NBSIR 73-175

Estimates of Vehicular Collisions

with Multistory Residential Buildings

Stuart L. Fribush

Deborah Bowser

Robert Chapman

Technical Analysis Division

National Bureau of Standards

Washington, D. C. 20234

April 1973

Final Report

Prepared for Office of Policy Development & Research

Department of Housing and Urban Development

Washington, D. C. 20410

NBSIR 73-175

ESTIMATES OF VEHICULAR COLLISIONS

WITH MULTISTORY RESIDENTIAL BUILDINGS

q;-

Stuart L. Fribush

Deborah Bowser

Robert Chapman

Technical Analysis Division

National Bureau of Standards

Washington, D. C. 20234

April 1973

Final Report

Prepared for Office of Policy Development & Research

Department of Housing and Urban Development

Washington, D. C. 20410

U. S. DEPARTMEIMT OF COMMERCE, Frederick B. Dent, Secretary

NATIONAL BUREAU OF STANDARDS, Richard W. Roberts, Director

List of Figures

Figure 2 .

1

Figure 2 .

2

Figure 2 .

3

Figure A.l

Figure A.

2

Figure A.

3

Figure A.

4

Figure A.

5

Figure A.

6

Figure B.l

Figure B.2

Figure B.3

Figure B.4

Set Theory Interpretation of Building

Collision Accidents

Highway Accidents in U.S. 1967-71

Runoff Accidents in U.S. 1967-71

Runoff Accidents by Location

Frequency of Collision vs. Dollar Damage

to Building

Frequency of Collision vs. Speed at Impact

Highway Classification for Runoff Accidents

Frequency of Truck Accidents in Built-up

Areas vs. Speed

Truck Accidents vs. Type of Truck

Building Collision Accidents by Road Type

Building Collision Accidents by Population

Building Collision Accidents by Vehicle Type

Building Collision Accidents by Dollar Damage

TABLE OF CONTENTS

SUMMARY

1.0 INTRODUCTION

2.0 NATIONAL FORECASTS OF BUILDING COLLISION

ACCIDENTS

2.1 Introduction

2.2 Proportionality Model

2.3 Regression Model

2.3.1 Theoretical Development

2.3.2 Model Input

2.3.3 Model Output

2.3.4 Model Extensions

2.4 Discussions

2 . 5 Chapter Summary

APPENDIX A - OKLAHOMA VEHICULAR IMPACT ANALYSIS

A.l Introduction

A. 2 Analysis

A. 3 Summary

APPENDIX B - ILLINOIS VEHICULAR ir^ACT ANALYSIS

B. l Introduction

B.2 Analysis

B.2.1 Building Collision Accidents by

Road Type

B.2. 2 Building Collision Accidents by

Population

B.2. 3 Building Collision Accidents by

Vehicle Type

B.2. 4 Building Collision Accidents by

Dollar Damage to Property

B.2. 5 Building Collision Accidents by

Driving Conditions

B . 3 Summary

APPENDIX C - MULTISTORY RESIDENTIAL BUILDINGS

APPENDIX D - MOTOR VEHICLE REGISTRAT^IONS 1960-1970

List of Tables

Table 2.1 National Motor Vehicle Accident Data

Table 2.2 Regression Results

Table 2.3 Estimated Building Collision Accidents

Table 2.4 Estimates for BC(US) for 1970 and Values of

(BC/RO) for 1970.

Table 2.5 Estimated Building Collision Accidents Resulting

in Substantial Damage to Multi-story Residential

Structures

Table A.l Group A Summary

Table A. 2 Group B Summary

Table B.l Distribution of Building Collision Accidents by

Number of Vehicles Involved

Table B.2 Summary Comparison: Maximum Damage Building

Collision Accidents vs. All Building Collision

Accidents

Table C.l 19 70 Housing Data

Table C.2 1960 Housing Data

Table C.3 Summary Comparison of Housing Data 1960-1970

Table D.l Motor Vehicle Registrations

Table D.2 Growth of Motor Vehicle Registrations 1960-1970

Table D.3 Motor Vehicle Registrations in U. S. 1965-1970

SUMMARY

This study was designed to investigate the frequency

with which motor vehicles collide with buildings in the

United States. In particular, the objective was to assess

the extent to which motor vehicles collide with multistory*

residential buildings, potentially causing substantial damage.

It was soon discovered that statistics for "building collision"

accidents are not available on a national level; data therefore had to be drawn from state motor vehicle accident

records. Two States, Oklahoma and Illinois, provided data

on accidents which occurred within their respective jurisdictions in 19 70. In general, it was learned that "building

collision" accidents are largely the result of single vehicles

running off the road in urban areas.

National estimates of "building collision" accidents

have been generated, with information extracted from the

Oklahoma and Illinois analyses as inputs. It is estimated

that the annual number of "building collision" accidents

which occur nationally is of the order of twenty to forty

thousand. However, when this estimate is modified to account

for only those accidents causing substantial damage to multistory residential buildings, the magnitude is then decreased by

roughly a thousandfold. Therefore, an estim.ated value for

the number of vehicles colliding with multistory residential

* Multistory here means buildings with four or more floors.
buildings and causing substantial damage in 1970 is of the

order of 40. The annual probability of a given building

being affected by such an accident was approximately one in

ten thousand. A discussion then follows on existing data

collection systems v/hich might be modified to provide better

estimates of the number of "building collision" accidents.
1.0 Introduction

Since 1968 there has been growing international concern

that buildings, particularly multistory residential buildings,

may be subjected to loading conditions not normally considered

in design, i.e., abnormal loadings. In that year there occurred

the much-publicized collapse of an apartment house at Ronan

Point in England. In this 22-story building of precast

concrete panel construction, collapse was triggered by an

accidental explosion of gas that leaked from the connection

of a gas range located in an apartment on the 18th floor. As

a result of the explosion, one corner of the building

collapsed for the height of the apartment unit.

In November 1971, the Department of Housing and Urban

Development requested the National Bureau of Standards to

make a study of all aspects of abnormal loading and the

problem of progressive collapse. The recognized sources of

abnormal loading are discussed elsewhere.* Several of these

types of Ipading were considered to have a frequency of

occurrence large enough to warrant particular attention. The

collision of motor vehicles with a building is one of these.

In an attempt to assess the probability of vehicular

collisions with building that might be structurally

Somes, N. F. , Abnormal Loadings on Buildings and

Progressive Collapse, Building Practices for Disaster

Mitigation, Building Science Series 46, January, 1973.

1
significant, this study of related U. S. statistics was initiated.

The interpretation of these statistics with regard to design

against progressive collapse is beyond the scope of this report.

There are essentially two classes of accidents,

namely (a) those involving a vehicle within its normal

operating domain (i.e., an automobile in a parking garage)

and (b) those accidents that occur as a result of a

vehicle leaving its normal operating path or domain

(i.e., an automobile leaving a roadway and colliding

with a building) . This report discusses only the latter

type of accident.

In order to ascertain damage done to buildings as

a result of vehicular impact, detailed accident records

must be examined. Probably the best source of such

information would be records maintained by insurance

companies. An attempt was made to obtain such records,

but this did not meet with any success.

It was then decided that motor vehicle accident

records would be the next best available source for the

information desired. Ideally, nationwide data should

be used since one of the objectives is to obtain a. national estimate of the frequency with which motor

vehicles collide with buildings. However, neither the

National Safety Council nor the National Transportation

Safety Board maintain statistics regarding those

accidents where motor vehicles have collided with buildings

("building collisions").
Apparently, the reason no national statistics exist is

that not all states maintain records of "building collision"

accidents. However, two states which do keep records of

sufficient accuracy and detail to meet the purposes of this

study are Oklahoma and Illinois. Both states graciously

consented to provide any available information regarding 19 70

motor vehicle accidents involving "building collisions."

This information was used as the starting point for the

analysis. The details of the Oklahoma and Illinois analyses

appear in Appendices A and B, respectively. In Chapter 2,

some crude estimates of national building collision accidents

are made using information gained from the Oklahoma and

Illinois studies.

3
2.0 National Forecasts of Building Collision Accidents

2.1 Introduction

In this chapter, two methods are described and used to

obtain rough estimates of the incidence of vehicular

collisions with buildings on a national scale. The first of

these is a simple proportionality model which assumes that

"building collision" accidents are proportional to either motor

vehicle registrations or population. This method estimates

only the gross number of "building collisions" and makes no

attempt to account for either the type of building struck or

the degree of damage caused. The second method uses linear

regression to project "building collisions" based on

historical accident data. This method is then modified to

account for only those accidents which cause substantial

damage to multistory residential buildings. Although both

methods are first efforts based on scant data, they provide

a perspective on the magnitude of "building collision"

accidents on a national level.

2.2 Proportionality Method.

The underlying assumption on which this method is

based is simply that the number of "building collision"

accidents which occur in a given area is directly proportional

to the motor vehicle registrations (population) in that area.

It then becomes possible to estimate the total number of

"building collisions" which take place throughout the United

States (US) by means of extrapolation, as shown below:

4
BC (US) ^ BC (sample)

MV (US) MV (sample) (1)

where BC denotes building collisions and MV denotes

motor vehicle registrations.

Assuming that BC (sample) , MV (sample) , and MV (US) are

available, the calculation of BC (US) then becomes obvious.

Using the same proportionality technique, similar

estimates can be made separately for urban and rural

building collisions:

BC^ (US) ^ BC^ (sample) ^2)

MV^ (US) MV^ (sample)

where the superscript denotes urban.

The rural estimate is obtained as follows:

BC^ (US) ^ BC^ (sample)

MV^ (US) MV^ (sample)

where the superscript denotes rural.

The sample available for this study consists of the

traffic accident records of Oklahoma and Illinois. Details

of the accident records are provided in Appendix A and B

respectively with the most relevant statistics summarized

below

Total "Building Collision" Accidents in 1970

Oklahoma 50

Illinois 1229

Total 1279

5
*Urban "Building Collision" Accidents in 1970

Oklahoma 34

Illinois - 1137

*Rural "Building Collision" Accidents in 1970

Oklahoma 16

Illinois 92

Using the Oklahoma data on total motor vehicle

registrations** we obtain estimates for the Building

Collisions (US) to be 3200. Using Illinois data we obtain

the estimate 25,000. Since the collision data for Illinois

does not include all of the Chicago data, this last estimate

should be revised upward. Note that these estimates are

about an order of magnitude apart, so we are in an unfortunate

position. If one could assume that the Oklahoma estimate was

a representative low one and the Illinois estimate a

representative high one, then a pooling of the data (giving

equal weight to each) would yield an estimate of about 20,000.

*The numbers for rural and urban "building collision"

accidents in Oklahoma and Illinois do not correspond to the

numbers given in the summaries of Appendices A and B because

of a difference in the definition of urban. In Appendices

A and B municipalities of over 5,000 population were

considered urban. However, the Bureau ot the Census uses a

breakpoint of 2,500 to distinguish between urban and rural.

Hence the urban and rural population figures needed in this

proportionality analysis are based on this breakpoint. Therefore, the sample "building collision" accident figures were

adjusted to correspond to a population breakpoint of 2,500.

**See Table D.l, page 64.

6
If one were to employ population in place ot motor

vehicle registrations, one would obtain from the pooled data

for Oklahoma and Illinois that BC(US) = 19,000,

BC^(US) = 16,000 while BC^(US) ^ 3,000. Both the urban and

total US values are smaller than they should be because of

the unrecorded Chicago collisions.

The population statistics needed for the analysis are

given below.*

Total Population 1970

6 U.S. - 203.2 X 10

6 Oklahoma - 2.6 x 10

6 Illinois - 11.1 X 10

Urban Population 1970 (municipalities i 2,500)

6 U.S. - 149.3 X 10

6 Oklahoma - 1.8 x 10

6 Illinois - 9.2 x 10

Rural Population 1970

6 U.S. - 53.9 X 10

6 Oklahoma - 0.8 x 10

6 Illinois - 1.9 x 10

Certainly the BC(US) is greater than 1279, the total

of those in Oklahoma and Illinois. If we consider the states

with high motor vehicle registrations, like New York, New

Jersey, Pennsylvania, Ohio, California, et cetera, a reasonable

bound for building collisions in the United States might be on

the order of 10 ,000 .

* Taken from Statistical Abstracts of the United States

1971, Table 17.

7
These admittedly crude estimates are useful in providing

insight as to the order of magnitude for the number of

buildings struck by vehicles per year. Thus the above

estimates suggest that the annual number of vehicular

collisions with buildings is on the order of tens of

thousands and possibly near 20,00 0. However, there are

other means of obtaining estimates which may prove more

reliable.

2.3 Regression Model

2.3.1 Theoretical Development

1

. Before introducing this model it may be instructive

to review the necessary antecedent conditions for "building

collision" accidents. An obvious major prerequisite is

that the vehicle first runs off the road. Therefore, the

set of all "building collision" accidents is a subset of the

set of all runoff accidents. Similarly, the set of all

runoff accidents is a subset of the set of all motor vehicle

accidents. This relationship is depicted schematically in

figure 2.1.

/ AC AC = All Motor

\ Vehicle

\ Accidents

RO ] ! RO = Runoffs

I BC = Building

/' ' Collisions

Figure 2.1

SET THEORY REPRESENTATION OF BUILDING COLLISION ACCIDENTS

8
Mathematically, this relationship can be represented

in terms of frequency ratios as follows:

N (BC) ^ N(BC) N (RO) (4)

N(AC) N(RO) N (AC)

where N() denotes the number of the expression in ()

.

If one denotes these numbers simply as BC, AC, .... then we can write

BC = (AC) (RO/AC) (BC/RO) (5)

that is, the total number of "building collisions" is equal

to the total number of vehicle accidents multiplied by

the proportion of accidents which are run-off times the

proportion of "building collisions" to run-offs.

From the Oklahoma and Illinois studies (Appendices A

and B respectively) , it was learned that rural "building

collision" accidents and urban "building collision"

accidents are really two different phenomena. In rural areas,

the probability of runoff is relatively high while the

probability of colliding with a building, given a runoff,

is low. Conversely, in urban areas the probability of

vehicular runoff is low, but the probability of vehicular

collision with a building given a runoff, is relatively

high. This suggests that an estimation model for the

number of "building collisions" should have both urban and

rural components. Therefore:

9
Table 2.1. National Motor Vehicle Accident Data

All All (^R0\o Accidents Acc idents V AC/ \ AC / URBAN (10^^ RURAL ^-^^^^ URBAN RURAL

1967 9.8 3.9 4.9 27.3

1968 10.4 4.2 5.7 27.3

1969 11.1 4.4 4.8 30.2

197 0

- 11.5 4.5 4.9 29.4 : 1971 ' 11.8

! 4.6 4.9 29.4

*Taken from Accident Facts , published bv the National

Safety Council, Chicago, Illinois, 1968-1972.

10
16

14

12

O

t-HX

W

4-> c 10

•HU

U

<

rH 8 U

•H

x:>

0

4-) Mo

4-1O

M

<D Si

NUITI 4

Total

—V Urban

-X Rural

1967 1968 1969 1970 1971

Figure 2.2 Highway Accidents in U. S. 1967-1971

11
32

28

24

20

16

12

X, --x Rural

-=>^ All

-y^ Urban

1967 1968 1969 1970 1971

Figure 2.3 Runoff Accidents in U. S. 1967-1971

12
BC = BC^ + BC^ (6)

where the superscripts U and R refer to urban and

rural respectively

BC^ = (AC)^ (RO/AC)^ (BC/RO)" (7)

and BC^ = (AC)^ (RO/AC)^ (BC/RO) ^ ' ' (8)

If this model is intended for use in the prediction of

future occurrences of "building collision" accidents, it is

necessary to include a time factor in the model. This

requirement is indicated by:

BC^ = BC^ + BC^ (9)

where the subscript t denotes the year

and BC^ = (AC)^ (RO/AC)^ (BC/RO) ^ (10)

BC^ = (AC)^ (RO/AC)^ (BC/RO)^ (11)

Equations 9-11 constitute the prediction model. The . terms on the right hand sides of equations (10) and (11) must

now be estimated for future years. The following section

deals with the methodology employed in generating such

estimates

.

2.3.2 Model Input

The National Safety Council maintains annual national

statistics regarding both the number of motor vehicle

accidents and the percentage of those accidents which were

runoffs. This information is available for 1967 through

19 71, and is shown in table 2.1. Figures 2.2 and 2.3 provide

general representations of the data in table 2.1. In general,

it appears that the number of motor vehicle accidents has

13
increased over time (figure 2.2), while the percentage of

accidents which are runoffs has remained relatively constant

(figure 2.3). Simple linear regression was applied to the

4 columns in table 2.1, using time as the independent

variable to test the hypotheses stated above. The values of

time (t) used in the analysis were the last two digits of

the year in question (67, 68, ...71) . Also, the number of

accidents is predicted in millions, while the proportion of

runoffs is predicted in percent. The results of the analysis

appear in table 2.2.

Table 2.2. REGRESSION RESULTS

Intercept Slope 2P(t)

(1) All Urban -24.27 . 51 .975 >.995

(2) All Rural - 7.41 .17 .938 > .995

(3) %R0 Urban 10.56 -.08 .116 <.95

(4) %R0 Rural -14.75 .63 .555 <.95

*R , the square of the correlation coefficients, is a simple

measure of the degree of goodness of fit between the

input data and the calculated regression line. An R^

equal to 1 indicates perfect fit and any R"^ greater than

0.9 is considered to be a good fit. The use of R^ does not

imply in any way that time itself is a determinant of

accident behavior, however.

**Pi(t) refers to the percentage point of a Student 's-t

distribution with 3 degrees of freedom corresponding to the

ratio of the slope of calculated regression line to its

estimated standard deviation.

14
2 The high levels of R , for rows 1 and 2 of table 2.2

indicate that the regression lines are good estimators of

R U both (AC)^ and (AC)^, at least over a short period of time.

S tudent ' s-t tests were applied to the estimates of the slopes

of the lines in rows 1 and 2 in order to test the hypotheses

that the slopes could be zero. The results of the t-tests

indicate that the hypotheses that the slopes are zero must

be rejected at the 99.5% confidence level. Therefore, the

U R i best estimates for (AC)^ and (AC)^ are the following:

(AC)^ = (-24.27 + .51t) 10^ t = 70, 71 (12)

(AC)^ = (-7.41 + .17t) 10^ t = 70, 71 (13)

Unfortunately, the regression results for (RO/AC)^ and

(RO/AC)^ were not as significant. The relatively low levels

2 of R m rows 3 and 4 of table 2.2 indicate a poor correlation.

In addition, applying t-tests to the estimates of the slopes

in rows 3 and 4 reveals that the hypotheses that the slopes

are zero cannot be rejected at the 95% level. Therefore,

U R

it is not unreasonable to assume that (RO/AC)^ and (RO/AC)^

remain constant over time. For modeling purposes, then,

TJ p (RO/AC)^ and (RO/AC)^ will be set equal to the means of

(RO/AC)^ and (RO/AC) ^ from 1967 through 1971:

(RO/AC)^ = .05 for all t (14)

(RO/AC)^ = .29 for all t (15)

15
Unfortunately, the National Safety Council does not

maintain statistics regarding building collision accidents.

U R Estimates for (BC/RO) ^ and (BC/RO) ^ have therefore been

obtained from the information gathered from Oklahoma and

Illinois. Since only 1970 data was gathered, the estimates

U R

of (BC/RO) ^ and (BC/RO) ^ were, of necessity, point estimates.

At this point we can digress and check the estimates

for the year 1970. Here the national AC and (RO/AC) figures

are known while the (BC/RO) values are known only for

Oklahoma and Illinois. Using these data one generates the

estimates for 1970 in Table 2.3.

Table 2.3 ESTIMATES FOR BC(US) FOR 1970 AND VALUES OF

(BC/RO) FOR 1970.

BC Based on: BC/RO Based on

Okla.

Data

111.

Data

Pooled

Data

Okla.

Data

111.

Data

Pooled

Data

Urban 11800 27600 26500 Urban .021 .049 .047

Rural 7300 14500 13200 Rural .0055 .011 .010

U. S. 19100 42136

It is interesting to note that using Illinois or pooled

data from Oklahoma and Illinois, one again has an estimate

of about 40,000 building collision accidents for the U. S.,

and that the Oklahoma data alone would yield an estimate of

19,100. The agreement with the estimates based on

proportionality assumptions may well be fortuitous, but it

is certainly not discouraging to have this agreement.

16
At this point we return to the estimation of BC^ and

BC^ over time. Using the least squares linear regression

for AC^ and AC^, and assuming that the ratios of (RO/AC)|^ =

.05 and (RO/AC)^ = .29 for all times and that the ratios

(BC/RO)^ = .047 and (BC/RO)^ = .010 are constant with time

and equal to the pooled values for Oklahoma and Illinois

for 19 70, the estimation equations reduce to:

BC^ = -57035 + 1199t (Urban Component) (16)

BC^ = -21490 + 493t (Rural Component) (17)

Because of the data limitations we rounded off these

equations to

(BC)^ = - 57000 + 1200t (18)

(BC)^ = -21500 + 500t (19)

Combining equations 18 and 19 yields a total estimate

for the number of building collision accidents in a given

year:

BC^ = BC^ + BC^ (20)

BC^ = -78500 + 1700t t = 70, 71 (21)

Equations 9, and 18 through 21 constitute the prediction

model. In the next section the output from this model will

be presented and interpreted.

2.3.3 Model Output

Table 2.4 presents the output generated by the model.

17
Table 2.4 ESTIiVlATED BUILDING COLLISION ACCIDENTS

Urban Rural

Year

Building

Collisions

Building

Collisions Total

1967 23400 12000 35400

1968 24600 12500 37100

1969 25800 13000 38800

1970 27000 13500 40500

19 71 28200 14000 42200

19 72 29400 14500 43900

1973 30600 15000 45600

1974 31800 15500 47300

1975 33000 16000 49000

No attempt has been made to extrapolate past 197b.

Because of the linear nature ot the estimation m.ethod, the

number of building collision accidents may be expected

to continue to increase at a constant rate. However, longterm extrapolation is hardly warranted considering the

limitations inherent in the development of the method. The

constant growth projection is essentially due to the fact

that the total number of motor vehicle accidents increased

at an approximately constant annual rate from ly67 to 1971.

The magnitude of the estimates generated iDy the model

requires further examination. Mathematically, the method

is structured so that the number of building collision

accidents is directly proportional to BC/RO. This implies

that if, for example, the estimate of (BC/RO) ^ were halved,

18
then the resultant predictions for BC^ would be halved for all

times. The estimate for (BC/RO)^ was calculated by combining

both Oklahoma and Illinois data. However, if only the Oklahoma

data had been used, the estimate would have been 32/14 89 =

.0215. The estimate of (BC/RO)^ used for modeling purposes

may well be biased on the high side.

2.3.4 Model Extensions

One conclusion which can be drawn from these estimates

is that, regardless of the exact proportion of runoffs which

result in building collisions, the number of vehicles which

annually collide with buildings is on the order of tens of

thousands. However, the number of accidents provides no

information about either the type of building hit or the

kind and amount of damage caused. One goal ot this study

is to assess the extent to which vehicular impact might be

responsible for substantial damage to multistory

residential buildings, and perhaps leading to progressive

collapse. Two questions therefore, remain at issue:

(1) What proportion of "building collisions" cause

substantial damage?

(2) What proportion of the substantially damaged

buildings in (1) are multistory residential

buildings?

With regard to the first question, $1,000 in building

property damage was chosen as a convenient breakpoint which

certainly eliminates non-substantial damage, although

19
larger damage amounts may not be necessarily significant.

Examining the Illinois and Oklahoma data, it was found that

of the 1279 reported "building collision" accidents, 119 or

9.3% had building property damage estimates of at least

$1,000. For purposes of crude estimation, it is assumed

that approximately 10% of the "building collision" accidents

result in substantial damage to the building.

With regard to the rumher of buildings which are

multistory residential, rougn estimates were derived from

the 1970 Census of Housing. Details of the approximation

appear in Appendix C. A summary follows.

In summary, there are approximately 5 0 million

residential buildings m the United States, of which less

than 1% (that is, 400,000; are multistory in the sense of

having four or more floors above ground. It must be

emphasized that the figures cited above are based entirely

on housing data, with no account taken of commercial

structures, for whicn no information was available, since

earlier calculations have been based on building collisions,

it can readily be seen that the multistory residential

buildings must be significantly less than 1% of the total

number of buildings . Assuming that the combined Oklahoma and Illinois

damage distribution is somewhat representative of the nation

as a whole, and that the percentage of building collision

accidents which affect multistory residential buildings is

20
similar to the proportion of residential structures which

are multistory, then one can derive a national estimate for

the number of multistory residential buildings which are

likely to be affected by vehicular collision annually. This

estimate is generated by using the model output described

in section 2.3.3 and adjusting it for (a) damages exceeding

$1000 and (b) multistory residential buildings. Since

damages exceeding $1000 account for 10 percent of all

"building collisions," and multistory residential

structures constitute approximately 1 percent of all residential buildings, estimates of the total number of motor

vehicles which collide with multistory residential structures

and which cause significant damage can be determined by

dividing the data in table 2.4 by 1000. Table 2.5 presents

the adjusted estimates.

These admittedly crude estimates indicate that the

number of vehicles colliding with multistory residential

buildings and causing substantial damage is small compared

to the total number of estimated "building collisions, and

that the rate of increase is also relatively low. When

one considers the manner in which the damage, multistory

residential, and "building collision" components of the

method were calculated, the entries in tabel 2.4 are felt

to be on the high side. The actual number of significant

accidents for any given year probably lies somewhere

21
Table 2.5 ESTIMATED BUILDING COLLISION ACCIDENTS RESULTING

IN DAMAGE TO MULTISTORY RESIDENTIAL STRUCTURES

^ WHICH EXCEEDS $1000.

ACCIDENTS

35

37

39

40

42

44

46

47

49

between zero and this estimated high value, implying that the

collision of a motor vehicle with a multistory residential

building and causing substantial damage to it is a relatively

rare phenomenon in the United States. In fact, the annual

probability of a given multistory residential building

being struck by a vehicle with suJDStantial damage resulting

can be crudely estimated as 40/400, UOO = O.OOOl and

possibly smaller. This estimate was obtained by aividing

the number of significant accidents by the number of multistory residential buildings. Unfortunately, there appears

to be no readily available information to validate the

prediction results developed and discussed above.

22

YEAR

1967

1968

1969

1970

1971

1972

1973

1974

1975
2.4 Discussion

In this chapter, estimates were made of the number of

vehicles which collide with multistory residential buildings

and cause substantial damage. These estimates were based on

accident statistics maintained by the National Safety

Council, statistics regarding "building collision" accidents

maintained by the states of Oklahoma and Illinois, and

housing statistics maintained by the U. S. Census Bureau.

However, this entire estimation procedure would not have

been necessary had statistics regarding "building collision"

accidents been aggregated on a national level.

Such an aggregation would naturally require that each

state maintain "building collision" statistics. These

statistics should include such information as already

maintained by the states of Oklahoma and Illinois and

detailed in Appendices A and B, respectively. This includes

information as vehicle type, road type, and locality of

accident. However, in addition, such information as the

type of building struck, as well as a more detailed

explanation of the damage caused would be extremely useful.

If statistics concerning the number of vehicles

impacting multistory residential buildings are to be gathered

the need for maintaining the type of building struck is clear

Detailed damage explanations are needed as a supplement to

dollar estimates because the dollar estimates by themselves

do not convey very much information with regard to the type

23
of damage done. In this chapter, substantial damage was

considered to be any reported damage of at least $1000.

However, there is no way of determining whether this damage

is structurally significant. For example, it may be possible

that in some cases, a plate glass window was shattered and

the resultant damage was in excess of $1000.

If someone is considering changes to the existing

data systems , we suggest that changes along the line of the

above discussion be considered. The data then could be

employed in more meaningful analyses.

2.5 Chapter Summary

In this chapter, attempts were made to forecast the

number of building structures impacted annually by motor

vehicles. A crude proportionality model was established and

results obtained from Oklahoma and Illinois were extrapolated

for the nation as a whole. The model indicates approximately

20,000 building collision accidents for 1970, with the

greater number occurring in urban areas.

An alternative model, based on regression analysis,

was then developed to predict future occurrences of building

collision accidents as a function of historical accident

data. This model estimates roughly 40,000 instances ot

building collision accidents tor 1970, ot which 27,000 were

urban. It is felt that these estimates may be high because

of the seemingly high estimated value used for the

24
proportion of urban run-offs which resulted in buildxng

collisions (as used in the second model)

.

The results of the second model were adjusted to

consider only those cases where vehicles collided with multistory residential buildings and caused damage m excess of

$1000. It is estimated that about 40 such accidents

occurred in 1970, and it is felt that this is more likely a high value than the true value. On the basis of the magnitude

of the modified estimates, it is tentatively concluded that

the annual probability of vehicular collision with multistory

building is of the order of one in ten thousand.

25
Appendix A

Oklahoma Vehicular Impact Analysis

A.l Introduction

A study of 1970 Oklahoma motor vehicle accident

reports was undertaken in an attempt to determine the

frequency with which motor vehicle accidents involved

collision with a building, and to discover the most

prevalent circumstances attendant on such a collision.

Oklahoma was chosen because individual traffic accidents

reports include a specific code for vehicular collision

with a building and because a data tape was available.

A. 2 Analysis

The data for Oklahoma show a total of 65,183 motor

vehicle accidents in 1970. Of these accidents, 50 were

collisions with a building. The category in the Oklahoma

Investigators Collision Report Coding Guide under which

collision with a building occurs, is specified as "Object

Struck - First Contact." Discussion with personnel of the

Oklahoma Department of Public Safety indicated that a

vehicle deflecting from one object (e.g., grazing a sign

post or curb) and then hitting a building is not coded as

hitting a building. It would probably be coded under Type

of Collision as "running off road." A total or 4791 such

accidents occurred. Many of the following computations

were made using "run off road" data, and are noted as such.

26
The data indicate that roughly 2/3 of all run-off

accidents occurred in non-built-up areas, where there is

less chance of a building being struck. This is shown in

figure A.l, which exhibits the frequency of both run-off

accidents and building collision accidents by location.

Most collisions with buildings caused minor dollar

damage to the building, as is shown in figure A. 2, a

chart displaying the frequency of collision vs. property

damage. By Oklahoma standards for coding, property damage

refers to all property damage excluding damage to the

vehicle. Figure A. 3 shows the frequency of collision with

buildings at various speeds of impact. Further evaluation

of the data shown in these three figures yields the following:

(1) The fraction of the run-off accidents '

that were reported to hit a building

was 50/4791 = .0104 - .010. The average property

damage when a vehicle strikes a building was

$622, and the average speed at impact was 14 mph.

(2) * For non-built up areas the fraction of the run-off

accidents that were reported to hit a building was

5/3219 = .00155 - . 002. Here average property

dcimage was $800 , and the average speed at impact

was 38 mph.*

1

The sum of accidents in built-up areas and non-built-up areas

is less than the totals shown in (1) because 12 8 run-off

s

and 3 building collisions did not specify location. (See

Figure A.l)

27
3400

3218

3200

3000

O <

u 1000

;3U

O

O

>i 800

u

>^ 600

869

400

200

Z

501

28

56

I

' , II J

I' I

A - Residential

B - Business

C - Industrial

D - Not Built-up

E - School

F - Not Stated

G - Other

Run-off Total - 4791

Building Col--

lision Total - 50

82

19

46

A B D E

Figure A.l Run-off Accidents and Building Collision

by Location

28
!

26

24

O

c

(U

S-l u

uo

o

>1o

c

0)

cr

QJ

10

4 i

<50 50- 201- 401- 601- 801- 1001-5001-

200 400 600 800 1000 5000 10000

Dollar Damage to Building

^i^dre h,2 Frequency of Collision Versus Dollar Damage to Building

29
Frequency of Collision

00

cn o w

fi- (i- o

00

Ln

30
(3) * For built-up areas (defined in the Oklahoma

Coding Guide to be business, industrial,

residential or school areas) the fraction of

the run-off accidents that were reported to hit

a building was 42/1445 = .029. Average property

damage in such areas was $585 , and the average

speed at impact was 11.5 mph.

(4) For a business area the fraction of the run-off

accidents that were reported to hit a building

was 2 8/5 01 = ,056, with average property damage

of $698 and average speed at impact of 10.1 mph.

(5) For a residential section, the fraction of the

run-off accidents that were reported to hit

a building was 13/869 = .015. In this instance,

average property damage was $19 4 , and the

average speed at impact was. 12 . 7 mph.

(6) For an industrial section, the fraction of the

run-off accidents that were reported to hit a

building was 1/56 = .018. Average property

damage was $25 00 and the average speed at impact

was 35 mph.

"5E The sum of accidents in built-up areas and non-built-up areas

is less than the totals shown in (1) because 128 run-offs

and 3 building collisions did not specify location. (See

Figure A. 1)

.

31
It sh.ould be noted that eacli of the categories described

above contains a very small number of accidents in which

buildings were struck. The cited statistics must therefore

be used with great care, especially since coding errors are

considered to be not at all unlikely. In addition, speed and

extent of damage were frequently left unreported, contributing

further to possible unreliability of the average values

shown. No meaningful correlation was found between speed,

dollar damage, and location of accident, but a far larger

data sample would be required to verify the validity of

this apparent lack of relationship.

Run-off and hit-building accidents were then separated

by highway classification, as is shown in figure A. 4, It

should be noted that city streets were the scene of run-off

accidents more frequently than any other type of road, while

limited access highways (interstates and turnpikes) had

significantly fewer reported. In addition, although only

32.6% of run-off accidents occurred on city streets, 76% of

the total hit-building accidents took place on this type

of road (where, of course, more buildings are to be found).

Thus the probability that a building will be hit/ given

that a run-off accident has occurred on a city street, is

estimated as 38/1561 = .024. The average property damage

reported in such cases was $5 31, and the average speed at

impact was 14 mph. Not surprisingly , there were no accidents

on interstate highways or turnpikes that involved hitting
1.6% Turnpikes (75)

LO-0%J

/

/ 19.8%

U.S. Highways

(951)

C4 - 8C1

32.6%

!

I City Streets

(1561)

[38 - 76%J

19.2%

State Highways

(917)

L5 - 10%3 22.2%

County Roads

(1063)

P - 6%J

I

/

/

4.7% Highway that are not turnpikes

(224) [0-0%]

(4791)* (OKLAHOMA, 1970) [50-100%]**

*The number in parentheses is the total number of run-off

accidents for that class.

**The numbers in brackets are the number of buildings hit for

a highway class and the % of the total buildings hit.

Figure A, 4 Highway Classification for Run-Off Accidents

33
Truck accidents were examined separately from automobile

accidents. An accident involving a heavy truck, such as a semi-trailer, might do more structural damage to a building

than would a car (given otherwise identical circumstances)

due to the increased size and weight of the vehicle. A

total of 423 accidents involving trucks were reported,

including eight specifically listed as hitting buildings.

The number of accidents involving the various types of

trucks is shown in figure A. 5. The high number of pick-up

truck and single unit truck accidents is probably due to the

large number of such vehicles compared to other truck

types. The accidents considered in this histogram are coded

as follows:

Code 1 - run-off road

Code 10 - collision on road with fixed object

Code 11 - collision an road with other object

(excluding such objects as guard rails, other cars, utility

poles, etc., which are specified in other codes).

These three types include all accidents where a building

was struck. Truck accidents occurring in built-up areas

were further broken down by speed at impact and type of

truck. The results are shown in figure A. 6. The

percentages on the graph are an indication of the proportion

of accidents occurring at higher speeds. The frequency of

accidents where the truck was traveling at greater than or

equal to 30 mph, or greater than or equal to 4 0 mph, should

34
be noted, because a higher speed at impact combined with the

heavier weight of a truck could cause considerable

structural damage. In 1/3 of the accidents within a given

category the speed at contact was not reported, hence

those cases were omitted from the percentage

calculations

.

In addition to the 50 vehicular accidents which

were listed as having hit buildings, an additional 17

run-off accidents were considered as possible "building

collisions." These all had listed a first-struck object

of little value, but had reported total property damage

greater than $800. In all of these cases (65%), the

object struck was specified as "other". Further inquiry

of the Oklahoma Department of Public Safety (which supplied

the data) yielded no clue as to what this might indicate.

A total of 94% of these possible "hit-building" accidents

were on undivided highways and 61% were on roads with

legal speed limits greater than or equal to 40 mph. The

vehicle was traveling at speeds greater than or equal to

40 mph in all specified cases, and greater than or equal

to 30 mph at contact in 83% of the specified cases.

Twenty-six individual accident reports were requested

from the Oklahoma Department of Public Safety. The

individual reports have a short summary and description of

the collision, as well as a diagram of the scene of the

accident. These reports provided infoinnation which had

not been coded and hence was not on the computer tape. The

35
o[Siuo

m

-p

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240 237

200

160

120

80

40

Truck Types

134 A - B - C -

D - E -

F -

Pick-up Truck

Single Unit Truck

Truck Tractor, SemiTrailer

Truck and Trailer

Pick-up Truck with

Trailer

Pick-up Truck with

Camper Body

A B C D E

Type of Truck

Figure A. 5 Truck Accidents (Code 1, 10, 11)

VS.

Type of Truck

36
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37
reports could be divided into 2 categories, the first of

which (identified hereafter as Group A) includes 14 accidents

which had been coded as collisions with buildings and which

had involved damage (other than to the vehicle) in excess of

$800. Also in Group A is one accident which involved four

vehicles and a building, but damage to the building of only

$50. A summary of the information obtained from Group A is

shown in Table 1. A second set of 12 accidents (known as

Group B) was chosen from those accidents referred to in the

immediately preceding paragraph. These were accidents not

coded as having involved collisions with buildings but which

were suspected to have involved buildings due to high

property damages. A summary of these reports appears in

table 2.

The diagram and descriptive summary of the individual

accident reports permitted the derivation and analysis of

data pertaining to the distance that the vehicle traveled

before impact, the distance from the edge of the road to

the object, and the physical location of the accident. The

reported information was in many cases based on estimates

only; in other cases no data was provided at all. Based on

information gathered from the 14 accident reports in Group

A (coded as having struck buildings) , the following results

are obtained.

(1) 13 of the accidents occurred on two lane roads,

one on a four lane road; none of these was on a

divided highway.

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(2) 10 were on city streets, two on state roads, one

on a county road, one on a U. S. highway.

(3) Nine occurred in business districts, two in notbuilt-up areas, one in industrial, one in

residential, one not stated.

(4) Four took place in areas with less than 500

population, four in areas with greater than or equal

to 100,000 population.

(5) 12 involved cars, one involved a tractor trailer,

one a single unit truck.

(6) Five occurred on roads with legal speeds greater

than or equal to 4 0 mph.

(7) Three involved unsafe speeds (marked with "*" on

Figure 7) and four more did not specify "speed

before contact."

(8) Seven accidents involved a building on the same

side of the road as the vehicle, four on the

opposite side, one straight ahead (across an

intersection) , one not stated.

(9) The average distance traveled by the vehicle

before impact in eight specified cases was 132.6 ft.

(10) The average distance of the building from the road

was 39.5 ft. in eight specified cases.

(11) There seems to be no correlation between distance

the vehicle traveled before impact, or distance

from the road to the building, and property damage.

41
: (12) Only one collision involved swerving to avoid

another vehicle. Most were due to mechanical

failure of the vehicle, or unsafe or negligent

f driving.

(13) One accident took place at a curve , seven

occurred at or approaching intersections.

Although Group A constitutes a small sample, the

information above does not vary significantly from the

statewide statistics. Items (7) through (13) provide

information not on the computer tape.

The 12 accidents reports in Group B (coded as having

hit something other than a building) yielded the following:

(1) Three of the accidents involved collisions with

buildings where the building was not the first

object struck. An additional accident involved

a vehicle striking a large lumber rack.

(2) In four of the accidents, gas pumps were the first

-

.. . object struck; all four occurred in business

districts and three accidents were on city stijj'eets

(3) 10 of the 12 accidents occurred on city streets,

one was on a U. S. highway, one on a state highway

(4) Eight accidents occurred on two-lane roads, four

on four-lane roads; none of these were divided

highways

.

42
(5) Seven accidents took place in business districts,

four occurred in residential areas, and one

occurred in a not-built-up area.

(6) Five accidents were in areas with population

greater than or equal to 100,000, one in an area

with population less than 500.

(7) 10 involved cars. Two accidents involved pick-up

trucks . .

(8) Six took place on roads with legal speeds greater

than or equal to 40 mph.

(9) Seven involved unsafe speeds.

(10) Three had the object struck on the same side of

the road as the vehicle, three on the opposite

side, four straight ahead (across an intersection

or at a curve)

.

(11) The average distance the vehicle traveled before

hitting the major object (building, gas pumps, etc.)

was 163.5 ft.

(12) The average distance of the object from the edge

of the road in eight specified cases was 34 ft.

(13) There seems to be no correlation between distances

traveled, or object distance from the road, and

property damage.

(14) Only one collision involved swerving to avoid other

vehicles. Most resulted from mechanical failure

of the vehicle, or unsafe or negligent driving.

43
(15) Three took place at a curve, six occurred at

or approaching an intersection.

Again, the information in this amall sample seems to

follow the trend for the state as a whole. In addition to

the four cases involving gas pumps, two others involved a

natural gas meter and a natural gas main. This seems to

account for many of the high damage value accidents with

"other" coded as the first object struck. Many ot the

accidents in Group B involved vehicles deflecting from

traffic signs and making further contacts, or running

through fences into other obstacles. It is significant that

three of the 12 collisions examined involved buildings,

suggesting that the estimated number of buildings hit in

accidents, as derived from the coded tape, is low. The

only way to determine how many additional buildings were

struck would be to examine all of the reports, a task

which is not feasible from the point of view of the time,

effort, and cost which would be necessary.

A. 3 Summary

Of a total of 65,183 accidents which occurred in

Oklahoma in 1970, 4,791 or 7.3% were run-off accidents. Of

the total run-off accidents, 50 or 1.1% are known to have

involved collisions with buildings. 67.2% of the run-off

accidents occurred in rural areas, but 76% of the known

collisions with buildgins occurred in cities. The highest

chance of hitting a building, given that a run-off accident

44
has occurred, is in the business district; it is approximately

.056. The highest average speed of impact (38 mph) and the

highest average property damage ($800) occurs in non-builtup areas. The number of buildings coded as being hit (50)

is low, due in part, to the fact that an additional number

of buildings were undoubtedly hit, but not reported because

they were not the first object struck.

Using municipalities with a population of 5,000 as a

breakpoint between urban and rural, 1,4 89 ot the 4,791 runoffs can be classified as urban. Similarly, 32 of the 50

"building collision" accidents occurred in urban areas. The

respective rural figures are 3,302 run-off s and 18 "building

collisions." Of the 50 "building collision" accidents, six

resulted in estimated damage in excess of $1,000. Ot these

six, one resulted in damage in excess of $5,000.

It should be noted that a very small number of hitbuilding accidents occurred in each of the various subdivisions used above. Information on speed and damage was

omitted frequently enough that the data were considered

insignificant. Mistakes in coding are also a possible source

of error. A correlation between speed, dollar damage, and

location of the accident was sought, but not found to exist

at a meaningful level. However, a larger data sample would

be required to verify this apparent lack of correlation.

Run-off and hit-building accidents were then separated

by highway classification, as is shown in figure A. 4. It

45
should be noted that city streets were the scene of run-off

accidents more frequently than any other type of road, while

limited access highways (interstates and turnpikes) had

significantly fewer reported. In addition, although only

32.6% of run-off accidents occurred on city streets, 76% of

the total hit-building accidents took place on this type of

road (where, of course, more buildings are to be found).

Thus the probability that a building will be hit, given that

a run-off accident has occurred on a city street, is estimated

as 38/1561 = .024. The average property damage reported in

such cases was $531, and the average speed at impact was 14

mph. Not surprisingly, there were no accidents on interstate

highways or turnpikes that involved hitting & building.

Most non-passenger-car accidents involved pick-up

trucks. Individual accident reports indicate that the average

distance traveled by a vehicle after run-off and before

impact is 146.4 ft., while the average distance from the road

of the struck object is 37 ft. In addition, slightly more

collisions occur with objects on the same side of the road

as the vehicle, and the fewest occur with an object straight

ahead. Many collisions occur near intersections, and most

are due either to mechanical failure of the vehicle or unsafe

driving. Eleven of the 26 cases studied individually were

based on collisions on roads where the legal speed was at least

as high as 4 0 mph, and all 2 6 took place on undivided highways .

46
Appendix B

Illinois Vehicular Impact Analysis

B.l Introduction

This chapter provides a statistical summary of building

collision accidents which occurred in the State of Illinois

during 1970. In conjunction with the Oklahoma data discussed

in the previous chapter, it attempts to provide additional

insight as to the nature of such accidents. Illinois motor

vehicle accident records are very comprehensive, reporting

up to three involvements for any vehicle in an accident.

Thus, a vehicle which collides with a stop sign, for

example, and then deflects into a building, will be coded

as having done exactly that.

B.2 Analysis

According to state records, 409,174 motor vehicle

accidents occurred in Illinois during 1970, Of these, 37,462

or 9.2% were recorded as run-off accidents. In turn,

1229 of these run-off accidents resulted in collisions with

buildings. Building collision accidents accounted for 3.2%

of the run-off accidents, or 0.3% of all motor vehicle

accidents recorded. However, motor vehicle accident records

maintained by the state include only a fraction of the

accidents which occurred in Chicago, namely those which

occurred on state or U.S. numbered routes and/or those

which resulted in fatalities. Of the 164,889 accidents

47
which occurred withm Chicago in 1970, only 37,043 or 22%

are maintained on state accident files. Therefore, the

state accident statistics are biased downward as a result

of this ommission.

B.2.1 Building Collision Accidents by Road Type

In figure B.l, the 1229 building collision accidents

are categorized according to the type of roadway on which

they occurred. As can be seen, 717 (or 58.3% of all building

collision accidents reported) took place on urban city streets

(category A). When the other urban categories (B, C, and D)

are combined with this figure, the resultant proportion of

building collision accidents which occurred in urban areas is

85%, even with Chicago largely disregarded. This is reasonable

since building collision accidents are likely to happen more

frequently in built-up areas than in non-built-up areas.

In addition, figure B.l indicates the low frequency with

which building collision accidents happen on controlled-access

highways. In all, only four reported building collisions

resulted from motor vehicle accidents on controlled-access

highways (categories D,E, and H) . This low incidence

is probably due to a general paucity of buildings in immediate

proximity to such highways, as well as safety features

commonly found on many urban expressways.

48
800

700

600

500

(58.3%)

717

A - Urban City Streets

B - Unmarked State Highways

(Urban)

C - Ctner Urban State

Numbered Highways

D - Urban Controlled-access

Highways (except toll

roads

E - Toll Roads

F - Rural County and Local

Roads

G - Other Rural State

Numbered Highways

H - Rural Controlled-access

Highways (except toll

roads

)

I - Unmarked State Highways

(Rural)

cn 400

300

200

100

(21.4%)

263

!(5.0%)!

61

'

(y.3%)

118

(0.3%) (Q^3^^) ^ 1

(4.6%)

57

B C D E F

Class of Trafficway

I (0.7%)

i 0 9

[ r~~~\

G H I

Figure B.2 Building Collision Accidents by

Road Type

49
B.2.2 Building Collision Accidents by Population

Figure B.2 depicts the distribution of "building collision"

accidents by the population of the location in which the

accidents occurred. The State of Illinois defines urban

areas as "locations in or adjacent to a municipality or

other urban areas of over 5,000 population." Therefore,

using 5,000 as a breakpoint, it appears that 86.1% of the

"building collision" accidents took place in areas with a population greater than 5,000. This agrees well with the

85% figure for urban "building collision" accidents

calculated from the Oklahoma data.

B.2. 3 Building Collision Accidents by Vehicle Type

As shown in figure B.3, 1090 (88.7%) of the "building

collisions" were caused by automobiles; of the remaining

11.3%, 8.9% were caused by trucks.

Information was also available as to the number of

vehicles involved in each accident, summarized in table B.l.

TABLE B.l DISTRIBUTION OF BUILDING COLLISION ACCIDENTS BY

NUMBER OF VEHICLES INVOLVED

No. of

Vehicles

No. of Bldg,

Collisions

Percent

2

>2

TOTALS

884

316

29

1775"

71.9

25.7

2.4

imrrcr

50
Figure B.2

o

u

O

COO

•H

CO -H

O

U

tJi C

•H

•H

CQIHO

U

e

360t

300

240

180

120

60

12 . 3%

152

27.2%

, 335 ,

19.0%

234 18. 8% — 2 30

107

6.4%

78

7.5!

92

Chi- >50K 25K- lOK- 5K- 2 . 5K- <2500

cago 50K 25K lOK 5K

Population

Building Collision Actcidents

By Population

51
1120

o

u

O

M-i

o

•H

•H

i-l -iHC

u

,a •HH

•H

iwO

CD f

:3

38.5%

1083

1080

1040

1000

>

80

'6 0

40

20

5 . 5%

0 .2%

2

A - Passenger Car

B - Passenger Car Towing Trailer

C - Truck or Truck Tractor

D - Truck Tractor and Semi-Tractor

E - Other Truck Combination

- Bus

G - Motorcycle

H - Truck To^.-zing Trailer

I - Other and Not Stated

2.7'

33

1 . 3%

16

0 .9%

5 0.3%

3

0.8%

7

0.3!

A B C D E F G

Vehicle Type (1st Vehicle)

H

Figure B.3 Building Collision Accidents

by Vehicle Type

52
The table indicates that 884 of 1,229 accidents involved only

one vehicle. Of these single vehicle accidents, 776 involved

only passenger cars. Therefore, of the 1229 "building

collision" accidents, 776, or 6 3%, involved a single

automobile

.

B.2.4 Building Collision Accidents By Dollar Damage to Property

Figure B.4 displays the distribution of building collision

accidents by dollar damage. This distribution is unfortunately

incomplete, for of the 1229 accidents used as the base, only

558 (45%) , had damage estimates associated with them. There is

no readily available information as to the methodology

employed to estimate damage, in general or in specific incidents,

nor as to whether the estimates are restricted to the damage

done to the buildings. However, since all accidents in the

data base are building collision accidents, an assumption

that damage estimates refer only to damage done to buildings

is not totally unreasonable. For the 558 accidents for which

damage was reported, 34 3 were estimated to have caused less

than $550 damage. Of the remaining 215 accidents, 132 reported

damage in multiples of $500, leading one to believe that

these estimates are little more than "ballpark" guesses as

to the amount of damage incurred. Nevertheless, because the

information is the best available, it is still useful to

perform a cursory analysis in an attempt to gain additional

insight as to the nature of building collision accidents.

Furthermore, to the extent that damages may have been

53
o

ci CO (1) O 00 4-1 o

LO

r

tri ?» -l-J 10

rtl ti

J rC (L) 1 \ D" c mUi 1 \ <U 1 1 o

rrlO

Q) -P 0) 1 . i w U n

-H -H 11 1 o o

(L) tJ o «H, {/} Q) u CTi

(yj (N (d • fN 6 w <W I TO *H i-l c •H

r?^ f n •H 4-)

C

UJ

C U) -H o H fl) \v TI\J fO fH

Q) <D

g >i-H C 0 0 fO J2

(U 4JO

00 o

LOO

O

ID 1 C

LOo O o

lDo o

cn 1 to J-) cr 0

LD c; (U o -H 13

I—1

CN! •H 1—1 U <U 1 •H r \ o ;3 LD O CQ 6

CX5 -H 0 O

Q 1 •H O (0 U

ID

a •H rd 00 .H iH j g o o O rd U Q

Ln

Q

'cr >^

C ^ 1 Jd -H o a^ rH T5

ID rH iH

(N 0 •H D :3 1 Oo

rH CN o o

CO

crCM 0Z.6T suoTSTXxoo buTpxTng jo #

54
sustained by objects other than the buildings affected by

collision, true damages are likely to be less than those

reported and analyzed bclcnv.

The average damage for the 558 accidents was calculated

to be $667. However, 412 (74%) had damage estimates less than

$650, and the median for the damage distribution is

approximately $300. (The substantial difference between the

m.ean and median can be explained by the occurrence of 6

accidents with damage estimated to be greater than $900,

tending to drive the mean upwards. In contrast, the occurrence

of 261 accidents with estimated damage of less than $250

tends to lower the median.)

A comparison of damage caused by trucks and that due to

automobiles reveals some notable differences. The average

damage caused by trucks colliding with buildings was calculated

to be $1,191, although the median was approximately $300,

Mean damage for automobile accidents was calculated as $614,

or approximately half of the truck mean damage. However, the

median for automobile accidents was also estimated at $300.

Certainly one would expect trucks to cause more damage to

buildings than automobiles, because trucks have greater mass

and therefore greater potential to cause extensive damage.

55
The average damage was also calculated separately for

urban and rural accidents. The mean damage for rural building

collisions was computed as $833, while the average damage for

urban accidents was $618. These figures were based on

a sample of 125 rural accidents and 443 urban accidents, because

there were only 558 accidents for which damage was reported.

The rural accidents probably entail higher average damage

because vehicles travelling on rural roads tend to be

driven at greater speeds than vehicles on urban streets.

It was decided to extract from the data base those

accidents which had damage estimates of at least $3000

and to submit these to further analysis. There were 24"

such accidents. In table B,2, a few summary statistics

derived from the sample are displayed in comparison

to statistics gathered from the entire data base.

TABLE B.2 SUMMARY COMPARISON: MAXIMUM DAMAGE (>$30 00)

BLDG. COLLISION ACCIDENTS VS. ALL BLDG. COLLISION

ACCIDENTS

Sample All

Frequency Percent Frequency Percent

No. in Sample 24 100% 1229 100%

Urban 16 67% 1044 85%

Automobile 17 71% 1090 89%

Single-Vehicle 16 67% 884 72%

56
These results appear to be consistent with the mean

damages discussed above: because urban accidents cause lower

average damage than do rural accidents and because passenger

vehicles do less damage than trucks, one would expect

lower incidences of both automobile and urban accidents

in a sample consisting of those accidents causing the

most damage.

The frequency of single vehicle accidents in the

sample does not appear to be significantly different from

the frequency of single vehicle accidents for all building

collision accidents. The occurrence patterns of the singlevehicle accidents were closely scrutinized in an attempt to

observe general trends. Of the total of 16 single vehicle

accidents, 13 ran off the road and collided with buildings

directly; the remaining three were deflected into buildings

after first colliding with a highway sign, a utility pole,

and a fence, respectively. Of these 16 accidents, 11 involved

automobiles and five involved trucks. Only one of the truck

accidents was a deflection. Based on this scanty evidence,

it is difficult to draw any conclusions concerning the nature

of major single-vehicle building collision accidents, although

the data does seem to indicate that direct collisions occur

more frequently than do deflections.

57
B.3 SuiTunary

1) The analysis was based on a total of 1229 building collision

accidents. These accidents constituted 3.2% of all run-off

accidents and 0.3% of all motor vehicle accidents. However,

the omission of 78% of the Chicago accidents introduces

a downward bias to many of the results presented here.

2) 1058 of the 1229 building collision accidents (86%)

occurred in urban areas and, 58% took place on city streets.

Similarly, 21,537 of the 37,462 run offs are classified

as urban.

3) Automobiles were the first vehicle involved in 89% of

the building collision accidents, while trucks accounted

for only 9%.

4) A single vehicle was involved in 72% of the building

collisions. 63% of the building collisions were caused

by a single automobile.

5) Only 45% of the building collision accidents had damage

estimates associated with them, with an average damage

calculated at $667. The average damage in truck-related

accidents was $1191, but automobile accident mean

damage was calculated at $614. The average building

• damage in rural accidents was $833 , whereas the a,verage

damage in urban accidents was $618. Damage was estimated

in excess of $1000 in 113 accidents. Of those 113, 10

carried damage estimates in excess of $5,000.

58
APPENDIX C

Multistory Residential Buildings

In Table 1, housing units for 1970 are partitioned

according to the number of units per building. For

example, the third row of the table can be interpreted

as follows:

There are (1.706) x 10 owner occupied

housing units located in buildings

which contain 2 units per building.

Therefore, these units account for

((1.706 X 10^) T 2 = (0. 853) x lo''

residential buildings.

Those buildings which we assumed to be multistory

residential are designated by a triple star. This

information was taken from 1970 Census of Housing,

Detailed Housing Characteristics, United States

Summary, Table 22, and is shown on the following

page.

59
Table C.l 1970 Housing Data

Units per

Building

Owner Occupied

1 detached

attached

and 4

or more

Mobile homes

Housing

Units (xlO°)*

34.396

1. 112

1.706

.454

. 463

1.751

39.882

Buildings (xlO )

34.396

1.112

0. 853

0.454

<0 . 463***

1.751

38.335

Renter Occupied

1 detached

attached

3 and 4

5 to 9

10 to 19

2 0 to 4 9

IjO or more

Mobile home

7.736

0.794

402

816

284

219

873

115

0.321

23 . 560

7.736

0.794

1.701

0.804

0.326

0.153***

0.054***

<0. 042***

0.321

11. 931

Vacant for Rent

1

2 to 4

5 to 9

10 or more

0.571

0.407

0.194

0.494

0.571

0.135

0.028

< 0. 049 *** 0.783

65.108 51.049 (total)

taken from 1970 Census of Housing, Detailed Housing

Characteristics, United States Summary, Table 22

calculated; all values are approximations due to varying size of

class intervals.

assumed to be multistory buildings (4 or more stories)

60
From the above information, the number of multistoryresidential structures (N) in 1970 is calculated as follows:

10"-^ N ^ 93,000 + 153,000 + 54 ,000 + 42 ,000 + 49 ,000 = 391,000

The proportion of residential structures which are multistory (P) is then calculated as follows:

P ^ 391/51,049 ^ .008 ~ 1%

However, the estimated number of multistory residential

buildings is somewhat questionable. In order to lend

credibility to the estimate, an alternative procedure was

performed using the number of residential units in structures

with four or more floors. According to the 19 70 Census figures,

there are 3,295,304 residential units located in structures

with four or more floors.* By making the extreme assumption

that there are only tour units per structure, the number of

multistory residential structures is estimated as 824,000.

On the other hand, if the previous estimate of 391,000 is used,

this would yield 8.4 as an average number of residential

units per structure. This still appears to be low. Thus,

the previous estimates of 391,000 appears to be a high

estimate for the number of multistory residential structures

in the United States in 1970.

Housing data for 1960 is given in table 2.

* 1970 Census of Housing, Detailed Housing Characteristics,

United States Summary, Table 24.

61
Table C2. I960 Housing Data

Units per

Building*

Owner Occupied

1 detached

1 attached

. 2

3 and 4

5 or more

Mobile home

Housing

Units (x 10^)

28.436

1.526

1.443

0.456

0.258

0.677

32.797

Buildings (x 10^)**

28.436

1. 526

0.722

0.130

< 0. 052***

0.677

31.543

Renter Occupied

detached

attached

and 4

to 9

10 to 19

2 0 to 4 9

50 or more

Mobile home

891

860

683

343

770

141

283

165

090

20. 227

7.891

1.860

1.342

0. 669

0.253

0. 079***

0. 037***

< 0 . 023***

0 . 090

12.244

Vacant for Rent

1

2 to 4

5 to 9

10 or more

0.603

0.378

0.154

0.291

1.426

0.603

0.126

0. 022

< 0.029 *** 0.780

54.450 < 44. 567

* Taken from 1960 Census of Housing, Detailed Housing

Characteristics, United States Summary, Table 5. ** calculated; all values are approximations due to varying size of

class intervals.

*** assumed to be multistory buildings (4 or more stories)

62
Table C. 3 provides' a summary comparison between the 1960

and 1970 figures.

Table C. 3. Summary Comparison of Lousing Data 1960-1970

1960 1970 Change % Change

Multistory Buildings 0.220 0.339 0.171 78%

(x 10^)

Residential Buildings

(x 10^) <44.567 <51.049 =6.482 =15%

Multistory Buildings >o.o05 >0.008 Residential Buildings

Multistory Units

(x 10^) 4.133 7.164 3.026 73%

Housing Units

(x 10^) 54.450 65.108 10.658 20%

Multistory Units Q^^^g q^^LIO Housing Units

Basically, the figures indicate that in the 1960 's,

multistory housing had grown at a rate considerably faster

than housing in general. Even so, in 1970, multistory

buildings accounted for less than 1% of the residential

buildings. However, these multistory buildings contained

59% of the 1970 housing units.

63
APPENDIX D

Motor Vehicle Registrations 1960-1970

Table D. 1. displays the number of motor vehicles

registered in the United States, as well as in the sample

states of Oklahoma and Illinois from 1960 to 1970. Data are

available at five year intervals for individual states and

the U. S., and on a yearly basis from 1965 to 1970 only for

the U.S.

3 Table D. 1. Motor Vehicle Registrations (xlO )*

U.S. Okla. 111.

1960 73,869 1,184 3,776

1965 • 90, 358 1,438 4,437

1970 108,436 1,713 5,238

In Table D.2, information regarding the growth of

motor vehicle registrations from 1960 to 1970 is provided.

Table D. 2. Growth ot Motor Vehicle Registrations 1960-1970

Difference (xlO") % Change Annual % Change

1960-1965

U.S. 16,489 22.3 ' 4.5

Okla. 254 21.5 4.3

111. 661 17.5 3.5

1965-1970

U.S. 18,078 20.0 4.0

Okla. 275 19.1 3.8

111. 801 18.1 3.6

1960-1970

U.S. 34,567 46.8 4.7

Okla. 529 44.7 4.5

111. 1,462 38.7 3.9

* Taken from Statistical Abstract of the United States 1971,

Table 849.

64
These figures indicate that motor vehicle registrations

have grown at the rate of roughly 4% annually throughout the

1960 's. In order to obtain more precise annual growth

estimates, the number of motor vehicle registrations should

be presented for every year, rather than for every five

years. Detail is availaJDle* for the United States as

a whole but not for individual states. Table 3 presents the

yearly registration data for the entire U. S. from 1965 to 1970.

Table D. 3. Motor Vehicle Registrations in U.S. 1965-1970*

3 3 Registration (xlO ) Difference (xlO ) % Change

1965 90,358 3,835 4.2

1966 94,193 2,738 2.9

1967 96,931 4,108 4.2

1968 101,039 4,058 4.0

1969 105,097 3,339 3.2

1970 198,436

According to the data in Table D.3, the average of the

annual rates of growth of motor vehicle registrations in

the United States between 1965 and 1970 is 3.7%.

TE From Statistical Abstract of the United States 1971, Table

847.

65
I

1^

i
FORM NBS-114A (1-71) U.S. DEPT. OF COMM. 1. PUBLICMTON OR REPORT NO. 2. Gov't Accessioa

BIBLIOGRAPHIC DATA No. SHEET NBSIR 73-175

3. Recipient's Accession No.

4. TITLE AND SUBTITLE

Estimates of Vehicular Collisions with Multistory

Residential Buildings.

5. Publication Date

6, Performing Organization Code

7. AUTHOR(S)

Stuart L. Fribush, Deborah Bowser and Robert Chapman

8. Performing Organization

NBSIR 73-175

9. PERFORMING ORGANIZATION NAME AND ADDRESS

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, D.C. 20234

10. Project/Task/Work Unit No. 431 442fi

11. Contract/Grant No.

12. Sponsoring Organization Name and Address Center for Building Technology

National Bureau of Standards

13. Type of Report & Period C overe d Final

14. Sponsoring Agency Code

15. SUPPLEMENTARY NOTES

16. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a significant

bibliography or literature survey, mention it here.) Through analysis of data from Oklahoma and Illinois along with national

statistics, estimates are made of the number of vehicular collisions

with buildings on an annual, nation wide basis. The best estimate is

on the order of tens of thousands. However, since the impetus for

the study was on m.ultistory buildings and the likelihood of their

being subject to progressive collapse the calculations have been

refined to apply to substantial damage to multistory residential

buildings. In 19 70, such accidents were only on the order of 40,

hence the probability of a given building being so affected in a single year is approximately one in ten thousand. Some discussion

is provided on improvement for data collection for the future.

17. KEY WORDS (Alphabetical order, separated by semicolons) CollisionS Of VehicleS with

buildings; multistory buildings; progressive collapse of buildings;

residential buildings; vehicular impact.

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