ITARDA 25th Work Shop Report2022
25th Presentation Session for Traffic Accident Investigations, Analysis, and Research“Characteristics of Pedestrian Accidents in Low-Speed Range”
Taisuke Watanabe
Senior Researcher, Research Division

1

ITARDA 25th Work Shop Report

1. Background and objectives

0 to 6 years old(infants and young children)
7 to 12 years old(school children)
13 to 64 years old(adolescents and adults)
65 to 74 years old(younger elderly persons)
75 years old and above(older elderly persons)

Ratio

Among traffic-accident parties, pedestrians havecontinued to have the highest number of fatalities inJapan since they surpassed occupants of four-wheel
vehicles in 2009 (Figure 1). Furthermore, looking at
the past 10 years, pedestrian fatality & serious injury

Danger-recognition speed of vehicle (km/h)

accidents have decreased very little in the low-speed

Figure 3. Ratios of 2011 to 2020 & 1991 to 2000

range (20 km/h or below), and thus addressing this

2. Examination method

seems to be an upcoming challenge (Figures 2, 3).

Chart 1 shows the examination objectives, the accident

Against this backdrop, in this research, we investigateddata used, and a list of the analytical methods. In the(1) the causes of accidents not decreasing in the low-case of Objective (1) mentioned above, we gained an

speed range, and (2) mechanisms that lead to fatalities

understanding of actual conditions through cross& serious injuries in the low-speed range.

tabulation, and estimated causal relationships throughan RCT (randomized controlled trial). As for Objective

Pedestrians

Four-wheel vehicles

Motocycles
Bicycles(2), we narrowed targets through non-hierarchicalComposition ratio

Number of fatalities

Composition ratio of pedestrians

cluster analysis (the k-means method), and then carriedout cross tabulation.

(1)
(2)

Number of fatalities & serious injuries per million
people of population (ppm/year)

Figure 1. Trend in traffic-accident fatalities

Number of fatalities & serious injuries per million
people of population (ppm/year)

Data

Objective

Analytical method

・Cross tabulation
ITARDA macro data
•Person-to-vehicle accidents ・RCT (randomized controlled trial)•1995/2010 to 2021
•n = 1,830,000/540,000 cases

Understanding ofactual conditions
Estimation of
causalrelationships
Information
compression

・Non-hierarchical cluster analysis+ Cross tabulation Estimation of causes

Chart 1. Overview of examination method
3. Results

0 to 6 years old(infants and young children)
7 to 12 years old(school children)

3.1 (1) Causes of accidents not decreasing in low-

13 to 64 years old(adolescents and adults)
65 to 74 years old(younger elderly persons)
75 years old and above(older elderly persons)speed range

Danger-recognition speed of vehicle

First, we checked annual changes in the danger-

1991 to 2000

recognition speeds of pedestrian accidents. The averagefor six vehicle types ranging from “motorcycle” to
0 to 6 years old(infants and young children)“cargo” followed a consistent trend in which it

7 to 12 years old(school children)
13 to 64 years old(adolescents and adults)
65 to 74 years old(younger elderly persons)continued to fall, and over 26 years, it declined by 975 years old and above(older elderly persons)

km/h (Figure 4).
Danger-recognition speed of vehicle

2011 to 2020

Figure 2. Relationship between speed and number offatalities & serious injuries per population

2

Average danger-recognition speed (kph)

ITARDA 25th Work Shop Report

Primary-party danger-recognition speed(person-to-vehicle, fatality & serious-injury accidents)
K-passenger
Passenger
K-cargo
Cargo
MotocycleBus
All vehicle types-9.0 kph/26 years

Year of accident

Figure 4. Trend in danger-recognition speed(pedestrian accidents)

Looking at the change in the number of accidents
divided by speed, while the number has decreased for 30
km/h and above, it has stayed at the same level for 20
km/h or below. As a result of this, the composition ratioof 40 km/h and above has decreased, and thecomposition ratio of 10 to 20 km/h has increased (Figure5). In other words, amid an overall trend in which speeds
have been decreasing, the number in the medium tohigh-speed range has been flowing into the low-speed
range, and this is why even though accidents have been
decreasing overall, they have not been declining in the
low-speed range.

3

ITARDA 25th Work Shop Report

Number of cases (10,000 cases)

Over 160

Decreasing

Staying at same level

Year of accident

Vehicle-alone accidents

Average danger-recognition speed (kph)Number of accidents

Passenger
K-passenger
K-cargo
Cargo
MotocycleBus
All vehicle types

Year of accident

Figure 6. Trend in danger-recognition speed(vehicle-to-vehicle / vehicle-alone)*In all cases, the speed is the primary-party danger-recognition speed(fatality + serious-injury + slight-injury).

Composition ratio
Over 160

In order to verify this hypothesis, it is necessary to

Decreasing

identify factors that have a causal relationship withdanger-recognition speeds, and to check how suchfactors are connected with social changes. Since it is

Increasing

difficult to evaluate causal relationships (≠ correlative
relationships) with common statistical methods, in ourYear of accident

Figure 5. Change in number of accidents by dangerrecognition speed (pedestrian accidents)

research, we attempted the application of an RCT. RCTs
are positioned at the highest rank in the evidence*In all cases, the speed is the primary-party danger-recognition speed(person-to-vehicle, fatality & serious-injury).

pyramid for clinical medicine, and thus they are deemed

Next, we checked the trends regarding other accident

to be the method that has the most persuasiveness.

types (vehicle-to-vehicle accidents and vehicle-alone

Under this structure, a pair of populations regarded as

accidents). As expected, the average speed of all vehicle

being equivalent is prepared, and an intervention

types has been continuing to drop in a consistent manner

operation such as drug administration is only carried out(Figure 6), and thus there appears to be a declining trend

regarding one of the populations. The results are thenthat is not limited to pedestrian accidents and is sharedcompared, and this enables the effects of theby all of the main accident types. In light of this, it canintervention to be verified (Figure 7).

be inferred that significant forces related with social
Randomized controlled trials

changes of some kind have been at work in the

Average danger-recognition speed (kph)background.

Non-randomized controlled trials
Analytical epidemiologicalstudies

Vehicle-to-vehicle accidents(cohort studies, case-control studies, etc.)

Passenger
K-passenger
K-cargo
Cargo
MotocycleBus
All vehicle types

Observational studies

Case reports
Opinions and ideas of editorials and experts

Evidence pyramid
SourceYear of accident

4

ITARDA 25th Work Shop Report

Application to this research
Populations withequivalent factor
composition ratios

Assigning ofdifferences only
regarding focus
factors

Comparing of average
danger-recognition
speedsYes
No
Equivalent
populations

Intervention
operation

Comparisonof results

RCT (randomized controlled trial)
Figure 7. Overview of RCT (Reference 1)
and application to accident analysis
In this research, we first of all made a list of factors
considered to be related with speed (Chart 2), andextracted multiple groups in such a way that theaccident-number composition ratios of the factors
would be equivalent with the overall population (witherror within 0.1%). We then assigned differences
regarding composition ratio only to factors that we
focused on, and investigated disparities in the average
danger-recognition speeds that could be calculated as aresult of the above*.
*To learn about the limitations, etc. of this method,
see the Appendix at the end of this document.

On this occasion, we extracted 2,000 cases for eachlevel from the total data of 540,000 cases. Thus, theextraction rate was less than 1%, and due to throwing
out over 99%, it became possible to create the type of
artificially-controlled data set described above.

5

ITARDA 25th Work Shop Report

Chart 2. Factors (= explanatory variables) regarding danger-recognition speed, and their levels
PersonYear ofaccident

Road

VehicleIntoxicati
Vehicle typeRoad
on while Purpose Behavior type
Vehicle age configurationof travelof party
driving

Age

Gender0 to 24 years IntoxicatedBusiness Starting/backin
old
g up

Regularpassenger

Traffic
light

Level 1

Male

Level 2

Not
25 to 44 years
NonCommutin Going straight K-passenger 3 to 5 years
Flashing
Femaleintoxicated g to workoldintersection
ahead, etc.

Level 345 to 64 years
oldLevel 465 to 99 years
oldPersonal

Turning

6 to 8 years

Railway
crossing

Cargo

9 or more
years

General
area

Other

Lit

0 to 2 years Intersection

Other
passenger

Terrain

Day/
night

DID

Day

Urban

Night

Nonurban

None

Day

Day/
night

Night

Non-urban

DID

Terrain

Urban

None

Lit

General area

Nonintersection
Railway
crossing

Intersection

Flashing

Traffic
lightRoad
configuration

9 or more years

3 to 5 years

6 to 8 years

Cargo

0 to 2 years

K-passenger

Other passenger

Other

Regular-passenger

Going straightahead, etc.
Turning

Personal

Vehicleage

Vehicle typeStarting/backing up

Business

Commuting to
work

IntoxicatedNot intoxicated65 to 99 years old

45 to 64 years old

0 to 24 years oldAge

25 to 44 years old

Male

GenderIntoxi Purpose
cation of travel

Change in composition ratio (1995 to 2021)

Behavior type
Year ofaccident

FemaleWhencomposition
ratio of focus
level is 100%

Average danger-recognition speed in RCT (km/h)

FactorFigure 8. Average danger-recognition speeds for focus factors (levels), and changes in composition ratio

The results for the total of 39 groups that were

“intersection” and “non-intersection” and slower for

extracted are shown in Figure 8. The bar graph shows“parking area”), traffic light (faster when present),

the average danger-recognition speed (when theterrain (slower for urban areas), and day/night (faster forcomposition ratio of the focus level equals 100%), and“night”).

the line graph shows the change in the composition ratio

Next, by multiplying the average danger-recognitionof the focus level (from 1995 to 2021). Furthermore, aspeed by the change in composition ratio, we calculated

t-test was carried out regarding each of the factors, andthe actual amounts of contribution of the factors to thethe significant factors (with a p-value of ≤ 5%) arechange in speed over the past 26 years (Figure 9). The

shown in red text. First of all, looking at the group (onresults show that behavior type particularly stands out,

the far left) regarding which only “year of accident” hasand that following this, age, road configuration, andbeen changed and the other factors & levels have been

terrain have contributed to the decrease in speed.

made the same, there is a result in which there are noAmount of contribution to dangerrecognition speed (kph)differences in the average speeds, and thus it is clear that
there was no insufficiency in the types of factors(explanatory

variables)

that

were

extracted.

Furthermore, results were obtained that did not seemparticularly unusual, and these included age (slower

Traffic light

Day/night

Vehicle age

Vehicle type

GenderPurpose of
travelTerrainIntoxication

slower for “turning/starting”), vehicle type (faster forRoad
configuration

behavior type (faster for “going straight ahead” andAge

Behavior
type

speed as age increases), intoxication (faster speed),

Figure 9. Contribution of various factors to dangerrecognition speed (1995 to 2021)“K” and “cargo”), road configuration (faster for
6

ITARDA 25th Work Shop Report

Upon carrying out trial calculations with the number of

3.2 (2) Mechanisms that lead to fatalities & serious

divisions ranging from 2 to 10, in both groups, wheninjuries in low-speed range

the number of divisions was 4, a cluster with a high
The analysis procedure is shown in Figure 10.

fatality & serious-injury rate became apparent, and(1) First of all, we made a list of 27 factors related with

we set these as focus clusters.

the level of injury of pedestrians, and then created a(4) In regard to the focus clusters, we compared thedata set.

distribution of all 27 factors, and thereby checked the(2) Next, based on the danger-recognition speed, wecharacteristics of the low-speed group.

divided the data set into the two groups of “medium

Upon comparing the frequency distribution of each of

to high-speed” and “low-speed.”

the factors in (4) above, it was possible to recognize(3) We carried out non-hierarchical cluster analysis

notable difference between the focus clusters regarding

based on the k-means method regarding each group.

12 factors (Figure 11). These are compiled below.

(1) Creation of data set (27 factors related to injuries of pedestrians)
Road
TerrainRoad
Traffic Road
configurlight linearity
ation

Person (pedestrian)

VehicleRoad
width

Acciden
Gendert type(2) Division into two groups based on
speed range

Vehicle Behavio Purposetype
r type of travel

Speed
limit

DangerIntoxica Vehicle
recogniti
tion
width
on speed

VehicleheightDate offirst
registration

Vehicleclass Gender

Medium to high-speed group(40 to 60 km/h)
Total n = 69,659

Sample size(3) Information compression based on
non-hierarchical cluster analysis(k-means method)

Age

Focus
cluster

Age

Purpose Level ofof travel injury

Light
injurySerious
injuryFocus
cluster

Fatality
Cluster

Low-speed
group
Cluster 0(4) Comparison of factor distribution
Medium toof focus cluster
high-speed
group
Cluster 1

Figure 10. Procedure for analysis of factors that lead to fatalities& serious injuries in low-speed range

7

Injured Injury
part of inflictin
body g object

Low-speed group(0 to 20 km/h)
Total n = 366,928

Sample size

Year of Accident Day/
accident content night

ClusterITARDA 25th Work Shop Report

Low-speed
group
Cluster 0Day

Elderlypersons

Intersection

DID

Lit
Young
persons

Medium tohigh-speed
group
Cluster 1

Turning

Personal

Regular sedan

Commuting towork

Cargo vehicle,
etc.

Starting

FemaleRoad
surface

Lower
limb

Elderlypersons
Children

Tire

Factors with notable differences(total of 12)

Figure 11. Frequency distribution comparison of all factors & levels in focus cluster• Road: In the low-speed group, daytime, DID,

in the RCT, and these seem to exclusively be the factors

intersection, and lit traffic light present were common.

that are related with the reduction in speed. Thus,• Vehicle/driver: In the low-speed group, regular sedan,

examination with two different approaches has shown

starting turning, personal, and elderly driver wereconsistent results.
Chart 3. Factors & levels more common
in low-speed groupcommon.
• Person (pedestrian): In the low-speed group, lower-• Slowing factors: factors (levels) that “reduce speed” also confirmed with RCTlimb• Injury-related factors: factors (levels) related with “injury intensification at low
speeds”

injury,injuryinflicted

bytire/road,andchildren/elderly person were common.

Road
VehicleWithin the focus clusters (fatality & serious-injury

Day, urban, intersection, traffic light
Regular sedan, starting/turning, personal, elderly driverChildren, elderly woman

Age

PersonMain part of body injured(pedestrian)clusters), the factors & levels common for the low-speed

Injury inflicting object

group are shown in Chart 3. “Road” and “vehicle”

Lower limb
Tire, road surface

include numerous factors that had also been confirmed(Pedestrian age)

Male

(Pedestrian age)
Medium to highspeed group(Injury inflicting object)(Number of cases)

A 0 to 3 years old
FemaleLow-speed group

B 7 years old

C 20 to 54 years old D 70 to 84 years old

Other
Road
surface
Car
body
Tire
Total
Abdo Lower Upper
body Head Chest men limb limb

Total
body

Lower Upper
Head Chest Abdo
men limb
limb

Total
body Head

Lower Upper
Chest Abdomenlimb limb(Main part of body injured)

Total
Abdo Lower Upper
body Head Chest men
limb limb

Other
Road
surface
Car
body
Tire
Total
body

Lower Upper
Head Chest Abdo
men limb limb

Total
body

Lower Upper
Head Chest Abdo
men limb limb

Total
Abdo
body Head Chest men

Lower Upper
limb limb

Figure 12. Differences regarding injury-related factors according to speed group

8

Total
body Head

Lower Upper
Chest Abdo
men limb limb

ITARDA 25th Work Shop Report

Meanwhile, since it seems that factors related withamong elderly women, there are many cases in which“personinjurytolerance is decreased due to osteoporosis, and this canintensification in the low-speed range, we analyzed

be inferred to be the cause of (2). Furthermore, althoughdifferences among these according to the speed groupthe degree of injury significantly declines in the low-

(Figure 12). The bar graphs on the left show the

speed range when the cause is “car-body contact,” there

frequency distribution of each age group and gender,

is not much change when the cause is “road-surface

and the bubble charts on the right show the frequency ofcontact.” Therefore, the injury-inflicting potential of

occurrence of each “main part of body injured” and“road surface” relatively increases in the low-speed“injury inflicting object” with a drawing of four cross-range, and this seems to be the cause of (3).

(pedestrian)”arerelatedwithsections that are in accordance with the age group of theOverall decrease in injuryinflicting potential →Decrease in tolerance due toosteoporosis →pedestrian (A: infant to 3 years old, B: 7 years old, C:
20 to 54 years old, and D: 70 to 84 years old).

Elderly womenLifesaving efforts
not possible

Degree of injury

First of all, looking at the medium to high-speed group,in the frequency distribution, there is no uneven portion
regarding children or bias toward women, and in the
bubble chart, regardless of age group, the most common

Mortal injury

Critical injury
Severe injury

Contactwith carbody

ModerateinjurySignificant decrease ininjury-inflicting potential
Mild injury
of “car body” ↓cases are those in which the injury is inflicted by a car
body and the head is the main part of the body injured.

Injuries inflicted byroad surface

Meanwhile, in the low-speed group, four characteristics

Impact velocity

can be recognized: (1) a high number of cases involving
children (A & B), (2) a high ratio of women among
elderly persons (D), (3) the injury inflicting object often

Figure 13. Trend regarding degree of injury and injury
inflicting object & impact velocity (Reference 2)

being “road surface” and “tire” in addition to “car body”

Figure 14 shows the typical collision behavior(particularly in A), and (4) an abundance of cases inregarding pedestrians (in a time series). When adultwhich “lower limb” is the main part of the body injured.

pedestrians are hit by a car with a bonnet, first of all,Next, we inferred the mechanisms in the backgroundtheir legs are swept by the bumper, they fall over and hit

behind these characteristics. With accident analysis

their head on the bonnet side, and then their body rotates.

alone, it is difficult to try to identify physicalThen, since the car then applies the brakes, they slide

mechanisms, so we inferred these based on general

down and fall off the front of the car. At medium to highknowledge in the field of crash safety related to the

speeds, the pedestrians forcefully strike their head, theirprotection of pedestrians. Figure 13 shows the trend

body somersaults from this position, and then they fall

regarding degree of injury according to injury inflicting

onto the road surface from mid-air. At medium to highobject and impact velocity. There is a trend in which the

speeds, the head is often the main part of the body

injury tends to be a mild one when the speed is lower,

injured due to the timing with which the car body andand in the low-speed range of 20 km/h or below,

head come into contact. Nevertheless, at low speeds, thegenerally “mild injury” and “moderate injury” arelevel of force is weak, so often the main part of bodycommon. Nevertheless, as for accidents under the same

injured is determined by the timing with which the leg

conditions in which there is a severe injury, it seems thatis first hit, and this seems to be the cause of (4).

there is an increase in the percentage of pedestrians who

Furthermore, in the case of children, who are shorter inhave a low level of physical tolerance. In particular,

height, rather than a leg, the upper body comes into
9

ITARDA 25th Work Shop Reportcontact with the bumper, so they fall onto the road-the driver’s seat of such vehicles. Because of this, there

surface side, which is the opposite from adults.

are many accidents that occur close to home in which

Furthermore, since the speed is low, they are not thrownchildren are run over during vehicle starting. Since suchfar, and it seems that they are more frequently run overaccidents occur during starting, they are of course low-

by a tire. This seems to be the cause of (3) (infliction of

speed accidents. B is accidents involving first graders.

injury to young child by tire).

When children begin school, they quickly become

Lastly, although this differs from mechanisms ofactive in a much wider area, and this is what causes

injury intensification, we will touch upon theaccident frequency to sharply increase for this age groupbackground of (1) (a high number of cases involving

alone. Such children often get into accidents whenchildren [A & B]). Based on accident analysis carriedcommuting to and from school, so it seems that they areout separately by ITARDA (Figure 15), A seems to beprone to low-speed accidents along school-commuting

accidents involving a young child during vehicle

routes.

starting. In particular, since children of age 3 and below
have a height of around 90 cm, when they are standing
in close proximity to vehicles with a high seating
position, such as minivans, they are difficult to see from
Low speed →Easing of head contact →Injury inflicted to lower
limb by car bodyLow speed → not thrown →low height → falling forward →running over with tire(child)Adult

Low speed
25 kphSliding down
and falling

Child

Head

Lower limb

Repulsion

Falling*The vehicle brakes.

Somersault

Medium tohigh speed
40 kphInjury inflicted to headby car body

Figure 14. Differences regarding injury-related factors according to speed group (Reference 2)
A: Accidents involving young
children during vehicle starting

B: Accident involving first graders[Causes]• Low in height → difficult to see from driver’s seat
• During starting → accidents are low-speed
accidents[Causes]• Becoming active in much wider area after
starting school → sharp increase in accident frequency• Commuting to and from school → large number of lowspeed accidents along school-commuting routes

Figure 15. Low-speed accidents involving children(References 3, 4, 5)

10

Falling frommid-air

ITARDA 25th Work Shop Report

4. Conclusion

seems that measures to prevent collisions fromIn regard to pedestrian accidents in the low-speed

occurring are needed.

range (danger-recognition speed of 20 km/h or below),
we investigated (1) the causes of accidents notdecreasing, and (2) mechanisms that lead to fatalities &serious injuries at low speeds. As a result, the following
became clear.
(1) Since 1995, there has been an ongoing trend inwhich danger-recognition speeds have been
decreasing among almost all accident types. As
a result of this, the number in the medium tohigh-speed range has been flowing into thelow-speed range. This seems to be why
accidents have not been decreasing in the lowspeed range (even though they have been
decreasing overall). The main factors related todecreases in danger-recognition speed are (i)behavior type (less accidents for going straightahead and more for turning), (ii) age (lessaccidents for 44 years old or below and morefor 65 years old or above), and (iii) roadconfiguration(lessaccidents

for

non-

intersection and more for intersection/parkingarea). As for social changes in the background,although population aging can first of all bepointed to, taking into consideration changes
regarding (i) and (iii), it seems that the urban
concentration of the population may also be
having an impact.
(2) In the low-speed range, injury factors become
more diversified, and compared with themedium to high-speed range, there are
increases in the percentages of: (a) lowtolerance

pedestrians,(b)injuries

being

inflected by the road surface, (c) pedestrians
being run over, and d) lower-limb injuries.
Addressing (a) through (c) with conventional
collision safety measures (structures forprotecting pedestrians) would be difficult, so it
11

ITARDA 25th Work Shop Report

References1)
https://twitter.com/katalab_s/status/141295715452121
4978
2) Koji Mizuno, Jidosha no Shototsu Anzen Kiso-ron(Introduction to Crash Safety of Passenger
Vehicles), The University of Nagoya Press, 2018, p.
207/220.
3) Institute for Traffic Accident Research and Data
Analysis, “ITARDA Information No. 141,” June
2022.
4) Institute for Traffic Accident Research and Data
Analysis, “ITARDA Information No. 116,” June
2016.
5) Institute for Traffic Accident Research and Data
Analysis, “ITARDA Information No. 121,” March
2017.

12

ITARDA 25th Work Shop Report

Appendix: Limitations, etc. of RCT method in this research
In the case of the method that we adopted on this occasion, even if there is a linkage relationship between thefactors as shown in Figure A, the effects of the upstream factors (in red text) are nullified through adjustments tothe composition ratios of the downstream factors. Accordingly, causal relationships can only be evaluated regarding
the factors (in green text) that are directly connected with danger-recognition speed (objective variable). As formethods for evaluating the upstream factors with linkage relationships, although there seem to be approaches suchas “changing the objective variable to each of the downstream factors, and repeating the RCT with a brute-force
approach,” if the number of factors is large, realistically implementing this is difficult.
Evaluation no possible
Factor

Factor

Factor
Factor
Factordangerrecognition speed

FactorFigure A. Scope of evaluation of factors

Furthermore, in this research, the amounts of contribution of the various factors to danger-recognition speed,which are shown in Figure 9, are calculated by multiplying (1) the sensitivity of the factors/levels (independent) by(2) the change in the composition ratio of the factors/levels (Figure B).
The former is the danger-recognition speed when the composition ratio of the focus factors/levels is 100%, andthat of the other factors/levels is equivalent with the population. Thus, the sensitivity has been calculated under thecondition that there are no effects from the other factors, such as confounding and interactions, and nonlinear
response characteristics have not been taken into consideration. On this occasion, although there was a 13% error
between the actual measurement value of the amount of change in danger-recognition speed (accident data), and thecalculated value (∑ sensitivity of each level × change in composition ratio), it is possible that this is the effect of
nonlinear characteristics. The latter is the actual measurement value from accident data, and as a result, all of thevarious interactions between the factors are included.
As for the the sensitivity of the factors/levels in a broader sense, although it seems that there must be inclusion of(2), in other words, the effect of the change in the composition ratio of the relevant factors/levels on the change inthe composition ratio of the other factors, this would require the implementation of RCTs regarding the composition
ratios of all the factors/levels, so this was skipped on this occasion.

12

ITARDA 25th Work Shop ReportΣ Each level (sensitivity × change in composition ratio)

Day/night

Traffic light

Vehicle age

Vehicle type

GenderPurpose of travel

Terrain• Taking into consideration nonlinear characteristics
of sensitivity was not possible.
• The change in composition ratio is an actual
measurement value so it includes confounding andinteraction between the factors.
Actual change: -7.9 kph (excluding motorcycles)
Calculated value: -8.9 kph→ Error: 13% (possibly from nonlinear
characteristics?)

Figure B. Process of calculating amount of contributionof various factors to danger-recognition speed

13

Intoxication

Age

Road
configuration

Behavior type

recognition speed (kph)Amount of contribution to
speed by various factors
Amount of contribution to danger-

Period of change in composition ratio (1995 to 2021)

Change in composition ratioof factors/levels
Accident analysis results

2010-122013-152016-18
2019-21
Male
Female0 to 24 years old
25 to 44 years old
45 to 64 years old
65 to 99 years old
IntoxicatedNot intoxicatedBusiness
Commuting to work
Personal
Starting/backing up
Going straight ahead, etc.
Turning
Other
Regular-passenger
K-passenger
Other passenger
Cargo
0 to 2 years
3 to 5 years
6 to 8 years
9 or more years
Intersection
Non-intersection
Railway crossing
General area
Lit
Flashing
None
DID
Urban
Non-urban
Day
Night

Average danger-recognition speed in RCT (km/h)

Sensitivity of factors/levels(independent)
RCT results