effect of road geometrics on accidents

7/31/2019 Effect of Road Geometrics on Accidents

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EFFECT OF ROAD GEOMETRICSON

ACCIDENTS AND SAFETY


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Around 2,38,000 people die in road crashes every year in South

Asian countries

The latest annual statistics indicate that over 80,000 people are

killed on Indian roads

Riding a vehicle in India is by large becoming a dangerous

experience, and Indian roads like those of other Asian countries

are becoming virtual death traps


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Fatality Rates in Selected Developing Countries

Deaths per 1000 vehicles


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WHOARESUFFERING?

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Australia

India

Indonesia

Japan

Malaysia

Netherlands

Norway

Sri Lanka

Thailand

US

Pedestrian Cyclist Two Wheeler Four Wheeler Other

Road users Killed in various modes of transport


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Road accident Statistics of India 1970-2004

Source: Ministry for Road Transport & Highways


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ROADACCIDENT SCENARIOOF INDIA1970-2004

295131

325864

429910

56278 60113 6038064463 70781

74665 76977

153200

114100

282600275541

284646

371204 373671

385018386456 391449

405637407497

406726

351999

2400014500

54100

79919 81966 78911 80888 8467485998 92618

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

500000

1970

1980

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Year

No.ofAcc

idents

No. of Road Accidents No. of persons Killed



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ACCIDENT STATISTICSIN ANDHRA PRADESH

Year

No. of Accidents No. of Deaths

Total per Day per Hour in year per Day per Hour

2001 28902 79.2 3 8248 22.6 1

2002 34133 93.5 4 9523 26.1 1

2003 34826 95.4 4 9679 26.5 1

2004 38937 106.7 4 11046 30.3 1

2005 38339 105 4 11076 30.3 1



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ACCIDENT SCENARIOIN ANDHRA PRADESH

28902

34133 34826

38937 38339

82489523 9679

1104611076

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

2001 2002 2003 2004 2005

Year

No.o

fAc

cidents

No. of Accidents No. of Deaths



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OBJECTIVES Identifying the Blackspot locations

Identify road design elements that affect road safety.

Identify how a variation in standards for design elementsaffects the safety of roads in different environments.

Develop models for practitioners to determine the

appropriate balance between road design standards,road safety.


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BLACKSPOTIDENTIFICATION


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BLACK SPOT IDENTIFICATIONMETHODS

Statistical methods

Bio-medical engineering approach

Engineering methods

Subjective assessment techniques

Empirical Bayes Method


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STATISTICAL METHODS

Crash Frequency Method

Crash Density Method

Crash Rate Method

Frequency-Rate Method

Accident rate based on traffic flow

Weighted severity index method

Quantum of accident method

Accident prone index

Multi factor approach


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1.Crash Frequency Method

This Method summarizes the number of crashes at location

and the stretches having the more number of crashes are

taken as accident prone stretches

Advantages:

o Simple to use

o Doesnt require additional information beyond number and

location of crashes

Disadvantage :

Traffic volume is not accounted


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2. Crash Density Method

Crash Density = the number of crashes per mile for HighwaySections

3. Crash Rate Method

days/year365*n

1000000*DEV

CrashesofNumberrate/MEVCrash

n: Analysis Time Period, generally taken as 5 years

For links 0.6 miles or longer, the DEV is determined using the

following equation:

DEV*0.3

LinklengthABSDEV

ABS is Absolute value


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4.Frequency-Rate Method

This method is a combination of the Crash Frequency andCrash Rate Methods. Locations are first ranked by Crash

Frequency and the worst locations re-ranked using Crash

Rate.

The rational of combining Crash Frequency and Crash Rate

is to eliminate or minimize the bias of the two individualmethods


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5. Accident Rate based on Traffic Flow The accident rate per unit traffic flow for the stretch is

calculated and stretch having more accident rate is taken as

accident prone stretch.

6.Quantum of accident method

In the quantum of accident method consequent three years

of data is considered for analysis

i

i)i(

stretchon theyearintrafficTotal

stretchon theyearinaccidentofno.TotalRateAccident


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7.Weighted Severity Index Method

3

1i

ii)j( A*WWST

)j(

)j()j(

PCU

K*WSTWSI


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Based on the values of WSI, mean, standard deviation, the

accident prone locations are identified and divided into three

types.

Accident prone locations of First OrderWSI = Mean + 2SD

Accident prone locations of Second Order

Mean + 2SD > WSI Mean + 1.5 SD

Accident prone locations of Third Order

Mean + 1.5 SD > WSI Mean + SD


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8.Accident Prone Index

Consistency

Consistency means how frequently the accidents are

taking place at the location.

Tendency

Tendency means whether the numbers of accidents at

the location are increasing regularly or it is consistent or

reduced. Level

Level means that the magnitude of accidents in

quantitative terms.


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Rating Of Analysis Elements For Accident Prone Index

Sr.No.Element of

AnalysisAccident Scenario Points

1Consistency(max. of 40points)

Number of accidents > 3 every year 40



No accident 0

2Tendency (max.of 20 points)

2 times increase in 3 years 20

1 times increase in 3 years 15

No increase in 3 years 10

No accident 0

3Level (max. of40 points)

Number of accidents in 3 years are 6 or>6 40

Number of accidents in 3 years arebetween 3 & 5

30

Number of accidents in 3 years arebetween 1 & 2

20

No accident in 3 years 0


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9.Multi factor approach

Multi factor approach assigns weight to different accident

reflecting severity, type of road user involved and accident

cost information.

This has been mainly recommended for identifying black

spots with higher pedestrian accidents.


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ENGINEERING METHODS

Speed profile method

Safe coefficient method

Traffic conflict studies

Wheel path study of vehicle

Accident coefficient method


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Accident Coefficient Method

In this method the relative accident proneness of a road section is

obtained as a continuous product of partial accident coefficients

which have been obtained from different geometrical conditions,

traffic volume and others.

Relative accident coefficient of a section is obtained as:

K= k1* k2* K3**k14


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Classification of Locations based on Summary of AccidentCoefficient Method

Summary Accident Coefficient (K) Type of Location

1250 Very Dangerous


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BIO-MEDICAL ENGINEERING APPROACH

Drivers characteristics or response at the location is taken

into consideration.

The bio-medical techniques are difficult to be used byorganizations lacking in the necessary expertise for carrying

out field studies


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SUBJECTIVE ASSESSMENT TECHNIQUES

Based on the result of the safety evaluation by a group of

drivers, traffic engineers, experts of traffic safety and others.

Multi dimensional perceptual study of road safety is the

ultimate aim of the subjective assessment methods.

In video logging, the whole road can be brought to thelaboratory and safety evaluation can be performed by group

of experts.


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EMPIRICAL BAYES METHOD

This method is used for identification of high crash locations.

The EB method controls the randomness of crash data by

using an estimate of the long-term mean number of crashes at

a location.

It is used for predicting crashes in the future and then ranking

based on the predicted number of crashes.


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Main disadvantage

Extensive data requirements.

Two sets of data are required to use the Empirical

Bayes method:


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Cross-section

Sight distance

Horizontal alignment

Vertical alignment

Drainage

Medians and barriers

Curbs ,Shoulders and Grading

GEOMETRICS DESIGN EFFECT ONACCIDENT RATE


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CROSSSECTION

Road way width, m 4.5 5 5.5 6 6.5 7 7.5 8 9

Relative accident

rate2.2 1.7 1.4 1.3 1.1 1.05 1.0 0.9 0.8

Relative accident rate with roadway width


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HORIZONTALALIGNMENT

Accidents on horizontal curves tend to be of two main types

Running off the road and hitting an object

Lost control and Rolled over

Reasons for this are

Driver entering the bend at too high a speed

Driver was paying insufficient attention or because he misjudged

the severity of the bend.


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Accident rate per million vehicle kilometers with radii of horizontal curvesRadius of curve, m 50 150 200 250 500 1000

Accident rate 3.2 2.8 1.6 0.9 0.8 0.4

Radius of horizontal

curvature

=2000

Relative accident rate 10 5.4-4.6 2.25 1.6 1.4 1.25 1

Relative Accident rate relating with the radii of horizontal curves


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VERTICAL ALIGNMENT

The alignment should be properly coordinated with

the

Natural topography

Available right-of-way

Utilities

Roadside development

Natural and man-made drainage patterns


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Relative Accident Rate in relation with Vertical GradientGrade, % 2 3 4 5 7 8

Relative accident rate 1 1.5 1.75 2.5 3 4


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SHOULDERS

According to V.F.Babkov (1975), a vehicle stopped on a

shoulder does not affect the path of vehicles travelling along

the road only if it is at least at a distance of 2.7metres from the

edge of the pavement, and does not affect their speed if this

distance is at least 1.5 meters.


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Shoulder width, m 0.5 1 1.5 2 2.5 3

Relative Accident rate

(Ksh)2.2 1.7 1.4 1.2 1.1 1.0

Relative accident rate in relation with Shoulder width


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PAST REVIEWS

Pasupathy et al. (2000) and Davies (2000). These studies

have produced a range of multivariate models with quite

different relationships. The authors believe the reasons for

these variations are that the relationship between road

geometry and crash risk differs between regions and that the

parameters that influence crash risk are difficult to

characterise.


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Davies (2000) looked at the relationship between road

geometry and crash risk for all vehicle types. That study found

significant effects due to the horizontal average curvature,

difference between maximum and minimum horizontal

curvature, and the minimum advisory speed. Small effects

were also found for the gradient, direction, sealed carriageway

width and annual average daily travel. There are possibly

effects associated with surface age, surface type, wet or dry

surface, and accident type. There were no significant effects

due to cross section slope or vertical curvature.


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Milliken and de Pont (2000 used data for heavy vehiclecrashes on the State Highway network in New Zealand. They

estimated that heavy vehicle crash risk could be reduced by

8% per metre of widening for small increases in road width.

This result is backed up by McLean (1997) who estimated a

reduction in crash rate of 2% to 2.5% per 0.25 metres of

widening. However, there were other predictors such as AADT

that had a much stronger relationship with crash rate. Theseother predictors were not independent of seal width, so it was

not possible to confidently attribute an increased crash rate to

reduced seal width alone.


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METHODOLOGY


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Preparation of accident data format

Accident data Collection from secondary sources

Tabulation and General Analysis of Accident Data

Selection of Black spot Identification Method

Analysis and Identification of Black spots

Selection of Major Blackspots

Collection of Geometric features at selected Blackspot

Tabulation and General analysis of Geometric details

Model Development for the Determining the Relation

between Geometric Features and Safety

Crash Frequency Ranking MethodCrash Density Method


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SHORTEST POSSIBLE RANGE 1.5 m

GREATEST POSSIBLE RANGE 3000m

MEASURING TIME

Prism mode:


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DATA COLLECTION AND ANALYSIS


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STUDY AREA ON RAJIV RAHADARI

o Rajiv Rahadari is a State Highway passing thorough the

Rangareddy, Karimnagar and Medak districts, having a total

length of 227 Km

o On Rajiv Rahadari, 42 Km covered in Rangareddy, 81 Km

covered in Medakand 101 Km covered in Karimnagardistricts, all with the two lane bituminous surface.


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The study on Rajiv Rahadari (passing through the

Rangareddy, Karimnagar and Medak districts) has been taken

to analyze accident data and identification of accident prone

locations

The secondary data was obtained from the concerned police

stations for the above mentioned districts.

Accident data analysis was carried out for the period of 2002-

2006

The accident prone locations were identified using Crash

density method ,Crash Frequency method.


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From the crash density method of analysis

Average Crash Density 1.81

Critical Crash Density 3.13


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BLACKSPOTSIDENTIFIEDPolice

StationNo of

AccidentsAverage

Length ofstretch(km)

CrashDensity

L.M.D.ColonyStation

Timmapur231 46.2 10 4.62

Siddipet urban 39 7.8 2 3.90

Koheda 44 8.8 4 2.20

Shameerpet 175 35 20 1.75

Siddipet rural 82 16.4 12 1.37

Bollarum 67 13.4 10 1.34

Kukunurpally 182 36.4 28 1.30

Gajwel 259 51.8 40 1.30

Bejjanki 84 16.8 14 1.20

Knr rural 109 21.8 30 0.73

Alwal 14 2.8 12 0.23


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From the crash density method of analysis

Average crash frequency = 29.22

Critical crash frequency = 49.78


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S.No BlackspotTotal no. of

accidentsCrash frequency

1 Gajwel 259 64.75

2L.M.D.Colony

Station Timmapur231 57.75

3 Kukunurpally 182 45.50

4 Shameerpet 175 43.75

5 Karimnagar rural 109 27.25

6 Bejjanki 84 21.00

7 Siddipet rural 82 20.50

8 Bollarum 67 16.75

9 Koheda 44 11.00

10 Siddipet urban 39 9.75

11 Alwal 14 3.50

BLACKSPOTS IDENTIFIED


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For the study area

Critical Blackspot =LMD Colony (Timmapur)


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STUDY AREA ON RAJIV RAHADARI


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ANALYSISOFCRITICALBLACKSPOT

Geometrical details profiles were taken blackspot location

under the above stretch using total station.

The blackspot locations under LMD colony station are given

in table.

The total length of stretch considered (Alugunur to Timmapur)

for analysis is 6.43km.


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BLACKSPOTS UNDER ALUGUNUR TOTIMMAPUR STRETCH

BLACK SPOT CRASH RATE

ALGUNOOR 16

EENADU OFFICE 0.25

LMD COLONY 9

MAHATMA NAGAR 0.25

ST ANTHONY SCHOOL 0.25

THIMMAPUR 6


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Analysisand

model development.xls
http://localhost/var/www/apps/conversion/current/tmp/scratch_2/R.R.Accidents%20ANALYSIS.xlsxhttp://localhost/var/www/apps/conversion/current/tmp/scratch_2/R.R.Accidents%20ANALYSIS.xlsxhttp://localhost/var/www/apps/conversion/current/tmp/scratch_2/R.R.Accidents%20ANALYSIS.xlsxhttp://localhost/var/www/apps/conversion/current/tmp/scratch_2/R.R.Accidents%20ANALYSIS.xlsx


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CR = -11.99 + 18.8 614(CM) + 6.01(USW) - 15.0266(PSW) -

0.0153(RAD) - 6.5864(SE) - 0.0908(HSD)+7.4118(TR) -

0.0213(VSD)

R2 = 0.835

Variable T-test value99% significance

value

CM -1.74 1.884

USW -1.32 1.554

PSW -1.4 1.628RAD 1.38 1.615

SE -0.28 0.818

HSD 2.38 2.427

TR -0.38 0.819

VSD 6.61 6.87


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LOCATION ACTUAL VAUES OFCRASH RATE

PREDICTED VALUES OFCRASH RATE

ALGUNOOR 14.36 14.71

EENADU OFFICE 0.41 0.76

LMD COLONY 3.27 3.62

MAHATMA NAGAR 0.32 0.67

ST ANTHONY SCHOOL 0.57 0.92

THIMMAPUR 8.15 8.50


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Paved Shoulder

Width3 2.5 2 1.5 1

Crash Rate 6.17 13.68 21.20 28.71 36.22

Relative

Accident Rate1.00 2.22 3.43 4.65 5.87

Radius 1000 500 250 200 150 100 50

Crash Rate 0.87 8.52 12.35 13.11 13.88 14.64 15.41

Relative

Accident Rate1 9.75 14.12 15.00 15.87 16.75 17.62

Super Elevation 7 6 5 4 3 2 1

Crash Rate4.82 11.41 17.99 24.58 31.17 37.75 44.34

Relative

Accident Rate1 2.37 3.73 5.10 6.46 7.83 9.20


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Horizontal Sight

Distance200 150 125 75 50

Crash Rate 3.15 7.69 9.96 14.50 16.77

Relative Accident

Rate1 2.44 3.16 4.61 5.33

Total Rise 8 7 6 5 4

Crash Rate 31.81 24.49 17.08 9.67 2.26

Relative Accident

Rate1 0.77 0.54 0.30 0.07

Vertical Sight

Distance250 200 150 100 50

Crash Rate 10.53 11.59 12.66 13.72 14.79

Relative Accident

Rate1.00 1.10 1.20 1.30 1.40


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CONCLUSIONS

Along the horizontal curves, radius and sight distances are insufficient

for the vehicles which are moving at greater than the design speed

which is 60kmph.

Most of the accidents were occurred near the junction where there is no

proper sight distance for the vehicles coming from the minor road to

merge or diverge from major road. This problem can be overcome by

providing the service roads for the minor road vehicles so that they can

merge or diverge into major road traffic.

The number of culverts along the section are more. At these locations

the carriage way width was drastically decreased and because of

improper signboards the drivers are unable to judge the situation.

continued


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LIMITATIONS OF STUDY

Blackspots were identified based on the total number of accidents in

the given stretch, traffic volumes were not considered so it is not

possible to relate accident rate with traffic flowing along the road.

If the accident data is precise i.e. time and date of accident and gap

between two accidents occurred in a section is known then there will

be a possibility of fitting a better binomial or Poisson distribution

which is not possible with the present available data.

The model developed can be used for predicting the future crash rate

value, but accident rate does not solely depend on geometric

features but also the number of cross details, number of culverts and

traffic regulations maintained along road.


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SCOPE FOR FURTHER WORK

Blackspots can be identified by the other methods which consider the

traffic volume.

Accident cost analysis can be carried out to find the appropriate

balance between road safety benefits and costs.

Road safety audit can be done to get the further details like

encroachments, position of signboards, etc., and other improvements

can be stated by auditing along the road.


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effect of road geometrics on accidents

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