ITARDA 21th WorkShop ReportCreating New Accident Databases
・Developing SIP accident patterns
・In-depth accident case studies from D-Call Net®
Toru Kiuchi, Senior Researcher, Research Division

Preface
In 2017 the number of traffic accident fatalities came to 3,694. Even though this fell by 210 from the previous
year, far too many precious lives are still being lost. ITARDA’s traffic accident databases have been broadly
divided up into macro database and micro database, both of which are put to their own respective effective uses.
This paper will introduce SIP accident pattern analysis as a new form of macro database, as well as in-depth
accident case studies from D-Call Net® as a new form of micro database.

1. Trends in the number of traffic accident fatalities
Trends in the number of traffic accident fatalities in Japan (1948 – 2017) and the government’s targets (2020)are shown in Fig. 1. Two peaks can be confirmed within this figure. The peak of 16,765 fatalities that occurredin 1970, which was the year that saw the largest number of fatalities in the post-war period, came to be known
as the “Traffic War I.” The Traffic Safety Master Plan was formulated the following year in 1971, and by 1979
the number of fatalities had fallen to 8,466. Thereafter, the number surpassed the 10,000-person mark again in
1988, ultimately peaking at 11,452 fatalities in 1992 in what came to be known as the “Traffic War II.” ITARDA
was then established in March of that same year.

Fig. 1. Trends in the number of traffic accident fatalities and the government’s targets

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ITARDA 21th WorkShop Report

The Tenth Traffic Safety Master Plan that was formulated in 2016 set a target value for the number offatalities five years later in 2020 at 2,500 fatalities. However, as of 2017 the number of fatalities was 3,694,
indicating that there is still a gap of 1,194 fatalities. Given that the downward trend in the number of fatalities
has been slowing down in recent years on account of factors like the increase in the number of elderly people,
this indicates that further effort will be needed in order to achieve this target.

2. ITARDA’s accident databases
As shown in Fig. 2, ITARDA’s accident data exists in the form of micro studies and macro analysis, which
form the two wheels of said data. The SIP accident pattern database that will be introduced in this paper as a
new type of accident database is based on macro analysis. Conversely, the in-depth accident case studies from

Two wheels of
ITARDA’s accident
databases

Macro analysis

Micro studies

D-Call Net® are derived from micro in-depth case studies for specific purposes.

Fig. 2. The two wheels of ITARDA’s accident databases

3. SIP accident patterns database(1) Strategic Innovation Promotion Program (SIP)
At the 107th meeting of the Council for Science and Technology Policy held in March 2013, Prime
Minister Abe kicked off the proceedings by stating, “We will once again aim to become ‘the No. 1 in theworld.’ In order to aim to be the No. 1 in the world, innovation will be important above all. I would liketo clearly state that the Abe administration will, as its new policy, focus on innovation.” At the 114th
meeting of the Council for Science and Technology Policy held six months later in September 2013, Prime
Minister Abe offered the following statement, “the Cross-ministerial Strategic Innovation Promotion
Program (SIP) as part of the National Emphasis Program, which will be the key to paving the way for the
future of Japan. We will strongly push this program forward.” Based on this, SIP Programs were initiatedfor 11 areas as indicated in Fig. 3.

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ITARDA 21th WorkShop Report

Automated driving system

Innovative combustion
technology

Infrastructure maintenance,
renovation and management

Next-generation powerelectronics

Enhancement of societal resiliency
against natural disasters

Structural materials forinnovation

Cyber-security for criticalinfrastructure

Energy carriers

Technologies for creating nextgeneration agriculture forestry andfisheries

Next-generation technology for
ocean resources exploration

Innovative design /Manufacturing technologies

Fig. 3. List of SIP Programs
One of these programs, “Automated driving system,” consists of the following six promoted initiatives.
(1) Develop/verify automated driving systems(2) Advance basic technologies to reduce traffic fatalities and congestion(3) Promote international cooperation(4) Deploy next-generation urban transportation(5) Conduct large-scale field operational tests(6) OtherOn the theme of “Develop technologies for estimating the impact on reducing traffic fatalities; create acommon national database” found in “(2) Advance basic technologies to reduce traffic fatalities andcongestion,” which is one of these programs, ITARDA has continued with research and studies spanninga four-year period.
(2) SIP traffic accident patterns
SIP traffic accident patterns were established with the objective of estimating the impact of factors like
automated driving systems, and were premised on the following four requirements.
Should consist of a classification method that can be applied to a variety of different safety measuresand devices.



The database should be of a size that is easy to use for estimating impacts.

Should be capable of being graphically represented to facilitate easy comprehension of the details of
the classified accidents.

Should be classifications that cover more than 80% of the number of traffic accident fatalities from
2013.

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ITARDA 21th WorkShop Report

ITARDA’s
macro data

Accident pattern sheet2013 accident
database
Fatalities:
4,373 people
Serious injuries:
44,547 people
Slight injuries:
736,947 people

Pattern overview sheet
Sort
patterns

Accident pattern sheet
Aggregate
patterns

Accident pattern sheet

No. of fatalities from all patterns≥ 3,499 people (80%)

Fig. 4. Process for establishing traffic accident patterns
Fig. 4 shows the process for establishing SIP traffic accident patterns. ITARDA macro data from 2013
was used to classify the accidents into patterns. Since these needed to cover more than 80% of the numberof traffic accident fatalities of 4,373, the patterns were set so that the total number of fatalities for theclassified patterns came to more than 3,499. The set patterns were then organized into respective accident
pattern sheets.

Fig. 5. Classification items for the traffic accident patterns

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ITARDA 21th WorkShop Report

The classification items for the traffic accident patterns are shown in Fig. 5. Multiplying all of theseclassification items together would result in a net total of 31,500 patterns, so to achieve a database sizethat is easier to use it was necessary to narrow down the patterns. Table 1 shows the number of fatalities
for each combination of primary and secondary party by road and accident type found in the macro datafrom 2013. As for the threshold values within the table, when the number of fatalities found within eachindividual SIP accident pattern consists of five or more, four or more, or three or more people, then this isdeemed to be the total number of fatalities for that pattern. As such, the smaller the number of people thelarger the total number of fatalities. Moreover, when the threshold value has been set at four or more or
three or more people, this indicates the number of patterns and the coverage rate at that time.
Table 1. Number of fatalities, number of patterns, and coverage rate by threshold value (2013)
Road
PublicroadType ofaccident
Vehiclevehicle

Primary
party
Car

Secondary
party
Car

Car
Motorcycle
Car
BicycleMotorcycle Car
Motorcycle Motorcycle
Motorcycle Bicycle
BicycleCar
BicycleMotorcycle
SinglevehicleVehiclePedestrian

Express Vehicleway
vehicleSinglevehicleTotal

Threshold values
5 or
4 or
3 or
more
more
more
people people people

No. of patterns
4 or
3 or
more
more
people people

Coverage rate
4 or
3 or
more
more
people people

CTC

636

546

574

583

25

28

90.3%

91.7%

CTM
CTB
MTC
MTM
MTB
BTC
BTM

283
359
204
13
8
132
5

172
272
133
0
0
80
0

196
276
137
0
0
80
0

211
300
140
3
3
89
0

17
20
12
0
0
4
0

22
28
13
1
1
7
0

69.3%
76.9%
67.2%
0.0%
0.0%
60.6%
0.0%

74.6%
83.6%
68.6%
23.1%
37.5S
67.4%
0.0%

Car

SCA

650

501

525

552

38

47

80.8%

84.9%

Motorcycle

SMA

214

120

148

163

18

23

69.2%

76.2%

Car

Pedestrian

CTP

1297

1143

1143

1173

40

50

88.1%

90.4%

Motorcycle Pedestrian
Pedestrian Car
Pedestrian Motorcycle

MTP
PTC
PTM

37
126
6

23
100
0

23
100
0

26
106
0

3
8
0

4
10
0

62.2%
79.4%
0.0%

70.3%
84.1%
0.0%

Car

HCTC

95

66

66

72

6

8

69.5%

75.8%

Car
Motorcycle
Motorcycle Car
Motorcycle Motorcycle

Car

HCTM
HMTC
HMTM

7
7
0

0
0
0

0
0
0

3
0
0

0
0
0

1
0
0

0.0%
0.0%
0.0%

42.9%
0.0%
0.0%

Car

HSCA

82

51

63

69

8

10

76.8%

84.1%

Motorcycle
VehiclePedestrianCode

No. offatalities

HSMA

18

0

4

4

1

1

22.2%

22.2%

Pedestrian

HCTP

14

0

0

3

0

1

0.0%

21.4%

Motorcycle Pedestrian

HMTP

0
4193

0
3207

0
3335

0
3500

0
200

0
255

0.0%
76.3%

0.0%
80.0%

Car

The total field at the very bottom row of the table shows that the 255 patterns with threshold values ofthree or more people have a coverage rate of 80%.

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ITARDA 21th WorkShop Report

Accident overview
Pattern No.
Road
Road configuration
By party (primary)
By party (secondary)Type of movement(primary party)
Direction of motion(secondary party)
Type of accident

CTC-01
Public road
Expressway
Within intersection (Signalized Non-signalized) Near intersection
Curve Tunnel/Bridge Other non-intersection General traffic
location
Car Motorcycle (including mopeds) Bicycle Pedestrian
Car Motorcycle (including mopeds) Bicycle PedestrianStarting up/going straight Passing/overtaking Changing lanes
Turning left Turning right Reversing Crossing Other
Same direction Oncoming From left From right Stopped
Vehicle-vehicle (Head-on collision Rear-end Crossing
While passing/overtaking While passing While turning left
While turning right Collision/contact Other )Aggregated results
No. of accidents / %No. of casualties / %Fatalities
21
0.5%
22
0.5%

Serious injuries
287
0.8%
326
0.8%

Slight injuries
4,907
1.0%
7,249
1.2%

Casualties
5,215
1.0%
7,597
1.1%

Total No. of accidents
Total No. of casualties

Fatalities
4,028
4,117

Serious injuries
37,012
38,959Slight injuries
495,859
627,064

Casualties
536,899
670,140

Fig. 5. Classification items for the traffic accident patterns
Fig. 5 shows one example of an accident pattern. This example shows a crossing collision accident at anintersection with a traffic signal between the primary party of a car starting up and going straight (the redcar) and the secondary party of a car coming from the right (the blue car). It was given a Pattern No. of
CTC-01. The “Aggregated results” section shows the number of people who suffered fatalities, serious
injuries, and slight injuries due to this pattern, as well as the number of casualties which is the sum totalof all of these. In addition, the numbers of fatalities, serious injuries, slight injuries, and casualties from2013 for both the total number of accidents and the total number of casualties were added as well to serve
as a reference.

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ITARDA 21th WorkShop Report

By time of day
Morning
Afternoon
Dusk
Nighttime
Weather
Clear
Cloudy
Rainy
Foggy
Snowy
Road conditions
Dry
Wet
Frozen/snow-covered
UnpavedNo. ofComposition
accidents
rate
303
6.4%
2,865
60.9%
416
8.8%
1,120
23.8%
No. ofComposition
accidents
rate
2,879
61.2%
1,102
23.4%
601
12.8%
8
0.2%
114
2.4%
No. ofComposition
accidents
rate
3,762
80.0%
794
16.9%
148
3.1%
0
0.0%

Median strip andstructures
Central median strip
Median line
No median strip
General traffic
location

No. of Composition
accidents
rate
450
9.6%
2,262
48.1%
1,992
42.3%

Type of roadNo. ofComposition
accidents
rate
898
19.1%
847
18.0%
2,939
62.5%
20
0.4%

National highway
Principal local road
Public local road
Other

Topography
Urban Concentrated
area
population
Other
Non-urban area

0

0.0%

No. ofaccidents

Composition
rate

1,833

39.0%

1.578
1,293

33.5%
27.5%

Legal violation (primary party)

No. ofComposition
accidents
rate
Human factors (primary party)
3669
78.0%
Internal
2
0.0%
1
0.0%
2
0.0%

Ignoring traffic signal
Transit demarcations
Speeding violation
Violating a crossing or other bans
Failing to maintain inter-vehiculardistance
Violating a ban on changing lanes
Improper overtaking
Violation when turning right
Violation when turning left
Obstructing the right of way, etc.
Vehicle traveling down
an intersecting road
SafelyVehicle turning righttraveling
through an from opposite direction
intersection Pedestrian
Other
Obstructing pedestrians, etc.
Obstructing a crossing bicycle, etc.
Violating low-speed areas
Failing to come to a temporary stopin designated locations
Driving operation error
Failure to pay attention
Violation forwardofFailure to observe
mandatory surrounding trafficsafe
movement
driving
Failure to confirm safety
factors
Other
Other violation
Uninvestigable / no violation

Nodding off
Other type of
distracted driving
Droppedsomething, trying0
0.0%
to retrieve
something0
0.0%
Watching TV/0
0.0%
Failure to pay
navigation system,00.0%
attentionetc. (while driving)
2
0.0% Delay inforwardDistracted byExternal
52
1.1% noticing
searching for road,182
3.9%
guide sign, etc.
Distracted by1
0.0%
scenery, object, etc.
Distracted by other1
0.0%
vehicle/pedestrian19
0.4%
Other distraction0
0.0%
Failure to
Failed to confirm safety factors
0
0.0%
confirm safety Failed to adequately confirm
11
0.2%
factors
safety factors
Neglected to pay attention on
167
3.6%
Failure to
assumption that other party
observe22
0.5%
would yieldsurrounding
traffic
Other type of failure to observe117
2.5%
movement
surrounding traffic movement
Mistaken impression while
Error in
16
0.3%
driving (speed, road width,
judgment,
distance, etc.)
etc.
Failure ofAssumed other party would
418
8.9%
prediction
follow rules/yield
9
0.2%
Other type of failure of
9
0.2%
prediction
4
0.1%
Traffic environment
Misapplication of brakes/
DangerPrimary party
Secondary party
accelerator
Errorsandperception
No. of Composition No. of Composition
Applied brakes with insufficient
the like
speed
accidents
rate
accidents
rate
force, delay in braking
Operating
10km or less
433
9.2%
180
3.8% while
Braked suddenly
error
20km or less
637
13.5%
1178
25.0% operating
Improper steering
30km or less
877
18.6%
1231
26.2% vehicle
Steering while braking
40km or less
1745
37.1%
1434
30.5%
Other type of operating error
50km or less
735
15.6%
530
11.3% Uninvestigable / no human factors
60km or less
221
4.7%
128
2.7%
80km or less
44
0.9%
16
0.3% Age
Primary party
Secondary party
100km or less
2
0.0%
1
0.0%
No. ofCompositi
No. ofCompositi
Over 100km
1
0.0%
0
0.0%
accidents
on rateaccidents
on rateUninvestigable
90.2%
60.1% 6 and under
0
0.0%
0
0.0%
7 – 15
0
0.0%
0
0.0%
16-24
631
13.4%
416
8.8%
25-49
1760
37.4%
2438
51.8%
50-54
325
6.9%
382
8.1%
55-64
755
16.1%
758
16.1%
65-74
740
15.7%
532
11.3%
75 and older
493
10.5%
178
3.8%

Fig. 6. A sample of the detailed analysis sheets
Fig. 6 shows a sample of the detailed analysis sheets. For each corresponding accident pattern, the numberof accidents and composition rate for ITARDA’s major macro data items were aggregated into these sheets.
Using these sheets makes it possible to perform detailed analyses on the patterns.
(3) Example of using SIP traffic accident patterns
This section will show an example of using the SIP traffic accident patterns.
For the accident pattern involving a secondary party car moving straight ahead (Pattern No. CTC-26) andthe accident pattern involving a stopped secondary party car (Pattern No. CTC-25), which are both counted
among rear-end collision accidents at non-intersections, the trends in the number of accidents and thenumber of fatalities over the nine-year period from 2008 to 2016 were investigated.

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ITARDA 21th WorkShop ReportCarmovingstraight
ahead

Source: JNCAP footage from NASVA

Fatalities

50 people

No. of accidents
50,000 accidents

Drastic
reduction’08’09 ’10

’11

’12

’13

’14

’15

‘16

(2016: 22,275 accidents)

Versus oncoming car moving at
a fixed rate of speed (45km/h)

Fig. 7. Trends in the number of CTC-26 accidents and fatalities and NASVA footage

Source: JNCAP footage from NASVA

Fatalities

50 people

No. of accidents
100,000 accidents

Stoppedcar

Marginal
reduction’08’09 ’10

’11

’12

’13

’14

’15

‘16

(2016: 71,777 accidents)

Versus stopped car (30km/h)

Fig. 8. Trends in the number of CTC-25 accidents and fatalities and NASVA footage
As for the number of accidents in 2008, there were twice as many accidents involving rear-end collisions
with stopped vehicles, seeing as how there were approximately 40,000 CTC-26 accidents versus
approximately 80,000 CTC-25 accidents. However, for the number of accidents in 2016, CTC-25
accidents saw a marginal reduction to approximately 70,000 such accidents, whereas CTC-26 accidents
fell dramatically to approximately 20,000 accidents. Even in footage from tests assessing harm reduction
brakes from the Japan New Car Assessment Program (JNCAP) by the National Agency for Automotive
Safety & Victim’s Aid (NASVA), the test vehicles stopped without colliding with the vehicle moving
forward (balloon), but collided with the stopped vehicle (balloon).
According to a publication by the Ministry of Land, Infrastructure, Transport and Tourism, thedissemination rate for new vehicles equipped with harm reduction brakes had risen to 66.2% in 2016, up
from 4.3% in 2013. This expanded dissemination of harm reduction brakes is thought to have contributed
to the drop in the number of accidents when it comes to the dramatic fall in the number of CTC-26
accidents.
(4) Finalizing the SIP traffic accident patterns
Fig. 9 shows the changes in the coverage rate for SIP traffic accident patterns over the four-year period
lasting from 2013 to 2016. For 2013 there were 255 accident patterns that were set so as to achieve a
coverage rate of 80%. Yet due to the drop in the number of fatalities the coverage rate fell to 77% thefollowing year in 2014, and then down to 75.5% in the fourth year after this in 2016.

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ITARDA 21th WorkShop Report

80.0%
80.0%

77.0%

76.8%
75.5%

75.0%

Total
No. of全死者数
fatalities

5000
4000

No. offatalities
抽出死者数
selected

3000
2000
1000
0

H25
2013

H26
2014

H27
2015

H28
2016

Fig. 9. Changes in the coverage rate for SIP traffic accident patterns
The study on traffic accident patterns came to an end in the fifth year. The plan for this final year was toraise this coverage rate by means of eliminating and consolidating some traffic accident patterns together
and creating new ones, thereby making these patterns a shared national traffic accident database.

4. Database of D-Call Net® in-depth accident case studies(1) Overview of D-Call Net®
Fig. 10 shows an overview of D-Call Net®. HELPNET is an emergency notification system that serves as
an automatic traffic accident notification system which began operating in 2000. When an accident severe
enough to cause the airbags to deploy occurs, the GPS location information is sent from the vehicle to acall center, where a third-party notification that an accident has occurred is sent by an operator to a fire
department in which they request that an ambulance be dispatched to the scene.

9ITARDA 21th WorkShop Report

Fig. 10. Overview of D-Call Net®
D-Call Net®, which is automated emergency notification system, began operating in conjunction with
HELPNET on a trial basis in 2015. In addition to the GPS location information provided thus far, it also
obtains vehicle information such as the impact direction, the impact severity (∆V), whether or not seatbelts were being worn, and whether or not there were multiple collisions from the Event Data Recorder(EDR), and sends this to the call center. The call center uses an algorithm for estimating the fatality/serious
injury rate in order to calculate the probability that the occupants in the driver’s seat or passenger seats
were killed or seriously injured. The results are then displayed on a tablet at a base hospital for medical
helicopters together with information on the vehicle and the scene of the accident. Upon checking thetablet, doctors can then rush to the scene of the accident as needed.
An example of the tablet’s display is shown in Fig. 11.

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ITARDA 21th WorkShop Report

Elapsed time from when
accident occurs

Fatality/serious injury rate
calculation results

D-Call Net®
Elapsed time(minutes)

Accident ctrl. No.
Time notified
Vehicle model
Color

Toyota car/Crown
Black

Coordinates
Address

Accident situation

Shows whether passengers were
present/if they were wearing their
seatbelt (Front seat only)

Driver seat
Fatality / serious
injury rate

1-1 Ichiba chō, Chuo-ku, Chiba city,
Chib

Accident

Passenger
seat
Fatality / serious
injury rate

- Impact direction- Impact severity (V)
Calculated for a frontal impact at ΔV 25 km/h

This display is for the purpose of training*1: Air bags of driver seat and front passenger seat
*2: Side airbag, curtain shield airbag

Fig. 11. Tablet display screen from D-Call Net®

Fig. 12. Effect of D-Call Net® on shortening response times
Fig. 12 shows the effect D-Call Net® has on shortening response times. Before this system had been
adopted, it used to take five minutes from when the accident occurred to notify the fire department and 15
minutes until a request for a medical helicopter could be placed. But the results of a demonstration
experiment performed by the Japan Automobile Research Institute in 2012 showed that these times fell to
one minute to notify the fire department and three minutes until a medical helicopter request was placed
as a result of using D-Call Net®, thus confirming that it was possible to shorten response times by 17
minutes.

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ITARDA 21th WorkShop Report

Adoption at cooperating hospitals as ofJune 15, 201837 helicopters at 42 hospitals in 31 prefectures

Locations ofcooperating
hospitals

Fig. 13. D-Call Net® cooperating hospitals
Fig. 13 shows D-Call Net® cooperating hospitals as of June 15, 2018. The figure shows hospitals that arebases for medical helicopters that lend their cooperation by dispatching medical helicopters when theyreceive a D-Call Net® notification with the consent of prefectural government offices and fire department
headquarters. This cooperative structure includes 37 helicopters from 42 hospitals in 31 prefectures.
(2) Structure for D-Call Net® in-depth accident case studies
D-Call Net® databaseof accident cases
Surveys on accident scenes /vehicles, etc.
Surveys on medical helicopter mobilization status / passenger injuries

Joint research(medical helicopter
base hospitals)

31 prefectures
42 hospitals

D-Call Net®
Research
objectives- Select themes- Assess thealgorithm* Emergency Medical Network of Helicopter andHospital (HEM-Net), a specified NPO

Consortium

National ResearchInstitute of Police ScienceFig. 14. Study structure for D-Call Net® in-depth accident case studies

12

Research cooperation

D-Call Net®
cooperating hospitals

ITARDA 21th WorkShop Report

Fig. 14 shows the structure for performing studies related to D-Call Net® in-depth accident case studies.
Emergency Medical Network of Helicopter and Hospital (HEM-Net), a specified NPO, and ITARDA arepromoting such studies through joint research with the goal of studying matters like selecting themes for
D-Call Net® and assessing its algorithm for estimating injuries. Automobile manufacturers have also taken
part in the form of the D-Call Net® Consortium.

Fig. 15. Process for D-Call Net® in-depth accident case studies

Fig. 16. Locations where the accidents from D-Call Net® in-depth case studies occurred

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ITARDA 21th WorkShop Report

Fig. 15 shows the process for D-Call Net® in-depth accident case studies, while Fig. 16 shows the locations
where accidents from D-Call Net® in-depth case studies occurred that had been surveyed in accordancewith this process. To date, 30 in-depth case studies have been performed. The number of cases in which
medical helicopters were actually mobilized is extremely small, and the majority of these were cases
involving slight injuries. Since the in-depth case studies have only just begun, it was decided that theseslight injury accidents are also to be surveyed as well.
(3) Example of a D-Call Net® accident (Chiba Prefecture)

Fig. 17. Image of the scene of a D-Call Net® accident caseA case that occurred in Chiba Prefecture in January 2018 will be introduced, since it was the world’s firstsuccessful case involving D-Call Net®. Fig. 17 shows an image of the scene of the accident case, while
Table 2 shows an overview of said accident. Sometime after 15:00 on a cloudy day in January 2018 Party
A (a woman in her 40s driving a small-sized passenger car) veered into the oncoming traffic lane despiteof it being a left curve, where she caused a head-on collision with Party B (a man in his 50s driving a
medium-sized passenger car equipped with D-Call Net®) who was coming in the opposite direction.
Table 2. Overview of a sample D-Call Net® accident
Date ofaccident
Party A

Party B

Jan. 2018 3-4 p.m.

Weather: Cloudy

Small-sized passenger car
Female driver in her 40s (seatbelt ON)
Cerebral concussion, Collarbone fracture (RH),
Sternum fracture
ISS 12Passenger car (D-Call Net® equipped)
Male driver in his 50s (seatbelt ON)
Contusion over entire body
ISS 1

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ITARDA 21th WorkShop Report

Party A was wearing her seat belt, but still suffered injuries such as a cerebral concussion, a fractured
collarbone, and a fracture of the sternum (ISS =12). Party B was also wearing his seatbelt, and sufferedinjuries in the form of contusions over his entire body (ISS=1).

Fig. 18. Extent of the deformities to the vehicles from the D-Call Net® accident caseFig. 18 shows the extent of the deformations to the vehicles of Parties A and B. These deformations did
not extend to the inside of the passenger compartment of either vehicle.
Table 3. Rescue events and elapsed time from sample D-Call Net® accidents
Elapsed time
（min）
0
3
7
9
19
36
50
58

Rescue event
Collision
D-Call Net® alert
Coming back from another mission & landing
Taking off from the hospital
Arriving over the spot
Landing at the R/PStart of medical treatment by the onboard doctor
Taking off from the R/P
Arriving at the hospital
Start of fundamental medical treatment at ER

The rescue events and elapsed time for this accident case are shown in Table 3. At the time when theincident was confirmed on the tablet at the base hospital, the medical helicopter was already in flight foranother incident. So after returning to the hospital, it took off once again nine minutes after the accident
occurred. While it arrived over top the scene 19 minutes later, it took 36 minutes before it was able totouch down at a rendezvous point close to the scene. Immediately after touching down the onboard medics
began administering medical treatment. In this case, the helicopter faced the challenge of being flying on
hold for 17 minutes.
Ideally, rendezvous points should be set in place and the helicopter should land at the scenes of accidents(with it being crucial to ensure safety in advance) in order to eliminate this time spent in flying on hold.
(4) Assessing the D-Call Net® algorithm

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ITARDA 21th WorkShop Report

An assessment of the algorithm was conducted based on the 30 accident cases surveyed. There wereneither serious injury cases (true positive cases) nor under triage cases among these 30 cases, so only theover triage rate was assessed. The results are shown in Fig. 19.

Fig. 19. Assessing the algorithm via D-Call Net® in-depth accident case studies
With D-Call Net®, the algorithm for estimating injuries cannot currently use the age of the driver of thevehicle because the vehicle cannot detect the age, and so age 65 was entered as the default value. Whenthe driver age is set to a standard level of 65 years old, the current threshold value for deploying a medical
helicopter of 5% had an overtriage rate of 40%. This falls below the 50% global threshold value, and istherefore at a level that is not deemed to be problematic.
For the future, it will be necessary to accumulate more D-Call Net® in-depth accident case studies andassess the algorithm for this, including the undertriage rate.
5.

Conclusion
This paper introduced the SIP accident pattern database and the database of D-Call Net® in-depth accident

case studies. These can be summarized as follows.
(1) SIP accident patterns had been developed from the macro data and they should be used extensively in theform of a nationwide database.
(2) Cases in which medical helicopters were dispatched via D-Call Net® were studied. Such cases willcontinue to be accumulated to reveal the issues of the system and to evaluate the algorithm.
(3) Activities to adopt the algorithm for estimating injuries from D-Call Net® as an international standard will
be initiated, and considerations for performing new in-depth accident case studies on automated drivingcars will begin.

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