The Contribution of various Risk Factors to Road Safety

Problems in Sweden

This appendix presents the data used to estimate the risks attributable to various risk factors in Sweden. The list of road safety problems is presented in chapter 2 of the main text of the report. The contributing risk factors have been sorted into the following five main categories:

• Inadequate system design

• Environmental risk factors

• Vulnerability of road users

• Road user behaviour

• Provision of emergency medical services

The tables below present estimates of the first order attributable risks of risk factors subsumed under each of these main categories. The estimation proceeded in the following stages:

1 The first order attributable risk of each risk factor in the target group was estimated.

2 Target group attributable risk was converted to population attributable risk.

3 Estimates were adjusted for correlations and overlapping accident categories.

4 The marginal contribution to injury prevention of removing each risk factor was estimated by applying the method of joint residuals.

Table 1 gives the data used in stage 1 of the analysis. Following table 1, the text explains how attributable risk was estimated in stages 1 and 2 for each risk factor.

Appendix 1, Table 1: Data used to estimate the risks attributable to various risk factors in Sweden

Road safety statistics Relative rates Attributable risks Risk factor Variable Categories Fatal Injured Exposure Fatalities Injuries Fatalities Injuries

Category A: Inadequate or bad system design

Traffic environment Urban/rural Mv 70 3 388 950 0,35 1,64 -0,009 0,009

Mv 90 5 299 2000 0,27 0,60 -0,022 -0,012 Mv 110 21 680 7400 0,31 0,37 -0,080 -0,073 Mtl 70 1 65 160 0,69 1,63 -0,001 0,001 Mtl 90 7 118 450 1,71 1,06 0,005 0,000 Mtl 110 9 120 520 1,90 0,93 0,007 -0,001 Av 50 39 2195 3950 1,08 2,24 0,005 0,066 Av 70 111 3956 9920 1,23 1,60 0,032 0,080 Av 90 198 4227 18215 1,19 0,93 0,048 -0,018 Av 110 34 493 2555 1,46 0,78 0,017 -0,008 Ag 30 2 201 920 0,24 0,88 -0,010 -0,002 Ag 50 80 7006 15860 0,55 1,78 -0,115 0,152 Ag 70 20 829 1830 1,20 1,82 0,005 0,021 Ev 50 2 67 700 0,31 0,39 -0,007 -0,006 Ev 70 20 796 2670 0,82 1,20 -0,007 0,008 Div 30 0 47 200 0,00 0,95 -0,003 0,000 Div 50 2 234 400 0,55 2,35 -0,003 0,008 Reference Rural 409 11142 44840 1,00 1,00 0,000 0,000 Risk factor Towns 145 10579 23860 0,67 1,78 -0,131 0,214

Appendix 1, Table 1: Data used to estimate the risks attributable to various risk factors in Sweden, continued

Road safety statistics Relative rates Attributable risks Risk factor Variable Categories Fatal Injured Exposure Fatalities Injuries Fatalities Injuries Road standard Motorway/other Mv 90 5 299 2261 1,00 1,00

Av >8000 31 504 3100 4,52 1,23 0,671 0,117 Roadside obstacles None/objects None 273 7362 20925 1,00 1,00

Objects 338 3714 5770 4,49 1,83 0,430 0,152 Highway signs Correct/erroneous 0,010 0,015 Junctions High risk/low risk 20 470 0,036 0,022

Crashworthiness Car mass 650--899 35 1284 4496 1,01 1,00 0,001 0,000 900--999 44 1840 6430 0,89 1,00 -0,011 0,000 1000--1099 68 2400 8990 0,99 0,94 -0,002 -0,009 1100--1199 78 2400 8350 1,22 1,01 0,027 0,001 1200--1299 73 2600 8990 1,06 1,01 0,008 0,002 1300--1399 78 3500 12845 0,79 0,96 -0,044 -0,009

1400--1499 73 2800 9630 0,99 1,02 -0,002 0,003 1500-- 44 1480 4498 1,27 1,15 0,019 0,011 Total 493 18304 64229 1,00 1,00 0,341 0,066 Heavy vehicles Heavy/light Small 455 18640 102650 1,00 1,00

Heavy 134 3032 16150 1,87 1,03 0,106 0,005

Appendix 1, Table 1: Data used to estimate the risks attributable to various risk factors in Sweden, continued

Road safety statistics Relative rates Attributable risks Risk factor Variable Categories Fatal Injured Exposure Fatalities Injuries Fatalities Injuries

Category B: Environmental risk factors

Darkness Night/day Daylight 341 11000 93572 1,00 1,00 Dark – no

lighting 107 1544 7950 3,69 1,65 0,150 0,041 Dark - yes 53 2266 12000 1,21 1,61 0,021 0,056

Dawn – no

lighting 25 763 5500 1,25 1,18 0,011 0,008 Dawn - yes 2 312 2500 0,22 1,06 -0,016 0,001

Total 528 15885 121522 1,19 1,11 0,165 0,107 Winter time Winter/summer Dry 295 9319 84022 1,00 1,00

Wet 117 3754 28195 1,18 1,20 0,040 0,044 Snow 111 2556 9305 3,40 2,48 0,155 0,102 Total 523 15629 121522 1,23 1,16 0,196 0,146 Animal crashes Animals involved No animal 580 20845 NA PAR = proportion of

Involved 9 827 NA accidents involving animals 0,015 0,038

Appendix 1, Table 1: Data used to estimate the risks attributable to various risk factors in Sweden, continued

Road safety statistics Relative rates Attributable risks Risk factor Variable Categories Fatal Injured Exposure Fatalities Injuries Fatalities Injuries

Category C: Vulnerability of road users

Road users Pedestrians 1 to 3 2 33 35 2,24 2,51 0,554 0,602 4 to 6 2 64 65 1,21 2,63 0,171 0,619 7 to 14 7 208 273 1,01 2,03 0,005 0,508 15 to 19 4 155 255 0,61 1,62 -0,626 0,383 20 to 24 8 138 302 1,04 1,22 0,037 0,179 25 to 34 8 180 393 0,80 1,22 -0,253 0,181 35 to 44 10 147 392 1,00 1,00

45 to 54 10 160 381 1,03 1,12 0,028 0,107 55 to 64 16 150 265 2,37 1,51 0,577 0,338 65 to 74 20 203 242 3,24 2,24 0,691 0,553 75 to 84 33 230 88 14,70 6,97 0,932 0,857

Total 120 1668 2691 1,75 1,65 0,428 0,395

Appendix 1, Table 1: Data used to estimate the risks attributable to various risk factors in Sweden, continued

4 to 6 1 30 51 3,27 0,89 0,694 -0,119 7 to 14 4 429 462 1,44 1,41 0,307 0,291 15 to 19 5 340 392 2,13 1,32 0,530 0,241 20 to 24 0 302 368 0,00 1,25 0,000 0,198 25 to 34 4 361 478 1,39 1,15 0,283 0,129 35 to 44 3 329 500 1,00 1,00

45 to 54 6 327 496 2,02 1,00 0,504 0,002 55 to 64 8 266 222 6,01 1,82 0,834 0,451 65 to 74 19 247 202 15,68 1,86 0,936 0,462 75 to 84 18 176 87 34,48 3,07 0,971 0,675

Total 68 2812 3270 3,47 1,31 0,711 0,235

Appendix 1, Table 1: Data used to estimate the risks attributable to various risk factors in Sweden, continued

20 to 24 47 1722 4025 4,78 5,61 0,791 0,822 25 to 34 66 2235 12149 2,22 2,41 0,550 0,586 35 to 44 42 1661 13765 1,25 1,58 0,199 0,368 45 to 54 41 1279 16782 1,00 1,00

55 to 64 38 802 8929 1,74 1,18 0,426 0,151 65 to 74 30 595 5824 2,11 1,34 0,526 0,254 75 to 84 24 303 940 10,45 4,23 0,904 0,764

Total 318 9458 64229 2,03 1,93 0,507 0,482

Appendix 1, Table 1: Data used to estimate the risks attributable to various risk factors in Sweden, continued

4 to 6 2 156 3647 1,00 1,00

7 to 14 7 419 6521 1,96 1,50 0,489 0,334 15 to 19 23 890 2559 16,39 8,13 0,939 0,877 20 to 24 20 704 2409 15,14 6,83 0,934 0,854 25 to 34 21 733 4708 8,13 3,64 0,877 0,725 35 to 44 13 458 5669 4,18 1,89 0,761 0,471 45 to 54 10 384 5844 3,12 1,54 0,680 0,349 55 to 64 9 328 1947 8,43 3,94 0,881 0,746 65 to 74 15 367 1781 15,36 4,82 0,935 0,792

75 to 84 12 196 254 86,15 18,04 0,988 0,945 Total 137 4769 37721 6,62 2,96 0,849 0,662

Appendix 1, Table 1: Data used to estimate the risks attributable to various risk factors in Sweden, continued

Road safety statistics Relative rates Attributable risks Risk factor Variable Categories Fatal Injured Exposure Fatalities Injuries Fatalities Injuries

Bus passengers 1 to 6 1 3 245 2,71 1,81 0,631 0,447 7 to 12 2 10 851 1,56 1,73 0,359 0,423 13 to 17 4 18 2657 1,00 1,00

18 to 24 5 20 1368 2,43 2,16 0,588 0,537 25 to 44 9 41 3564 1,68 1,70 0,404 0,411 45 to 64 10 40 1672 3,97 3,53 0,748 0,717 65 to 84 9 42 1756 3,40 3,53 0,706 0,717

Total 40 174 12113 2,19 2,12 0,544 0,528 Road users Protected/unprotected Pedestrians 132 1737 2691 12,09 5,07

Cyclists 71 2843 3270 5,35 6,82 Moped rds 17 870 200 20,95 34,15 Mc rds 39 1004 500 19,23 15,76 Car drivers 323 9500 64229 1,24 1,16 Car pass 141 4905 37720 0,92 1,02 Bus drivers 1 47 800 0,31 0,46 Bus pass 1 181 12112 0,02 0,12 Protected 466 14633 114861 1,00 1,00

Unprotected 259 6454 6661 9,58 7,61 0,320 0,266

Appendix 1, Table 1: Data used to estimate the risks attributable to various risk factors in Sweden, continued

Road safety statistics Relative rates Attributable risks Risk factor Variable Categories Fatal Injured Exposure Fatalities Injuries Fatalities Injuries

Category D: Road user behaviour

Speeding Violation rate Mv 70 1 260

Mv 90 2 309

Mv 110 28 589

Mtl 70 0 81

Mtl 90 4 130

Mtl 110 10 177

Av 50 50 2068

Av 70 102 3762

Av 90 233 4247

Av 110 30 390

Ag 30 1 197

Ag 50 94 7867

Ag 70 21 845

Ev 50 0 100

Ev 70 10 450

Div 30 0 100

Div 50 3 100

Total 589 21672

Appendix 1, Table 1: Data used to estimate the risks attributable to various risk factors in Sweden, continued

Road safety statistics Relative rates Attributable risks Risk factor Variable Categories Fatal Injured Exposure Fatalities Injuries Fatalities Injuries Drinking and driving Drivers Sober 497 20283 1,00 1,00

Drunk 40 1064 25,00 25,00 0,072 0,048

Total 537 21347

Seat belt wearing Drivers Yes (87) 197 7375 55879 1,00 1,00

No (13) 82 2045 8350 2,78 1,85 0,146 0,054

Total 279 9420 64229

Passengers Yes (91,5) 96 3871 34515 1,00 1,00

No (8,5) 16 662 3206 1,84 1,84 0,036 0,036

Total 112 4533 37721

Total 391 0,084 0,032

Appendix 1, Table 1: Data used to estimate the risks attributable to various risk factors in Sweden, continued

Road safety statistics Relative rates Attributable risks Risk factor Variable Categories Fatal Injured Exposure Fatalities Injuries Fatalities Injuries Other violations Male car driver No violations 50539 55032 1,00

Violations 10486 5990 1,91 0,082

Total 61025 61022

Female car driver No violations 23957 24618 1,00 Violations 1213 552 2,26 0,027

Total 25170 25170

Male bus drivers No violations 1374 1409 1,00 Violations 184 149 1,27 0,025

Total 1558 1558

Female bus drivers No violations 282 287 1,00 Violations 11 6 1,87 0,017

Total 293 293

Male truck drivers No violations 3590 3822 1,00 Violations 631 399 1,68 0,061

Total 4221 4221

Male motorcyclists No violations 2932 3503 1,00 Violations 1060 465 2,72 0,168

Total 3992 3968

Excessive driving 3% of town driving 0,005 0,015 Category E: Emergency medical services

Rescue services 100 5000 0,167 0,071

Category A: Inadequate or bad system design This category comprises the following risk factors:

A1: Traffic environment. This variable was defined in terms of a rural or urban environment.

A2: Substandard roads. This variable was defined as lack of motorway standard on roads with an AADT of more than 8,000.

A3: Roadside obstacles: This variable was defined as accidents in which a roadside obstacle was hit.

A4: Highway signs: This variable was defined as erroneous highway signing.

A5: Junctions: This variable was defined as high risk junctions.

A6: Car crashworthiness: This variable was defined in terms of the difference in crashworthiness performance between the best cars and the average car in a certain weight category.

A7: Heavy vehicles: This variable was defined as the additional fatality or injury risk posed by heavy vehicles compared to light vehicles.

The risk attributable to each of these variables was estimated as follows:

A1: Traffic environment

Roads were divided into the categories listed in the table. The data were taken from the road data bank, and are identical to Table 6 in the main text of the report.

Roads with a speed limit of 50 km/h or less were classified as belonging to an urban traffic environment. Some roads with a speed limit of 70 km/h were also classified as urban. Roads in rural areas was defined as the reference category.

Relative risk was estimated to 0.67 for fatalities and 1.78 for injuries in total. This results in an attributable risk of –0.131 for fatalities and 0.214 for injuries in total.

The risk of a fatality is, in other words, higher in rural areas than in urban areas.

For injuries in total, the reverse holds.

A2: Substandard roads

The concept of “substandard roads” is somewhat flexible. In this report, a rather narrow interpretation was chosen. All roads in rural areas with an AADT of more than 8,000 were regarded as substandard if they did not have motorway standard.

According to Andersson et al (1998), there are 750 km of such road. This appears to be a conservative estimate, but has been used. The annual mean number of fatalities during 1994-1996 was 31. The total number of injured road users was 504. The relative injury rate on these roads, compared to motorways is 4.52 for fatalities and 1.23 for the total number of injured road users, using injury rates on motorways as reference. Target attributable risk is 0.671 for fatalities and 0.117 for injured road users in total. This translates to a population attributable risk of 0.034 and 0.010, respectively.

A3: Roadside obstacles

Accidents were classified into those in which a fixed object is struck, and those that do not involve striking a fixed object. The number of accidents in each category was taken from a report by Schandersson (1979, Appendix 3, Table C5).

only were taken as a measure of exposure. The relative risks associated with fixed objects were estimated to 4.49 for fatalities and 1.83 for injuries in total.

Attributable risk in the target group of accidents is 0.430 for fatalities and 0.152 for injured road users in total. These estimates were then applied to official accident statistics for 1994, in which 229 of 589 fatalities involved hitting a fixed object, and 5,621 of 21,672 injured road users in total were injured in accidents in which a fixed object was struck. Population attributable risks were estimated to (229 × 0.43)/589 = 0.167 for fatalities, and (5,621 × 0.152)/21,672 = 0.039 for injured road users in total.

A4: Erroneous highway signs

No precise information exists concerning the current prevalence of erroneous highway signing in Sweden. In a Nordic survey in 1990 (Vaa et al 1990), 14% of 703 highway signs were classified as erroneous in Sweden. A sign was classified as erroneous if it did not comply with the current guidelines for highway signing.

A study quoted in the Traffic Safety Handbook (Elvik, Mysen and Vaa 1997) shows that correcting traffic control devices to make them conform to the US Manual on Uniform Traffic Control Devices, can reduce the number of injury accidents by 15% and the number of property-damage-only accidents by 7%.

Based on this study, it was assumed that the risk attributable to erroneous or substandard highway signs in Sweden is 0.010 for fatalities and 0.015 for injuries in total. These estimates are highly uncertain.

A5: High risk junctions

Andersson et al (1998) estimate that treatment of high risk four leg junctions in Sweden, of which they estimate that there is 875, can reduce the number of fatalities by 20 and the number of injured road users by 470. These estimates are taken to indicate the risk attributable to high risk junctions in Sweden, which then becomes 20/554 = 0.036 for fatalities, and 470/21721 = 0.022 for injuries.

A6: Car crashworthiness

The contribution of this risk factor was assessed by putting together information from two different sources. The Folksam car model safety rating 1991-1992 (Hägg et al 1992) provides information on the performance in crashes of many car models. The official accident statistics for Sweden shows the number of registered cars by mass (weight). This was taken as a measure of exposure. Official accident statistics also shows the rates of involvement of cars of different masses in fatal accidents and injury accidents. Cars were divided into the following groups with respect to mass in kilograms:

• Below 900

• 900-999

• 1,000-1,099

• 1,100-1,199

• 1,200-1,299

• 1,300-1,399

• 1,400-1,499

• 1,500-

In each of these groups, the number of cars involved in fatal accidents, the number of cars involved in injury accidents, and the number of registered cars was noted. There were 493 fatalities in 1994 in accidents in which passenger cars were involved. 391 of these were car occupants, the other 102 were other groups of road users, mainly pedestrians, cyclists and moped riders. It was assumed that injuries to other road users are independent of car crashworthiness. Only injuries to car occupants are affected.

The Folksam study presents a safety performance indicator called Z for cars in four groups by mass. These groups are somewhat broader than those used by Statistics Sweden, but were applied in the following manner:

• Folksam group 751-950 kg, comprises SCB groups –899 and 900-999 kg

• Folksam group 950-1,050 kg, comprises SCB group 1,000-1,099 kg

• Folksam group 1,050-1,250 kg, comprises SCB groups 1,100-1,199 and 1,200-1,299 kg

• Folksam group 1,250-1,550 kg, comprises SCB groups 1,300-1,399, 1,400- 1,499 and 1,500- kg

For each group, the contribution of inferior crashworthiness to fatalities and injuries was assessed by applying the ratio of the best Z value in each class to the average value for that class:

Class –899: 0.074/0.138 = 0.536

The Z value for the best performing car in this class was 0.074. The mean Z value for the class was 0.138. If all cars in the class had performed as the best car, the number of fatalities could have been reduced to 0.074/0.138 = 0.536 = 54% of the actual number. Similar estimates were made for each of the four Folksam classes.

Estimates were summed. For injuries in total, the effect of differences in crashworthiness were assumed to be 20% of those found for fatalities. It was found that 201 fatalities could be prevented, and 1,434 injuries in total. This give a population attributable risk for 1994 of 201/589 = 0.344 for fatalities and 1,434/21,672 = 0.066 for the total number of injured road users.

A7: Heavy vehicles

VTI report 387, part 3 (Nilsson 1994A) was used as the source for estimating the risks attributable to heavy vehicles. Relative risks were estimated to 1.87 for fatalities and 1.03 for injured road users in total. This resulted in an attributable risk of 0.106 for fatalities and 0.005 for injured road users in total.

Category B: Environmental risk factors

This category comprises the following risk factors:

B1: Darkness. This variable takes on five values: daylight, darkness with no road lighting, darkness with road lighting, dusk or dawn with no road lighting, and dusk or dawn with road lighting.

B2: Road surface condition: This variable takes on the values dry, wet and covered by snow or ice.

B3: Animals: This variable takes on two values: animal involved and animal not involved.

The risks attributable to these factors was assessed as follows:

B1: Darkness

Accidents were categorised by light conditions according to the official accident statistics for 1994. The risk attributable to darkness was estimated by assuming that 77% of all traffic is during daylight. This estimate is admittedly judgmental, but is in the right order of magnitude. The distribution of traffic between darkness, on the one hand, and dusk and dawn, on the other was also determined informally, by assuming that: (1) The relative risks during darkness (using daylight as

reference) are higher than during dusk and dawn, (2) The relative risks are higher on unlit roads than on lit roads. Applying these assumptions resulted in an

attributable risk of 0.165 for fatalities and 0.107 for injured road users in total.

B2: Weather/road surface condition

This variable takes on the value dry, wet, and covered by snow. As for daylight conditions, the exact proportion of exposure subject to wet or snow covered roads is not known. It was, judgementally, assumed that the relative risk of injury is about 1.2 on wet road surfaces, and about 2.5 on snow covered road surfaces. The risk attributable to road surface condition then came to 0.196 for fatal injury and 0.146 for any injury.

B3: Animals

The official accident statistics for 1994 recorded 9 fatalities in accidents in which animals were involved, out of a total of 589. A total of 827 people were injured in accidents involving animals, out of a total of 21,672. Since the exposure to

animals is unknown, and likely to be a very momentary nature, the risk

attributable to animals was set equal to the portion of accidents involving animals.

This comes to 9/589 = 0.015 for fatalities, and 827/21,672 = 0.038 for the total number of injured road users. It is evident that the accidents involving animals are less severe than injury accidents in general.

Category C: Vulnerability of road users

This category includes the following characteristics of road users that put them at a disproportionate risk of injury:

C1: Being an unprotected road user C2: Being a child

C3: Being a young driver C4: Being an older citizen

This risks attributable to these characteristics overlap to a considerable extent.

The first order risks attributable to each risk factor were estimated by relying on a detailed breakdown of injuries and exposure by age and group of road user. The most recent statistics giving such a breakdown are given in a report by Thulin and

Kronberg (1998). Additional reports giving this kind of information include Thulin and Nilsson (1994) and Thulin (1997).

C1: Unprotected road users

Unprotected road users include pedestrians, cyclists, riders of mopeds and riders of motor cycles. All other road users are protected. Based on statistics applying to 1992 (Thulin and Nilsson 1992), the risk attributable to being an unprotected road users was estimated to 0.320 for fatalities and 0.266 for the total number of injured road users. Truck drivers were not included in this estimate.

C2: Children

Road users aged less than 15 years were counted as children, all others as adults.

The risk attributable to being a child was estimated by group of road users, and the estimates added. The risk attributable to being a child came to 0.016 for fatal injury and 0.022 for the total number of injuries. Apparently, accidents involving children lead to less severe injuries than injury accidents in general.

C3: Young drivers

This risk factor applies to car drivers only. Young drivers were defined as those of the age 18 to 24 years. The risk attributable to young drivers was estimated to 0.086 for fatalities and 0.101 for the total number of injuries.

C4: Older road users

This category was defined as all road users of the age of 65 years or older. The risk attributable to being an older road user was estimated to 0.206 for fatalities and 0.068 for injured road users in total. Older road users are apparently at greater risk of being killed than road users in general, perhaps because older people have a reduced tolerance for biomechanical impacts.

Category D: Road user behaviour

This category includes the following risk factors:

D1: Speed limit violations D2: Drinking and driving D3: Not wearing seat belts

D4: Other violations of road traffic law D5: Excessive driving in towns

The risks attributable to these factors have been estimated as follows.

D1: Speed limit violations

Based on a report issued by VTI (Andersson et al 1998), fatalities and injuries were tabulated by category of road and speed limit. Data on the current mean speed of driving was mostly taken from the same source, but in a few cases from a report issued by the Swedish National Roads Administration (Isaksson 1997).

Speed was assumed to be normally distributed around the mean. It was further assumed that the entire distribution of speeds is contained within plus or minus 3 standard deviations from the mean (covering a range of six standard deviations in total). Perfect compliance with current speed limits was defined as a distribution in which 93% of all speeds (corresponding to the mean plus 1.5 standard

deviation above it) are at or below the speed limit. The effect of perfect compliance on the number of road users killed or injured was estimated by applying functions relating the number of fatal accidents and injury accidents to the mean speed of traffic. Hence, it was necessary to estimate how perfect compliance would affect the mean speed of travel. To give an example of such a calculation, information is reproduced below for the case of rural roads with a posted speed limit of 90 km/h.

Percent of traffic (cumulative) Mean speed today (km/h)

Mean speed in case of perfect compliance (km/h)

0 60,0 60,0 1 66,0 66,0 2 72,0 72,0 7 78,0 78,0 16 84,0 80,0 31 90,0 82,0 50 96,0 84,0 69 102,0 86,0 84 108,0 88,0 93 114,0 90,0 98 120,0 96,0 99 126,0 102,0 100 132,0 108,0

In the initial distribution, speeds range from 60 to 132 km/h. Mean speed (the 50 percentile speed) is 96 km/h. Perfect compliance is assumed not to affect the speeds of those driving at a speed of up to about 10 km/h below the speed limit.

All speeds higher than this are, however, reduced. The largest reductions occur for the highest speeds. The new mean speed is 84 km/h. 93% of all vehicles are assumed to stay at or below the speed limit. The effect of perfect compliance on accidents was estimated by applying power functions developed by VTI. For fatalities, for example, the effect of perfect compliance is estimated according to the following function:

Effect on fatalities (84/96)⁴ = 0.586 = 41.4% reduction in the number of fatalities.

For serious and slight injuries, the exponent is 3 and 2, respectively. The risk attributable to speeding was estimated by applying these functions to all types of

No documento Improving Road Safety in Sweden - Transportøkonomisk institutt (páginas 159-185)