Effect of Road Geometrics on Accidents

EFFECT OF ROAD GEOMETRICS ON ACCIDENTS AND SAFETY



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

 Asian countries 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



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

 Asian countries 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

Deaths per 1000 vehicles

Fatality Rates in Selected Developing Countries

WHO ARE SUFFERING? US Thailand Sri Lanka Norway Netherlands Malaysia Japan Indonesia India  Australia

0%

10%

20%

Pedestrian

30%

Cyclist

40%

50%

60%

Two Wheeler

70%

80%

Four Wheeler

90%

100%

Other 

Road users Killed in various modes of transport

Road accident Statistics of India 1970-2004

ROAD ACCIDENT SCENARIO OF INDIA 1970-2004 500000 450000 385018

400000   s    t 350000   n   e    d    i 300000   c   c 250000    A    f   o 200000  .   o    N150000

325864 284646

295131 282600

373671

386456 391449

405637

406726

275541

153200 114100

100000 54100

50000

371204 351999

429910

407497

56278

60113

60380

64463

70781

74665

76977

79919

81966

78911

80888

84674

85998

24000 14500

0    0    8    0    9    0    9  1    9    2    9    3    9  4    9    5    9    6    9    7    9    8    9    9    0    0    0  1    0    2    0    3    0  4    7    9    9    9    9    9    9    9    9    9    9    9    9    0    0    0    0    0   1   1   1   1   1   1   1   1   1   1   1   1    2    2    2    2    2

Year

No. of Road Accidents

No. of persons Killed

92618

ACCIDENT STATISTICS IN ANDHRA PRADESH No. of Accidents

No. of Deaths

 Year 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

ACCIDENT SCENARIO IN ANDHRA PRADESH 45000 38937

40000 34133

35000

  s    t   n 30000   e    d    i   c 25000   c    A    f 20000   o  .   o 15000    N

10000

38339

34826

28902

8248

9523

9679

11046

11076

5000 0 2001

2002

2003

2004

Year

No. of Accidents

No. of Deaths

Source: Ministry for Road Transport & Highways

2005

OBJECTIVES 

Identifying the Blackspot locations



Identify road design elements that affect road safety. safety.



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



Develop models for practitioners to determine the appropriate balance between road design standards, road safety.

B LACK SPOT  LACK  SPOT  I DENTIFICATION  DENTIFICATION 

BLACK SPOT IDENTIFICATION METHODS 

Statistical methods 



Bio-medical engineering approach 



Engineering methods 



Subjective assessment techniques 



Empirical Bayes Method 

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 

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: Simple to use Doesn’t require additional information beyond number and location of crashes o o



Disadvantage : Traffic volume is not accounted

2. Crash Density Method  Crash Density = the number of crashes per mile for Highway Sections

3. Crash Rate Method  Crash rate/MEV 

Number of  Crashes DEV

*

1000000 n * 365 days/year

n: Analysis Time Period, generally taken as 5 years For links 0.6 miles or longer, the DEV is determined using the following equation:

 Linklength 0.3 

DEV  ABS 

  * DEV

 ABS is Absolute value

4.Frequency-Rate Method  

This method is a combination of the Crash Frequency and Crash 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 individual methods

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.

Accident Rate ( i ) 

Total no. of  accident in year on the stretch i Total traffic in year on the stretch

i

6.Quantum of accident method  

In the quantum of accident method consequent three years of data is considered for analysis

7.Weighted Severity Index Method  3

WST(  j ) 

 Wi * Ai i 1

WSI(  j ) 

WST(  j ) * K PCU(  j )



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 Order  WSI = 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

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 means that the magnitude of accidents in  Level quantitative terms.

Rating Of Analysis Elements For Accident Prone Index 

Sr.No.

1

2

3

Element of   Analysis Consistency (max. of 40 points)

Tendency (max. of 20 points)

Level (max. of  40 points)

 Accident Scenario

Points

Number of accidents > 3 every year

40

Number of accidents > 2 every year

20

Number of accidents > 1 every year

10

No accident

0

2 times increase in 3 years

20

1 times increase in 3 years

15

No increase in 3 years

10

No accident

0

Number of accidents in 3 years are 6 or >6

40

Number of accidents in 3 years are between 3 & 5

30

Number of accidents in 3 years are

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 .

ENGINEERING METHODS  

Speed profile method 



Safe coefficient method 



Traffic conflict studies 



Wheel path study of vehicle 



Accident coefficient method 



 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

Classification of Locations based on Summary of Accident  Coefficient Method 

Summary Accident Coefficient (K)

Type of Location

1250

Very Dangerous

BIO-MEDICAL ENGINEERING APPROACH 



Driver’s characteristics or response at the location is taken into consideration.



The bio-medical techniques are difficult to be used by organizations lacking in the necessary expertise for carrying out field studies

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 the laboratory and safety evaluation can be performed by group of experts.

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.



Main disadvantage 

Extensive data requirements.



Two sets of data are required to use the Empirical Bayes method:

GEOMETRICS DESIGN EFFECT ON ACCIDENT RATE 

Cross-section 



Sight distance 



Horizontal alignment 



Vertical alignment 



Drainage 



Medians and barriers 



Curbs ,Shoulders and Grading 

C ROSS SECTION  Relative accident rate with roadway width 

Road way width, m

Relative accident rate

4.5

5

5.5

6

6.5

7

7.5

8

9

2.2

1.7

1.4

1.3

1.1

1.05

1.0

0.9

0.8

HORIZONTAL ALIGNMENT  

 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 .

 Accident rate per million vehicle kilometers with radii of horizontal curves  Radius of curve, m

50

150

200

250

500

1000

 Accident rate

3.2

2.8

1.6

0.9

0.8

0.4

Relative Accident rate relating with the radii of horizontal curves  Radius of horizontal

=2000

1

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

Relative Accident Rate in relation with Vertical Gradient  Grade, %

2

3

4

5

7

8

Relative accident rate

1

1.5

1.75

2.5

3

4

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.

Relative accident rate in relation with Shoulder width 

Shoulder width, m Relative Accident rate (K sh)

0.5

1

1.5

2

2.5

3

2.2

1.7

1.4

1.2

1.1

1.0

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 characterise.

that

influence

crash

risk

are

difficult

to



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.”



Milliken and de Pont (2000 used data for heavy vehicle crashes 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. These

other predictors were not independent of seal width, so it was not possible to confidently attribute an increased crash rate to

METHODOLOGY 

Preparation of accident data format

Accident data Collection from secondary sources

Tabulation and General Analysis of Accident Data

Selection of Black spot Identification Method

Crash Density Method

Crash Frequency Ranking 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

SHORTEST POSSIBLE RANGE

1.5 m

GREATEST POSSIBLE RANGE

3000m

MEASURING TIME