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
Source: Ministry for Road Transport & Highways
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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
Source: Ministry for Road Transport & Highways
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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
Source: Ministry for Road Transport & Highways
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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
Number of accidents > 2 every year 20
Number of accidents > 1 every year 10
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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