1_342_LR732 a Study of Accident Rates on Rural Roads in Developing Countries

8/3/2019 1_342_LR732 a Study of Accident Rates on Rural Roads in Developing Countries

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st udy of ac c ident rat es on rural roadsdevel op ing

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TRANSPORT and ROADRESEARCH LABORATORYDepatiment of the Environment

TRRL LABORATORY REPORT 732

A STUDY OF ACCIDENT RATES ON RURAL ROADSIN DEVELOPING COUNTRIES

byG D Jacobs

Any views expressed in the Repoti are not necessarilythose of the Depatiment of the Environment.

Overseas UnitTranspoti and Road Research Laboratory

Crowthorne, Berkshire1976lSSN 0305 1293


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CONTENTS

Abstract1. Introduction2. Data collection

2.1 Kenya data2.1.1 Geometric design parameters

2.2 Jamaica data2.2.1 Geometric design parameters

3. Analysis procedure4. Results

4.1 The relationships between accident rate and vehicle flow4.2 The results of the simple regression analysis4.3 Multiple regression analysis4.4 Comparison of the Kenya and Jamaica results with those from other countries

5. Conclusions6. Acknowledgements7. References

Page1122233455567999

@ CROWN COPYRIGHT1976Extracts fmm the text maybe reproduced, except forcommercial purposes, provided the source is acknowledged


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In order to study the relationships between personal injury accident rates and geometric design of theroad, data have been collected from Kenya and Jamaica. The primary objective of the investigation was toattempt to correlate tie number of accidents per million vehicle-kflometres on a length of road with thedesign characteristics of the road Iin order to obtain information which would be of use in formulatingprinciples for the design of safer roads in developing countries. However, relationships derived in thisinvestigation need to be verified by studies on roads in other countries. In particular it is hoped that therelationships derived here and in other countries wuld eventually be incorporated into the Road TransportInvestment Model developed by the Overseas Unit. This Model attempts to minimise the total transportcost of a given project by devising the optimum standard of road construction and design.

2. DATA COLLECTIONIn order to correlate accident rates with road design it is necessary to have, for each section of road, theprecise location of each personal injury accident taking place over a given period of time, an accuratemeasurement of traffic flow throughout the year and measurement of factors such as road width, horizon-tal and vertical curvature, surface irregularity etc. There were very few developing countries where suchdata were avtiable but it proved possible, by using data from various research studies, to obtain therequired information for Kenya and Jamaica.2.1 Kenya data

During 1974 a visit was made to Kenya by the author to collect information on dl personal injuryaccidents taking place in 1972. In 1975, fo~owing co~aboration with the Kenya Ministry of Works, detaflsof all injury accidents taking place in 1973 on the Nairobi:Mombasa Road (see Figure 1) were obtained.With this extra road accident data and with accurate traffic flow data avadable, it was decided to use thisroad for detaded study.

The Road Transport Investment Model, designed by the Overseas Unit to finimise total trans-portation costs on a given road project, was based on data collected in Kenya over the period 1969-1974.Test sections were set up throughout Kenya, particularly on the Nairobi-Mombasa Road, and the trafficflow and geometric design data collected durtig that period have been used for this investigation of roadaccident rates. The road in question was divided into various sections (see Figure 1) with the followinginformation avadable for each road length:

1)2)3)4)5)6)7)8)

personal injury accidents occurring in 1972 and 1973the length of each sectionthe average annual dady traffic flowthe average road width (metres)the number of junctions (per kilometre)the average horizontal curvature (degrees per km)the average vertical curvature (metres per km)surface irregularity (millimet res per km).

From 1) and 2) personal injury accidents per ktiometre per annum were obtained, and from 1),2)and 3) accidents per million vehicle-kdometres were obtained.2.1.1 Geometric design parameters The road width was the width of the surfaced section of the roadexcluding the gravel shoulders.

The vertical curvature of a road can be described most easily by its average gradient or its total

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vert icd rise and fall. Neither measure is fu~y satisfacto~ but rise ,and fd was preferred. A method ofmeasuring it from a specially designed and instrumented moving vehicle was developed for the study inKenya. The development of this technique allowed a high degree of accuracy to be obtained even on roadswith irregular surfaces.

Horizontal curvature is simply the bendiness of a road. A particular bend can be defined eitherby the radius of curvature measured in metres or by the degree of curvature, defined as the angle in degreesbetween the straight wctions of road which are joined by the curve. Mthought the latter definition doesnot dtiferentiate between bends of different radii, it is the most suitable for evaluating the overall effect ofa wries of bends on accident rates because it is additive and easier to measure. In developed countries wheresimdar studies have been carried oute, the accident rate has frequently been correlated with the exist ingradius of curvature. On busy roads where numerous accidents occur and where high levels of vehicle flowexist, this is probably the most convenient dimension to use.

Surface irregdarity is sometimes ca~ed the riding qutity of the road. A method of measuringsurface irregularity was developed from the principles of the bump integrator* in which the verticalmovements of the axle of a sin~e-wheel trader are summed over a test section by an integrating clutch.Though necessarily arbitrary, the system that was developed provided an index of the irregularity whichwas useful for comparing the surface renditions of the test sections.

The parameters obtained for the sections of road used in the analysis are given in Table 1.2.2 Jamaica data

In 1962 a team was sent by the Laborato~ to arry out urban and rural research work in Jamaica.During this period an unpublished report was produced which gave detads of accident rates and blackspots on rural A and B roads on the island. The accident rates (per million vehicle-mfies) were calculatedfor almost the entire A road network on the island (see Figure 2) and have been used in this presentreport after converting the rates into metric units. A detafled investigation was dso carried out on thedeficiencies of the rural road network in Jamaica, with detafled inventories being made of the existingrural road system. For each section of rural A road the foflowing parameters were obtained:

1) average width (feet)2) profde and gradients (per cent)3) average vertical curvature (ft/mile)4) average horizontal curvature (degrees/mile)5) average surface irregularity (in/rode)6) average sight distance (ft)7) the number of junctions (per mile)

2.2.1 Geometric design parameters Average road width was obtained by taking five measurements atequrd intervals every mile.

The vertical curvature was obtatied by measuring the elevation at every crest and hollow andaccurately measuring the distances in between. Thus, as in Kenya, the rise and fall per unit length of roadwas obtained. The gradient was obtained by using an Abney level mounted in a survey car and modified toread grade directly.

As in Kenya, the horizontal curvature was measured in terms of degree of curvature or bend iness perunit length of road, but in this case was obtained by taking measurements from 1 : 12,500 scale landvahrat ion maps.

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The average surface irregularity (see above) for each section of road was obtained by using a towedbump integrator where, as described above, the vertical movement of a wheel relative to its mounting wasmeasured, thus providing a measure of the unevenness of the road surface.

The average sight distance of each section was obtained by measuring how far ahead a driver could seean obstruction on the road. Measurements were made by putting down markers at 100 ft intervals along theroad and, at each marker, counting how many markers could be seen on the road ahead. Twelve-inch highrubber cones were used as markers. This method has the advantage of simplicity and speed. The averagesight distance was calculated from the total of the numbers of cones counted in the section and the numberof observations made within the section.

The parameters obtained for the sections of road used in this analysis are given in Table 2.3. ANALYSIS PROCEDURE

Regression analysis was used to establish and quantify relationships between one dependent variable andone or more independent variables. (The first variable, which is the quantity under study, is known as thedependent variable and the others are defined as the independent variables.)

In this investigation four dependent variables were studied separately.1) personal injury accidents per kilometre per annum Kenya2) personal injury accidents per kdometre per annum Jamaica3) personal injury accidents per million vehicle-kdometres Kenya4) personal injury accidents per million vehicle-kfiometres Jamaica.The choice of independent variables implied that they were sensibly related to the dependent

variable. In this study, a further condition in choosing independent variables was that they should be simpleto define and, for an engineer working in the field, reasonably easy to measure.

As a preliminary investigation of which variables were most closely correlated with accident rate,simple regressions of accident rate on each of the road features individually, were performed. Equationsderived were of the form:

y=atblxlwhere

y = independent variablex, = dependent variablea = regression constantbl = regression coefficient

However because many of the road design features are inter-related simple regression analysis maygive a misleading impression of the relationships that they have with accident rate. Multiple regression, inwhich the accident rate is expressed as a function of several independent variables simultaneously, is likelyto be a better guide.

Equations derived were then of the form:y=a+blxlt b2x2tb3x3 . . . .. bxnwhere y, xl, X2, Xn, bl, b2, bn were as above.

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For these estimates to be acceptable it was necessary to test the hypothesis that the value computedfor each regression coefficient was unlikely to have arisen by chance. To check this, the standard error ofeach regression coefficient was computed and tested for significance; variables with non-significant co-efficients were el~nated from the analysis.

The computer program used was part of a statistical package and had an automatic prowdure foreliminating non-significant variables and for testing such variables with other combinations and replacingthem where necessary. This technique is known as stepwise regression analysis.

Data obtained on rural roads in Kenya and Jamaica were analysed separately.4. RESULTS

From the analysis, equations were derived which related accidents per kilometre per annum to vehicle flowand accidents per tillion vehicle-kdometres to the geometric parameters. Table 3 gives the maximum,minimum and means of the parameters obtained on the rural roads studied. The standard deviation, whichmeasures the variance about the mean, is dso given.4.1 The relationships between accident rate and vehicle flow

The number of injury-accidents per kdometre of road per annum occurring on rural roads in Kenyaand Jamaica were regressed against the vehicle flow per hour occurring on each test section of road(averaged over a 12-hour 7am 7pm period). In both cases the accident rate was found to be related to thevehicle flow (with results being stat isticdly significant at the 5 per cent level). The equations derived wereas follows:

For Kenya y= O.l16t 0.0091xFor Jamaica y= 0.158t 0.0126xwhere y = persoml injury accidents per km per annum

x = average vehicle flow/hour.Figure 3 Wustrates how the accident rate in both countries increases with. increasing flow. (Figure 3

dso shows a relationship derived for a number of developed countries; this is discussed later). It can be seenfrom Fig 3 that, for a similar rate of vehicle flow, Jamaica has a higher accident rate than Kenya.

In order to investigate relationships between geometric design and accident rates, the number ofaccidents per kilometre of road per annum were divided by the vehicle flow per annum on each section ofroad, to obtain the number of personal-injury accidents per million vehicle-kilomet res. In this way therelationship between vehicle flow and the accident rate is taken into account.

4.2 The results of the simple regression analysisThe results of the simple regression analysis are given in Table 4. The t value is the ratio of the

regression coefficient to the standard error and was used to test whether the relationship was statistica~ysignificant (ie were unlikely to have occurred by chance). The tables indimte the relationships which werefound to be significant at the 5 or 10 per cent level. (Note: 5 per cent is the level usually accepted instatistical analysis, ie there is only a.5 per cent probabdity that the relationship could have occurred bychance. Bearing in mind the many factors affecting accident rates, a relationship found significant at the


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10 per cent level in this study could be considered satisfactory). The correlation coefficient r is also given.The value r2 provides a measure of the proportion of variabdity in y that is accounted for by variabdity inthe appropriate x value. Thus, for example in Kenya, junctions per kdometre was found to be the mostsignificant independent variable. The r2value of 0.49 indicates that 49 per cent of the variation inaccident rate is explained by variation in the number of junctions per kdometre alone.

In both countries the most significant parameter of those considered in this study was found to bethe number of junctions per kflometre. The correlation between the junctions and the accident rate wasgreater on the Nairobi-Mombasa road, Kenya, than in Jamaica but as can be seen from Figure 4, the rangeswere quite different in the two countries. In Kenya where there were never more than two junctions perkilometre an addition of one junction per kilometre was associated with an increase in the accident rate ofover one accident per million vehicle-kilometres. In Jamaica, where there were often as many as eightjunctions per kilometre, an increase of three junctions per kilometre would increase the accident rate byone accident per million vehicle-kilometres.

On the Jamaican A roads, road width was dso a very significant factor, the wider the road the lowerthe accident rate (see Figure 5). On the Nairobi-Mombasa road, there was very little variation in the roadwidth and the small amount of variation (see Table 3) has not provided a significant relationship withaccident rate.

In both countries the surface irregularity was related to the accident rate: the rougher the road thehigher the number of accidents per mfllion vehicle-kilometres. In Jamaica the relationship was statisticallysignificant at the 5 per cent level whflst in Kenya it was significant at the 10 per cent level. (Again, inJamaica, the range was greater than in Kenya). The effect of surface irregularity was very similar in bothcountries; an improvement in roughness of 2000 millimetres per kilometre was associated with a reductionin the accident rate of 0.8 accidents per million vehicle-kflometres per annum.

In Kenya the horizontal curvature was found to be significantly related to the accident rate, adecrease of 35 degrees per kilometre reducing the accident rate by one accident per million vehicle-kilometres. In Jamaica neither horizontal curvature nor sight distance was found to be a significantfactor. This is a somewhat surprising result since the range of horizontal curvature is much greater inJamaica than in Kenya.

4.3 Multiple regression analysisThe results obtained in the previous section show how various features of the road considered

separately were related to the accident rate. In order to determine how the combined factors are associatedwith the accident rate, multiple regression analyses as described in Section 3 were carried out.

Parameters were given the following not ationy=1 =X2 =x=3X4 =5 =6 =

accident rate per million vehicle-kilometresroad width (metres)vertical curvature (m/km)horizontal curvature (deg/km)surface irregulatiry (mm/km)junctions per kilometresight distance (metres)


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The regression equation of factors related to the accident rate (significant at the 5 per cent level) inKenya was as follows:

y = 1.45 + 1.02X5 t 0.017X3

(Note: The independent variables in this equation and those below are listed in order of significance;thus, in Kenya, junctions per kilometre was found to be the variable which had greatest effect on theaccident rate).

Other parameters were not significant at the 5 per cent level but were at the 10 per cent level.Although 5 per cent is the level normafly accepted in statistical analysis, in this case, taking into accountthe limitations of the data, the 10 per cent level might be considered acceptable. The equation for Kenyathen becomes

y = 1.09 t 0.031x3 + 0.62x5 t 0.0003x4 t 0.062x2

The effects of surface irregularity and verticrd curvature are considerably less than those of junctionsper kdometre and horizontrd curvature. Nevertheless they are worth including, particularly surfaceirregularity where, for example, the improvement under consideration is the upgrading of a gravel road to abituminous-surfaced road and the change in riding quality maybe considerable.

The multiple regression equation for Jamaica (with parameters significant at the 5 per cent level) wasas follows

y = 5.77- 0.755x1 t 0.275x5In this equation, road width was the variable most closely associated with the accident rate. In

section 4.2 it was seen that, in Jamaica, the accident rate was related separately to road width, junctionsper kilometre and surface irregularity (all at the 5 per cent level). In the multiple regression analysishowever, it was found that surface irregularity was not significmt (even at the 10 per cent level). Thereason for this is that surface irregularity and road width were themselves related, the correlation betweenthe two variables being significant at the 5 per cent level. The most significant factor (road width) entersthe equation first and since this is closely related to surface irregularity, it aslo explains most of thevariation associated with surface irregularity, which was therefore found to be non-significant.4.4 Comparison of the Kenya and Jamaica resulfi with those from other countries

In January 1973 Silyanov published the results of a comparison of accident rates on roads ofdifferent countries. This work has enabled a comparison to be made between the relationships derivedin Kenya and Jamaica with those obtained on rural roads in developed countries.

Silyanov examined the relationships between accidents per kilometre of road per annum and trafficflow derived by workers in Russia3, Sweden and Austrdia5 and, grouping the results from the differentcountries together, found that the relationship between accidents and vehicle flow could be expressed bythe formula:

y= 0.256 t 0.000408N t 1.36 (10-7)N2for40


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This equation has been plotted on Figure 3 and, as can be seen, the accident rate in Kenya andJamaica is very much greater than in the above developed countries for a similar level of vehicle flow. (Thiscomparison shodd be treated with caution because there were few instances where Sdyanov found flowlevels below 200 vehicles per hour). For example, at a flow level of 100 vehicles/hour, the accident rate inKenya is over three times greater than in the developed countries and the rate in Jamaica is almost fivetimes greater. These greater accident rates could be due to the quality and condition of the road or possiblyto driver behaviour.

The number of junctions per kilometre was found to be significantly related to accident rates inKenya and Jamaica and this has been compared with results of early work carried out in Great Britain in1946-48. The result of this work has been plotted on Figure 4 and it shows that for the same number ofjunctions per kilometre the accident rate per million vehicle-kdometres was similar in Great Briatin and inJamaica. (The accident rate in Kenya, however, was very much higher than in either Jamaica or GreatBritain but the range of the number of junctions per kilometre is obviously different from that in the othertwo countries and comparisons are not really valid).

In Jamaica the road width was an important factor and, by combining results from Great Britain,France7, Hungary, West Germany and the United States~, Sflyanov also related accidents to road widthaccording to the formula:

y = l/(0.173B 0.21)where y = number of accidents per million vehicle-kflometresB = road width in metres (4 < B < 9)

This relationship has been plotted on Figure 5 and it can be seen that, for the same road width, theaccident rate is greater in Jamaica than in the developed countries. This difference is greatest at low roadwidths; for example with a road width of 5 metres, the accident rate in Jamaica is over twice that of thedeveloped countries.

Although sight distance was not found to be a significant factor in Jamaica it is interesting tocompare the accident rates in Jamaica for different levels of sight distan~ with those found by Silyanov.According to Sflyanov the sight distance can be related to accidents by the formula:

y = 1/(0.20+ 0.001 ld t 0.0000009d2)for 25< d


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5. CONCLUSIONSFrom data obtained from various sources in Kenya and Jamaica, it has proved possible, using multipleregression analysis to relate the accident rates on rural roads in these countries to certati design,,charact-eristics of the road. In Kenya, the accident rate per million vehicle-kilometres was significantly related tothe number of junctions per kilometre, the horizontal curvature, the vertical curvature and the surfaceirregdarity. In Jamaica, it was found to be related to road width, and, again, junctions per kflometre.

Engineers*and planners may wish to use these regression equations in other developing countries toobtain estimates of changes in accident rate following given road improvements where traffic and roadconditions are simdar to those described here. However since the equations derived are different k Kenyaand Jamaica it would be difficult to decide which equation to use. Where conditions were similar to thoseon the Nairobi-Mombasa road, the equation derived from the Kenya data would appear the moreappropriate. Similarly, where there were greater extremes of horizontal and vertical curvature and surfaceirregularity the equation derived for Jamaica may be more appropriate. However there was littlevariation in road width on the Nairobi-Mombasa road and this did not appear to be a significantparameter, whereas in Jamaica it was the most important parameter. Thus where a road was being widenedit might be difficult to decide which was the most sensible equation to use particularly if conditions weresimilar to those on the Nairobi-Mombasa road.

Before the equations can be used with certainty and, for example built into the Road TransportInvestment Model, they need to be verified and adjusted by carrying out similar analyses on other roads inother countries.

It would appear from the comparison of results obtained in Kenya and Jamaica with those fromdeveloped countries that the accident rate in the two developing countries was considerably higher than inthe developed ones for simdar levels of vehicle flow and geometric design. It is quite likely therefore thatother factors are involved, such as road user behaviour and vehicle condition and maintenance. It isintended that these factors wdl be the subject for future research.

6. ACKNOWLEDGEMENTSThe work described in this report forms part of the research programme of the Overseas Unit (Unit Head,Dr E D Tingle) of the Transport and Road Research Laboratory. The author wishes to acknowledge theassistance provided by the Kenya Government in the collection of road accident data, in particularAssistant Commissioner Ravd, Kenya Police. r.

The author dso wishes to acknowledge the assistance provided by Miss S Shamsuri, BradfordUniversity and Mr Scott, Accident Investigation Division, TRRL, with the analysis of the data.

7. REFERENCES1. JACOBS G D and I A SAYER. A study of road accidents in Kenya in 1972. Department of the

Environment, TML Report SR227UC. Crowthorne, 1976 (Transport and Road Research Laborato~).2. OKYERE J N. Research into road safety problems in Ghana. Building and Road Research Institute,

Kumasi, Ghana, 1972 (Unpublished).3. KEMP RN, I D NEINON, G C STRAUGHTON and H A WILUNS. A prelimkary report on an

on-the-spot survey of accidentsBepartment of the Environment, TRRL Report LR434. Crowthorne,1972 (Transport and Road Research Laborato~).

4. BENNETT G T. An investigation and report into four years fatal accidents in Oxfordshtie. OxfordCounty Council, 1937.

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5. ROBINSON R, H HIDE, H W HODGES, J ROLT and S W ABAYNAYAKA. A road transportinvestment model for developing count ries. Department of the Environment, TML Report LR674.Crowthorne, 1975 (Transport and Road Research Laboratory).

6. COBURN T M and A E WALKER. The effect of horizontal curvature on accident rates on some roadsin Buckinghamshire. Department of Scientific and Industrial Research, Roti Research LaboratowResearchNote No RN/1509. Harmondsworth, 1951 (Unpublished).

7. COBURN T M. An investigation into the effect of road layout on accidents in rural Bucks.Department of Scientific and Industrial Research, Road Research Laborato~ Research Note No RN/2340. Harmondsworth, 1974 (Unpublished).

8. HILLIER J A and J G WARDROP. Effect of gradient and curvature on accidents on London Birmingham motorway. Traffic Engineering and Control, 1966, ~, ( 10) 617 21.

9. SCOTT W JO. Roads and their riding qualities. Institution of CivilEngineersRoad Paper No 25.London, 1948 (Institution of Civil Engineers).

10. Accident rates and black spots in Jamaica, 1963. Caribbean Road Traffic and Planning Research UnitRD77/P26 (Unpubhshed).

11. ROAD RESEARCH LABORATORY (TROPICAL SECTION. A study of road needs in rural Jamaica.Ministry of Transport, Road Research Laboratory, 1965.

12. SILYANOV Dr V V. Comparison of the pattern of accident rates on roads of different countries.Traffic Engineering and Control, January, 1973 pp 432 34.

13. SILYANOV Dr V V. Carriageway marking tests in the USSR. Traffic Engineering and Control, 1968~(8),409 12.14. ROOSMARK P and R FRAKI. Studies of effects produced by road environment and traffic

characteristics on traffic accidents. Paper presented to Symposium on the use of statistical methodsin the analysis of road accidents. Road Research Laboratory, Crowthorne, 1969.

15. McKERRAL J M. An investigation of accident rates using a digital computor. Paper No. 53 Vol 1.in Proc. Aust. Rd. Res. Board, 1962.

16. MANNING JR. Accident rates on the classified roads of Buckinghamshire. Department of Scientificand Industrial Research, Road Research Laboratoy ResearchNote No RN/ 1094. Harmondsworth,1949 (Unpublished).

17. GOLDBERG S. Detailed investigateion of accidents on national roads in France. Irrt.Rd. Safety andTraffic Review, ~ (2) 1962.

18. BALOG T. AZ utjellemjok es a forgalombizt onsagi kapcsolata a magyarorsjagi vizsgalatok eredmenyeiszerint. Konzuti Forgalombizt onsagi Konferencia, 1965, Budapest, 1966, 64 72.

19. BITZL F. Accident rates on German expressways in relation to traffic volume and geometric design.Rds. Rd. const, 25 (409) 1957, 18 20.

20. RAFF M S. The Interstate highway accident study. Public Rds. 27(8), 1953, 170 186.

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SectionNumber

12345678910111213141516171819

TABLE 1Data for sections on Nairobi-Mombasa Road, Kenya

Personal InjuryAccidents/MdIion Veh-km/annum2.932.172.322.823.541.672.832.671.082.112.421.884.601.961.331.541.911.672.29

AverageRoadWidth(metres)

6.16.16.16.16.16.16.16.16.16.16.17.07.07.07.07.07.07.07.5

VerticalCurvature(m/km)

10.021.3605.405.4014.614.614.615.015.01.412.628.885.594.455.0413.021.9111.12

HorizontalCurvature(deg/km)

19.424.590.7312.5435.5340.7249.7416.982.0831.51.195.0753.7912.734.787.2238.9010.4230.44

Surfaceirreg(mm/km)

2300230030303264308630863308330833083308330833083308163316331633163316331488

Junctions/km

0.660.391.430.670.430.560.320.350.120.430.320.171.940.140.120.260.210.310.56

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Section

i2a2b3a3b4a4b5a5b68910a10bllallb1213a13b14a14b15a15b1617a17C17d17e18

TotalInju~Accidents

per mfllionveh-km2.163.051.963.703.712.471.371.043.016.172.781.611.952,251.853.371.521.884.063.023.195.003.312.251.730.951.173.402j61

TABLE 2Data for sections of rural A roads, Jamaica

Averagewidth(metres)

7.326.926.106.135.896.256.686.345.645.436.226.416.506.595.865.225.095.375.094.975.345.435.765.556.657.595.985.435.40

VerticalCurvature(metres/km)2.433.2712.419.2935.9351.3619.108.937.568.0911.231.7714.736.0811.3120.9412.7911.5015.6818.8913.3615.5817.2129.798.8722.3139.5629.6240.87

HorizontalCurvature(deg/km)

25.4047.70134.41102.86274.16322,42130.62111.12124.66184.66199.6397.95148.32106.71133.04249.63273.04243.48339.57250.75180.62138.32219.13368.32161.1838.32423.60346.34232.42

Surfaceirreg.(mm/km)

2822.83879.44904.54809.95109.54557.54368.33122.54604.84967.64257.93800.64872.93311.74683.74967.66103.05850.76039.94731.04857.25456.46087.24936.04321.02192.06087.269865945.3

SightDistance(metres)

237.29209.54126.58146.4096.38106.75139.69168.67133.29127.80122.00163.79134.81169.58131.76110.41110.41105.8495.4792.11118.34135.73111.6380.52117.73149.4568.6371.9896.99

Junctionsper

kflomet re

4.806.646.817.738.324.845.882.513.188.195.403.763.124.614.497.173.276.504.535.754.244.426.378.012.834.351.173.882.61

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,Par~eter

KEWAAverage width (metres)Vertical curvature (m/km)Horizontal curvature (deg/km)Surface irregularity (mm/km)Junctions/kmJAMNCAAverage width (metres)Vertical curvature (m/km)Horizontal curvature (deg/km)Surface irregularity (mm/km)Junctions/kmAverage sight distance (metres)

TABLE 3Variation in Parameter values~Maximum

7.:015.0053.80

33081.947.651.35423.66991.18.32237.3

Minimum

6.1000.73

14880.124.971.77

25.42193.01.1768.6

Mean

6.507.9120.0

26250.496.0017.26193.44783.05.01126.9

StandardDeviation

0.5005.4117.33770.50.4600.6812.33102.861081.71.8937.60

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TABLE 4Results of simple regression analysis

IndependentVariable

Av width (m)Verticalcurvature( m/km)Horizontalcurvature(de~km)Surfaceirregularity(m~km)Junctions

perkflometre

IndependentVariable

Av width (m)Verticalcurvature(m/km)Horizontalcurvature(deg/km)Surface

tiregularity(mm/km)Av sightdistance(metres)Junctionsperkdometre

RegressionConstant

a3.9166

2.2846

1.7674

1.1837

1.6855

RegressionConstant

a7.6658

2.6969

2.3654

0.7750

3.1711

1.1082

a) KenyaRegression CorrelationCoefficient Coefficient

b r-0.2482 0.1507

0.0022 0.0145

0.0268 0.5645

0.0004 0.3984

1.2476 0.6968

b) JamaicaRegressionCoefficient

b-0.8418

-0.0033

0.0014

0.00039

-0.0042

0.3054

CorrelationCoefficient

r0.4802

0.0346

0.1224

0.3643

0.1324

0.4847

tvalue

-0.6287

0.0601

2.8201

1.7907

4.0061

tvalue

-2.8445

-0.1798

0.6407

2.10

-0.6941

2.8797

Level ofStatisticalSignificancenot sig at 1Wo

not sig at l~o

sig at 5%

sig at 1070

Sigat 5?0

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ABSTRACTA study of accident ratas on rural roads in developing countries: G D JACOBS: Department of the Environ-ment, TRRL Laboratory Report 732: Crowthorne, 1976 (Transport and Road Research Laboratory). Ilepurpose of the study described in this report was to investigate relationships between personal injuryaccident rates on rural roads in Kenya and Jamaica and factors such as vehicle flow and road geometry.Regression analysis was used to derive equations which can be used to estimate changes in accident ratesfollowing improvements to the geometric design of the road.The accident rate per kilometre per annum was found to be significantly related to the vehicle flow whflstthe rate per mfllion vehicle-kflometres was found to be significantly related to the physical characteristicsof the road tested, such as junctions per kflometre, surface irregdanty and road width.Comparisons were made with similar relationships derived in a number of developed countries, the accidentrates in Kenya and Jamaica were found to be consistently greater for similar values of vehicle flow andgeometric design.ISSN 0305 1293

ABSTRACTA study of accident rates on rural roads in developing countries: G D JACOBS: Department of the Environ-ment, TRRL Laboratory Report 732: Crowthorne, 1976 (Transport and Road Research Laboratory). Thepurpose of the study described in this report was to investigate relationships between personal injuryaccident rates on rural roads in Kenya and Jamaica and factors such as vehicle flow and road geometry.Regression analysis was used to derive equations which can be used to estimate changes in accident ratesfollowing improvements to the geometric design of the road.The accident rate per kilometre per annum was found to be significantly related to the vehicle flow whflstthe rate per million vehicle-kdometres was found to be significantly related to the physical characteristicsof the road tested, such as junctions per kdometre, surface irregularity and road width.Comparisons were made with similar relationships derived in a number of developed countries, the acciclentrates m Kenya and Jamaica were found to be consistently greater for similar values of vehicle flow andgeometric design.ISSN 0305 1293

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1_342_LR732 a Study of Accident Rates on Rural Roads in Developing Countries

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