ROAD ACCIDENT MODELS FOR LARGE METROPOLITAN CITIES OF INDIA

P. P. VALLI

ROAD ACCIDENT MODELSFOR LARGE METROPOLITAN CITIES OF INDIA
P. Pramada VALLI, Ph.D.
Scientist, Central Road Research Institute
New Delhi, India(Received August 5, 2004)
In most of the metropolitan cities in India, the road use patterns are very different from those of developed countries. In Indian cities, roads
are shared by non- motorized vehicles in large numbers. The rapid urbanization in India after independence has resulted in the faster development of
23 metropolitan cities as per the 2001 census. While the alarming increase in road accidents has become a major concern in the country, which takes
away more than 90,000 lives every year, a significant share of it is from the major cities.
The aim of the present paper is to develop models by analyzing the road accident data at an all India level as well as for major metropolitan
cities. The data for the 25 year period from 1977 to 2001 were analyzed to build models to understand the nature and extent of the causes ofaccidents using the concept of Smeed’s formula and Andressen’s equations.
On the basis of population and motor vehicle growth rates, which were derived from the empirical formulae, the above models were used for
estimating road accidents in seven metropolitan cities for the years 2007 and 2010. As the variation between model and observed values is negligible, models seem to perform well for both equations.
Key Words: Road accidents, Fatalities, Injuries, Regression, India

1. INTRODUCTION
Most of the metropolitan cities in India are witnessing phenomenon of ever increasing growth of vehiculartraffic due to population explosion coupled with largescale socio-economic activities. This has resulted in severe traffic problems on roads in terms of road accidents
and deterioration in the eco-friendly environment due toan increase in noise and air pollution.
Rapid growth of road accidents in most of the large
metropolitan cities is increasingly becoming a great concern to which takes away as many as 90,000 lives adding to human miseries. Causation of accidents can bewell understood with the help of analysis of accident statistics, which can provide clues to many factors of roadaccidents. Many researchers including Smeed have devoted their research to the area of road accidents and reported pioneering work on the analysis of road accidents.
Smeed1 further investigated the variations in the patternof accident rates in different countries and their causes.
Livneh2 conducted a detailed case study on road accidents
in Israel. Using employment and population data,
Partyka3 developed simple models with a view to understand the various factors affecting the increase in acci-

dents in developing countries. Andreassen4 investigatedthe origin of Smeed’s formula in detail and concluded that
Smeed’s formula cannot be applied universally to all
countries. Mekky5 used the time series data for the analysis and studied the effects of a rapid increase in motorization levels on fatality rates in some developing countries.
Mohan6 attempted to understand total crash patterns in
Delhi. Jacobs and Cutting7 attempted further to update therelationship based on earlier studies. The study on the effect of speed limits on road accidents has been carriedout by Fieldwick8. It was found that speed limits havea considerable effect on safety both in urban and rural
areas.
Adams9 worked on Smeed’s law and provided someinsights in the analysis. Minter10 discussed an application of the two models (Wright and Towell) for roadsafety problems and finally developed a model for estimating the road accidents in U.K. Pramada et al.,11 investigated the variations in the pattern of road accidents
in various States and Union Territories of India. Emanalo
et al.12 established the trend curves for the road accidents,
casualties, and other relevant quantities for Zambia.
Pramada et al.,13 developed a road accident modelby using the additional parameter of road length. Ameen
and Naji14 presented a general modeling strategy to foreIATSS RESEARCH Vol.29 No.1, 2005 •57

TRANSPORTATION

cast road accident fatalities in Yemen.
It is therefore extremely desirable to understand basic factors responsible for causing road accidents. Thoughanalyses of road accidents based on data15 have been directed towards understanding road accidents at an all India level and large metropolitan cities, author is moreemphatic on developing models for metropolitan cities inthis paper.

2. GROWTH OF DIFFERENT METROPOLITANCITIES IN INDIA
Urbanisation in India has been phenomenal since
independence resulting in the development of metropolitan cities (one million plus population). With only one
metropolitan city, Kolkata in 1901, India has now added
another 23 metropolitan cities over a period of 100 years.
The number of metropolitan cities during the period 19772001 has nearly doubled from 12 to 23. The share of the
population in metropolitan cities in the year 2001 constituted about 5.34% of the total population of 1.2 billion. There is a large variation in the sizes from 13.2
million (Mumbai) to 1.62 million population (Ludhiana)
in 2001.

3. PATTERN OF ROAD ACCIDENTS IN
DIFFERENT CITIES IN INDIAOf the total of 23, only seven major metropolitan
cities have been considered for detailed analysis of roadaccidents for the period of 25 years, i.e., (1977-2001).
Rapid growth of population along with numerous economic activities have acted as the major catalyst for thesteady growth of the motor vehicle population and has
subsequently resulted in the increase of road accidents.
Table 1 presents the growth rates of motor vehicles, population and accidents for selected metropolitan cities in India. It can be seen from the table that the ratio of averageannual growth of motor vehicles to population varies from
0.27 to 0.72. Except cities like Mumbai and Chennai,
the five metropolitan cities exhibit a positive growth rateof accidents that is a great concern for road safety. Table
2 presents a useful result in terms of rate of changes inroad accident indices.
Over a period of ten years (1990-2000), all the sevencities have experienced a decline in rate of change ofroad accident indices. As far as the change in the ratioof accidents to population is concerned, cities like Delhi,
Hyderabad and Kolkata indicate positive values.

4. ROAD ACCIDENT SITUATION IN INDIA
The magnitude of road accidents in India has gone
up to an alarming proportion. About 80,000 persons were
killed and 382,700 persons were injured in India in theyear 2001. This clearly demonstrates the gravity of roadtraffic problems in India, which continues to be worse
primarily due to the ever-increasing growth of motor vehicles and the inadequacy of the road system to cope withthe saturated traffic flow.
The actual trend in the total accidents, fatalities andinjuries in India for the period 1970-2001 is illustrated

Table 1 Growth rates of motor vehicles, population and accidents for major metropolitan cities in India
from 1990 to 2000
Name of theMetropolitan
City

AAGR of
Motor
Vehicles(NR)

AAGR of
Population(PR)AAGR of Total
Accidents(CR)AAGR of
Fatalities(FR)AAGR of
Injuries(IR)

CR/NR

NR/PR

Ahemadabad

1.749

4.88980.01000.50000.15000.00640.3600Bangalore

2.468

3.48000.39700.46000.30000.16000.7100Chennai

1.315

1.8254-0.1000

0.70000.2000-0.0600

0.7200

Delhi

1.406

2.75060.52000.44000.31010.37000.5100Hyderabad

1.350

2.08510.70000.37001.05300.51500.6500Kolkata

0.741

1.61370.55770.0682-0.0300

0.75000.4600

Mumbai

0.520

3.1678-0.0100-0.2900-0.1800-0.0100

0.2700

AAGR: Average Annual Growth Rate

58 • IATSS RESEARCH Vol.29 No.1, 2005

ROAD ACCIDENT MODELS FOR LARGE METROPOLITAN CITIES OF INDIA

in Fig. 1, which clearly reflects the increasing trend ofroad accidents during this period. The total number ofroad accidents and fatalities have gone up significantly
from 114,000 to 394,000 and from 14,500 to 80,000 resulting in an increase of 3.46 times and 5.51 times respectively over a period of 32 years.
As compared to an all India level, the total roadaccidents in the seven metropolitan cities namely
Ahemedabad, Bangalore, Mumbai, Kolkata, Delhi,
Hyderabad and Chennai were about 21.5% of the total
accidents during 1977, which marginally came down by
5% to 16.9% in 2001. The fatalities and injuries during
this period exhibit a declining trend significantly from10.52% to 6% and from 23.28% to 8.96% respectively.
This decline in most of the selected cities reflects not only
the relative expansion of the road-network, but also theextent of safety measures taken by concerned city authorities.

P. P. VALLI

5. DEVELOPMENT OF ACCIDENT MODELS
5.1

Smeed’s model
Smeed16 examined the relationship on a number ofroad fatalities with those of motor vehicles and the populations of 20 countries in 1938 in the following form:
D / N = 0.0003 (N / P) -0.67 ........................................................... (1)where D, N, P are deaths, motor vehicles and population
respectively.
It was observed that 10 of the values of D calculated by this formula were within 15% of their actual values, 19 were within 40% and one was in error by 67%.
The reason for the use of this formula was explained as“a formula is fitted to a set of data to summarize themand make it easier to reflect the properties of the data”.
There was no suggestion made to use the formula for prediction purposes. Smeed and Jaffocate17 further attempted

Table 2 Rate of change in road accident indices from 1990 to 2000
Name of the Metro
Politan City

C/N

F/N

I/N

C/P

F/P

I/P

Ahemadabad-0.630-0.4700-0.5800-0.3700-0.1000-0.2900

Bangalore-0.597-0.5790-0.6280-0.00800.3700-0.0800

Chennai-0.600-0.2400-0.4800-0.2300

0.46000.0009Delhi-0.370-0.4000-0.4600

0.16000.0980-0.0012Hyderabad-0.280-0.4200-0.1300

0.40000.11000.6700Kolkata-0.110-0.3900-0.4400

0.3300-0.0898-0.1707

Mumbai-0.350-0.5300-0.4600-0.1800-0.4100-0.3300

500,000
450,000
400,000

300,000
250,000
200,000

C/N = 0.00085(N/P)–0.75

150,000

I/N = 0.0012(N/P)–0.57

100,000
50,000F/N = 0.00032(N/O)–0.58

0

19
70
19
71
19
72
19
73
19
74
19
75
19
76
19
77
19
78
19
79
19
80
19
81
19
82
19
8
19 3
84
19
85
19
86
19
8
19 7
88
19
8
19 9
90
19
9
19 1
92
19
93
19
94
19
9
19 5
96
19
9
19 7
98
19
9
20 9
00
20
01

Accidents

350,000

Observed C
Model C
Observed F
Model F
Observed I
Model IYear

Fig. 1 Comparison of model values with the observed values of road accidents in India
IATSS RESEARCH Vol.29 No.1, 2005 •59

TRANSPORTATION

to present the accident figures (1960-1967) of 70 countries, including India, through the above formula. It may
be mentioned that except five out of 70 countries namely,
Gibraltar, Sarawak, North Borneo, India and South Africa, Smeed’s model successfully predicted the numberof fatalities. Using the same method as Smeed18, Jacobs7
carried out analysis of fatalities in developing countries
for different years and established significant relationships
between fatality rates and levels of vehicle ownership.
The analysis was repeated for the year 1980 using datafrom 20 developing countries and a relationship was derived which is as follows:
D / N = 0.00036 (N / P) -0.65 ....................................................... (2)Equation (2) is very similar to that derived by
Smeed suggesting that the situation in developed countries in 1938 is similar to that in developing countries in1980.
Again this analysis of 35 developing countries was
carried out for the years (1978-1980). The following
equation was derived:
D / N = 0.00039 (N / P) -0.64 ........................................................ (3)It can be seen that equation (3) was similar to thatderived for 1980 and Smeed’s equation.
5.2

Andreassen’s equation
Smeed’s analysis was heavily criticized byAndreassen19 for model accuracy. He argued that the
Smeed’s formula cannot be applied universally to all
countries. The generalized relationship Andreassen produced in 1985 is of the form:
D = const * (N) M1 * (P) M2 .......................................................... (4)The failure of Smeed’s model to predict fatalities
in many developed and developing countries (see Jacobs
and Cutting7, Adams9 and Andreassen4,19) motivated theauthors to modify the same by including other variables
like road length to improve its predictability.
5.3 Road accident models for India
Based on Smeed’s formula as described earlier, anattempt has been made to develop relationships among
the parameters, namely, road accidents, the number ofregistered motor vehicles and population. The data onroad accidents in terms of total number of accidents (C),fatalities (F), injuries (I) and the number of registeredmotor vehicles (N) were used from road accidents in India. The model developed by Smeed does not however,explain the discrepancy for the data on India, as the data
do not give good fit to the formula.
It is, therefore, proposed to investigate the data anddevelop appropriate models based on the concept of

60 • IATSS RESEARCH Vol.29 No.1, 2005

Smeed’s formula and Andreassen’s formula.
The models developed by Pramada et al.13 relating
fatalities (F) to vehicles and population in India for theyears (1960-1991) is as follows:
F / N = 0.00073 (N / P) -0.4 ........................................................... (5)The parameter coefficient -0.4 in equation (7) is not
the same as the parameter’s coefficient in Smeed’s formula. The constant term (i.e., 0.00073) also differs with
Smeed’s formula (i.e., 0.0003).
In the present paper the regression analysis was carried out using Smeed’s model for the years (1970-2001)
for India and the following equations are derived:
C / N = 0.0008 (N / P) -0.75 ........................................................... (6)F / N = 0.0003 (N / P) -0.58 ............................................................ (7)I / N = 0.0014 (N / P) -0.57 .............................................................. (8)The constant term (i.e., 0.0003) in equation (7) isexactly the same as the constant term in Smeed’s formulain equation (1). Whereas, using Andreassen’s approach
for accident data on India, the following equations were
derived for the above period:
C = e 21.26 * N 0.495 * P -0.83 .......................................................... (9)F = e 13.47 * N 0.613 * P -0.63 ......................................................... (10)I = e 13.47 * N 0.604 * P -0.54 ......................................................... (11)A comparison of observed and model values using
the above two approaches as presented in Table 3, reveals
that the observed values compare quite favorably withthose of Smeed’s and Andreassen’s equations. A goodcomparison of values from Smeed’s model with observed
values in terms of total accidents, fatalities and injuries
is shown in Fig. 1. The model seems to fit very well until
1985. Due to a sudden spurt in two wheelers and four
wheelers the variation took place in urban India as shownin Fig. 1.
5.4

Road accident models for selected metropolitan
cities of India
That the accident characteristics in metropolitan cities of India varies in these cities is evident from the analysis.
The mathematical form of the model is as follows:
A / N = B1 (N / P) B ........................................................................ (12)Where
A / N = Accidents per 1000 motor vehicles;
N / P = Motor vehicles per 1000 population;
B and B1 are model parameters.
Table 4 presents the models for total accidents, fatalities and injuries for selected cities. Model values onroad accidents obtained from equation (12) and observed
values for three cities Bangalore, Kolkata and Mumbai
are shown in Figs 2-4. Table 5 demonstrates the sensi-

ROAD ACCIDENT MODELS FOR LARGE METROPOLITAN CITIES OF INDIA

P. P. VALLI

Table 3 Comparison of Smeed’s and Andreassen’s Equation values of accidents for India from 1970 to 2001
Year

Total Accidents

Fatalities
Smeed’s Model

Injuries

Smeed’s Model

Andreassen’s
Equation

Andreassen’s
Equation

Smeed’s Model

Andreassen’s
Equation

Actual Predicted % PD

Predicted % PD

Actual Predicted % PD Predicted % PD

Actual Predicted % PD Predicted % PD

1970

114,000 105,138-7.77

109,929-3.57

14,50014,372-0.88

14,867

2.53

70,10064,598-7.85

66,618

1971

120,200 110,681-7.92

115,707-3.74

15,00015,4543.03

15,984

6.56

70,70069,497-1.70

71,667

1.37

1972

122,300 115,139-5.86

118,877-2.80

16,10016,278

1.11

16,678

3.59

76,40073,227-4.15

74,859

-2.02

1973

121,600 117,982-2.98

118,485-2.56

17,60016,706-5.08

16,760-4.77

79,30075,152-5.23

75,350

-4.98

1974

114,300 122,922

7.54

122,108

6.83

17,30017,643

1.98

17,554

1.47

76,70079,3973.52

79,0033.00

1975

116,800 126,881

8.63

123,500

5.74

16,90018,336

8.50

17,9646.29

77,00082,5297.18

80,956

5.14

1976

124,700 131,862

5.74

126,641

1.56

17,80019,281

8.32

18,6995.05

82,50086,811

5.23

84,363

2.26

1977

135,400 140,463

3.74

136,464

0.79

20,10021,157

5.26

20,6992.98

95,60095,331-0.28

93,402

-2.30

1978

146,300 146,515

0.15

140,965-3.65

21,80022,3902.70

21,743-0.26

99,500 100,920

1.43

98,210

-1.30

1979

144,400 153,317

6.17

146,559

1.49

22,60023,825

5.42

23,0241.87 102,900 107,435

4.41 104,082

1.15

1980

153,200 160,106

4.51

151,747-0.95

24,60025,263

2.69

24,255-1.40 109,100 113,964

4.46 109,751

0.60

1981

161,200 170,027

5.48

162,5110.81

28,40027,575-2.91

26,645-6.18 114,000 124,480

9.19 120,598

5.79

1982

166,200 176,156

5.99

170,833

2.79

30,70029,120-5.15

28,450-7.33 126,000 131,521

4.38 128,731

2.17

1983

177,000 183,631

3.75

181,8172.72

32,80031,081-5.24

30,848-5.95 134,100 140,460

4.74 139,505

4.03

1984

195,000 190,947-2.08

192,537-1.26

35,10033,030-5.90

33,239-5.30 156,200 149,354-4.38 150,256

-3.81

1985

207,000 199,135-3.80

205,093-0.92

39,20035,280 -10.00

36,079-7.96 163,400 159,623-2.31 163,001

-0.24

1986

215,500 207,667-3.64

218,451

1.37

40,00037,680-5.80

39,157-2.11 176,400 170,588-3.29 176,812

0.23

1987

234,000 218,367-6.68

236,593

1.11

44,40040,824-8.05

43,386-2.28 189,000 184,961-2.14 195,739

3.57

1988

246,700 228,731-7.28

254,255

3.06

46,60043,935-5.72

47,6102.17 214,800 199,197-7.26 214,642

-0.07

1989

270,000 237,940 -11.87

269,466-0.2

50,70046,724-7.84

51,353

1.29 229,700 211,960-7.72 231,414

0.75

1990

282,600 246,987 -12.60

284,3540.621 54,100

49,505-8.49

55,094

1.84 244,100 224,697-7.95 248,181

1.67

1991

293,400 279,290-4.81

270,068-7.95

56,30055,806-0.88

54,402-3.37 255,000 253,274-0.68 247,529

-2.93

1992

260,300 289,268 11.13

279,453

7.358 57,200

58,627

2.50

57,112-0.15 267,100 266,175-0.35 259,975

-2.67

1993

280,100 298,605

6.61

287,232

2.546 60,700

61,2380.89

59,460-2.04 287,900 278,111-3.40 270,822

-5.93

1994

320,400 308,209-3.80

295,160-7.88

64,00063,952-0.07

61,887-3.30 310,800 290,525-6.52 282,043

-9.25

1995

348,900 318,896-8.60

304,794 -12.6

70,70067,068-5.14

64,805-8.34 322,900 304,783-5.61 295,487

-8.49

1996

355,100 331,416-6.67

317,555 -10.6

71,90070,874-1.43

68,613-4.63 330,000 322,217-2.36 312,954

-5.17

1997

290,400 343,430 18.26

328,926

13.26

61,00074,52222.17

72,121

18.23 290,800 338,929

16.55 329,100

13.17

1998

298,100 351,320 17.85

331,979

11.38

62,70076,63322.22

73,41017.04 296,700 348,578

17.48 335,290

13.01

1999

386,425 368,089-4.745 351,574-9.02

82,04582,1250.10

79,313-3.28 375,000 373,772-0.33 362,261

-3.40

2000

391,463 380,292-2.854 361,976-7.53

78,95585,9078.80

82,7484.88 399,300 391,111-2.05 378,161

-5.29

2001

445,221 461,356

3.6241 320,193 -18.9

99,456 102,385

2.95

77,594-3.04 432,555 465,920

7.71 360,962-5.6801-4.97

Table 4 Road accident models for metropolitan cities of India
METROPOLITAN CITYTOTAL ACCIDENTSFATALITIESINJURIES

Ahemadabad

C/N = 0.000248(N/P)-1.29

F/N = 0.002603(N/P)-0.02

I/N = 0.0002651(N/P)-1.24

Bangalore

C/N = 0.001364(N/P)-1.01

F/N = 0.000119(N/P)-0.98

I/N = 0.0009539(N/P)-1.08

-1.31

-0.82

F/N = 0.000134(N/P)

I/N = 0.0006257(N/P)-1.07-0.58

I/N = 0.0041983(N/P)-0.64

Chennai

C/N = 0.000408(N/P)

-0.65

Delhi

C/N = 0.0004288(N/P)

F/N = 0.0009(N/P)

Hyderabad

C/N = 0.001022(N/P)-0.84

F/N = 0.00191(N/P)-0.87

Kolkata-0.96

C/N = 0.004391(N/P)-1.50

I/N = 0.0007624(N/P)-0.84-0.83

I/N = 0.0009733(N/P)-1.20-2.91

F/N = 0.000292(N/P)

MumbaiC/N = 0.000572(N/P)

F/N = 1.57E-07(N/P)

I/N = 4.006E-05(N/P)-1.97

ALL INDIA

C/N = 0.000817(N/P)-0.75

F/N = 0.000315(N/P)-0.58

I/N = 0.001453(N/P)-0.57

IATSS RESEARCH Vol.29 No.1, 2005 •61

TRANSPORTATION

tivity of the model during the years between 1995 and2000 for seven major metropolitan cities of India wherein
the percentage difference between model and observed
values is quite high for Kolkata and Hyderabad, in termsof total accidents and injuries in the year 2001. Fatalities and injuries models have fitted well with the data for
Delhi and Mumbai. But, only the fatalities model is performing well with respect to four Indian cities i.e.

10,0009,000
8,000

Accidents

7,000
6,000

Observed C
Model C
Observed F
Model F
Observed I
Model I

C/N = 0.0013(N/P)–1.01

5,000

I/N = 0.0001(N/P)–1.08

4,000
3,000
2,000

F/N = 0.0001(N/P)–0.98

1,000
0
1975

1980

1985

1990

1995

2000

Year

Fig. 2 Model and observed values of road accidents in Bangalore
12,00010,000C/N = 0.0044(N/P)–0.96

Accidents

8,000
Observed C
Model C
Observed F
Model F
Observed I
Model I

6,000

4,000

I/N = 0.001(N/P)–1.2

2,000
F/N = 0.0003(N/P)–0.83

0
1975

1980

1985

1990

1995

2000

Year

Fig. 3 Model and observed values of road accidents in Kolkata

62 • IATSS RESEARCH Vol.29 No.1, 2005

2005

ROAD ACCIDENT MODELS FOR LARGE METROPOLITAN CITIES OF INDIA

P. P. VALLI

35,00030,00025,000

Accidents

C/N = 0.00057(N/P)–1.5
Observed C
Model C
Observed F
Model F
Observed I
Model I

20,00015,00010,0005,000

I/N = 0.00004(N/P)–1.97
F/N = 1.57E-07(N/P)–2.91

0
1975

1980

1985

1990

1995

2000

2005

Year

Fig. 4 Model and observed values of road accidents in Mumbai
Table 5 Comparison of actual and predicted road accidents of metropolitan cities in India for the year 1995 and2000
Name of theMetropolitan City
Ahemadabad
Bangalore
Chennai
Delhi
Hyderabad
Kolkata
MumbaIYear

Total Accidents

Fatalities

Injuries

ActualPredicted% PD

ActualPredicted% PD

ActualPredicted

% PD

1995

2,354

2,936

24.73

189

200

6.04

2,670

2,781

4.16

2000

3,014

3,168

5.11

223

206

-7.76

2,905

3,056

5.21

1995

8,127

7,963

-2.02

690

657

-4.77

6,545

6,386

-2.43

2000

8,391

9,174

9.33

659

757

14.91

6,347

7,042

10.96

1995

4,987

4,750

-4.75

598

564

-5.65

4,987

4,395

-11.86

2000

4,878

4,567

-6.37

692

640

-7.58

4,496

4,581

1.89

1995

10,138

9,317

-8.10

1,981

1,963

-0.91

7,643

7,279

-4.76

2000

10,245

11,773-14.91

1,918

2,532

32.04

7,683

9,248

20.37

1995

1,901

1,665

-12.41

350

324

-7.47

1,892

1,254

-33.70

2000

2,492

1,819

-26.99425

348-18.20

2,357

1,365

-42.09

1995

899

8,286

-7.83

476

470

-1.35

2,980

2,529

-15.13

2000

10,9998,385-23.77

452

488

8.02

3,316

2,425

-26.88

1995

28,76529,567

2.39

391

481

22.92

7,321

8,099

10.63

2000

26,45023,473-11.26449

217-51.65

7,122

6,586

-7.52

IATSS RESEARCH Vol.29 No.1, 2005 •63

TRANSPORTATION

Ahemadabad, Chennai, Hyderabad and Kolkata. However, some variations in model values are observed inthese cities as shown in the figures concerned. Bangalore is the only city where accident models have fitted
well with the data.
A further attempt was made for prediction of roadaccidents for seven metropolitan cities for the years of2007 and 2010 as presented in Table 6. The summary of
the statistical tests for the regression equations is presented in Table 7.

could not be fitted collectively to express the accident
model properly. Hence, different models were developed
for different cities depending upon the data trends of eachcity. However, the fatalities model accuracy is acceptable for all cities.
The above models have been used to predict roadaccidents for the years 2007 and 2010. In order to minimize accidents, major policy may be evolved to reduce
the growth of personalized vehicles and simultaneously
to encourage the use of public transport vehicles. Dueto data limitations, other metropolitan cities could not be
considered.
Though a limited study has been reported so far in
the area of road accidents, it is desirable to look into thecauses and effects of road accidents to be carried out not
only at a macro-level but also at a micro-level.

6. CONCLUSIONS
The data collected for selected metropolitan cities

Table 6 Estimates of road accidents, population and motor vehicles of metropolitan cities in India for the Year2007 and 2010
Name of theMetropolitan
City/Year

Population in 1000
2007

Motor Vehicles

2010

2007

Total Accidents

2010

2007

2010

Fatalities
2007

Injuries

2010

2007

2010

Ahemadabad

8,718

9,386

1,114,515

1,269,082

3,534

3,723

223

232

3,447

3,645

Bangalore

8,302

9,200

1,897,884

2,041,953

11,657

12,918

962

1,066

8,976

9,972

Chennai

7,107

7,389

1,627,153

1,815,221

4,624

4,705

733

772

4,929

5,100

Delhi

13,872

14,446

4,262,188

4,656,458

11,726

12,220

2,690

2,845

9,370

9,862

Hyderabad

4,513

4,719

1,212,191

1,459,282

1,915

1,951

362

368

1,433

1,460

Kolkata

14,366

14,871

824,597

886,338

8,475

8,512

503

511

2,352

2,320

Mumbai

14,873

15,762

14,872,956

15,765,530

22,47022,401

158

144

6,722

6,881

INDIA

1,324,815

1,387,958

73,629,596

84,212,864

531,756

569,255

122,272

132,962

556,925

605,857

Table 7 Summary of the statistical tests for the regression equations
Name of theMetropolitan
City

Parameter values for
Total Accidents (C)A

Ahemadabad

7.545-1.2943 0.94

0.15

Bangalore

7.314-1.0139 0.99

Chennai

8.156-1.3080 0.97

Delhi

5.965

Hyderabad

R2

Parameter values
for Fatalities (F)R2

Std. FError value

7.262 -1.2436

0.96

0.11

999

7.447 -1.0849

0.99

0.05 1,789

484

6.922 -1.0700

0.97

0.13

767

406

5.896 -0.6458

0.96

0.11

583

4.381 -0.8741 0.98

0.14 1,733

5.596 -0.8493

0.92

0.38

237

4.499 -0.8294 0.99

0.04 2,674

8.319 -1.2082

0.88

0.22

150

173 10.450 -1.9780

0.92

0.12

234

0.98

0.07 1,590

A

1.141 -0.0228 0.90

0.20

174

0.03 3,512

4.685 -0.9860 0.98

0.06

0.15

867

3.669 -0.8220 0.95

0.13

-0.6529 0.97

0.10

683

3.927 -0.5838 0.95

0.11

5.836

-0.8419 0.98

0.15 1,466

Kolkata

8.086-0.9565 0.70

0.31

48

Mumbai

9.818-1.5022 0.96

0.06

503 11.352 -2.9114 0.89

INDIA

9.045

-0.7540 0.99

0.08 1,824 6.4470 -0.5754 0.98

288

A

B

R2

Std. FError value

B

Std.
FError value

Parameter values
for Injuries (I)A, B & R are the regression constant, regression coefficient and correlation coefficient64 • IATSS RESEARCH Vol.29 No.1, 2005

0.21

0.08 1,228

7.93

B

-0.57

501

ROAD ACCIDENT MODELS FOR LARGE METROPOLITAN CITIES OF INDIA

P. P. VALLIREFERENCES
1. Smeed, R.J. Variations in the pattern of accident rates in different
countries and their causes. “Traffic Engineering & Control” 10(7): pp.
364-371. (1968).
2. Livneh, M. and Hakkert, A.S. Some factors affecting the increase ofroad accidents in developing countries, with particular reference toIsrael. “Accident Analysis & Prevention” 4: pp.117-133. (1972).
3. Susan, C. Partyka. Simple models of fatality trends using employment
and population data. “Accident Analysis & Prevention” 16(3): pp.211222. (1984).
4. Andreassen, D. Linking deaths with vehicles and population. “Traffic
Engineering & Control” 26(11): pp.547-549. (1985).
5. Mekky Ali. Effects of rapid increase in motorization levels on road
fatality rates in some rich developing countries. “Accident Analysis &Prevention” 17(2): pp.101-109. (1985).
6. Dinesh Mohan. An analysis of road traffic fatalities in Delhi, India.
“Accident Analysis & Prevention” 17(1): pp.33-45. (1985).
7. Jacobs, G.D. and Cutting, C.A. Further research on accident rates indeveloping countries. “Accident Analysis & Prevention” 18(2): pp.119127. (1986).
8. Fieldwick, R. and Brown, R.J. The effect of speed limits on road
casualties. “Traffic Engineering & Control” 28: pp.635-640. (1987).
9. John, G.U. Adams. Smeed’s law: Some further thoughts. “Traffic
Engineering & Control” 28: pp.70-73. (1987).
10. Minter, A.L. Road casualties improvement by learning processes.
“Traffic Engineering & Control” 28: pp.74-79. (1987).
11. Pramada Valli, P. and Sarkar, P.K. Variation in the pattern of roadaccidents in different states and union territories in India. Proceedingsof the third national conference on transportation systems studies:
Analysis and Policy pp. 1X-5 to 1X-9. December. (1993).
12. Emenalo, M., Pustelli, A., Ciampi and Joshi, H.P. Analysis of roadtraffic accidents data in Zambia. “Traffic Engineering & Control” 28:
pp.635-640. (1987).
13. Pramada Valli, P. and Sarkar, P.K. Models for road accidents in India.
“Highway Research Bulletin, Indian Roads Congress” 56: pp.1-11.
(1997).
14. Jamal, R.M. Ameen and Jamil A. Naji. Causal models for road accident
fatalities in Yemen. “Accident Analysis & Prevention” 33: pp.547-561.
(2001).
15. Motor transport statistics of India. Ministry of surface transport. New
Delhi. (1996-1998).
16. Smeed, R.J. Some statistical aspects of road safety. “Journal of Royal
Statistical Society” 112 CX11 (Part 1,Series 4): pp.1-24. (1949).
17. Smeed, R.J. and Jaffocate. Effects of changes in motorization invarious countries on the number of road fatalities. “Traffic Engineering& Control” 12(3): pp.150-151. (1970).
18. Smeed, R.J. The usefulness of formula in traffic engineering and roadsafety. “Accident Analysis & Prevention” 4: pp.303-312. (1972).
19. Andreassen, D. Population and registered vehicles data Vs. Road
deaths. “Accident Analysis & Prevention” 23(5): pp.342-351. (1991).

ACKNOWLEDGEMENTS
The author acknowledges and thanks Dr. P.K. Nanda, Director, Central Road Research Institute, New Delhi, India for hiskind permission to submit the research paper for publication. She
wishes to thank Shri. Sudhir Mathur, head of the Geotechnical Engineering division, Central Road Research Institute, New Delhi,
India for his encouragement to prepare this research paper. She isalso thankful to Shri. I. Prasada Rao for his assistance for this paper.

IATSS RESEARCH Vol.29 No.1, 2005 •65