gate, WRP weighbridge

5 356.56 177.44 16 96 78.0488 Security Office traffic Signal, West Plant level Crossing

7 456.22 1205.2 9 105 85.3659 Cabin 4

2 1064.1 224 8 113 91.8699 Slag Road Gate

1 127.2 492.2 5 118 95.935 West Peripheral Road

3 131.2 935.8 5 123 100 Near Power House 3 Gate

Clustering


Belong to cluster 4

Belong to cluster 3

Belong to cluster 1

Cluster Analysis for 2012-2013


on injury

Fatal

LTI

First Aid

No Injury



13 to 18

7 to 12

1 to 6


19 to 25

13 to 18

7 to 12

1 to 6

Clustering of

incident location

sRepresents

cluster

Cluster

Xcord Ycord count cum. percent Location

2 805.423 387.308 26 26 18.30986 LD#2 Traffic Signal, G Blast furnace turning

4 389.962 199.692 26 52 36.61972 Security Office Traffic Signal,coke plant drop gate

6 366.077 1121.96 26 78 54.92958 cabin#4 drop gate, near merchant mill

7 1229.57 779.13 23 101 71.12676 HSM Gate, WRP Weigh Bridge

5 850.118 916.647 17 118 83.09859 LD#2 Traffic Signal, CRM Island

3 164.222 512.778 9 127 89.43662 west side peripheral road

1 517.375 578.25 8 135 95.07042 East plant drop gate

8 1155.71 315.143 7 142 100 slag road gate

Clustering


Belong to cluster 1

Belong to cluster 2

Belong to cluster 4

2013-14 2012-13

Distribution of Time between LTIs


Distribution Name: Weibull Alpha: 16.8

Beta: 1.21





Alpha: 23

Beta: 1.34

Expression: -0.5 + WEIB(23, 1.34)


Sample Mean = 20.8Sample Std. Dev = 15.8


CONTROL CHART FOR LTI

2013-14 2012-13

4/19/2

013

6/11/2

013

8/3/2

013

8/11/2

013

9/2/2

013

10/1/2

013

10/1/2

013

10/7/2

013

10/17/2

013

11/6/2

013

11/17/2

013

12/4/2

013

12/23/2

013

1/9/2

014

2/5/2

0140

10

20

30

40

50

60

70

80



D A T E

T B

I

4/1/2

012

4/29/2

012

5/14/2

012

5/21/2

012

6/21/2

012

6/22/2

012

6/30/2

012

7/30/2

012

8/4/2

012

8/9/2

012

8/16/2

012

8/19/2

012

8/22/2

012

9/17/2

012

10/4/2

012

11/2/2

012

12/1/2

012

12/17/2

012

12/25/2

012

2/1/2

013

2/1/2

0130

10

20

30

40

50

60



D A T E

T B

I

Distribution of Time between First Aid



Alpha: 6.88

Beta: 1.27



2013-14 2012-13


Sample Mean : 5.88Sample Std. Dev : 5.32



Alpha: 9.32

Beta: 1.15



CONTROL CHART FOR FIRST AID2013-14

2012-13

4/18/2

013

5/21/2

013

6/13/2

013

6/29/2

013

7/10/2

013

7/26/2

013

8/20/2

013

9/6/2

013

9/11/2

013

9/27/2

013

10/7/2

013

10/14/2

013

10/31/2

013

12/17/2

013

12/23/2

013

1/15/2

014

1/26/2

014

2/7/2

014

2/17/2

0140

5

10

15

20

25

30



D A T E

T B

I

5/7/2

012

5/9/2

012

5/29/2

012

6/19/2

012

7/10/2

012

7/25/2

012

8/2/2

012

8/16/2

012

8/31/2

012

9/13/2

012

9/26/2

012

11/10/2

012

12/5/2

012

12/26/2

012

1/11/2

013

1/28/2

013

2/16/2

013

3/7/2

013

3/19/2

013

3/22/2

0130

5

10

15

20

25

30

35

40



D A T E

T B

I

Distribution of Time between No Injury

2013-14 2012-13



Alpha: 5.18

Beta: 1.1



Sample Mean = 4.47




Alpha: 8.76

Beta: 1.24




CONTROL CHART FOR NO INJURY2013-14

2012-13

4/5/2

012

4/13/2

012

5/7/2

012

5/26/2

012

6/12/2

012

6/15/2

012

6/26/2

012

6/27/2

012

7/12/2

012

7/23/2

012

8/14/2

012

8/25/2

012

8/30/2

012

9/29/2

012

10/12/2

012

10/27/2

012

11/17/2

012

11/29/2

012

12/10/2

012

1/10/2

013

1/18/2

013

2/1/2

013

2/22/2

013

3/6/2

013

3/14/2

013

3/16/2

013

3/25/2

0130

5

10

15

20

25

30


TBI Linear (TBI) UCL LCL

D A T E

T B

I4/1

/2013

4/19/2

013

5/4/2

013

5/14/2

013

5/27/2

013

6/26/2

013

7/23/2

013

8/16/2

013

8/22/2

013

9/11/2

013

10/1/2

013

10/28/2

013

11/2/2

013

11/10/2

013

11/24/2

013

12/1/2

013

12/5/2

013

12/30/2

013

2/1/2

014

2/6/2

014

2/18/2

014

3/9/2

014

3/11/2

014

3/19/2

0140

5

10

15

20

25

30

35


TBI Linear (TBI) UCL LCL

D A T E

T B

I

Effect of Speed violations on Total incidents

MonthSpeed

Violations

No. of incidents(over

all)

April 334 6

May 414 10

June 326 9

July 410 7

August 296 11

September 321 11

October 330 18

November 311 9

December 231 13

January 215 8

February 126 13

March 114 8

0

50

100

150

200

250

300

350

400

450

334

414

326

410

296321 330

311

231215

126 114

6 10 9 7 11 11 18 9 13 8 13 8

Speed violations vs Road Incidents

Speed Violations No. of incidents(overall) MONTH

FR

EQ

UEN

CY

Effect of Heavy vehicle inspection on Total incidents

MONTHSNo. of

inspectionsTotal no. of incidents

April 434 6

May 362 10

June 403 9

July 368 7

August 308 11

September 330 11

October 334 18

November 417 9

December 395 13

January 528 8

February 421 13

March 288 8

1 2 3 4 5 6 7 8 9 10 11 120

100

200

300

400

500

600

434

362

403368

308330 334

417395

528

421

288

6 10 9 7 11 11 18 9 13 8 13 8

No. of HV inspection vs Total no. of incidents

No. of inspections Total no. of incidents

MONTH

FR

EQ

UEN

CY

Effect of R-SAP on Total incidents

MonthR-SAP

ConductedNo. of

Incidents

April 106 6

May 98 10

June 116 9

July 101 7

August 89 11

September 92 11

October 110 18

November 102 9

December 115 13

January 104 8

Apr'13 May'13 June'13 July'13 Aug'13 Sep'13 Oct'13 Nov'13 Dec'13 Jan'140

20

40

60

80

100

120

140

10698

116

101

89 92

110102

115

104

6 10 9 7 11 1118

913

8

R-SAP conducted Vs No. of incidents

R-SAP Conducted No. of Incidents

MONTH

FR

EQ

UEN

CY

USING DECOMPOSITION METHOD (Quarter wise)

Forecast for No. Of IncidentsPolynomial Quarter Actual

29.7 Q1 2828.3 Q2 26.9 Q3 23.4 Q4 22.2 Q1 19.8 Q2 17.5 Q3 13.0 Q4

Forecast for First AidMonth Polynomial Actual FY-15





March 7.82

Forecast for LTIMonth Polynomial Actual FY-15





March 0.95

Usefulness to Tata Steel Here with the help of Cause and Effect diagram we can

identify all the possible causes responsible for the incidents taking place here related to human, vehicle, road, environmental and external factors.

Next with the help of Pareto Chart we can identify major factors responsible for the occurrence of incidents. Using this we were able to identify major type of vehicles, type of injury, type of causes, months which constitutes maximum (here 80%) of incidents.

Moreover, We can also use Pareto chart for identifying highly frequent incident occurring areas which were identified by Cluster Analysis, explained in next slide.

Cluster Analysis :

This is a mathematical tool for grouping a set of objects in such a way that objects in the same group are more similar and within cluster sum of squares is minimum.

This can be used in Tata Steel to group all incidents in a few clusters identified by their co-ordinates on a map of size (width: 1332px, height: 1447px) .

Also, these clusters can be utilised as input for predicting various injury type incidents in a particular area. Here we have used k-means clustering and have taken k =8.

Probability Distribution:

We have fit time between occurrences of incidents both frequency wise and injury type wise (No injury, First aid, LTI).

Using this fitting, we will be able to say what is the pattern of time between occurrences as well it's parameters

can be used in prediction.

E.g.- We have found that TBO follows Weibull distribution with parameters alpha = 1.26 and beta =3.64. So we can say that a particular incident will occur with a particular probability after a certain number of days.

road safety final

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