united states passenger-vehicle crashes by crash geometry: direct costs and other losses

Pergamon

Accid. Anal. and Prev., Vol. 23, No. 3, pp. 343-352, 1997 8 1997 Else&r Science Ltd

All rights reserved. Printed in Great Britam ooot-4575.‘97 Sl7.00 + 0.00

PII: SOOO1-4575( 96)00087-5

UNITED STATES PASSENGER-VEHICLE CRASHES BY CRASH GEOMETRY: DIRECT COSTS AND

OTHER LOSSES1

TED MILLER’* DIANE LESTINA,’ MAURY GALBRAITH,~ TIM SCHLAX,~

PAMELA MABERY~ and RICHARD DEERING’

‘National Public Services Research Institute, 8201 Corporate Drive, Suite 220, Landover, MD 20785,

U.S.A. and ‘Crash Avoidance Department, General Motors Safety and Restraints Center,

NAO Technical Center, 30200 Mound Road, Warren, MI 48090-9010, U.S.A.

(Received IO October 1995; in revisedform 23 October 1996)

Abstract-The personal and societal losses caused by motor-vehicle crashes are significant. This paper provides tools that describe these losses for 30 different crash geometries. Persons involved with the development and implementation of crash countermeasures can use these tools to prioritize their countermeasure approach.

Multiple vehicle crashes currently account for much larger direct costs but only slightly more years lost than single vehicle crashes. Direct expenditures on multiple vehicle crashes exceed $41 billion per year; they claim 974,000 years of life and functioning. Direct expenditures on single vehicle crashes exceed $18 billion per year; they claim 937,000 years of life and functioning. 0 1997 Elsevier Science Ltd

Keywords-Crashes, Years lost, Crash geometry, Costs, Who pays

INTRODUCTION

The losses caused to both individuals and society by motor-vehicle crashes are significant. Blincoe and

Faigin (1992) estimate the economic cost of 1990 motor-vehicle crashes in the United States at $137.5 billion. Viner ( 1993) and Miller et al. ( 1991) eval- uated the losses from motor-vehicle crashes by first harmful event and most harmful event. The automotive safety community continues to pursue countermeasures to mitigate crash losses. Selection of the appropriate countermeasure approach for a given

crash geometry depends on a number of factors, including: anticipated effectiveness (the expected percentage reduction in the harmful events the counter-

measure addresses); technology maturity (since the benefits of new technology often are known with less certainty and unanticipated side effects like airbag injuries may arise); product, program or system cost; and maximum harm reduction opportunity (i.e., the amount of harm associated with the crash problem the countermeasure addresses). This paper provides tools that describe the personal and societal conse-

*Author for correspondence. ‘The estimates, opinions, and conclusions in this paper are

strictly those of the authors’ and are not necessarily those of the National Public Services Research Institute or General Motors.

343

quences associated with 30 different crash geometries. The frequency and losses from a specific crash sce- nario can then be used to evaluate the maximum harm reduction opportunity available from various

countermeasures.

METHODS

To evaluate the relative losses, in terms of both direct costs and years lost, of passenger car and light truck crashes we used the National Highway Traffic Safety Administration’s (NHTSA) Crashworthiness Data System (CDS), National Accident Sampling System (NASS), General Estimates System (GES), and Fatal Accident Reporting System (FARS). We

identified five single-vehicle crash geometries (e.g. ‘single-vehicle struck human’), and five multiple-vehi-

cle crash geometries (e.g. ‘multiple-vehicle cross paths’). We further differentiated multiple-vehicle crash geometries by location and vehicle actions (e.g. ‘multiple-vehicle cross paths at signal both vehicles straight’). Altogether we identified 30 crash geometries which provide an opportunity to set priorities for countermeasure development. We chose these categories by thinking about crash geometries that might be reducible in frequency or severity with innovations in vehicle and roadway design as well as

344 T. MILLER et al

driver training. Table 1 provides a description of each crash geometry. Many of the categories came from Massie et al. (1993).

We conducted a major upgrade of the related

highway crash cost estimates in Miller et al. (1991) Blincoe and Faigin ( 1992), and Miller ( 1993a). We estimated the annual direct cost of passenger car and light truck crashes by crash geometry. To capture the long-term consequences of crash injuries, we also provided estimates of years of life and functioning lost due to the crash injuries by crash geometry.

‘Direct costs’ are the actual dollar expenditures related to crash injury and damage. Direct costs

include amounts spent for hospital, physician, rehabilitation, prescription, and related medical costs. Also included are coroner and premature burial costs for fatalities, and the legal and insurance processing expenses that result from loss compensation through insurance and the courts. The loss-compensation administrative-cost estimates omit time that plaintiffs

and defendants spend on the loss-recovery process. The costs of police, fire, ambulance, and helicopter services are included. Also included in the category is the cost to repair or replace damaged vehicles and property, including the costs of damage compensation. Finally, this category includes costs of short-

term work loss, employer productivity loss, and travel delay.

‘Years of life and functioning lost’ (called ‘Life Years’ in the table headings) is the number of years of life lost to fatal injury plus the number of years of

functional capacity lost to non-fatal injury. Years of functional capacity lost are taken from Miller et al. ( 1991) which defines the losses on seven dimensions: mobility, cognitive, self care, sensory, cosmetic, pain, and ability to perform household and wage work.

We discount future years of life and functioning lost at a 2.5percent discount rate. This discount rate is toward the conservative end (a high-discount rate, which yields low loss estimates) of the l-3 percent range typically used in courtroom compensation of personal injury (U.S. Supreme Court, 1983). Discounting of future life years models health decision-making described in general population surveys and revealed by safety behavior (see, e.g., Agee and Cracker, 1996; Kashner, 1990; Moore and Viscusi, 1990a,b; Olsen, 1993; Viscusi and Moore, 1989; Viscusi, 1995). Most estimates of discount rates used in valuing future life years range between one and three percent, but some are as high as 14 percent. From thousands of survey responses, Cropper et al. (1991, 1992) conclude that the discount rate for

Table I. Definition of crash geometries

Single-Vehicle police-reported crashes are classified according to first harmful event. ‘Struck Human’ includes collisions with pedestrians and pedalcyclists. ‘Struck Animal’ includes collisions with domestic and wild animals. ‘Struck Object’ includes collisions with objects both on and off the roadway. ‘Rollover’ includes crashes both on and off the roadway where the vehicle overturns. ‘Struck Parked Car’ includes collisions, in any manner, with a parked vehicle.

Multiple-Vehicle police-reported crashes are mainly classified by the actions of the first two involved vehicles, ‘Unspecified’ refers throughout to collisions where the actions are unknown.

Cross Puth crashes are classified by type of intersection, signalized, signed, and no/unknown signage, and by vehicle actions, ‘Both Vehicles Straight’ includes collisions where a driver disobeys or fails to see a stop. ‘One Vehicle Turns Right’ includes collisions where one vehicle turns right. ‘One Vehicle Turns Left’ includes collisions where one vehicle turns left.

Rear-End crashes are classified by vehicle actions: ‘Lead Vehicle Stopped’ includes collisions where the lead vehicle is stopped when struck. ‘Lead Vehicle Turning’ includes collisions where the lead vehicle is turning when struck. ‘Lead Vehicle Straight’ includes collisions where the lead vehicle is moving forward when struck. ‘3 + Vehicles’ includes pile-ups of three or more vehicles.

Sideswipe crashes are classified by vehicle actions: ‘Lane Change’ includes collisions where a driver changes to an occupied lane. ‘Vehicles Straight’ includes collisions where a driver fails to hold his/her lane.

Opposite Direction crashes are classified ‘Non-intersection’/‘Intersection’ by vehicle actions. ‘Both Vehicles Straight’ includes head-on or offset frontal collisions. ‘One Vehicle Turns Left’ includes collisions where the left-turning driver fails to yield.

Backing includes collisions where a driver backs into moving traffic,

Undefined crashes are those not classifiable elsewhere.

Unreported crashes are those not reported to police.

Losses from passenger-vehicle crashes 345

future life years declines with time (i.e. that people

discount losses 30 years hence less than losses next

year). Upgrades and updates of Miller et al. (1991),

Blincoe and Faigin (1992), and Miller (1993a)

include:

(a) Non-fatal medical care and coroner costs

came from Miller (1993a). However, the costs in that article were reinflated from their 1983 base year with

Medical Spending per Capita rather than the Consumer Price Index-Medical Care. The switch in

inflators better accounts for changes in medical tech-

nology and procedures. It causes the inflated costs

per day of hospital stay for crash injury to keep pace

with the American Hospital Association’s index of

average cost per hospital day (Bureau of the Census,

1994) and is consistent with recent research on medi-

cal care cost inflators (Newhouse, 1992). (The switch raised the previously published medical care costs for

1988 by 15 percent.)

(b) Medical payments per fatality came from national workplace injury data (National Council on

Compensation Insurance, 1989) rather than the less representative data used by Miller (1993a).

(c) Property damage costs from Blincoe and

Faigin (1992) and vocational rehabilitation, prema-

ture burial and emergency services costs from Miller

(1993a) were inflated using the Consumer Price

Index-All Items.

(d) The short-term wage loss and travel delay

costs from Miller (1993a) were inflated with the

Employer Cost Index.

(e) Employer productivity losses were recom-

puted with the assumptions in Miller (1993b). We assumed supervisor and co-worker staff-time lost to

a permanently disabling injury equalled the losses for

a fatality. (f) We improved the insurance administrative

and legal expense models. The new models placed a

$100,000 average policy limit on liability claims and

a $500,000 limit on average court awards for cata-

strophic injuries. Legal costs were reestimated with unit costs from Kakalik and Pace ( 1986) and proba-

bilities of lawsuit from Hensler et al. (1991), as well

as the updated medical care and productivity loss

estimates. For these computations, the lifetime earn-

ings and household production-loss models in Miller et al. ( 1991) were updated with 1990-1991 demo-

graphic data, earnings profiles, and life tables (replac- ing 1985 data).

These upgrades result in crash costs per injury

by body region and Maximum Abbreviated Injury

Scale (MAIS) severity (AAAM, 1985). The Occupant Injury Coding (OIC) AIS scheme is a detailed medical

classification developed by physicians as a basis for

rating the survival threat that injuries pose. MATS is

simply the maximum AIS among the multiple injuries

a victim suffers. The purpose of the AIS scale is to

differentiate injuries by survival threat, not the cost,

functional losses, or course of recovery that they

involve. For example, loss of teeth is an AIS- injury

that can involve substantial costs and lifetime pain

and suffering. Conversely, timely surgery often allows complete and rapid recovery from ruptured spleens

and other AIS 3-5 internal injuries. Nevertheless,

estimates by MAIS and body region successfully sort

costs (Miller, 1993a).

Incidence of non-fatal injury by crash geometry

is available only by police-reported KABCO severity

(National Safety Council, 1990). Estimating the total

harm from U.S. motor-vehicle crashes, thus, requires

estimating costs per police-reported non-fatal involve-

ment by crash geometry and KABCO severity. The

KABCO scale allows police officers to classify crash

victims as: K, killed; A, disabling injury; B, evident injury; C, possible injury; 0, no apparent injury.

KABCO ratings are coarse because officers must

typically classify victims without a hands-on examina- tion. This is somewhat problematic. Miller et al.

(1991) documented the great diversity in KABCO

coding across cases and the systematic undercount of

police-reported cases in NASS/GES/CDS. Blincoe

and Faigin (1992) confirmed the undercount. Miller

et al. (1987) analyzed the systematic variation in

police-reported injury counts by severity caused by differing state accident reporting thresholds. O’Day

(1993) more carefully quantified the great variability

in use of the A-injury code between states that already was known to exist. Viner and Conley (1994)

explained the contribution to this variability of differ-

ing state definitions of A-injury. To minimize the

effects of variability in police coding we turned to

NHTSA’s CDS and old NASS. These files contain

nationally-consistent injury coding by OIC/AIS.

First, we tabulated a proportional distribution

of injuries by crash geometry, injury location, and MAIS severity. This proved difficult due to the differ-

ing crash descriptors in the NHTSA data sets. For

crash strata covered by NHTSA’s CDS, we computed the distribution of injury victims by body region and

MAIS for years 1988-1991. The CDS does not indi-

cate if a crash occurred at an intersection with a stop

signal or a stop sign; we applied the GES frequency of these situations to the CDS cross-path crashes. For passenger vehicle strata not covered by the CDS

system (pedestrian and most non-tow-away crashes),

we computed the victim injury severity distribution from 1982-1986 NASS data. In these computations,

346 T. MILLER et al.

Table 2. Number of injuries in passenger-car and light-truck crashes in 1992 by police-reported injury severity and crash geometry

K A B C 0

Killed Disabling Evident Possible None Total

Single-Vehicle Crashes 20056 169348 34583 1 297667 1901836 2735000 Struck Human 5387 35075 76397 52198 225120 394000 Struck Animal 96 1111 3808 8175 348953 362000 Struck Object 10909 105407 201454 180378 878571 1377000 Rollover 3211 20132 40314 29522 83578 177000 Struck Parked Car 453 7623 23858 27394 365614 425000

Multiple-Vehicle Crashes Cross Paths At Signal

Both Vehicles Straight One Vehicle Turn Right One Vehicle Turn Left

At Sign Both Vehicles Straight One Vehicle Turn Right One Vehicle Turn Left

No Signage Both Vehicles Straight One Vehicle Turn Right One Vehicle Turn Left

Unspecified

13742 252253 521358 1453741 9729244 11971000 4647 101893 212182 470790 3131918 3922000

727 24339 46885 109162 447822 629000 18 735 2695 6835 75616 86000 0 5958 12492 24989 160476 204000

2381 34342 70538 134929 706882 949000 66 1606 3607 8633 106150 120000

1455 13243 25105 56797 384983 482000

0 7985 14363 33171 231628 287000 0 1350 4227 12983 134899 153000 0 8567 21469 49144 397655 477000 0 3768 10801 34147 485807 535000

Rear-End 1649 57519 138482 64202 1 3723420 4563000 Lead Vehicle Stopped 359 23610 60178 323174 1960719 2368000 Lead Vehicle Turning 99 4013 8524 36052 234905 284000 Lead Vehicle Straight 703 13534 30984 142422 888184 1076000 3 + Vehicles Straight 488 14913 36604 133204 571281 756000 Unspecified 0 1449 2192 7169 68331 79000

Sideswipe 296 Lane Change 179 Vehicles Straight 117 Unspecified 0

11316 7021

952 3343

80429

34379 6996

3916 35138

21001 11962 2768 6271

70243 43686

9548 17009

255859

47954 32918

14077 160910

1110639 1214000 724041 787000 165193 179000 221405 248000

Opposite Direction Non-intersection

Both Vehicles Straight One Vehicle Turn Left

Intersection Both Vehicles Straight One Vehicle Turn Left

Backing

Undefined

Unreported

Total

7096

6070 0

367 659

54

2394

1096

37833

145244

42592 15296

9972 77384

4449

65956

1821000

407000 253000

100000 1061000

14828

188361

36192 460000 930000 1940000

1332449

275898 198205

71192 787154

430818

1427273

17672165

30730000

451000

1722000

17672000

34100000

when two injuries had the same AIS severity, victims were assigned to body regions with the following hierarchy: spinal cord, brain, lower limb, upper limb, trunk/abdomen, face/neck, and minor external. The GES provided weights to place on the CDS and NASS data to arrive at synthesized estimates of annual passenger-vehicle occupants by MAIS injury severity, body region, crash geometry, and police- reported KABCO severity. Multiplying costs by body region and MAIS times injury counts by crash geome-

try, body region, and MAIS severity yields costs per police-reported non-fatal involvement by crash geometry and reported KABCO severity.

Incidence of non-fatal injury by crash geometry and police-reported severity are from 199 1~ 1993 GES data. A three-year average was chosen to account for small sample variability. We multiplied by factors from Blincoe and Faigin ( 1992) to account for a systematic undercount in the GES sample. To add injuries in crashes not reported to the police, we


Table 3. Annual direct cost, present value years of life and functioning lost (life years), and number of crashed vehicles by crash geometry

Direct cost ($) Life years # Crashed vehicles

Single Vehicle Crashes: $18678 M Struck Human 4236 M Struck Animal 641 M Struck Object 10261 M Rollover 2348 M Struck Parked Car 1192 M

Multiple Vehicle Crashes Crms Paths At Signal




Unspecified

41553 M 14523 M

2948 M 213 M 123 M

4414 M 312 M

1854 M

1033 M 403 M

1417 M 1206 M

Rear-End Lead Vehicle Stopped Lead Vehicle Turning Lead Vehicle Straight 3 + Vehicles Straight Unspecified

13906 M 6824 M

763 M 3015 M 3101 M

203 M

SidemQe Lane Change Vehicles Straight Unspecified

2794 M 1712 M

395 M 687 M




Backing

Undefined

Unreported

Total

9517 M

3261 M 968 M

545 M 4743 M

813 M

5835 M

22354 M

$88420 M

931 K 251 K

4K 523 K 131 K 28 K

973 K 341 K

72 K 3K

12K

138K 5K

62 K

16K 4K

19K 10 K

204 K 78 K 10K 47 K 67 K

2K

36 K 20 K

8K 8K

387 K

220 K 27 K

20 K 120 K

5K

147 K

150K

2209 K

2412 K 346 K 321 K

1221 K 155 K 369 K

10483 K 3398 K

544 K 74 K

179K

830 K 114K 429 K

253 K 142 K 392 K 441 K

4028 K 2063 K

254 K 938 K 670 K 103 K

1089 K 733 K 167 K 189K

1589 K

361 K 188 K

106K 934 K

379 K

1613 K

11736 K

26243 K

$ = 12/93 dollars; M = millions; K = thousands.

multiplied 1988 unreported injuries from Miller et al. ( 1991) by the ratio of 1992 passenger car and light- truck-involved crashes to total 1988 crashes. These procedures provide an estimate of 1992 non-fatal

injury incidence by crash geometry and police- reported severity.

The 1992 FARS was used to estimate fatalities by crash geometry. FARS does not provide detailed descriptions of crashes where one vehicle is turning left (i.e. it does not break the left-turn count into

‘cross path signal’, ‘cross path sign’, ‘opposite direction’ ‘non-intersection’, and ‘opposite direction intersection’ crashes). We computed the split of fatalities between these crash geometries from weighted GES sample data on fatalities from 1988-1992 (the best available national probability sample describing the distribution of these fatalities) and used it to break apart the exact left-turn fatality count from FARS. Table 2 presents 1992 injury incidence by police- reported injury severity.

T. MILLER et al.

Total Direct Costs $88 Billion Annually (1993$‘s) Crashes shown by Major Classification (reported crashes only)

Reported Crashes 75% of total direct costs Reported Crashes 55% of all crash involved vehicles

mv rear ‘end 0

I I I 0% 5% 10% 15% 20% 25%

Percent Involved Vehicles

Fig. 1. Direct cost per crashed vehicle and percentage of crashed vehicles for police-reported crashes by crash geometry

30%

I.0

0.9

0.8

0.2

0.1

0 l-

Total Years Lost 2.2 Million Annually, 1993 estimate Crashes shown by Major Classification (reported crashes only)

Reported Crashes 93% of total years lost Reported Crashes 55% of all crash involved vehicles

500,000 constant years lost

v animal mv backing 0 mv sideswipe mv rear end-

00 I I I I I I 0% 5% 10% 15% 20% 25% 30%

Percent Involved Vehicles

Fig. 2. Years of life and functioning lost per crashed vehicle and percentage of crashed vehicles for police-reported crashes by crash geometry


RESULTS

The direct cost of passenger-car and light-truck crashes in the United States totals $88.4 billion per year. Multiple-vehicle crashes contribute $41.6 bil-

lion, single-vehicle crashes $18.7 billion, unreported crashes $22.3 billion, and undefined crashes $5.8 billion to this total. ‘Multiple-vehicle cross path’

crashes at $14.5 billion, ‘multiple-vehicle rear-end’ crashes at $13.9 billion, ‘single-vehicle struck object’

crashes at $10.3 billion, and ‘multiple-vehicle oppo-

site-direction’ crashes at $9.5 billion have the highest direct costs. These four crash geometries account for 54 percent of the annual total.

In addition, over 2.2 million years of life and functioning are lost each year as a result these crashes.

Multiple-vehicle crashes dominate single-vehicle crashes in direct costs; yet, in years of life and functioning lost, they are nearly equal, with 973,000

and 937,000 years lost, respectively. ‘Single-vehicle struck object’ crashes at 523,000 years lost, ‘multiple-

vehicle opposite-direction’ crashes at 387,000 years

lost, and ‘multiple-vehicle cross-path’ crashes at 341,000 years lost are the largest contributors. These

three crash geometries account for 57 percent of the years of life and functioning lost. Table 3 shows annual direct costs, present value years lost, and number of crashed vehicles by crash geometry.

The direct cost per crashed vehicle and the percentage of crashed vehicles by crash geometry can

be used to compare the relative cost reduction opportunities from crash-geometry-specific countermea-

sures. Figure 1 plots these measures for police-

reported crashes. To aid crash-geometry comparisons we added ‘iso-cost curves’. Iso-cost curves represent lines of constant direct cost. Crash geometries lying

on the same iso-cost curve have equal maximum cost- reduction opportunity. Going up the curve means

fewer crashes (a lower proportion of total crashes) but larger direct costs per crash; total cost, the product of these two factors, is constant along the

curve. Crash geometries lying on iso-cost curves located farther from the origin have higher maximum

cost reduction opportunities than those lying on iso-

cost curves located closer to the origin. ‘Multiple-vehicle cross-path’ crashes and

‘multiple-vehicle rear-end’ crashes dominate. They

have the second- and first-highest frequency of crashed vehicles and the fifth- and sixth-highest costs

per crashed vehicle, respectively. ‘Single-vehicle struck object’ and ‘multiple-vehicle opposite-direction’ crashes have the next highest cost-reduction opportunities. Both of these crash geometries are

located near the $10 billion iso-cost curve; we would assign these crashes equal priority in the pursuit of

countermeasures from a maximum cost-reduction

standpoint. Figure 2 plots years lost per crashed vehicle and

the percentage of crashed vehicles by crash geometry. ‘Iso-loss curves’ have been added to aid comparisons of relative loss-reduction opportunities by crash geometry. ‘Single-vehicle struck object’ crashes dominate, followed by ‘multiple-vehicle opposite-direction’

and ‘multiple-vehicle cross-path’ crashes. Table 4 compares the rankings of the loss-reduc-

tion opportunities by crash geometry in a side-by- side fashion. The first ranking is based on direct costs. The second ranking is based on years of life and functioning lost. We have excluded undefined and unreported crashes. The order of the crash geometries differs greatly between rankings. Only ‘single- vehicle struck parked car’ (8th), ‘multiple-vehicle

backing’ (9th), and ‘single-vehicle struck animal’ (10th) receive the same rank on both scales.

Importantly, the composition of the top five positions is the same on both scales. Direct expenditures on these five ‘standout’ crash geometries total $52 billion per year. Over 1.7 million years of life and functioning

are lost per year. These five ‘standout’ crash geometries cost insur-

ers $49.2 billion, government $4.6 billion, and victims’ employers $2.7 billion in 1992 (Table 5). The third- party compensation for crashes includes not only many direct costs, but some compensation for work losses, and sometimes even non-economic damages. Consequently, total third-party compensation exceeds $85 billion per year. Auto insurers pay over 75

percent of these costs. Health insurers and the government pay the next largest shares followed by victims’

employers, workers compensation, and life insurers. Uncertainty in our incidence estimation is f 15

percent, according to the confidence intervals

NHTSA publishes for GES, CDS, and NASS data. We roughly and judgmentally estimate the uncertainty in the direct costs at around f30 percent. The uncertainty in years of life lost for fatalities is very

Table 4. Priority rankings of loss reduction opportunities by crash geometry based on direct costs and years of functional life lost

(excludes undefined and unreported crashes)

Rank order Direct costs Years lost

I mv-cross path sv--struck object 2 mv-rear end mv-opposite direction 3 sv-struck object mv-cross path 4 mv-opposite direction sv-struck human 5 sv-struck human mv-rear end 6 mv-sideswipe sv-rollover 7 sv-rollover mv-sideswipe 8 sv-struck parked car sv-struck parked car

9 mv- backing mv--backing 10 sv- struck animal sv---struck animal


Table 5. Costs, expressed in dollars, of 1992 crashes to selected payers by crash geometry

Single Vehicle Crashes Struck Human Struck Animal Struck Object Rollover Struck Parked Car

Multiple Vehicle Crashes Cross Paths At Signal




Unspecified

3423 M 234 M

1489 M

768 M 299 M

1087 M 867 M

Rear-End 10323 M Lead Vehicle Stopped 5067 M Lead Vehicle Turning 572 M Lead Vehicle Straight 2216 M 3 + Vehicles Straight 2257 M Unspecified 151 M

Sideswipe Lane Change Vehicles Straight Unspecified

2047 M 1270 M 297 M 480 M




7734 M

2122 M 812 M

444M 3756 M

569 M Backing

Undefined

Unreported

Total

Auto insurer

14975 M 3387 M

442M 8614 M 1640M 892 M

31844 M 11171 M

2275 M 158M 571 M

4457 M

14113 M

65391 M

Health insurer Life insurer

2899 M 775 M

18M 1521 M 486 M

99 M

260 M 65 M

2M 148 M 36 M

9M

4084 M 1505 M

361 M 130M

366 M 14 M 68 M

30 M 1M 6M

534 M 21 M

173 M

46 M 2M

18 M

115 M 9M 27 M 2M

116M IOM 71 M 6M

1141 M 553 M

53 M 226 M 296 M

13 M

98 M 44M

5M 20 M 28 M

1M

170M 15M 84 M 8M 21 M 2M 65 M 5M

1240 M

467 M 101 M

74 M 598 M

28 M

584 M

658 M

8321 M

116M

49 M 10 M

7M 50 M

2M

51 M

66 M

736 M

Workers compensation Government

Victims’ employer

635 M 167M

4M 346 M

99 M 19 M

712 M 253 M

55 M 2M 9M

99 M 4M

43 M

14M 3M

15 M 9M

173 M 72 M

8M 38 M 53 M

2M

26 M 14 M 4M 8M

256 M

144 M 14 M

14 M 84 M

4M

106 M

88 M

1540 M

1925 M 474 M

13 M 1095 M 270 M

73 M

3163 M 1152M

213 M 11 M 58 M

391 M 17 M

139M

85 M 23 M 98 M 57 M

944 M 436 M

44 M 184M 269 M

11 M

140 M 76 M 18 M 46 M

906 M

307 M 90 M

53 M 456 M

21 M

435 M

529 M

6051 M

$ = 12/93 dollars; M = millions

low, since this information comes directly from national life tables. An analysis of the range of loss scores for each dimension of functional capacity (Miller et al., 1995) suggests uncertainty in years of functional capacity lost for non-fatal injuries may be on the order of k25 percent.

Of necessity, this paper focuses on crashes reported to the police and subsequently captured in NHTSA data sets. Large numbers of primarily minor crashes are not reported to the police, both in the

1010 M 227 M

27 M 584 M 115 M 57 M

2055 M 718 M

148 M 10 M 36 M

224 M 14 M 95 M

50 M 18 M 69 M 54 M

652 M 315 M

36 M 144 M 148 M

9M

132M 81 M 20 M 31 M

517 M

181 M 58 M

30 M 248 M

36 M

289 M

457 M

3810 M

United States and worldwide. The incidence and severity of unreported crashes in the U.S. is difficult to estimate despite intensive study (Miller et al., 1991; Blincoe and Faigin, 1992). Rather than ignore these crashes, the tables show them in a row labelled ‘unreported’. They account for an estimated half of all people involved in passenger-vehicle crashes, perhaps 45 percent of crash-involved vehicles, a quarter of direct crash costs (largely related to property damage), and perhaps seven percent of years lost to


crashes. Lack of detailed knowledge about these

generally minor crashes obviously threatens validity

not only of the present study but of virtually all police-report-based studies of highway safety worldwide.

CONCLUSIONS

An understanding of the losses from motor- vehicle crashes can be useful in prioritizing countermeasure design. This paper estimates these losses by crash geometry. The estimates yield insight into the significance of different crash geometries relative to the total problem. Multiple-vehicle crashes currently account for more losses in both direct costs and years lost than single-vehicle crashes. Direct expenditures

on multiple-vehicle crashes exceed $41 billion per year; they claim 974,000 years of life and functioning. Direct expenditures on single-vehicle crashes exceed $18 billion per year; they claim 937,000 years of life and functioning. Crash geometries that cause the largest losses include: ( 1) Multiple-vehicle cross path (2) Multiple-vehicle rear end

(3) Single-vehicle struck object (4) Multiple-vehicle opposite direction (5) Single-vehicle struck human

An understanding of the large losses associated with these crash geometries provides helpful information for vehicle and roadway designers. They may integrate this information with estimates of other important factors such as injury causation, design and manufacturing feasibility, cost, and customer acceptance. Also important in making design choices will be: careful scientific and engineering judgements about how the use of a countermeasure for one kind of crash may affect severity for another kind of crash;

how driver response to new countermeasure design may reduce its effectiveness (e.g the decision to drive faster because the car handles better with radial tires); and how personal responsibility of drivers and passen- gers should contribute to preventing crashes.

Acknouledgemmts~We thank John Viner of the Federal Highway Administration and an anonymous referee for their excellent comments on earlier drafts. We also thank Dawn Massie and Ken Campbell of the University of Michigan Transportation Research Institute who developed the crash taxonomy used here and ran the FARS analysis.

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united states passenger-vehicle crashes by crash geometry: direct costs and other losses

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