Acrid. Anal. & Pm-s. Vol. I I, pp. 261-270
@ Pergamon Prcrs Ltd.. 1979. Prmted m Great Br~latn
MEASURING
THE EFFECT OF POLICE SURVEILLANCE
ON THE PREVENTION
OF TRAFFIC ACCIDENTS
AKIHIR~
Abe
Laboratory,
Department
HASHIMOTO
of Social Engineering,
Tokyo Institute
Meguro-ku,
Tokyo, 152, Japan
(Received
13 July
1978; in revised form
of Technology,
25 April
2-12-l.
0-okayama,
1979)
Abstract-An
approach for estimating the effect of preventive measures taken against traffic accidents is
presented.
A simple stochastic
model as to the process of accident occurrence
is provided
and the
relationship
among vulnerable
behavior, collisions, and police surveillance
is formalized.
Here vulnerable
behavior is defined as the state prior to the collision in the process of accident occurrence,
and police
surveillance
is selected as one of the preventive
measures against the accident.
The procedures
by which the model can be applied in practice are shown for the case of intersection
accidents, and the effects of some intersection
surveillance
forms on accident prevention
are calculated.
The results suggest that police surveillance
which affects vulnerable
behavior reduces accidents and that
each collision category differently
has the most effective surveillance
form.
It is concluded that vulnerable behavior, which increases the likelihood of accident occurrence, provides a
more sensitive safety measure than accidents.
INTRODUCTION
The prevention of traffic (automobile) accidents is as important as any other traffic safety
measure. However, all preventive measures against the accident cannot be adopted. Therefore,
alternative
pereventive
measures must be evaluated so that we can allocate the limited
resources among them. Such being the case, an approach for measuring the effect of the police
surveillance is presented in this paper, but it can be applied to the cases of other preventive
measures against the accident, too.
The automobile accident is a collision of a vehicle with another vehicle, with a pedestrian, or
with a stationary object. Accidents are caused by a variety of failure modes affecting the
informational
and mechanical interactions
linking together man, vehicle, and environment
[Kontaratos, 19741. It has been suggested that human factors are involved in a causal way in
more than 90% of accident cases [National Highway Traffic Safety Administration,
19721 or that
most of accidents are caused by human error [McFarland and Mosely, 1954, National Safety
Council, 19661. Therefore, collisions can be divided into two types: the collision caused
primarily by driver failure and that caused primarily by pedestrian failure. Only the former
were selected as the subject of this study, so that driver behavior is a key to the analysis of this
kind of collision. As for the latter, pedestrian behavior, the approach in this paper can similarly
be applied.
MODEL
Preventing collisions requires an understanding
of the process of collision
Therefore, we begin with the description of the process preceding the collision.
generation.
Process toward collision
Goeller[ 19691 demonstrated conceptual framework for thz process of collision generation.
Modifying the Goeller framework so that it is applicable to measuring the effect of the police
surveillance, the process toward the collision is, in this study, drawn simply as Fig. 1.
As soon as a vehicle starts on a road, the driver of the vehicle makes decisions (about 20
decisions per mile[Platt,
19591) and takes actions not to cause the collision based on his
perceptions of traffic events (e.g. other vehicles, pedestrians, and signposts). However, the
driver occasionally gets into danger for one of the following reasons: failing to perceive traffic
events, making misjudgements,
failing to operate the vehicle, or running a risk. Strictly
speaking, it is not obvious whether any of these actions or failures is actually dangerous, they
261
A. HASHIMOTO
262
( vehicle
PHASE
starts
)
I
23
COLLISION
Fig. 1. Process toward collision.
should be called the potential dangers. Moreover, we define the driver behavior that causes
such a potential danger to be vulnerable behavior (phase 1).
Even though a driver’s behavior may be vulnerable, a collision does not always occur. For
example, let us consider a driver at an intersection with automatic traffic signals. Suppose that
the driver goes into the intersection in spite of the red traffic signal; then we say that his
behavior is vulnerable because the state in which the driver and vehicle exist is potentially
dangerous. However, if there are no other vehicles and no pedestrians in the intersection, the
potential danger is not really dangerous and a collision does not occur. On the other hand,
suppose that the potential danger is really dangerous (e.g. another vehicle comes from the
other crossroad). Even in such a case, if the evasive action taken by a driver is successful, the
collision does not occur. Otherwise, the collision occurs (phase 2).
In this way, the process toward the collision can be considered as a stochastic chain in
which chance plays a very large part.
Vulnerable behavior, collision and police surveillance
Vulnerable behavior is driver behavior that has the possibility of resulting in a collision. If
all drivers refrain completely from vulnerable behavior, collision (caused by the driver) never
occurs. Therefore, the vulnerable behavior is not a sufficient condition, but a necessary
condition for the collision.
On the other hand, the impact of police surveillance on the process leading up to a collision
(Fig. 1) is as follows. Noticing the policemen, a driver generally intends to perform safe
behavior (i.e. vulnerable behavior is reduced) in phase 1. However, the process in phase 2 is
beyond the preventive effect of the police surveillance, because once the behavior becomes
vulnerable, the transition to the collision or evasion depends on factors such as chance, quick
judgement, and driving skill, which have nothing to do with the police surveillance. Thus, the
police surveillance only reduces vulnerable behavior, but has an effect on the prevention of
accidents that is similar to cutting off the stochastic chain toward the collision at the earliest
point. Therefore, the effect of police surveillance on accident prevention can be measured by
observing the reduction of vulnerable behavior if the relationship between the vulnerable
behavior and the collision is established.
Measuring
the effect
of police
263
surveillance
In evaluating traffic safety measures, an indicator like the accident rate has been used.
However, the value of such an indicator is too small to identify the efficiency of the measures
on the small scale experiment. Since the vulnerable behavior occurs more frequently than
collisions, it is much easier to observe vulnerable behavior. Therefore, the concept of vulnerable behavior is useful for the statistical analysis of accidents,
Furthermore, the concept of vulnerable behavior is an external one to explain the collision
occurrence by means of overt behavior that can be observed. Therefore, this analysis does not
require treating the internal, (i.e. psycholigical or physical) factors of the driver, which are
difficult to quantify. For example, considering the impact of alcohol on an accident, we do not
say that a collision occurs due to the drunkenness,
but that the collision occurs due to the
vulnerable behavior that may be committed due to the drunkenness. Thus, the present analysis
has nothing to do with the reason for the vulnerable behavior.
Formalization
Based on the foregoing concepts, the stochastic process toward the collision can be
formalized as follows.
Let Vi = act of vulnerable behavior, Cj = collision category.
Since all the collisions caused by driver failure result from some vulnerable behavior (i.e. the
vulnerable behavior is the necessary condition for the collision), we can consider the following
conditional probability for every collision: p(ci/Vi) = the probability that collision category cj
occurs due to the vulnerable behavior ui, given that the driver commits the vulnerable behavior
Vi. Therefore, we can obtain the joint probability as follows. Let p(u;) = the probability that a
driver commits the vulnerable behavior vi, p(ni Cj) = the probability that the collision category
cj occurs due to the vulnerable behavior Vi, then
p(U,,
Cj)
(i = 1,
= p(Ui)p(Cj/Ui)
. . . ,
n, j = i.. . . , m).
The formalization for measuring the effect of the police surveillance is described as follows.
Let k = index of police surveillance forms (k = 0, 1,. . . ,K). Here, k = 0 means no police
surveillance (i.e. the normal state). Then, pk( Vi),Pk(cj/ui) and pk(ui, cj) denote the probabilities in the
case where the police surveillance form k is executed, corresponding to p(s), P(cj/Vi) and p(q, cj).
Police surveillance influences the probability that a driver’s behavior will be vulnerable, but
does not influence the transition probability to a collision. Therefore, we can write
PdCjlS) = Pk(Cj/U,)= P(Cj/U,)
(i = 1,. . ., n, j = 1,. . ., m, k = 1,. . ., K).
Here, we can obtain the probability that a collision of category Cj occurs
vulnerable behavior item u, in the case where the surveillance form k is executed:
Pkt”i9 Cj) =
Pk(S)p(Cj/Vi)
(i=l,...,
n, j=l,...,
m, k=O,
I,...,
due to the
K).
(1)
Since
PktCj) =,z
Pk(Ut, Cj)
(j=l,...,
m, k=O,
I ,..., K)
is the probability that a collision of category cj occurs in the case where the surveillance
is executed, we can measure the effect of the surveillance form k on the prevention
collision category Cj by the following indicator:
&(Cj)
= 1 - [Pk(Cj)lPdCj)l
(j = I,. . ., m, k = 1,. . ., K).
form k
of the
(3)
CASE STUDY
In order to demonstrate the formalization described above, accident and police surveillance
at intersections with automatic traffic signals (A.T.S.) were selected as the subject of the study.
AAP Vol. II, No &R
264
A. HASHIMOTO
Measurement of pk(ui)
The vulnerable behaviors at the intersection were set up as shown in Table 1, referring to
Road Traffic Law, discussions with the police, and the experiment carried out by Smeed[ 19641.
(In the Smeed investigation, a list similar to Table 1 was provided and the effect of police
presence on driver behavior at intersections
was measured.) Since vulnerable behavior is
behavior that has the possibility of resulting in a collision, as discussed before, these behaviors
are not necessarily illegal.
To produce Table 1, a preliminary analysis of intersection accidents was undertaken to
make the vulnerable behaviors reliable and valid. That is, these vulnerable behaviors were
adopted or rejected by the examination through the Traffic Accident Record of the police so that
they would represent the primary determinants
of intersection
accident causation. It was
suggested by the analysis that all the drivers that caused intersection accidents when driving at
excessive speed had committed some vulnerable behavior in Table 1, e.g., failing to stop at red
(u,) or failing to turn right correctly? ( zi4).Therefore, going too fast for the conditions generally
identified as an important accident cause by past accident studies, does not appear as
vulnerable behavior.
The police surveillance forms at the intersection were set up as follows: form 0 = no police
surveillance (i.e. the normal state), form 1 = the intersection is watched by a policeman at the
cornet, form 2 = the intersection is watched by two policemen, one at the corner and another at
the diagonal corner of the intersection, form 3 = the intersection is watched by a policeman on a
stand placed in the center of the intersection,
form 4 = the intersection
is watched by a
policeman at the corner, as the form 1, and at each of the two adjacent intersection corners
respectively. Here, all the policemen mentioned above are wearing uniforms.
These surveillance forms were set up in the following three surveillance series:
formO-form
formO-form
formO-form
1-form2.....seriesA
I-form3.....seriesB
I-form4.....seriesC.
Table I. Vulnerable behavior items at intersection
vulnerable
notation
failing
"1
to stop
starting
“2
‘3
v4
v7
“8
to stop
at amber
to turn
right
drivers
in ooposite
when
'10
direction
to take
evasive
right*
left
two-wheeled
vehicle
riders
evasive
action
turning
left*
causing
pedestrians
to take
evasive
offside
when
lane*
or
cyclists
action
to take
even
if
on green
failing
signal
passing
over
parking
"12
from
**
causing
overtaking
"11
turning
correctly*
turning
proceeding
“9
at red
on red
failing
action
“6
item
failing
causing
“5
behavior
intention
stop
line
or changing
or stopping
when
stopping
lanes
within
near
or at
5 meters
intersection
of approach
to
intersection
* Note
that
this
** This
item
includes
lane,
tNote
turning
right
is for
traffic
driving
on the
the
following:
turning
in a row,
and making
a detour
that this is for traffic driving on the left.
left.
right
when
from
nearside
turning
right.
Measuring
the effect of policesurveillance
265
That is, series A is for identifying the effect of increased police surveillance; series B is for
discriminating
the effect of surveillance by a policeman located at a different part of the
intersection;
and series C is for identifying the durable effect of the police surveillance at
adjacent intersections.
A certain intersection that was a junction of four roads with four A.T.S. and four pedestrian
crossings was selected. The frequency of each vulnerable behavior that occurred there in the
case of each surveillance form was counted. (This experiment can be carried out relatively
easily because the vulnerable behavior is the overt behavior.)
Let Nik = the frequency of the vulnerable behavior O, in the case where the surveillance
form k is executed, Qk = the number of vehicles passing through the intersection in the case
where the surveillance form k is executed, then the probability of ui in the case of the form k is
obtained as follows:
Pk(Vi)
=
k = 0, 1,. . . ,4).
12,
(i=l,...,
Nik/Qk
The results are shown in Table 2.
Accident
analysis
The collision categories at the intersection were set up as shown in Table 3.
In order to obtain the transition probability from the vulnerable behavior to the collision, 26
general intersections
(i.e. junctions
of four roads with four A.T.S. and four pedestrian
crossings) were selected and the actual accidents caused there were examined through the
Traffic Accident Record of the police. Classifying the collected cases of accidents according to
the collision category and the vulnerable behavior item that is considered as the primal cause of
the collision, the following matrix (Hii) is obtained; IIii = the number of the collision category ci
caused by the vulnerable behavior item Di.
Here, we should note that the vulnerable behavior of a third person not actually involved in
the collision may be the primal cause of the collision. For example, suppose that one vehicle is
Table
2. Probability
of vulnerable
vulnerable
behavior
v1
vz
"3
v4
v5
v6
v7
v8
v9
v10
v11
v12
behavior
u; in the case of surveillance
form
k*
police surveillance form
0
1
2
3
4
0.69
0.53
0.26
0.25
0.42
0.24
0.13
0.13
0.07
0.06
1.37
1.33
0.98
0.88
1.18
0.57
0.53
0.44
0.20
0.37
0.64
0.53
0.21
0.24
0.43
0.08
0.07
0.06
0.08
0.07
0.05
0.01
0.01
0.05
0.00
0.45
0.15
1.16
0.53
0.33
0.57
0.52
0.32
0.11
0.37
2.54
2.36
2.34
2.90
1.80
0.13
0.13
0.05
0.04
0.03
0.01
0.03
0.00
0.01
0.01
* All values are multiplied by 100.
Note: At the same intersection on 10 days with good weather, 3
hours (1:OO p.m.-2:30 p.m. and 3:00 p.m.-4:30 p.m.) per day, 2 days for
each surveillance form, the number of vehicles passing Qk = 23804,
23633, 23731. 23391, 22628.
This experiment was carried out at an
intersection in the city of Yokohama.
266
HASHIMOTO
A.
Table 3. Collision categories at intersection
notation
collision
head-on
cl
collision
when
collision
c2
rear-end
c3
flank
c4
c5
'6
c7
* Note
proceeding
in parallel
collision
collision
or side
between
collision
vehicles
when
collision
between
vehicle
and
pedestrian
collision
between
vehicle
and
stationary
this
is for
category
vehicle
and
another
so that
all
the
turning
traffic
includes
vehicle
collision
right*
I
collision
that
** This
category
only
**
driving
on the
collisions
coming
straight
categories
left.
between
from
object
a turning
the opposite
are mutually
right
direction,
exclusive.
struck in the rear by the vehicle behind when the driver brings the first vehicle to a sudden stop
in order to evade a third vehicle rushing out into the intersection in disregard of the red signal.
In such a case, the primal cause of the rear-end collision (~3) is the vulnerable behavior, failing
to stop at red (II,), committed by the driver of the third vehicle, and we count it as Hr3.
The results of the accident analysis are shown in Table 4.
Hence, the probability p(cj/ui) is calculated as follows:
p(cjjvi)
=
Pd”iy(7)_ 4/Q
PO(Vi)
(i = 1,. . , 12, j = 1,. . . ,7),
PO(Q)
Table 4. Results of accident
vulnerable
collision
behavior
analysis*
category
total
cl
'2
'3
'4
'5
'6
1
1
25
51
3
13
94
5
5
1
11
27
12
4
20
65
8
11
29
7
v1
"2
v3
2
"4
3
6
36
v5
'6
1
23
'7
238
7
1
65
2
276
3
1
35
17
v7
17
26
v8
74
100
0
v9
v10
1
"11
8
191
11
16
* All
the
intersections
1970
to Jun.
Note:
sections
cases
8
385
in the Metropolitan
total
Jan.
collected
number
1970
83
Police
and
1973
274
accidents
122
that
district
4
occurred
in Tokyo
942
at the 26
from
Jan.
analyzed.
of vehicles
to Jun.
200
3
of human-injury
1973 were
The
from
58
1
71
"12
total
4
3
52
passing
= Q = 2.536
through
x 10'.
the
26 inter-
Measuring the effect of police surveillance
267
Table 5. Results of pk(c,) and Rk(c,)
police
collision
category
surveillance
form
2
3
4
0
1
Cl
0.63 x10-8
0.60 x~O-~
C2
0.23 x~!I-~ 0.16 x~O-~ 0.11 x10-7 0.16 x1o-7 0.10 x10-7
C3
0.15 x1o-6 0.14 x10-6 0.10 x10-6 0.12 x1o-6 0.94 x10-7
C4
0.33 x10-7 0.27 x~O-~
(0.05)
(0.30)
(0.06)
(0.18)
C5
(0.57)
(0.45)
(0.51)
(0.30)
(0.21)
(0.33)
(0.54)
(0.55)
(0.38)
0.17 x10-7 0.14 x1o-7 0.21 x1o-7
(0.57)
(0.49)
(0.36)
0.11 x10-6 0.90 x1o-7 0.41‘x1o-7 0.41 x10-7 0.73 x1o-7
(0.17)
'6
(0.62)
(0.62)
(0.33)
0.48 x~O-~ 0.30 x1o-7 0.21 x1o-7 0.25 x~O-~ 0.25 x~O-~
(0.37)
C7
total:
0.35 x10-8 0.27 x~O-~ 0.29 x1o-8
(0.47)
(0.57)
(0.47)
0.16 x~O-~ 0.14 x1o-8 0.94 x1o-g 0.89 x10-' 0.11 x1o-g
C
0.37 x10 -6
(0.13)
(0.40)
(0.44)
(0.32)
0.31 x1o-6
0.20 x1o-6
0.22 x1o-6
’ 0.23 x~O-~
(0.16)
(0.47)
(0.41)
(0.39)
here, Q = the total number of vehicles passing through the 26 intersections during the period of
the accident analysis.
Effect of police surveillance
Based on the probabilities pk(ui) and p(cj/ri) obtained above, the probability pk(Vi, ci) is
calculated through the eqn (1). The probability that the collision category cj occurs in the case
where the surveillance form k is executed and the preventive effect of the form k on the
collision category cj are also calculated through eqns (2) and (3).
The results are shown in Table 5.
RESULTS
AND DISCUSSIONS
In Table 5, what should be noted first is the probability of a collision at the intersection in
the normal state: PO(C)= 0.37 X 10T6.This value means that collisions occur at a rate of only
twice a month even at the intersection that has the largest volume of traffic among the 26
intersections selected for tbe accident analysis. Thus, in case we intend to measure the effect of
some traffic safety measure at the intersection by such a probability (e.g. the accident rate), it is
impossible to identify it through a one-day or a two-day observation at an intersection,
However, as seen in Table 2, since the probability that the vulnerable behavior occurs is much
larger than the probability of the collision occurrence, it can be measured on the small scale
experiment. Then the effect of the traffic safety measure can be estimated indirectly using the
probability of the vulnerable behavior. Thus, the approach using vulnerable behavior is valid
for the statistical analysis of accidents.
As seen in the parentheses in Table 5, the preventive effect of the police surveillance is
measured for each surveillance form. These values, along each of the three surveillance series
mentioned before, show a general tendency for surveillance forms 2, 3, and 4 to be more
effective than the form 1. That is, both the effect of increased police surveillance, series A, and’
the durable effect of the police surveillance, series C, are recognized. Moreover, in series B, we
find that the police surveillance in the center of the intersection is more effective for almost all
collision categories than surveillance from the corner of the intersection.
The effect of each surveillance form on the collision as a whole is displayed in graphical
A. HASHIMOTO
268
100
r
SURVEILLANCE
Fig. 2. Preventive
effect
on the collision
FORM
as a whole.
form in Fig. 2. Assuming that all of the true vulnerable behaviors had been identified,
were free of measurement error, and that the regression of accidents on the behaviors
to unity, we find here that even the surveillance form 1 can. prevent 16% of the
caused in the normal state, that the form 2 can prevent about a half of those, and so
However, what is interesting is that the police surveillance affects each collision
differently. Therefore, the seven collision categories can be divided into three groups
to the most effective surveillance form.
that they
was close
collisions
on.
category
according
(1) [CS, ~1: Form 2 is the most efectiue
The collision between vehicle and pedestrian (cg) is generally caused at the pedestrian
crossing when the vehicle goes into or goes out of the intersection. Therefore, it is convincing
that the form 2 is most effective for this collision category because the policemen in this form
are located near the pedestrian crossing. But what we should note here is that this category is
the one for which form 1 is most effective for preventing collisions among the seven categories.
Therefore, we can say that this category is most sensitive to the police surveillance (see Fig.
3.1).
(2) [cI,c4,c5,c7]: Form 3 is the most efective
Here we recognize the greatest effect of the police surveillance on a collision category. That
is, 60% of the collisions when turning rightt (cS) can be prevented by either form 3 or form 2.
Therefore, we can say that this category is most reducible using police surveillance.
Many accident analyses of the past indicate that the collision categories in this group are
mainly due to reckless driving or running a risk. Hence, form 3 seems to have the greatest effect
on risk-taking drivers by causing them to drive more carefully than they usually do, at least at
the intersection where the police are present (see Fig. 3.2).
(3) [c2,cJ: Form 4 is the most efective
The rear-end collision (cJ), which occurs most frequently (i.e. forms about 40% of all the
collisions at the intersection as seen in Table 4), is the category that is least influenced by police
surveillance. Therefore, we can say that this category is least sensitive to police surveillance.
Moreover, it can also be said from past analyses that collision categories in this group
mainly occur due to the carelessness of drivers. Hence, we find that police surveillance at the
previous intersection has the effect of making the driver more alert, and it is durable at least at
the next intersection (see Fig. 3.3).
Bee
previous
footnote.
269
Measuring the effect of police surveillance
3.1
3.2
COLLISION
VEHICLE
BETWEEN
AND
3.3
COLLISION
PEDESTRIAN
TURNING
Cc,)
this
COLLISION
RIGHT*
Cc,)
Cc,)
SURVEILLANCE
* liote that
REAR-END
WHEN
is for
traffic
driving
on the
FORM
left.
Fig. 3. Preventive effect on some collision categories.
In this way, the police surveillance at the intersection seems to have two kinds of effects on
the driver. One is that it makes the driver behave better and another is that it makes the driver
more alert. The former is temporary and the latter is durable. Therefore, the effect of
surveillance form 3 and that of form 4 on each collision category are in contrast to each other.
Moreover, form 2 has an even effect on every collision category (see Figs. 2 and 3).
Proving the results of this study actually requires a long-term study of accident rates at
locations with specific surveillance being carried out continuously. However, this is impracticable because accidents occur so infrequently that continuous surveillance is costly. For that
reason, conversely, the theory and methodology presented in this paper were developed.
Therefore, what would be needed would be identifying the true vulnerable behaviors that are
consistent with the theory by the analysis of many more past accident cases. Then the
procedures presented in this study would be more valid to evaluate measures to improve traffic
safety, i.e. to improve our ability to determine the benefits of various traffic safety programs
related to accident avoidance.
CONCLUSIONS
The procedures for measuring the preyentive effect of the police surveillance on accidents
at intersections have been developed using the notion of the vulnerable behavior. The adoption
of the vulnerable behavior concept makes the quantitative analysis of accidents relatively easy
because vulnerable behavior occurs much more frequently than accidents and because it is the
overt behavior that we can observe externally. Moreover, the notion of vulnerable behavior can
be applied to collisions caused by pedestrians’ vulnerable behavior and can also be applied to
collisions on the road as well as at intersections. Therefore,,the procedures developed in this
study are useful for measuring the effect of various types of preventive measures taken against
traffic accidents.
Acknowledgments-This
I would
study was conducted under the leadership of Assistant Professor Kodama of Saitama University.
to express my gratitude to Professor Kodama. 1 also wish to acknowledge
the
like to take this opportunity
perceptive comments of the referees.
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A. HASHIMOTO
270
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