April 12, 2024 Research study report meeting

Project No. 2306B<Government/Organization Cooperation>>>Research on Efficient Accident Prevention
Measures Using Artificial Intelligence

Project Leader: Akinori Morimoto, Waseda University

１．Research Summary1

1.1 Project Members
PL

MemberPostdoctoral
Researcher

2

Akinori Morimoto

Professor, Faculty of Science and Engineering, Waseda University

Kazusei Kato

Professor, Faculty of Business and Commerce, Keio University

Rumiko Iwasada

Automobile Journalist

Akihiro Nakamura

Professor, Faculty of Economics, Chuo University

Hidekatsu Hamaoka

Professor, Graduate School of Engineering Science, Akita University

Yuka Nakagawa

Professor, Faculty of Law, Chukyo University / Attorney at Law, Nakagawa Law & Consulting

Nobuaki Takubo

Deputy Director, Research Department, Institute for Traffic Accident Research and Data Analysis

Daisuke Kamiya

Associate Professor, Department of Engineering, Faculty of Engineering, University of the Ryukyus

Kyoko Manaka

Associate Professor, Faculty of Economics, Ryutsu Keizai University

Osamu Furukawa

Advisor, International Association of Traffic and Safety Sciences
Senior Assistant to the President, Professional University of Electric Mobility Systems

Keidai Kurashina

Waseda University Graduate School, Masterʼs degree 2nd year

Yasushi Nishida
Research
Collaborators

Toyoki Kurihara

Waseda University, 4th year undergraduate

Observer : National Police Agency, Metropolitan Police Department, Ministry of Land, Infrastructure,
Transport and Tourism, CTI Engineering Co., Ltd., NEC Corporation, Informatix Inc.
Research on Efficient Accident Prevention Measures Using Artificial Intelligence2

1.2 Background, Purpose, and Expected Effects

3

l The 11th Traffic Safety Basic Plan "promotion of traffic
guidance and control that contributes to the deterrence oftraffic accidents" as a priority measure.
l Digital Agency was established in 2021. Standardizationand other measures are needed in the field of traffic safety.
l IATSS has published a Traffic Enforcement Handbook
since 2014 to provide ongoing information to those involvedin traffic enforcement.

Developed a fundamental model that will lead to progress in accident
deterrence countermeasures, utilizing AI, which is rapidly becoming
more widely used.
Development of general-purpose applications to enable deployment indiverse regions and accumulation of countermeasure effects to study
community-based countermeasure proposals
Research on Efficient Accident Prevention Measures Using Artificial Intelligence3

1.3 R&D Flow

4

FY2022

Outsourcing
Application
Development

Examination of system
specifications

App Development

System Development

Creating Input Data

Prototype System Construction
FY2023On-site verification
work

Trial Operation of Prototype Systems

Improving Input Data

System Improvements

App Improvements

Revision of System Specifications

Outsourcing
Application
Improvements

Establishment of this system
Final year（FY2024）
Expansion to other regions and revision of
the handbook
Research on Efficient Accident Prevention Measures Using Artificial Intelligence4

２．Overview of the Traffic Guidance
and Control Activity Support System5

2.1 Overview of the Traffic Guidance and Control Activity Support System
System6● Support the determination of control points by combining the "Accident Risk Prediction Model" andthe "Control Effectiveness Evaluation Model" and visualizing the model calculation results in a series.
Accident Risk Prediction
Model

User
Specify the area,
Enter the area and date and time date and time

for accident risk assessment .

Check the accident risk map

Function①

Accident Risk
Assessment

Enforcement EffectivenessEvaluation Model
Accident Risk
Assessment
Results

Visualization of accident risk
assessment results

Function②
Accident risk
assessment at the timeof traffic control

Confirmation of the
proposed enforcement

Designation of traffic
control points

Consideration of
Enforcement Proposals
based on feasibility

Determination of
enforcement points

Analyze the effectivenessof enforcement

function③

Review of enforcement points

Confirmation ofeffectiveness throughenforcement

Analyze the optimal
enforcement location

Accident risk predictionof enforcement and nonimplementation
Visualization of accident risks
with/without traffic control

Research on Efficient Accident Prevention Measures Using Artificial Intelligence6

2.2 Image of the Utilization of the Traffic Control Activity Support System

7● STEP①：Select the jurisdictional area where traffic control activities will be
conducted①Select the target jurisdiction area

②Refer to the current accident risk
Select the jurisdictional area you
wish to view.

③Set mandatory activity locations④Enter the weather for the date

⑤Simulation⑥Confirmation of prediction results

Research on Efficient Accident Prevention Measures Using Artificial Intelligence7

2.2 Image of the use of the enforcement activity support system

8● STEP②：View the current accident risk in the selected jurisdiction
StreetActivity
Results①Select the target jurisdiction area

②Refer to the current accident risk

③Set mandatory activity locations④Enter the weather for the date

⑤simulation⑥Confirmation of prediction results

Darker areas are areas with a higher risk of accidents

Research on Efficient Accident Prevention Measures Using Artificial Intelligence8

2.2 Image of the Utilization of the Traffic Control Activity Support System

9● STEP③：Select locations if traffic control locations are pre-determined①Select the target jurisdiction area

②Refer to the current accident risk

③Set mandatory activity locations④Enter the weather for the date

⑤Simulation⑥Confirmation of prediction results

Forecast
conditions
Research on Efficient Accident Prevention Measures Using Artificial Intelligence9

2.2 Image of the Utilization of the Traffic Control Activity Support System

10● STEP④：Enter the weather conditions on the day of the enforcement
activity with reference to the weather forecast, etc.
①Select the target jurisdiction area

②Refer to the current accident risk

③Set mandatory activity locations④Enter the weather for the forecast date

Weather data entry
Projected Date:
Precipitation:
Snowfall:Daylight hours:
Maximum wind speed:⑤Simulation⑥Confirmation of prediction results

Traffic AmountPrediction
Data
Research on Efficient Accident Prevention Measures Using Artificial Intelligence10

2.2 Image of the Utilization of the Traffic Control Activity Support System

11● STEP⑤： Calculate the effect of conducting control activities within thejurisdictional area
Model
Prediction
Results①Select the target jurisdiction area

②Refer to the current accident risk

③Set mandatory activity locations

Optimization simulation results
Optimization simulation results
Optimization simulation results④Enter the weather for the date

⑤Simulation⑥Confirmation of prediction results

Research on Efficient Accident Prevention Measures Using Artificial Intelligence11

2.2 Image of the Utilization of the Traffic Control Activity Support System

12● STEP⑥: When the location of the enforcement activity is specified, theeffectiveness of the enforcement in that case is displayed①Select the target jurisdiction area

②Refer to the current accident risk

③Set mandatory activity locations△ □, 2023
4 hours in total
151 mesh 3 hours
172 mesh 1 hour (required)④Enter the weather for the date

⑤Simulation⑥Confirmation of prediction results● The prototype support system was verified in actual road traffic, and issues related to theconvenience and adaptability of the system to practical work were identified.

● The 2022 model has been improved and the basic model and system have been improved.
Research on Efficient Accident Prevention Measures Using Artificial Intelligence12

３．Improvement of the Basic Model

133.1 Model Overview

142022 Traffic Guidance and Enforcement Support Model①Accident Risk Prediction Model
Method：Multiple Regression Bayes
Predict accident risk in units of 50 meters
per day
Objective
Variables②Enforcement Activity Proposal
Model
Method：Deep Q-Network
Proposing enforcement activities tomaximize the amount of reduction in
accident risk value

Accident risk value
Enforcement activity
value[h]Explanatory
variables

VirtualEnforcement Activities

Number of intersections

Accident risk after
virtual activities

Risk of accidents in theabsence of activity

Top speed[km]Iterative learning

Traffic amount

Output data

Output data

Accident Risk Prediction
Mesh

Predicted
value

Optimal location and time forstreet activities
mesh

Event time[h]

No.1

0.2

No.4

2

…

…

No.10

1

No.16

0.7

Research on Efficient Accident Prevention Measures Using Artificial Intelligence14

3.2 Improvements

15

l Improvements were made to the model for proposed enforcement activities. Refinements were made to themodel.
②Enforcement Activity Proposal Model

Method：Deep Q-Network
Proposing enforcement activities tomaximize the amount of reductionin accident risk value

Virtual Enforcement
Activities
Risk of accidents

in the absence ofactivity
Iterative learning

Accident risk
after virtual
activities

Output data
Optimal location and time forstreet activities
Mesh

Event time[h]

No.4

2

No.10

1

Focus for improvement
Ø Concentration of calculation results in one placewhen calculating activities for multiple hours→Discrepancy between the correct value and thecalculated value of the modelØ The model was difficult to converge

Implemented 3 points
Ø Review of Reward→In addition to the reduction of accident risk value
reward based on traffic amount was added
Ø Review hyper parameters→ Perform sensitivity analysis of parameters
Ø Review of measures→ Change the rules for conducting the study.

Research on Efficient Accident Prevention Measures Using Artificial Intelligence15

3.3 Improvement results

16（1）Review of Reward● Points are exogenously added to traffic concentration points such as intersections between main roads andhighways.

Location-specific reward
Main road：+0.5

Intersections between arterial roads：+0.5Road occupancy 0%：-1.0

Outside the jurisdiction of the target police
station：-1.0

So far

Before control☆：Control Activity Locations

After the change

After control

Before control

After control☆：Control Activity Locations

Research on Efficient Accident Prevention Measures Using Artificial Intelligence16

Review of control activity values

17■Distribution of the amount of accident risk reduction for the entire area during 1 hour of activity in eachmesh
Ø Calculation of ripple effects that takes into account "arterial road connectivity" instead of "being located in
the center of the target area" as in the past
Ø The effectiveness of the activity could be calculated based on the number of witnesses (traffic) to theactivity.
Highly effective in reducing
accidents

Low accident reduction
effectExisting modelResearch on Efficient Accident Prevention Measures Using Artificial Intelligence

Review of ripple effects

173.3 Improvement Results

18(2) Review of hyperparametersl Appropriate values are set by sensitivity analysis.

Experience Regeneration
Removing the correlation of experiences used when
updating parameters and stabilizing learning

Agent (the main body that learns)
The subject performing the action
nb_steps_warmupWarm-up period without
learning

limit

Maximum number of training
sessions to recordbatch_size

Batch size

ignore_episode_boundaries

Use the experience of anotherepisode or nottarget_model_updateHow to update the target
model

enable_dueling_network

Conduct Dueling Network
Insert or not

Neural network

PolicyEstimation of an approximation of theaction value function (Q value)

Number of
Nodes

Policy when choosing an action
εRandom Action Choice
Probability

Number of intermediate
layersResearch on Efficient Accident Prevention Measures Using Artificial Intelligence18

3.3 Improvement Results

19(3) Review of measuresl The learning rule is changed so that the calculation target is changed according to the number of activity
locations desired by the user.
Users enter the number of Event Time・Activity Locations（Undecided is also possible）

So farSince the strategy is to select the mesh with thegreatest effect, Activities of multiple hours at
one location only are likely to be recommended

After the change
It is possible to change the number ofactivities to be recommended according to thenumber of activities entered by the user.

Activity Locations = 1

Activity Locations = 2

Activity Locations = 1

Activity Locations = 2

Activity Locations = 3

Activity Locations = 4

Activity Locations = 3

Activity Locations = 4

Research on Efficient Accident Prevention Measures Using Artificial Intelligence19

4. Trial operation of the traffic guidancecontrol activity support system20

4.1 Summary of the Trial Operation System

21(1) System Overview①System Overview Diagram"Traffic Guidance Control Activity Support System"
developed during trial operation
Control Officer
conditions①Database Server

Static data

②Rating system
Accident Risk
Assessment
Model

Control Effect E
valuation Model

Research Institutes
conditions

Accident Risk
Assessment

View analysis results
Database Updates

Analytic data output

Dynamic Data

Accident risk
assessment at thetime of control
implementation③Drawing system
Accident risk
prediction forcontrol
implementation andnonimplementation

Analyze theappropriate
control position

Analyze impact
of control

System Administrator

Research on Efficient Accident Prevention Measures Using Artificial Intelligence21

4.1 Summary of the Trial Operation System

22(1) System Overview(2) Procedure for confirming the effectiveness of control implementation

Visualize analysis results
・Accident Risk Map
・Locations of support

Terms andConditions Input

ControleffectSpecifiedcontrolsupportsystemcontrolsupportsystemControl Officer

Control Officer

Control
point
specified

Control
enforcement

Control Officer
Re-specify
control point②Analytic Data Extraction Procedure
Use for research activities

Terms andConditions Input

Analytic data output
controlsupportsystemResearch Institutes

Research on Efficient Accident Prevention Measures Using Artificial Intelligence22

4.1 Summary of the Trial Operation System

23(1) Purpose of implementation
Ø A prototype system was built to develop a support system for traffic guidance and enforcement activities.
Ø With the cooperation of the Shinjuku Office, a prototype system will be operated on a trial basis to verify theeffectiveness of the system implementation, as well as to improve the model and revise the system specifications.

(2) Location of implementation
Ø Shinjuku Police Station
Ø See next page for areas subject to trial operation.

Trial operation(3) Implementation period
Ø Monday, October 2, 2023 - Tuesday, October 31, 2023
Ø Equipment installation and operation training will be held on Friday, September 29, and trial operation will begin on
Monday, October 2.
Ø Equipment was removed on Wednesday, November 1, and a hearing survey was conducted in conjunction withthe equipment.

Research on Efficient Accident Prevention Measures Using Artificial Intelligence23

4.2 Outline of Trial Operation(4) Area subject to trial operation

Research on Efficient Accident Prevention Measures Using Artificial Intelligence24

2 km square (1,600 mesh)24

4.2 Outline of Trial Operation

25(5) Equipment used
No

Equipment

Remarks

1

Terminal for operating theenforcement activity support
system

1 laptop2

Dedicated terminal for weather
forecast checking

1 laptop (terminal owned by the Metropolitan Police Department)

3

Simplified Operation Manual

See Appendix

Installation image
Research on Efficient Accident Prevention Measures Using Artificial Intelligence

Operation manual(Simplified Operation Manual)25

4.3 Verification Items

26

Effectivenessverification

Effects of
SystemIntroduction

Accident
Reductioneffectivenessverification【Direct Effects】
Number of accidents【Indirect Effects】
Number of arrests
Travel speed
Abrupt behavior

Model
Improvements

Accident risk prediction
Verification of accuracy

Evaluation of street activity
effectiveness
Verification of accuracyØ Accident risk
Prediction results
Consistency

Ø Proposed point
and activity area
consistency

Research on Efficient Accident Prevention Measures Using Artificial Intelligence

Systemrevision of
specifications

Convenience
Verification
Ø Systemprocessing timeØ operability

26

4.4 Verification of Effectiveness27(1) Accident Reduction Effectiveness Verification(a) Number of accidents
The accident data currently available for 2023 are preliminary figures, and the number of accidents in 2023 issignificantly smaller than that of 2022.
Below is an image of the analysis using the preliminary data.

Incidents during trial operation: 18
2022[Cases]
Month

1

2

3

4

5

6

7

8

9

10

11

12

People-tovehicle

5

6

8

6

9

11

5

7

12

6

10

9

Vehicle-tovehicle

19

13

20

16

16

17

31

31

19

23

32

26

Vehicle single

1

1

3

0

0

1

0

2

4

2

1

1

sum

25

20

31

22

25

29

36

40

35

31

43

36

[Cases]

2023
Month

1

2

3

4

5

6

7

8

9

10

11

12

People-tovehicle

2

10

8

6

5

8

6

3

2

６

-

-

Vehicle-tovehicle

15

13

21

14

11

13

13

11

16

11

-

-

Vehicle single

0

1

1

0

1

0

1

0

1

1

-

-

sum

17

24

30

20

17

21

20

14

19

18

-

-

Research on Efficient Accident Prevention Measures Using Artificial Intelligence4.4 Verification of Effectiveness28(1) Accident Reduction Effectiveness Verification(a) Number of accidents
There is a strong possibility that accidents along Meiji-street and Ome-kaido are on an
attenuating trend.

2022.10

Research on Efficient Accident Prevention Measures Using Artificial Intelligence2023.1028

4.4 Verification of Effectiveness29* All near-miss incidents in this(1) Accident Reduction Effectiveness Verification
document are calculated by multiplying
the magnification factor.
(b) Sudden behavior (ETC2.0 probe information)
Total number of cases multiplied by expansion factor for each month2023 data is analyzed from the two months of September and October as preliminary data.

Month

1

2

3

4

5

6

2022

,1953,941,1926,462

1,110,166

1,043,251

1,048,242

1,062,208

2023

-

-

-

-

-

-

Month

7

8

9

10

11

12

2022

1,100,850

1,079,846

1,067,921

1,100,398,1,688,902

1,107,623

2023

-

-

1,039,290

1,072,688--•

Total numberof cases••Front and rear acceleration 0.25G or lessSuccessful Matching
Number of data that satisfies thetarget 2064mesh

Number of data satisfying the above
Research on Efficient Accident Prevention Measures Using Artificial Intelligence

4

Magnification coefficient32
1
02022/12022/22022/32022/42022/52022/62022/72022/82022/92022/102022/112022/122023/12023/22023/32023/42023/52023/62023/72023/82023/92023/10ETC Probe Form 1-4

29

4.4 Verification of Effectiveness30(1) Accident Reduction Effectiveness Verification(b) Sudden behavior (ETC2.0 probe information)
Comparison of the number of near-misses (2022.10 number of near-misses , 2023.10 number of nearmisses)

There is no change in the tendency for incidents to occur on major arterial roads suchas Koshu-kaido and Yamate-street.

2022.10

Research on Efficient Accident Prevention Measures Using Artificial Intelligence2023.1030

4.4 Verification of Effectiveness31(1) Accident Reduction Effectiveness Verification(b) Sudden behavior (ETC2.0 probe information)
Difference in the number of incidents (2022.10 near-misses - 2023.10 near-misses)
The number of near-misses on Meiji-dori and Koshu-kaido streets near Shinjuku Gyoen has been continuously
decreasing.

Difference in thenumber of nearmisses (cases)

Increase

Decrease

Research on Efficient Accident Prevention Measures Using Artificial Intelligence31

4.4 Verification of Effectiveness32(1) Accident Reduction Effectiveness Verificationc) Differential analysis of differences in the number of traffic accidents and near-misses
Analysis Image• The period covered was analyzed in two ways: on all days and after the 10th of each month.
For all days

After 10 days

→Data for all days of the period covered is used.

→The following is a list of the data for each month after
the 10th day of the month.
Used data only

1st

1st

30/31

2022.092022.092022.102022.10
DID

10th

30/31

DID

2023.092023.092023.102023.10

Research on Efficient Accident Prevention Measures Using Artificial Intelligence

Time period to beanalyzed32

4.4 Verification of Effectiveness33(1) Accident Reduction Effectiveness Verificationc) Differential analysis of differences in the number of traffic accidents and near-misses
Differential analysis of September and October differences for 2022 & 2023 (for all days)•

The DID analysis for all days did not show a decrease in both the number of traffic accidents and near-misses.

Number of near-misses

Number of traffic accidents
1,150,000

35
31

30
20

19

Number of near-misses [cases]

Number of accidents [cases]

40

18

10
0
2022.09 2022.10

2023.09 2023.10

1,100,000

1,100,398
1,072,688

1,067,921

1,050,000

1,039,290

1,000,000
2022.09 2022.10

2023.09 2023.10[Cases]

September

October

Oct-Sep[Cases]

September

October

Oct-Sep

2022

35

31

-4

2022

1,067,921

1,100,398+32,477

2023

19

18

-1

2023

1,039,290

1,072,688+33,398

DID+3 (increase)

DID

+921
(increase)

Research on Efficient Accident Prevention Measures Using Artificial Intelligence33

4.4 Verification of effectiveness34(1) Verification of accident reduction effectsc) Differential analysis of traffic accidents
Differential analysis for September and October 2022 & 2023（Applicable after the 10th of each month）•

In the DID analysis of the 10th day onwards, there was a downward trend in the number of nearmiss incidents.
Number of near-miss incidents

Number of traffic accidents
800,000

785,237

24
22

20

10

10

12

0
2022.09 2022.10

2023.09 2023.10

Number of near-misses [cases]

Number of accidents [cases]

30

770,513750,000

753,336
739,064700,000

650,000
2022.09 2022.10
September
October

2023.09 2023.10
Oct-Sep[Cases]

September

October

Oct-Sep[Cases]

2022

24

22

-2

2022

753,336785,237

31,901

2023

10

12

+2

2023

739,064770,51331,449DID+4(Increase)

DID

452(Decrease)

Research on Efficient Accident Prevention Measures Using Artificial Intelligence34

4.4 Verification of Effectiveness35(2) Accuracy verification of street activity effectiveness evaluation(a) Street activity implementation records

Research on Efficient Accident Prevention Measures Using Artificial Intelligence35

4.4 Verification of Effectiveness36(2) Accuracy verification of street activity effectiveness evaluation(b) Verification of consistency between proposed and implemented locationsl During the trial operation, the system proposed 12 locations. 4 of the 12 locations have a 100%
implementation rate, and 6 have an implementation rate of 80% or higher.
l Few proposals were made by the system along Koshu-kaido road and were implemented in non-proposed
areas at the discretion of the police officers.
■Percentage of implementation at proposed locations

Research on Efficient Accident Prevention Measures Using Artificial Intelligence36

4.4 Verification of effectiveness37(3) Verification of conveniencea) Processing time of the system● The time required for the simulation is between 69 minutes ~ 75 minutes, and the result is that the blur is small.
● 71.9 minutes on average、The maximum frequency 72 minutes, and the standard deviation 1.35 minutes.
76
75

所要時間（分）

74

73
72
71
70
69

68
67

2023/10/31

2023/10/30

2023/10/29

2023/10/28

2023/10/27

2023/10/26

2023/10/25

2023/10/24

2023/10/23

2023/10/22

2023/10/21

2023/10/20

2023/10/19

2023/10/18

2023/10/17

2023/10/16

2023/10/15

2023/10/14

2023/10/13

2023/10/12

2023/10/11

2023/10/10

2023/10/9

2023/10/8

2023/10/7

2023/10/6

2023/10/5

2023/10/4

2023/10/3

2023/10/2

66

試行運用日
12
10

日数（日）

Statistics

time

Average

71.9 minMode

72 minMaximum Value

75 minminimum value

69 minStandard deviation

1.35 minNumber of trial days

24 days

10
7

8
6

3

4
2

1

1

1

1

0
69分

70分

71分

72分

73分

74分

75分

Research on Efficient Accident Prevention Measures Using Artificial Intelligence37

4.4 Verification of effectiveness38(3) Verification of convenienceb) User opinion● Regarding the overall picture and direction of the system, the results showed a high level of satisfaction among users (police
officers).
● Collected opinions and suggestions for improvements to the operability of the system and the proposed parts.
Convenienceand operability〇•

The ease of viewing and basic operability of the screen were satisfactory

×

•
•
•

Regarding the display content, request to add the address and intersection name of the recommended place
Request to reduce the number of operations, such as automatically deleting pop-outs that have already been entered.
Request frequently used tabs to be pinned all the time.

〇

Opinion on the model

×

other

Impre
ssions-About the proposed location•
A major intersection was proposed, and it was generally in agreement with the police's perception• Locations where they do not normally work were also proposed, and it was possible to prevent police officers from getting into arut.
• The proposed location changed between the beginning and the end, and I felt that the street activities had an effectOpinion on the proposed location•
Since the history of accidents at the proposed site is unknown, details such as where to stand will be determined at the site.
•
A point where there is no place of activity was proposed• When the same location was proposed every day, the sense of satisfaction with the proposal decreased.
• Couldn't find a proposal for a place to do the activity.
Desire for additional features
It is easy to use if the proposal is for a certain period of time, such as one week.
It would be desirable if there was a function to improve the efficiency of activities, such as obtaining the same effect with a small numberof places→At the time of verification, we proposed it for 4 locations per day, but the same effect can be obtained at 3 locations• It is a reference for determining the layout of the street, and I would like to continue using it• Felt that it would be easier to use if it could be linked with other police systems.

Research on Efficient Accident Prevention Measures Using Artificial Intelligence38

5. How the system is madeavailable to the public39

5.1 Methods of releasing the Traffic Guidance and Enforcement Activity
Support System

40

Since FY2022, we have been working on building a system under the "Research on
Efficient Accident Prevention Measures Using Artificial Intelligence" project. This system
will be owned by the International Association of Traffic Safety Sciences, with theassumption that it will be open to the public.
Since the system construction will be completed this fiscal year, we examined the method
for opening the system to the public starting next fiscal year, based on the following
requirements.
〈Requirements〉• The method of acquisition must be publicly available.
• The system should be open to anyone (organization) interested in the system.
• A record must be kept of when and by whom it was acquired.
• The impersonation of the acquirer (organization) is not likely to occur.

〈Publishing Method〉• The system will be placed on a DVD or other medium and mailed to the client.
• The cost of mailing is paid by the acquirer.
• Application will be obtained on the IATSS website.
• Applicants need to fill in the name of the organization, the name of the individual, andthe address.
Research on Efficient Accident Prevention Measures Using Artificial Intelligence40

5.1 Methods of releasing the Traffic Guidance and Enforcement Activity
Support System

41〈Newly added〉

Traffic Guidance and Enforcement
Activity Support System

Research on Efficient Accident Prevention Measures Using Artificial Intelligence41

5.1 Methods of Publishing the Traffic Guidance and Enforcement Activity

42<Newly built page>.

■Street activity support system using artificial intelligence Sending application

Purpose of system use [Required].

Research on Efficient Accident Prevention Measures Using Artificial Intelligence42

5.1 Methods of Publishing the Traffic Guidance and Enforcement Activity
Support System

43〈Distribution disk〉<System data image stored in the system

Research on Efficient Accident Prevention Measures Using Artificial Intelligence43

5.2 Toward the operation of the Traffic Guidance Control Activity Support
SystemOperational image of the public system
IATSS model to be published
Traffic Guidance Control Activity
Support System

Prefectural Police
External data
SystemAdministrator

44

Research Institutes andother development institutions

Control Activity Support System
Accident Risk Evaluation Model

DatasetControl effectEvaluation Models
improvement

Database Updates

Static data

Accident Risk
evaluation

Dynamic Data

Accident Risk
Evaluation with
controlObtaining Open Source

Control Officer
Input

input

Drawing system

View analysis results
outputAccident Risk
Prediction
with/without
control

Suitable Control
Location
Analyse

Systemimprovement, etc.

Control
EffectAnalysisMap Information System（Opensoftware or commercial software
such as QGIS ）Research on Efficient Accident Prevention Measures Using Artificial Intelligence44