5

Data Connection and Use to Achieve
Society 5.0

Building and Designing a
Geographic Architecture(Overview)
Raita Hiraoka, Hiroaki Kimura (Cabinet Office)in 2019 we began the development of "cross-field" and "field-

1

by-field" data coordination infrastructures, as well as the

Backgroundconstruction and testing of an architecture based on aframework for deepening the views and understanding among

In the society that we should aim for as Society 5.0, newbusiness models will be born through the advanced fusion of
cyberspace and physical space, and innovations born from theutilization of big data and AI will create new value in various
fields, leading to a paradigm shift in the economic and social
system. In order to realize such innovations, it is important tocoordinate data not only by field, as has been the case in thepast, but also across fields.

In SIP "Big-data and AI-enabled Cyberspace Technologies,"

all stakeholders, ensuring interoperability among theseinfrastructures,anddeveloping

technologies

andstandardization that enable mutual coordination andcooperation (hereinafter referred to as the Society 5.0
Reference Architecture; Fig.1). This initiative is being
expanded to smart cities, geographic information-related
fields (automated driving, agriculture, disaster prevention, andinfrastructure), and personal-related fields. In the SIP-adus(Cross-ministerial Strategic Innovation Promotion Program(SIP) Automated Driving for Universal Services), we havebegun constructing an architecture for geographic data.(1)Organizations

Implementing agencies, councils,
governing bodies, etc.

Businesses

Services, solutions, agreements
and contracts ecosystem, FOTs, etc.

FunctionsIndividual functions to realize
services, solutions, etc.

Data

Datasets, vocabularies/
codes, data catalogs, etc.

Data coordination

Data collection, data integration, data
cleansing, IoT device management, etc.

AssetsSensors, actuator hardware,
networks, etc.

2

Activities in SIP-adus

In the SIP-adus, we put together an architecture ofautomated driving fields related to geographic data (hereinafterreferred to as the Automated Driving Architecture) (Fig.2)
based on the Society 5.0 Reference Architecture.
Since the first phase of SIP-adus , we have been working on
the generation and distribution of high precision 3D maps anddynamic road traffic environment data necessary for automatedetc.

Laws, regulations, etc.

eld
s,

Rules

Fi

Vision, scope, etc.

Semantic axis

Cybersecurity, authentication

Strategy and policy

Temporal and spatial axes

driving, but cost reduction is a major issue for their widespreaduse. On the other hand, it is highly possible to reduce costs by
utilizing 3D point group data and vehicle probe data, which are

Fig.1: Society 5.0 Reference Architecture

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Building and Designing a Geographic Architecture (Overview)

Functional
layer

Driving
assistance

Automated
driving
Emergency
vehicle assistanceTransportation
plan

Transportation
management

Bus location

Data
layer

Vehicletrajectory

Asset
layer

Radar

Images ofsurroundings
ABS activation

Baggagedelivery
Travel plan

Vehicle data

ETC gate

Road regulations

Traffic signal
light color

Bus route map

Railroadroute map

3D point groups

Traffic lights

CameraLiDAR

Transfer guideRoad management

2D mapGNSS

and other functions that promote matching of needs and seeds.

Signal controls
Congestion
forecast

Navigation

G sensor

DSRC device
Traffic
counter

Portal functions

Update map

Pedestrian
positionthe creation of new services by enabling diverse users to utilize
road traffic environment data for various services through an
open portal web site called "MD communet" (https://info.adus-

Railway
car location
Baggagelocationarch.com/) (Fig.4), the portal website was established and madeMeteorologicalinformation

available in October 2020. After that, efforts were made to

Taxi stand

expand the number of users, chiefly through data providers,
Dynamic

Smartphone

ElectronictagMeteorologicalsatellite

MMS
VehiclesThis portal supports the promotion of data distribution andMobility
supportSemidynamic

and in April 2021, the website was made available to the generalpublic with 11 participating companies and organizations.

Semi-static
StaticTraffic infrastructure

People, otherFig.2: Automated Driving Architecture

the data layer for generating such road traffic environment data,
in other fields as well.
Business
matching

We believe that the problem is an imbalance between data
users who do not know that data exists and data providers who

Datasearchdo not know the needs for their data. To address this, we

Needs/seeds
registration

decided to work on the construction of a portal site with datacatalog and data search functions.
In addition, since it is important to create examples of datautilization using this portal site, we held an "App Contest to Solve
Issues Related to Transportation in Kyoto, a Tourist City" tobroaden the scope of data utilization efforts and to gather
opinions on how to improve the usability of the portal site.
In addition, we promoted the broader utilization ofgeographic data in collaboration with other SIP-adus projects,
such as the use of probe vehicle data to improve logistics
efficiency and support road management operations.
In 2020, we surveyed and analyzed examples of mobilityrelated data utilization in Japan and abroad, and based on the
results of interviews with data providers, service providers, datacollectors, and academic experts, we held discussions at a study
group consisting of experts and compiled draft guidelines fordata handling in the mobility field as well as a proposal for
public-private data coordination.

Technical
supportUse
cases

Communications

Servicedevelopment
supportFig.3: MD communet, a data matching website

To promote the use of the portal site, we have improved theuser interface by incorporating the opinions of "MD communet"
users, and have otherwise worked to make it a portal site that iseasier to understand. We have held various events to enliven
matching activities, including an "Idea Creation Event" inNovember 2021, a "Business Matching Event (MD com Match)"
in March 2022, and an "Idea Creation Workshop" in March
2022, and have worked to create many examples of portal
utilization. As of the end of July 2022, 43 companies andorganizations had registered to participate, and 7,316 catalog
data items had been posted. [See Section 5 1) for more details]
When we look at trends overseas, we see that the EU made2.1. Construction of the Portal for Road Traffic Environment Data "MD communet®"

it mandatory for member states to establish National AccessIn constructing the road traffic environment data portal,use of mobility data, and stipulated that data on transportation

while assuming services in the functional layer based on theautomated driving architecture, we aimed to aggregate,visualize, and catalog data layers such as road traffic environment
data to serve as a trigger for matching data providers and data
users, thereby enabling one-stop browsing. (Fig.3) In addition,
the system has an advanced data search function that uses not
only keywords but also area and purpose of use as keys, searches
for similar data using machine learning, a suggestion function,

Points (NAPs) in 2017 in order to encourage the collection andand travel must be provided through NAPs by the end of 2030.
In Germany, the Federal Ministry for Digital and Transport
started operating a mobility data platform called Mobilithek inJuly 2022, which will serve as an NAP. Like "MD communet," itis a portal site with data cataloging and search functions.
As described above, efforts to promote the utilization oftransportation data are becoming more active overseas, and it isnecessary to develop "MD communet" in Japan. Even after the

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5 Data Connection and Use to Achieve Society 5.0﻿
Building and Designing a Geographic Architecture (Overview)completion of SIP, NTT DATA will operate "MD communet,"and plans to improve usability, enhance services and content,
increase awareness, and create usage examples.

Recommends less crowded spots by estimating
the sightseeing spots around the pre-entered
destination based on congestion statistics data
(API).

Nearbyrecommendations

Destination

Calculates congestion based oncongestion statistics data

Facilities that offer points, goshuin stamp, and otherbenefits in Teburan app

Fig.4: MD communet website

Fig.5: KYOTO Raku Mobi Contest

2.2. Holding of App Contests

The SIP-adus Award Winning Apps

In order to create use cases that help to solve transportationrelated issues by utilizing road traffic environment data andmobility-related data, we held a contest (the first "App Contest
to Solve Tourism and Transportation Issues," nicknamed the"KYOTO Raku Mobi Contest") in October 2020 to solicit apps
and ideas for solving tourism and transportation-related issues
faced by Kyoto, a world-class tourist city. The contest was
conducted with the cooperation of the Kyoto City Transportation
Bureau and other operators, and included data on stations,stops, routes, timetables, and fares for public transportation
systems such as buses and railroads; data on temporary luggage
storage and delivery services and stores for logistics; congestion
forecasts for sightseeing facilities; information on sightseeing
spots; center guide service API, and congestion statistics data.
Although there were excellent applications (Fig.5) and ideas in
the first KYOTO Raku Mobi Contest, they were not put into
practical use due to the fact that the data could not be distributed
continuously after the contest. However, the contest did
generate momentum to open up bus information, especially in
the Kyoto City Transportation Bureau, and expectations for thesecond contest were raised. Municipal and private bus
companies in Kyoto City will create bus information in GTFSJP format and register it in "MD communet." Information suchas probe vehicle data from logistics companies and tire
manufacturers will also be provided on "MD communet." The2.3. Development of Support Systems for Local Automated Driving Services
We clarified common issues in socially implementing
automated driving services in rural areas, and developed acommon reservation and operation management system called"Mobisuke" (location management of vehicle positions, safety
monitoring using camera images inside and outside the vehicle,
reservation

management,

boarding/exiting

information

management, etc.) with an interface that is easy to use foreveryone, including the elderly. In developing this system, we
conducted field operational tests (FOTs) in collaboration with
the initiatives of Higashiomi City, Shiga Prefecture; Miyama
City, Fukuoka Prefecture; and Takahata Town, Yamagata
Prefecture, in addition to Kamikoani Village, Akita Prefecture,and Iinan Town, Shimane Prefecture, which are promotingsocial implementation of automated driving services under the
SIP-adus, to improve functions based on feedback from service
providers and users.
In the future, while providing "Mobisuke" as an inexpensive
and convenient system, we aim to commercialize support
services, including the system, in the interests of strengthening
interregional cooperation and with a view to utilizing thecommunication function of the road traffic environment data
portal.

Issues" is scheduled to be held in December 2022. Through this2.4. Improving Logistics Efficiency Using Probe Vehicle Data

contest, we aim to raise awareness of the road traffic environment

With the aim of improving cargo wait times in transportation

second "App Contest to Solve Tourism and Transportation

data portal and obtain feedback from data users who entered
the contest, which we will use to make improvements to theportal. [See Section 5 2) for more details]

operations, improving work times for checking daily inspection
items based on vehicle and probe data, improving operation
schedules and control by real-time understanding of load
weight and tire information, and making other improvements
demanded of the logistics industry, we clarified an architecture

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5 Data Connection and Use to Achieve Society 5.0﻿
Building and Designing a Geographic Architecture (Overview)for future data coordination/utilization in which data obtained[References]

from logistics trucks is utilized to improve logistics efficiency.

(1) The Second Phase of SIP-Automated Driving for Universal Services Research andDevelopment Plan, https://www8.cao.go.jp/cstp/english/04_autodrive_rdplan.
pdf, (accessed 2022.06.30)

In order to improve the wait times for cargo, we obtained
and analyzed the data from the carriers' actual vehicles. Bothcarriers and consignor companies shared their awareness of thecircumstances behind the occurrence of wait times for cargo.
We analyzed the causes of wait times and considered measures[Contacts]
Secretariat of Science, Technology and Innovation Policy Cabinet Office, 1-6-1
Nagata-cho, Chiyoda-ku, Tokyo 100-8914, 03-6257-1334 Contact: Raita Hiraoka,
Hiroaki Kimura

for improvement. (Fig.6)
Vehicle data

Shippers

Motion management data

Trucking companies

Fig.6: Schematic of vehicle and motion management data collection and sharing

To improve work times for checking daily inspection items,
truck manufacturers' own vehicles were used to check the statusof trucks for daily inspection items remotely by identifying
event data that contributes to the checking of daily inspection
items from vehicle and probe data. In addition, we proposed
standardizations of data formats for cooperative areas of vehicle
information such as probe data, and provided information(data catalogs) to "MD communet" to promote further
utilization of data in the future. [See Section 5 3) for moredetails]

2.5. Utilization of Probe Vehicle Data in Road Management
As examples of the use of probe data collected from vehicles,
we studied the detection of slippery road surface conditions
caused by bad weather conditions such as rain, the detection offlooding on roads and the provision of alert information, as wellas the use of the information for road maintenance management
by estimating the amount of rutting from the amount of water
film detected on the road surface.
In cooperation with OEMs and tire manufacturers, wecollected information on ABS operation, vehicle skid control
operation, tire spin control operation, wiper operation status,
tire grip level information, and other data from vehicles inmotion. The accuracy of generated detection information basedon statistical processing was verified through FOTs on actualroads. [See Section 5 4) for more details]

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5

Data Connection and Use to Achieve Society 5.0

1) Design of Geographical Data Architecture— Building and Promoting a Traffic
Environment Data Portal Site
Naoki Iso (NTT DATA Corporation)(Abstract) The data collected for automated driving, such as high precision map data, road traffic data, and vehicle probes, areexpected to be used as road traffic environment data in various other industries as well as the automotive industry. In order torealize a mechanism to allow this, we constructed and released "MD communet®," a portal site for road traffic environment
data that aggregates data in the mobility field and links it with other fields. Aiming for the continued operation of MD communet,
we have also promoted activities to spread the service, such as acquiring more member companies, increasing awarenessthrough various promotion, and creating offline/online matching opportunities. In addition, we have developed service examples
that effectively use road traffic environment data, which is the key to social implementation of this system, in combination withinformation from other fields, and have studied the support functions necessary for service development.
Keywords: Road traffic environment data, Society 5.0, data coordination, business matching, data utilization service creation

1

Construction and release of "MDcommunet," a portal for road traffic
environment data

development with a focus on the implementation of three major
functions: the promotion of business matching, the expansionof data, and UI/UX improvements based on user feedback.

1.1. Value provided by the portal site
The goal of this program is to contribute to solving social
issues, such as reducing traffic accidents, reducing traffic
congestion, ensuring mobility for road users with limited
mobility, and improving the shortage of drivers and reducing
costs for logistics and mobility services, which will be achieved
through the practical application and expansion of automateddriving, ultimately creating a society in which all people enjoy
high quality of life. This reflects our belief that contributing to
the practical application of automated driving is required torealize a safe and secure society, solve social issues, and create
new business value for these purposes.
To contribute to achieving the goals of this program, we
have created "MD communet," a portal site for road traffic
environment data, as a place where people, information, anddata can gather and create various ideas and services. As shownin Fig.1, this portal site aims to promote the utilization of data
and the creation of new businesses through the portal site byaggregating a wide variety of road traffic environment data inthe mobility field from around the world, and by creating
various opportunities for users to communicate with each other.
For this reason, after equipping the portal site with the basic
functions required from a catalog search site, we advanced

Matching
Automated driving
MaaS
Logistics/
transportation
Road environment

Catalog listing/searchNeeds/seeds
registration

Communications
Aggregate catalog dataon portal site

Matching support

Fig.1: Image of business creation through the portal site

1.2. Business matching on the portal site
As mentioned above, the development of this portal site'sfunctions focused on proactive search and communication
functions to facilitate business matching.
Fig.2 shows an overview of the functions provided byportal sites. The main functions provided by the portal site
are shown below.
(1) Data catalog registration and search functions
This function allows users to register catalogs and publish
them for other users, and provides various search methods
so that users can easily find the data they are looking for.
Specifically, keyword search, conditional search, and area
search functions have been implemented on the top screen,
and re-search and faceted filter search functions have been
implemented on the search results screen.

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﻿1) Design of Geographical Data Architecture — Building and Promoting a Traffic Environment Data Portal Site

Data catalog site
Catalog

Data information
registration screen

Data information
catalog

Data processing tool
registration screen

Data processing tools
catalog

Data request
registration screen

Data request catalog

Index

Extraction ofcharacteristics

Data user

CADDE
Connectors

SIP4D Catalog*CKAN support*CKAN support

Clustering

Activity
history
Data
Analysis

Data provider

Data user

Recommendation

G-Spatial Information
Center Catalog

Clusteranalysis

CKAN API

Presentation ofsimilar data

Registration andcollection functions

SearchData provider

Fig.2: Overall portal site functions(2) Communication functions
This function is designed to promote the discovery of
new data usage methods and direct business matching bydirectly connecting data providers and users.
Specifically, we implemented a function to evaluate posted
data and a message function to enable direct communication
among users.
(3) Recommendation function
This function recommends catalog data from the system
in order to provide users with unexpected discoveries.
Specifically, we implemented a function that provides
recommendations based on user access information, and asimilar data function and clustering function that uses
machine learning.
(4) Associative word support function for search terms
This function presents the user with related keywords
that are associated with the search term.
Specifically, we implemented a function that enables theretrieval of related keywords from an external repository(DBpedia) for each search term. This means that related term
information is not stored internally and the retrieval
destination can be set flexibly.
(5) Company profile
A function that allows each company to post theirachievements and activities, providing users with information
about the company itself and creating expectations formatching from perspectives other than those of the actual
catalogs.
Specifically, we implemented a function that allows
companies to introduce their initiatives and post text anddata such as editorials and use cases on the website fordissemination and promotion.
(6) Linked Data creation
A function that enables the operator to obtain sources for
information to be disseminated to member companies basedon user activity logs and data relevance.
Specifically, we implemented a function that enables

notification of recommendation information to member
companies by converting the metadata for registered catalog
data and logged-in user activity logs into Linked Data, which
can then be searched by management.
1.3. Portal site data expansion
In order to further promote business matching throughthe portal site, it is essential to expand the available data. As
a response to this requirement, data expansion was
implemented in terms of both the volume and types ofregistered data.
In terms of data volume, we also implemented linkage withexisting data platforms, such as the G-Spatial Information
Center and SIP4D. Furthermore, by implementing the
CADDE connector in the SIP inter-program data coordination,
as shown in Fig.3, it is possible to efficiently obtain datacatalogs from other programs.
In terms of data types, in addition to data informationand data requests, data processing tools can also be posted,
thereby expanding the data while increasing the number ofstakeholders involved in the creation of new services.
As data expansion progresses and the probability ofmatching increases when highly related data and logs arelinked, it is expected that strengthening both will create asynergy in the promotion of business matching.
1.4. Portal site UI/UX improvement
After the opening of the portal site to the public,
opportunities for business matching are expected to increase
due to the additional member companies and theaccompanying expansion of the data catalog, and the role ofthe portal site as a place where more data, people, andinformation can be gathered is expected to become more
important. In addition, for the purpose of attracting moremember companies, UI/UX improvement has been
continuously implemented in conjunction with various

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﻿1) Design of Geographical Data Architecture — Building and Promoting a Traffic Environment Data Portal Site

CADDE

MD communet

Service planner(MDC member)

DisplayMatching function
Collect
MDC catalog
CADDE Catalog
Save

SearchSearch results
Connector
coordination
SearchReturnSearch results

Present data ID
GUI

SearchReturnReturnSearch function

SIP Disaster
Prevention

SearchData providers inother fields
Data
registration

SIP otherfields

MDC catalog
CADDE Catalog

CKAN

CKAN collaboration

G-Spatial
Information Center

Data providers in
Data other fields
registration

Fig.3: Example of additional development for enhanced functionality
Concept for periodic metadata harvesting function

promotion activities, such as studying the optimization of
the system lines to link from the promotional activities
websites to the portal site, as shown in Fig.4.
Promotional website

Portal site

important prerequisite, and we specifically aimed to create
allies for this project. First, in order to identify the appealing
points of MD communet, which are vital in the creation of
new allies, we organized the worldview and values to beachieved (as shown in Fig.5). This was based on our objectives
and the project concept. We also defined the ecosystem that
we wish to form through this project.
Ecosystem formed by the project✓ We will utilize the assets and professional strengths of the various companies and organizations participatingin this project to form an ecosystem for the creation and promotion of data utilization businesses.
Service
Data

Fig.4: Optimization of lines from promotional activities websites

ServicePF
Data

2

Undertakings to promote MDcommunet

Asset provision(data, services, etc.)

Portal site

Business integration

2.1. Outline of activities

Service integration

PF

Data arrangement/reference–Roles we would like to ask project
participants to cooperate with.

Fig.5: Ecosystem formed by the project

In order to continue to operate MD communet, it isimportant to create new services and values by gaining wide
recognition, making allies, and promoting data utilization
through matching. In the process of creating allies, it isnecessary not only to find partners who agree with MDcommunet's initiatives and can create together, but also tobuild an operation and promotion system that will enable
users to conduct transactions smoothly. In the promotion ofdata utilization, it is necessary not only to collect a wide
range of road traffic environment data, but also to create
encounters with unknown data by publishing characteristic
data from both the public and private sectors, leading to thecreation of new services and values. Therefore, in FY2019
and FY2020, we focused on building a network, expanding
the data to be posted, and increasing awareness.

2.2. Creating allies for MD communet
For the continuous operation of the portal site, "matching"including personal relationships with each party is the most

Next, we surveyed and organized the stakeholders
necessary for the realization of the ecosystem in the public
sector and in the private. First, we visualized the flow ofvehicles and people on the road, assumed use cases in whichroad traffic environment data would be used, and investigated
the flow of data collection and distribution. As shown in Fig.6,
we classified stakeholders into the public and private sectors.
For the public sector, we assumed public offices and public
interest incorporated associations that own or have jurisdiction
over road traffic environment data. For the private sector, we
assumed businesses and service providers that offer telematics
services. The relationships among these stakeholders wereorganized and candidate service providers selected.
In FY2019, we conducted interviews with the mappedcandidate businesses to understand their level of interest inand sympathy with the world view and service contents thatthe portal was aiming to provide, their level of interest in andconfidence in their own business using the portal, and theirrequests for the portal's functions and data handling, with

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*Specific company names have been omitted

Business
category

Related company categories
Map/navigation
Logistics (home
Manufacturer (map information, delivery, transportation,navigation)business vehicles)

System/rule

Transportation(bus, taxi)Road/
infrastructure・・・

Academia andgovernment

・・・

・・・

Data
utilization/
Serviceprovision

・・・

・・・

・・・

・・・

・・・

・・・

・・・

Platform
operation

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・
・・・

Data
provision

・・・

・・・

・・・

・・・

・・・

・・・
・・・

・・・

〜

・・・

〜

・・・
・・・
・・・

・・・

LegendUser attributes

B to B to C
(including G to B
to C)

FY2021
4Q
Undisclosed
Undisclosed
Undisclosed
Undisclosed
Undisclosed

B to C (including
G to C)

Undisclosed
Undisclosed

Undisclosed
Undisclosed

FY2022
2Q

1Q

3Q
Undisclosed

Undisclosed

Undisclosed Undisclosed

Undisclosed

Undisclosed

Undisclosed

Undisclosed

Undisclosed

Undisclosed Undisclosed Undisclosed

Undisclosed Undisclosed

Undisclosed Undisclosed Undisclosed
Undisclosed
Undisclosed Undisclosed
Undisclosed Undisclosed
Undisclosed

Undisclosed

Undisclosed—Candidate—Implemented

Undisclosed Undisclose Undisclosed
Undisclosed
Undisclosed
Undisclosed
Undisclosed Undisclosed Undisclosed Undisclosed Undisclosed
LegendUndisclosed
Undisclosed
Undisclosed Undisclosed—Candidate Businesses—Key parties—Requested by SIP officials
Undisclosed
Undisclosed Undisclosed—Already implemented (revisited)
Undisclosed Undisclosed—Interview candidates

Fig.6: Mapping of candidate businesses

the aim of utilizing them in future promotion activities andportal site functions. Through these interviews, we were able
to obtain suggestions for future development of portal
functions and promotion activities such as portal sitemanagement, which we fed back into the development of theportal site and made an issue to be considered in the next
fiscal year. In FY2020, we considered the importance of
aggregating road traffic environment data from various
companies and publishing it as catalog data, so we decided toapproach potential data providers. As a result, we were able
to obtain membership from nine companies and twoorganizations that support the MD communet initiative.
After the public release in FY2021, in parallel withpromotional activities for the MD communet, we actively
approached data providers that possess characteristic roadtraffic environment data, and the data users and dataprocessors who are necessary for creating services. As a resultof various promotional activities, we were able to increase thenumber of members to over 60 companies. (Fig.7)
More than 7,000 items of catalog data were obtained via
the provision of catalog data from members. In addition,
through continuous efforts to reach out to business operators,
we were able to obtain the prospect of acquiring advanced
data such as vehicle probe data. We were able to include
characteristic public-private sector catalog data, including awide variety of vehicle probe data and cross-sectional traffic
volumes provided by the National Police Agency.

※50音順

Fig.7: List of MD communet members (partial)

2.3. Activities to raise awarenessIn FY2020, we also actively produced content andconducted various promotional activities to raise awarenessand interest in MD communet in preparation for the releaseof the portal site.
First, in conjunction with the release of information in
October 2020, a promotional website was created andreleased as a tool to introduce MD communet to candidate
businesses and to gain widespread recognition and interestin the future development of the site. (Fig.8) The purpose ofthis website was to inform visitors about the value andfunctions of the portal site and to motivate them to registeras members. The portal site was designed to provide an
overview of the services offered, catalog information, usecases, support details, membership registration methods,
and other information as the entrance to the data catalog, so

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﻿1) Design of Geographical Data Architecture — Building and Promoting a Traffic Environment Data Portal Sitethat visitors can understand the services and be guided tomembership registration and inquiries after becominginterested. In designing the site, we aimed for a site structurewith a UI that would not be bothersome for both users who
know about MD communet and those who don't. After the
release of the site, we worked to increase awareness throughvarious press releases and linkage with the SIP-adus programrelated websites, and also used the website as an introductory
tool when conducting promotional activities.

matching companies at the event.
Idea generation event/business-to-business matching event

Online seminars at SIP-café

Fig.10: Examples of past events

2.4. Toward social implementation from FY2023

Fig.8: Promotional website

Next, a promotional video was produced to promote
understanding of MD communet. (Fig.9) In addition to being
aired at various events, the video was released on the SIP-café
website, and was intended to attract interest and lead people
to the promotional website. This led to an increase in thenumber of visitors to the website from SIP-café, which in turnled to an increase in the level of recognition.

In order to continuously promote the creation of new datadriven businesses in MD communet, we will take both cyberand real approaches to the issues and concerns that members
have in creating services by utilizing data, and provide thenecessary matching and technical support for the creation ofsaid services. At the same time, MD communet will continue
to expand its membership and promote data utilization by
sharing use cases to help people visualize data utilization andby communicating the value of MD communet in an easy-tounderstand manner. Both cyber and real support will be
provided. We will continue to operate and grow a system thatcan promote the wider distribution and service utilization ofroad traffic environment data, as a platform that can bepromoted through public-private partnership.

3

Testing Project Promotion

3.1. Overview

Fig.9: SIP-café news release

In addition to the production of the above content,
activities aimed at raising awareness of MD communet
through events were held. (Fig.10) In order to increaseawareness of MD communet and appeal to the possibilities ofusing mobility data, events were held using idea generation ina semi-closed environment to rediscover the value of mobility
data, experience idea generation methods, and meet withpotential future partner companies. With the cooperation of
the Graduate School of System Design and Management at
Keio University, a wide range of companies involved in themobility industry shared various ideas through lectures andworkshops. The event also created opportunities for interaction
among the companies, and provided the possibility of

Based on the architecture pertaining to geographic data in
the field of automated driving, we conducted FOTs (Field
Operational Tests) using use cases to solve social issues extracted
from the study results of the FY2019 projects in the first phase of
SIP-adus, "the field of services for logistics companies" and "thefield of end-to-end multi-modal navigation services." As a result,
we found the possibility for new value-added services throughthe exchange of data among users.
Furthermore, in FY2020, based on a survey of issues facedby the logistics industry, we extracted candidate data that canbe used in cooperative areas related to the initiatives and issuesof logistics companies, with the aim of collaborating andutilizing cooperative data in the logistics field that will lead tosolutions to social issues common to the industry.
In addition, based on discussions with the participants
for the research and testing project for improving logistics
efficiency based on architecture utilizing information fromvehicle probes and other sources, we examined the issues
related to the usefulness and deliverability of the above data

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﻿1) Design of Geographical Data Architecture — Building and Promoting a Traffic Environment Data Portal Sitecandidates and the concept of service creation using theportal and based on the direction of the problem solving.
From FY2021, we explored the scope of possible
collaboration and utilization of vehicle probe data, which had
been an issue related to the availability of data due to its highconfidentiality. As a result, we were able to construct a scheme
to link and utilize data by bringing together a digital tachographmanufacturer that generates vehicle probe data for data
providers, a trading company that plans service creation forservice providers, and a data-holding delivery company that has
business issues for service users, and conducted FOTs to realize
service creation. In addition, in order to further promote data
coordination and utilization, we conducted testing that
contributed to solving issues faced by local governments inaddition to those faced by private companies.

incorporating multiple use cases and conducted functional
evaluation and problem identification by having participantsin the experiment use a service application that implemented
all use cases, as shown in Fig.12.

3.2. Fots overview

User information registration

User registers information
related to their own car

Navigation to the entrance of thedelivery destination and displayof rest spots along the route

Receives a reminder

By having drivers register
their vehicle information,
it is stored as driver
attribute information.

In addition to the route to the delivery
destination, rest stop locations are also
displayed. (The route factors in the widthof the car according to the vehicle type.)

When driving in front of atraffic sign or on a road where
there is no record of driving, awarning will be issued.

Fig.11: The field of services for logistics companies
Registration of travel spots

Catching up with dynamic information(1) Creation of services through data utilization by FOTs
participants
We set up use cases to solve social issues identified basedon the results of the study in the first phase of SIP-adus, andparticipants led FOTs.
Regarding social issues, in "the field of services forlogistics companies" we set the provision of a safe and secure
driving environment for truck drivers, including the need to
avoid dangerous routes when considering safe driving. In"the field of end-to-end multi-modal navigation services," wehave set the goal of providing stress-free transportation
support by combining personalized transportation means(including automated driving) with means of transportation
and route guidance that match user's personal data and are
flexible to changes in weather and congestion conditions.
The purpose of the "services for logistics companies" is toverify the extent to which the use of the road traffic environment
data portal can provide value, such as providing a safe and secure
driving environment and improving the working environment,
through data and data utilization methods that data users might
have been unaware of. In line with this objective, we conducted ademonstration of an application for cooperative provision andutilization of geographic road traffic environment data amongusers, as shown in Fig.11 Interviews were conducted with truck
drivers, operation managers with truck driving experience, andtraffic information service providers.
The purpose of the "end-to-end multi-modal navigation
services" was to verify that by using a road traffic environment
data portal, it is possible to provide support for a more stressfree form of mobility that includes more personalized meansof transportation (including automated driving) by providing
dynamic information through data and data utilization
methods that data users might have been unaware of. To
achieve this objective, we set up an FOTs scenario

By asking users to register theirgender, number of people, andgenres of interest, the system
accumulates personal data.

Refers to user's personal
data and recommends
spots that are popular oreasy to use accordingly.

The system collects dynamic
information, such as weather
information, operation information,and congestion compiled from Twitter,and notifies users of better spots.

Fig.12: The field of end-to-end multi-modal navigation services

As a result of the FOTs for the services for logistics
companies, we confirmed that route guidance that takes into
account static information such as the provision of a safe andsecure driving environment for truck drivers, loading docks,
rest areas, road information, and traffic signs, especially on
unfamiliar routes or routes where the road environment haschanged, can contribute to improvements. Regarding theusefulness and scope of the cooperative area data, we confirmedthat the data is useful as a cooperative area for public purposes,
and that there are cases where there are significant advantages tosharing the data depending upon its nature.
In the FOTs for the "end-to-end multi-modal navigation
services," we implemented a function that provides flexible
transportation and route guidance in response to changes indynamic information such as weather and congestion
conditions, while also taking into account information such as
user's personal data. In addition, the information collected via
the application, such as the results of actions taken and the timespent in the area, can be effectively used as cooperative area data
for marketing and other purposes. We therefore uncovered thepossibility for data utilization in other fields via data in thecooperative domain being distributed through the portal.

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﻿1) Design of Geographical Data Architecture — Building and Promoting a Traffic Environment Data Portal Site

Preparation (Probe data acquisition → processing → drive recorder settings)(1) Acquisition of driving records for the past several days
(2) Processing
Obtain probe data and location information for delivery
destinations from the service provider's customers

Create shipping destination profile

OfflineCooperation

Estimate delivery
entrance
Estimate parking
position

Development scope

Estimate parking
direction
Estimate driving route
Delivery entrance → Parking position(3) Drive recorder settings

Service provider's existing service
Delivery destination is set tothe drive recorder.
OfflineCooperation

Real field testing
Testing step 1
VehicleService provider testing

Delivery destination
Several stores based on actual
data (planned for Kansai area)

Audio route guidance

Audio guidanceexampleTurn left [xx]m ahead and enterfrom the delivery entrance.

Estimate delivery
entrance

Please back your car into the dedicatedparking lot at the delivery entrance.
Turn left after proceeding straight and back yourcar into the dedicated parking area.
Estimate driving route
Delivery entrance → Parking position

Estimate parking
direction
Estimate parking
position

Results of testing
We plan to evaluate functionality, effectiveness of introduction, and future issues for service improvement.

Fig.13: Overview of delivery destination profiling test(2) Testing of vehicle probe data coordination andutilization among private companies
A digital tachograph manufacturer, a trading company,
and the operator of MD communet collaborated to propose
the creation of a service utilizing vehicle probe data for delivery
companies (data holders), who are the direct customers ofdigital tachograph manufacturers, for the purpose of
improving the efficiency of delivery companies' operations.
Although the high confidentiality of vehicle probe data posed
problems regarding its availability, permission to use the data
was obtained for the sole purpose of using it to improve theefficiency of customer operations.
In setting the issues of delivery service providers, among
the issues recognized by the digital tachograph manufacturer
and trading company, we selected issues related to operational
efficiency that also have a high affinity with vehicle probe
data, such as the positions of delivery entrances and parking
lots at delivery destinations not being organized and drivers
having to park multiple times or drive around the perimeter.
The content of the FOT was to analyze and process thevehicle probe data to detect the location of the delivery
entrance, parking location, parking direction, and route fromthe delivery entrance to the parking location for each store at
the delivery destination, manage them as delivery destination
profiles, and test whether the information provided to drivers
can contribute to operational efficiency. (Fig.13) The analysis/
processing of the vehicle probe data was conducted from thestandpoint of service development support, which is one of
the roles of MD communet management.
As a result of the FOT, we were able to confirm theusefulness of vehicle probe data in this area by confirming
the contribution to a certain degree of operational efficiency
and the issues that need to be addressed for practicalapplication. We also confirmed the usefulness of planningsupport and data processing support in the process of service
creation as part of MD communet operation.
(3) Resolving issues faced by local governments
In order to further promote data coordination andutilization, we examined the possibility involving not only
private companies but also local governments and using
geographic data to resolve issues faced by local governments.
Based on the characteristics of data utilization, we selected
the theme of utilization in EBPM (Evidence Based Policy
Making) for local governments, and examined a plan togenerate and utilize objective evidence by combining
multiple instances of geographic data.
Specifically, we investigated in depth the issues related tosnow removal and snow clearance in Yokote City, Akita
Prefecture, a problem shared with every region that suffersfrom heavy snowfall, and the issue of illegal cab parking in
Kyoto City, Kyoto Prefecture, a problem shared with othertourist destinations, and established use cases to solve theseissues using geographic data.
3.3. Common efforts through testingThrough the testing, we found that the creation of services
by combining multiple geographic data sets required technicalknow-how to handle data in different formats at the same time.
In order to continue to create services in the future, it isnecessary to lower the hurdles mentioned above. We believe
it would be possible to speed up development and reduce
costs related to data utilization in the next and subsequent
years by creating a template as a general-purpose function
for processes, such as importing and processing, allowing forthe handling of data of different formats simultaneously, as

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﻿1) Design of Geographical Data Architecture — Building and Promoting a Traffic Environment Data Portal Site

Template
Data and creation functions
Potential roadflooding areas(Ministry of Land,
Infrastructure, Transport
and Tourism)・・・
Actual traffic data
(Pioneer)

Point data・・・

Use case introduction
content for Yokote City

Mesh data

Line data<Server side>
・Preprocessing
・Import processing
・Data processing
・Risk location detection
process<Client side>
・Visualization
・Routing (normal)
・Routing (considering risk)
・Display of various data

Network data
・・・Data that can beused fordemonstrationsclassified by data type

User (issue holder)

MD communet management
dissemination promotion tools anddemo apps

Design・Processing flow
Program・Samples byfunction

Data applications
Understanding concept
Idea
generation

Service provider
Realization of service
creationFor

Understanding ofdata/promotion of utilization
・Overview of functions

Finding problems/solution ideas

Service idea generation

Prototype apps
・During disaster
Data test

TargetFor

xx creation function group

・・Road network data
(HERE)

Published on MD communet
promotional website

Use case introduction
content/during a disaster・・・
1 km mesh analysis
volume (SIP4D)

Purpose and means

Deliverables

Shortening ofdevelopment time
Service developer
Understand development concept

For members Services for MD communet members
・Disclosure of design products
・Program provision
Service development
Shortening of period

Data provider
Data applications
Understanding applications

Fig.14: Geographic data utilization templating study

shown in Fig.14. We then created a design and program
based on this idea.
In addition, because there were cases in which users were
not able to start using geographic data because they did nothave a clear image of what the data would be used for, wecreated a demo application that allows users to experience
what the data can do.

4

Future Initiatives

In FY2022, the final year of the second phase of SIP-adus,
we carried out activities focused on preparations for social
implementation. After the second phase of SIP-adus ends in
FY2023, MD communet will see social implementation.
After social implementation, the MD communet willcontinue to serve as a hub for public-private partnerships
and continue the various activities that have been
implemented during SIP-adus. This is expected to increase
the number of allies, promote further utilization of data andcreation of new services, and solve social issues.

[Contacts]

NTT DATA Corporation, Smart Business Management Department, Social
Innovation Division, Social Infrastructure Solutions Business Headquarters,
Toyosu Center Building, 3-3-3 Toyosu, Koto-ku, Tokyo 135-6033, 050-55462450, Contact: Ayako Nakajima

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5

Data Connection and Use to Achieve Society 5.02) Resolving Social Issues in Cities Popularwith Tourists
Noriyuki Hayashi, Naohisa Komiyama, Yurie Toyama, Takahiro Kashiwa, Hiroki Ozu, Wataru Uchida (Mitsubishi ResearchInstitute, Inc.), Kazuhiro Noguchi, Taro Morita, Natsuha Makino, Taigo Mitsuhara (MRI Research Associates, Inc.)(Abstract) From the perspective of creating use cases to solve transportation-related issues in urban areas by utilizing
road traffic environment data, we held a contest (an App Contest to Solve Tourism and Transportation Issues (the"Kyoto Raku Mobi Contest") to solicit applications and ideas to solve social issues related to tourism and transportation
faced by Kyoto City, a world-class tourist city with many tourist resources. The contest was conducted with thecooperation of the Kyoto Municipal Transportation Bureau and businesses involved in transportation, logistics, andtourism, and organized and provided data including data on stations, stops, routes, schedules, and fares of buses,
trains, and other public transportation systems; data on services and stores for temporary baggage storage anddelivery services in the logistics sector; and information on tourist facilities and locations, past congestion statistical
data and future congestion forecast data, and map APIs in the tourism sector. Through the implementation of thecontest, we raised awareness of the portal site for road traffic environment data, and through discussions andcoordination with the various involved parties, we realized the collection and utilization of data to be posted on the
portal site (MD communetⓇ).
Keywords: public transportation data, GTFS-JP, tourist city, app contest, MD communet

1

Overview of the Project

The SIP-adus (Cross-ministerial Strategic Innovation
Promotion Program (SIP) Automated Driving for Universal
Services) is working on the generation and distribution of roadtraffic environment data that is indispensable for automateddriving, toward the realization of an automated driving society.
This project took place in the tourist city of Kyoto and aimed tobuild an ecosystem that promotes matching between
information owners and users so that a variety of users can use
road traffic environment data in a variety of services. Specifically,
the project planned and conducted two "Kyoto Raku Mobi
Contests," an App Contest to Solve Tourism and Transportation
Issues, and coordinated the development and procurement ofroad traffic environment data for use in the contests, with theaim of building momentum for the utilization of the data.

2

1st Kyoto Raku Mobi Contest

2.1. Planning and Preparation to the Launch (FY2019)
Based on the assumption of several use cases for solving

issues related to the movement of people and goods in urban
areas, we considered the planning of the "Kyoto Raku Mobi
Contest" as an app contest to solve issues using road traffic
environment data (various data on transportation, logistics,
and facilities), with the city of Kyoto, which faces issues
concerning tourism and transportation in particular, as the
subject.
In the consideration of the use cases, multiple use cases
were considered for services related to transportation andlogistics in urban areas, targeting various entities with different
attributes (residents, tourists, government and transportation
operators, etc.). Specifically, use cases that apply to tourists wereconsidered, for example, (1) guidance of sightseeing routes thatavoid the traffic flows of residents, (2) the recommendation of"empty-handed sightseeing" in which tourists can store theirluggage, (3) suggestions of destinations that include congestion
forecasts, and (4) guidance services that take into accountcongestion conditions and transportation service operation
conditions. (Fig.1)
Based on the consideration of the use cases, various data that
could be used to solve issues related to transportation andlogistics in the tourist city of Kyoto were organized into the three
categories of "transportation," "logistics," and "facilities", and an
explanation of the purpose of the project and a request forprovision of the data was made to the relevant parties such as theentities that hold such data, and the necessary data was procured.

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5.Data Connection and Use to Achieve Society 5.0
﻿2) Resolving Social Issues in Cities Popular with Tourists(9) (Walk) Use the route
guidance and walk to thenearest bicycle parking lot

Bicycleparking lot
Kinkakuji Temple(10) (Bicycle share) Easy route toyour accommodation with no stairs
Route with many stairs(8) (Car share) Fares canalso be paid in the app
Toei Kyoto
Studio Park
Recommendation of
Nijo Castle
highly-recommended (7) (Car share) Fares
sightseeing spots based can also be paid inon the user's attribute the app
information.
Congestion* Search, reservation, and payment are
all possible on a single smartphone app* Real-time weather information canalso be obtained, giving peace of mind
for travel by foot or bicycle

Accommodation(3) (Transport by truck)
Only the luggage is sent
to the accommodation.
Location information iscontinuously acquired.

(5) (Transport byrickshaw) Souvenirs
purchased along theway will also be sent tothe accommodation(6) (Transit bus)
Multilingual route
guidance. Passengers
Kiyomizu Temple(4) (Bus) Travel by thebest route and mode(2) Store luggage and go to
Movement of goods
sightseeing spots empty-handed of transportation
Movement of people(1) Arrive at
Kyoto Station Kyoto Station© OpenStreetMapcontributors

Fig.2: "Kyoto Tourism Assist" (tentative name)(1)[Local residents][Tourists]

Teburan Supporters
Teburan medal display

Button to read a QR CodeScan the city's QR code

Fig.1: Example of use case study (tourist case)

2.2. Contest Management to Judging and Awards(FY2020)
Based on the above consideration, the "Kyoto Raku Mobi
Contest" was planned and held from February to November
2020 to solicit apps and ideas that would contribute to solving
tourism and transportation issues in Kyoto City. In addition toproviding contest participants with road traffic environment
data and APIs, we explained the contents and specifications of
the data, conducted lectures and opinion exchanges on issues
related to transportation and tourism in Kyoto City, andprovided mentoring to individual contest participants.
The "Kyoto Raku Mobi Contest" consisted of two divisions,the app development division and the app idea division, andcalled for app products or ideas that contribute to solving issues
in Kyoto. After conducting a documentary review of thesubmissions and testing and evaluation by monitors, we held afinal judging session on Saturday October 17, 2020 and awarded
seven prizes at an award ceremony on Saturday November 7,
2020.
The "Walking Town Kyoto Award" for the top prize in theapp development division was awarded to "Kyoto Sightseeing
Assist" (s name) (Fig.2), an app that supports the sightseeing
activities of visitors to Kyoto by utilizing various road traffic
environment data, including data based on the "General Transit
Feed Specification Japan" (GTFS-JP) provided by the secretariat.
The "SIP-adus Award" for the top prize in the app idea division
was awarded to "Teburan - an app that makes you want to walkaround empty-handed" (Fig.3), which was an idea for an app
that discovers and provides new value of sightseeing spots when
visiting empty handed. Subsequently, meetings were held withthe creator of the winner of the "Walking Town Kyoto Award"
for top prize in the app development category, and work wascommissioned for the completion of the app towards social
implementation.

Number of visits to Kyoto
The number of visits to
Kyoto is displayed
Teburan point display
Display number ofpointsNumber of Teburan stamps
display
Number of Teburan stamps
collected

Stamp

Stamp

* (including Teburan Supporters)

Kyoto City residents
For Kyoto residents,"Resident" is displayed* Message function button* Luggage storage button* Map display button* Kyoto Friend button* Goshuincho button* Settings button

Fig.3: "Teburan - The app that makes you want to walk around emptyhanded"(2)3

2nd Kyoto Raku Mobi Contest

3.1. Planning and Preparation (FY2021)
Since FY2021, a portal site (MD communet) for searching
and matching data related to road traffic environment data has
been built and its use has commenced. As an example of solving
a regional issue using road traffic environment data, theplanning of the "2nd Kyoto Raku Mobi Contest" as a contest to
promote data utilization using MD communet was studied,with the aim of forming an ecosystem for data provision andutilization in the tourist city of Kyoto.
In this contest, contest participants were asked to set andspecify an issue that the contestant expected to resolve
themselves and which targeted local residents, tourists, logistics,
transportation, or tourism businesses, or local governments,
and propose an app or idea that would solve the issue. As
described below, the types of road traffic environment data
provided in the contest was expanded, so unlike the first contest,
the issues (use cases) expected to be resolved were not limited
to specific items. Local governments and businesses involved in
the contest were asked to propose "example issues" from theperspectives of public transportation, the region, tourism,
logistics, etc., and the information was disseminated to thecontest participants.
As in the first contest, data for the second contest wasorganized into the three categories of "transportation,"

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﻿2) Resolving Social Issues in Cities Popular with Tourists"logistics," and "facilities," and we then coordinated andprocured data from entities that possess such data in order tofurther expand the types of data.
Among these, for transportation data, we collaborated with
Kyoto City to develop data from transportation operators so it
could be provided in a standard data format (e.g., GTFS). In
addition to utilizing GTFS-JP, which was developed on a trial
basis for the future openness of bus data by transportation
operators, dynamic data such as the location of buses and trains
was developed and distributed on a trial basis. (Fig.4)
Transportation sector
Data contents
Data on Kyoto Municipal Subway stations, routes(lines), timetables, fares, etc.

Data provider
Kyoto City Urban Planning Bureau

GTFS

Data on Eizan Railway stations, routes (lines),
Eizan Electric Railway Co., Ltd.
timetables, fares, etc.
Data on Kyoto Municipal Bus stops, routes (lines), Kyoto City Urban Planning Bureau
timetables, fares, etc.
"Walking Town Kyoto" Bus and Train Transfer Information
Kyoto Municipal Bus vehicle location API
Dissemination System Consortium

GTFS，GTFS-RT
GTFS-JP

Kyoto bus stops, routes (lines), schedules, and fares Kyoto Branch, JTB Corporation

GTFS

Data on Yasaka Bus stops, routes (lines), timetables, Yasaka Bus Co., Ltd.
fares, etc.
Data on Kyoto Municipal Bus vehicle locations,etc.
Kyoto City Park-and-Ride Parking Lot data

GTFS-JP, GTFS-RT"Walking Town Kyoto" Bus and Train Transfer
Information Dissemination System Consortium
Kyoto City Urban Planning Bureau

Fig.4: Road traffic environment data (traffic data) provided by thesecretariat in the second contest

We also coordinated so that the secretariat could provide
data on logistics, facilities, and tourism.In particular, it was
decided to expand the data in the field of logistics bycollaborating with providers of data already available on MD
communet. (Fig.5)
The field of logistics
Data contents

Data provider

Data format

Location of data provision

Location data (individual vehicle
Yazaki Energy System Corpora- CSV
MD communet
point sequential data) and origintion
and destination data of logistics
vehicles in the 100 square kilometersof the main area of Kyoto City
(To be published
Sumitomo Rubber Industries, Ltd. in the future)(To be provided in the future)
Sensing Core (Kyoto Office)Vehicle driving data (statistics)

Aioi Nissay Dowa Insurance Co., CSV
Ltd.

MD communet

The field of facilities and tourism
Data contents

Kyoto City sightseeing spot
information

Data provider

Data format

Location of data provision

Kyoto City Tourism Association XLSX(DMOKYOTO)

MD communet

Congestion forecast information Kyoto City Tourism Association XLSXaround popular tourist spots(DMOKYOTO)

MD communet

Information on Wi-Fi spots in
Kyoto City

MD communet

Kyoto City Tourism Association XLSX(DMOKYOTO)

Yahoo Japan Corporation
Data (estimates) on human flows around tourist
attractions in Kyoto City (2nd Kyoto Raku Mobi Contest)
Location guidance service API

Zenrin Co., Ltd.

Data on temporary baggage storage and delivery Secretariat of the 2nd Kyoto
service stores and coin-operated locker locations Raku Mobi Contest

CSV

MD communet

API

Other data

CSV

Other data

Fig.5: Road traffic environment data (logistics and facility data) provided
by the secretariat in the second contest

In the second contest, the website and contents were
designed to encourage participants to be aware of and use MD
communet. In collaboration with NTT Data, a link to MD
communet was placed on the contest’s website and related
content was posted in the "Events" and "Columns" sections of
MD communet as reference information for participants. In

MD communet

Other data

Certification
Via MD communet

Data download(immediate)
Staying on the login screen after
downloading data
Data download URL

Fig.6: Cooperation between the contest and MD communet(3)(4)

addition, we coordinated a policy that the road traffic
environment data provided in the contest would in principle beposted in the MD communet catalog during the period of thecontest. (Fig.6)
In addition, following on from the first contest, we
coordinated with relevant entities including local data providers
and local governments to involve them in the contest. In
addition to Kyoto City as a cooperating entity, we expanded thenumber of sponsors, who were mainly local businesses in
Kyoto.

3.2.Contest Launch and Operation to Judging andAwards (FY2022)
The "2nd Kyoto Raku Mobi Contest" was planned andimplemented taking into account the above consideration. As
with the first contest, it consisted of two divisions, the app
development division and the app idea division, and called forapp products or ideas that contribute to solving issues in Kyoto.
A press release regarding the second contest was sent out
and entries were accepted from Monday, April 25, 2022. Road
traffic environment data and mentoring was provided to contest
participants to encourage them to consider submitting entries.
Entries were closed on Monday, October 24, 2022, and thesubmission period closed on Monday, October 31. A document
review was conducted and the final judging and awards
ceremony was held on Sunday December 11, 2022. As with thefirst contest, awards were given including the "Walking City
Kyoto Award (Kyoto Mayor’s Award)" for the top prize in theapp development division and the "SIP-adus Award (Cabinet
Office Secretary-General of Science, Technology and Innovation
Policy Award)" for the top prize in the app idea division, and it
is planned to commission the winner of the "Walking City
Kyoto Award" with the aim of social implementation within
FY2022.

4

Outcomes of this Project

The app and ideas developed through this contest were a
step toward the contest participants solving specific issues byutilizing road traffic environment data. Because the intellectual

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﻿2) Resolving Social Issues in Cities Popular with Tourists

property rights concerning the entries in the contest in principle
belong to the creators of the inventions, etc. (i.e. the contest
participants), it is possible for them to continue their efforts
toward social implementation after the contest has ended.
Through this project, discussions were held with local
governments and data providers for the distribution andutilization of road traffic environment data in Kyoto, and waslinked to the creation of momentum for the development andprovision of data in standard formats, including dynamic data
(e.g., GTFS-RT). In the future, it is necessary to promote
discussions with the related entities so that data can becontinuously developed and distributed in the region even after
the contest is over.

[References]
(1) Teruki Matsuoka: Kyoto Raku Mobi Contest Submission Materials(2) Team Teburan: Kyoto Raku Mobi Contest Submission Materials(3) Mitsubishi Research Institute: 2nd Kyoto Raku Mobi Contest,
https://web.contest.adus-arch.com/, (accessed on 29th August 2022)(4) NTT Data: MD communet, https://info.adus-arch.com, (accessed on 29th August
2022)[Contactts]
Mitsubishi Research Institute, Inc., Smart Region Headquarters, 10-3, Nagatacho
2-chome, Chiyoda-ku, Tokyo 100-8141, 03-6858-2722, Contact: Noriyuki HAYASHI210

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3)Research to Realize More Effective
Logistics System with Probe Vehicle Data
Yukihiko Akao, Masaaki Kanazawa, Daijiro Sato (NX Logistics Research Institute and Consulting, Inc.)(Abstract) This study conducted FOTs (Field Operational Tests) on data acquisition and utilization for three use cases:(1) ascertaining the occurrence of waiting time and sharing with related parties, (2) checking daily inspection items
using vehicle data, and (3) measuring load weight and tire data to ensure legal compliance and safety, and identified
the issues towards implementation and considered countermeasures, with the aim of utilizing various types of data
related to vehicle and probe information to improve operational efficiency and ensure safety in truck logistics. Throughthe verification of the hypotheses via the FOTs and discussions with carriers and related businesses based on the
results of the FOTs, each use case was evaluated highly from the perspective of its usefulness in improving theefficiency of trucking operations and ensuring safety. On the other hand, since each use case is still at the concept
level and the considerations towards implementation are still in their infancy, it will take a considerable period of time
before practical application, as there are various technical, business, and legal issues to be addressed in the detailed
considerations. In order to create the environment and momentum towards practical application, it is necessary to
promote awareness of each use case and to create related organizations and businesses that will serve as promoters.
Keywords: trucking operation efficiency improvement, transportation operation safety, waiting time, daily inspection, load weight,
tires, vehicle signal data

1

This study is scheduled to be completed by the end of
December 2022 and is still in the process of discussion andexamination at the time of writing this report. This paper isbased on the results of the FOTs and considerations up to theend of August 2022.

Outline of the Study

1.1. Contents of the StudyIn this study, FOTs using vehicle and probe information forthree use cases were conducted, and the issues to be addressed
towards implementation and who will be in charge of future
promotion were considered, with the aim of utilizing various
data, including vehicle and probe information, to improve
operational efficiency and ensure safety in truck logistics, witha view to the practical application of automated driving
technology in truck logistics in the future.
The three use cases are: (1) ascertaining the occurrence of
waiting time and sharing with related parties, (2) checking daily
inspection items using vehicle data, and (3) measuring load
weight and tire data to ensure legal compliance and safety.
Through the test study of these use cases, the extent to which
the acquisition and utilization of vehicle and other various data
can be achieved at present was confirmed. Based on this,identifying the image of the final goal and the issues to beaddressed with the aim of practical application of these
initiatives in the future, as well as consideration of the roles tobe played in promoting future implementation, are in progress.

1.2. Background of the Study
The truck logistics industry is facing a growing shortage oftruck drivers due to working environment problems such as
long working hours. There is concern that this will have anegative impact on the Japanese economy, as logistics will not
be able to adequately meet the future demand for freighttransportation. One effective solution to this problem is
expected to be the practical application of automated driving totruck logistics in the future.
On the other hand, inefficient truck logistics operations,such as long waiting times before loading and unloading, are atypical example of the background to the working environment
problems of truck drivers. This inefficiency problem cannot be
fundamentally solved by automated driving, so the factors
should be analyzed and countermeasures should be consideredseparately from automated driving.
It is also true that there are safety and maintenance concerns

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regarding the practical application of automated driving intruck logistics, and it will be necessary to consider maintenancemeasures and safety assurance measures for a new era based on
the assumption of saving manpower through automateddriving.
In considering how to address these issues, we became awareof the problem of whether the use of vehicle and probe
information can be linked to concrete measures. Based on thisawareness, three use cases were studied and FOTs wereconducted with the aim of correcting inefficiencies and ensuringsafety in truck logistics operations.

1.3. Positioning of This Study with a View to Implementation
Three FOTs were conducted to confirm the present technicalfeasibility of the concepts related to the use cases and to evaluate
their usefulness in operational practice by gathering theopinions of carriers. Therefore, the FOTs and considerations
related to the three use cases are still in the early stages oftechnological consideration, and there are various issues thatneed to be addressed before they can be materialized andimplemented in the future. This study will present the main
issues that need to be addressed for practical application of eachuse case. In addition, it is planned to explain each use case toentities and government agencies and request them to consider
measures to deal with these issues and promote their practicalapplication, and to encourage them to implement promotion
measures.

2

Details of the FOTs for the Three Use
Cases

2.1. Ascertaining the Occurrence of Waiting Timeand Sharing Among Related Parties
2.1.1. Details of the FOTs
This is an initiative to collect and analyze information to
study measures to reduce waiting time for cargo by utilizing
information obtained from information terminals installed ontrucks owned by trucking companies and from their in-housesystems, with the aim of improving productivity and long
working hours at logistics sites. Specifically, the occurrence of
waiting time for each vehicle, work day, pickup location, anddelivery location is confirmed by collecting and analyzing theoperation management data of trucks in actual operation, andvehicles and locations where long waiting times (i.e., waiting 30
minutes or more at one location) frequently occur are identified.
The usefulness of sharing the data which identifies the actual
status of waiting times between carriers and shippers inpromoting initiatives to reduce and improve long waiting times

was tested.
According to the Ministry of Land, Infrastructure, Transport
and Tourism's "Survey of Truck Transportation Conditions"(2021), drivers spend 94 minutes waiting for cargo during each
operation (12.6% of the total time per operation). Assuming
200 workdays per year, the annual waiting time is estimated to
be about 310 hours. Reducing such unproductive waiting timeis essential to improve the long working hours of drivers.
To this end, it is necessary for shippers and carriers to sharean awareness of the actual conditions of waiting for cargo, suchas "how many times long waiting occurs at which pickup/
delivery locations," and to discuss the causes of such waiting
and implement improvement measures. However, shippers are
generally unaware of the occurrence of trucks waiting to carry
the shipper’s cargo. On the other hand, many carriers do notaccurately ascertain and record the time spent waiting for cargoat pickup and delivery destinations, and are not able tocommunicate the actual status of waiting to shippers.
Therefore, we believe that the first step needed towards
reducing waiting time is to ‘visualize’ the actual status of waitingtime as numerical data and to have a common understanding
between carriers and shippers. Based on this idea, this testing
project analyzed the occurrence of waiting time based on theoperation management data from the digital tachographs oftrucks used in actual operation, and presented this data analysis
to the carrier and their shipper, and discussions were held toattain mutual recognition about the status of waiting time aswell as consider the causes of waiting and improvement
measures. The ultimate goal is to reduce the waiting time ateach location to less than 30 minutes through the spread ofthese initiatives.
The subject of this test study was processed food products,
which have particularly long waiting times. With the consent ofa transporter that mainly handles processed food products, andtheir largest customer who is the shipper of the goods, operation
management data related to the transportation and delivery ofcargo from the shipper company was acquired and theoccurrence of long waiting times for cargo was analyzed fromthis data. The results of the analysis showed that 374 (57%) of
the 656 vehicles in operation during a two-month period waited
for a load, and that the average waiting time (the average for thevehicles that waited for a load) was 43 minutes per vehicle perday, with a maximum waiting time of 5 hours and 46 minutes in
one day. In addition, 157 vehicles (24%) waited for a long time,
and the average waiting time for these vehicles was 1 hour and23 minutes per vehicle per day. The destinations with an average
waiting time of one hour or longer were listed (Fig.1), and thedate and time of waiting was clarified for the destinations withparticularly frequent waiting.
After explaining this and the other details of the analysis

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Location

No. of Ave. wait Max. wait Date of
Total wait occurmax. wait Day
time
time
time
rences
time
Thu.
Sat.
Sat.
Sat.
Wed.
Thu.
Tue.
Sat.
Sat.
Fri.
Tue.
Wed.
Fri.
Wed.
Fri.
Wed.
Thu.
Mon.
Fri.
Wed.
Fri.
Mon.
Sat.
Thu.

Fig.1: Analysis of locations, days of the week, and times
with high waiting times

data to the transportation company that provided the data andthe company that shipped the cargo, opinions were exchangedon the actual situation of waiting times and target sites, and arebeing used in the analysis of the factors behind long waitingtimes and the consideration of countermeasures.

2.1.2. Results of the FOTs and Future Issues
When we asked the carrier and the shipper that cooperated
in this testing project for their opinions on the usefulness of thetruck carrier sharing such data with the shipper, highevaluations were received such as, "we could numericallyconfirm the status of waiting for cargo, and also obtain new
insights" and "it is very meaningful for both sides to share datathat enables understanding of the overall picture of theoccurrence of waiting times and exchange opinions", and theresults showed that such visualization of the logistics on site canpromote discussions between carriers and shippers to eliminate
waiting time. In particular, there was an issue that it is difficult
for the shipper company to proactively address drivers' long
working hours because the waiting time occurred not at theirown facilities but at the facilities of their customers andconsignee destinations. If the sharing of data with truck
transportation companies enables the shipper to approach theconsignee for improvement based on specific data, theeffectiveness of the system is expected to be very high, as it willlead to improvement efforts from both sides of the supply chain.
On the other hand, a problem that needs to be addressed forthe implementation and spread of this initiative is whether
carriers are capable of cleansing, correcting, analyzing anddocumenting the operation management data. In other words,
the correction and analysis of operation management datarequires a considerable amount of time on the administrator,
placing a different burden on the carrier. In addition, many

managers at carrier businesses may not have the experience orskills to analyze the data. This is the biggest issue for the
implementation and dissemination of this use case.

2.2. Checking Daily Inspection Items Using Vehicle
Data
2.2.1. Details of the FOTs
This is an initiative to verify the feasibility, usefulness, andissues of using truck vehicle and probe data to check inspection
items, instead of checking by the driver, for the daily inspections
carriers are obliged to perform by law before the start ofoperation.
It is estimated that the time required for daily inspections,etc. that carriers are obliged to perform is 25 minutes per
operation, or approximately 83 hours per year. Although there
are several items that cannot be confirmed via vehicle data,
many carriers hope that even a portion of the time required fordaily inspections can be made more efficient. In addition, theopinion was often heard that qualitative benefits such as thecorrection of variations in the level of daily inspections among
drivers and a reduction in the psychological burden of drivers is
expected. Furthermore, in view of the future implementation of
automated driving, studying the reduction of the manpower
required for daily inspections should also be progressed. An
empirical investigation of this use case was conducted based on
the awareness of the above issues.
The Japan Automobile Manufacturers Association (JAMA)
has standardized the "data items provided by vehicle and probe
data" and the "production rules for the API used by users toacquire data from the back-end of heavy-duty truck
manufacturers" and progressing the establishment of a systemthat allows data users to acquire and use common data items
from each truck manufacturer using the same API if the service
menu is the same for that user. Based on this, how event datathat contributes to the confirmation of daily inspection items
can be extracted from vehicle and probe data, etc. wasconsidered, assuming the use of the API.
First, daily inspection items that can be expected to bechecked using vehicle and probe information were identified.
Of these items, those that could be checked using vehicle signalsthat could be obtained from the vehicle model of heavy-duty
trucks used for testing by the truck manufacturer thatcooperated with the FOTs were selected as the target items for
the FOTs. Specifically, the following eight items were selected.
(1) Parking brake signal, (2) Engine speed signal(3) Accelerator pedal opening signal(4) Windshield washer switch signal(5) Windshield wiper switch signal(6) Air tank pressure signal(7) Cooling water low level signal

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Monitor screen

Vehiclesignals

Provideddata

Manufacturer’s
side virtual
back-end server

Communication
unit
User’s sideOn-board device

Fig.2: Diagram of vehicle data acquisition and on-screen display of daily inspection items(8) Lamps and lights signals (vehicle width lights, front lights,
tail lights, number lights,
turning indicators, emergency flashing lights, reverse lights)
In the FOTs, the truck manufacturer provided vehicle signal
data for the above items. Fig.2 shows the sequence of this.
By using this vehicle signal data to display the items related
to the FOTs on the monitor screen shown in Fig.3, the possibilityof being able to use the truck vehicle signal data to confirm therelated daily inspection items was confirmed.

According to our research, the time required for the eightitems covered in this study accounts for approximately 40% of
the total time required for daily inspections, and it is estimatedthat the expected reduction of annual time per person throughimplementation of this is approximately 32 hours. In the future,
if truck manufacturers can provide data based on the API
production rules (both ECU and actuator signals may berequired depending on the daily inspection items), including
other items related to daily inspections that could not be
ascertained in the FOTs, the range of daily inspection items thatcan be inspected based on vehicle data will be expanded and the
reduction in time required for the daily inspections can also be
expected to increase.
Furthermore, if an algorithm is established that can
automatically determine the suitability of the results of daily
inspections from the vehicle data and the results are recorded
electronically, it may be possible to replace visual inspections
with inspections based on vehicle and probe data. As this is
expected to help prevent accidents caused by failure to performdaily inspections, inspection errors and inadequate checking, itis therefore desirable to confirm consistency with laws,
regulations, and public notices, and to consider any necessary
revisions together with the establishment of the technology.

2.3. Measuring Load Weight and Tire Data to Ensure
Legal Compliance and Safety
2.3.1. Details of the Load Weight Measurement FOTs
Fig.3: Sample of a monitoring screen

2.2.2. Results and Future Issues from the FOTs
Using the vehicle data obtained from the vehicles used in the
FOTs, the appropriateness of the relevant daily inspections
could be determined by visually confirming their condition on
the monitor screen. In addition, the disconnection of a brakelight, which was intentionally reproduced as a malfunction,
could also be confirmed on the monitor screen. Therefore, if
vehicle and probe data utilization and API production rules are
established, and if the provided data items can be utilized
remotely on a monitor screen, it may lead to a reduction in theworking hours and workload of drivers and maintenancemanagers.

This is an initiative that verified the usefulness in preventing
overloading, in which the driver measures the weight of theload using a load weight meter when loading and unloading
during actual operation, and confirms the load weight datafrom the driver's seat, while the sales office manager also
ascertains the relevant data via a terminal.
Overloading is an extremely dangerous practice thatinterferes with safe driving, such as braking and steering. For
this reason, carriers generally take care to avoid overloadingwhen loading and unloading cargo, but some shippers may
deliver cargo to carriers without knowing the weight of the
cargo, resulting in overloading. On the other hand, carriers
cannot refuse to accept cargo from a shipper unless they haveascertained that the cargo will be overloaded.

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Data confirmation in the office

Email text & dataattached as a csv file
PortablePC

Display unit image
On-siteconfirmation via
measurement &display screen(shaft weight andtotal value)

Installation of demonstration display unit

Fig.4: Diagram of loading weight measurement and checking

Based on this, the possibility of preventing overloading
through the carrier using a load weight meter to ascertain theweight of loaded cargo and gross vehicle weight when loading
the cargo at a delivery destination and keeping records of theresults was verified. This initiative aims to prevent theoccurrence of unintentional overloading in the course ofoperations.
In the FOTs, axle-weight sensors that measure the load
weight and devices for checking and transmitting the load
weight data were installed on four heavy-duty trucks used in
actual operation. The drivers measured and confirmed the load
weight each time they loaded and unloaded the trucks during
operation. The measured load weight data was also sent to thesales office by email so that the manager could ascertain thedata (Fig.4 shows an overview of this sequence). The abovementioned measurement of load weight, data transmission, andconfirmation by the driver and manager was repeatedly
performed.

2.3.2. 
Results of the Load Weight Measurement
FOTs and Future Issues
Through the above tests, it was confirmed that the load
weight meter can measure generally accurate load weights. It
was also verified that the driver can check the load weight data
each time cargo is loaded or unloaded, and that the data wasappropriately transmitted to the operation manager.
After the tests, interviews were conducted with the transport
operator (managers and drivers), who gave a good evaluation of
the load weight meter, including the following.
・For the operator, the load weight meter helped with safe
operation by preventing overloading.
・For the operator, it is possible to use the data to negotiate
rates with shippers.
・It is useful to be able to ascertain the axial weight on eachaxis.
On the other hand, it was pointed out that it is necessary tolink with devices such as digital tachographs to save labor inoperation and to arrange the sharing of data. In addition, an
opinion was provided that, as it is assumed that data sharing
will lead to collaboration in order to reduce drivers' working
hours, this could lead to increased efficiency through matchingand joint transportation and delivery. Another opinion was thatthe accumulation of load weight data will lead to managementby shipper and vehicle, and that if the data can be linked to fuelconsumption and mileage obtained from the vehicle’s
information, it will be possible to determine fuel consumption
according to actual vehicle weight. In particular, many held theopinion that linkage with digital tachographs is desirable fromthe viewpoint of labor-saving device operation, centralized
management of the terminals from which information to bechecked by operation managers is obtained, and the effective
utilization of data through linkage with fuel consumption anddistance traveled, so it can be said that this is a main issue forthe future.

2.3.3. Details of the Tire Data Measurement FOTs
In this initiative, as with the load weight meters, tires
equipped with a TPMS were installed on heavy-duty trucks
operated by four carrier companies to ascertain tire pressure
and temperature data, and it was confirmed whether it ispossible for this data obtained by the tire manufacturer’s server
could be shared with the carrier company’s operation managersof the carrier companies.
Tire-related problems account for the largest number ofvehicle breakdowns, and carriers have a strong awareness of the
problem of tire maintenance conditions. However, mattersrelated to tire condition that can currently be monitored using
signal data are limited to air pressure and internal tiretemperature, so the initial goal is to eliminate tire failurescaused by poor air pressure maintenance (excluding thosecaused by sudden events or force majeure).
Both tire pressure and temperature values remained basicallynormal during the period of the FOTs, but a tire puncture
occurred in one of the vehicles cooperating with the testing,and as shown in Fig.5, an alert was issued and the operation
manager was notified by email when the tire pressure decreased
below the threshold value. The operation manager noticed theemail and contacted the truck driver, who stopped the vehicle
and changed the tire on site. One mistake in the response could
have resulted in damage to the vehicle and the spread of damage
to the surrounding area due to scattering of tire fragments, butsuch damage was prevented and transportation time loss was

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minimized.
Pressure (kPa)

Pressure

1200

Temperature (°C)
Temperature
60

Tire puncture occurred at this time
50

800

40

600

30

400

20

200

10

0

0

8:04
8:13
8:22
8:31
8:41
8:50
8:59
9:08
9:17
9:26
9:36
9:45
9:54
10:03
10:12
10:21
10:31
10:40
10:49
10:58
11:07
11:16
11:26
11:35
11:44
11:53
12:03
12:12
12:21
12:30
12:40
12:49
12:58
13:07
13:14
13:23
13:32
13:41

1000

Time

Fig.5: Data showing air pressure reduction at the time of the tire puncture

2.3.4. Details of the Tire Data Measurement FOTs
When interviewing the carrier companies regarding the load
weight measurement, they were also asked about tire datameasurement, and most considered tire maintenance to be themost important factor in preventing accidents, and they highly
evaluated the usefulness of checking and managing tireconditions using data obtained while the truck is in motion. In
addition, many of them expressed an expectation for the use ofdata on tire wear and predictive signs of wheel breakage as
future tire data acquisition items.
On the other hand, as with the load weight meter, many held
the opinion of wanting linkage with digital tachographs or asystem to acquire tire data from digital tachographs. Also,
others wanted a system for direct communication to the truck
driver of information about the occurrence of an abnormality,
and that frequent transmission of data is unnecessary and that
notification only when an abnormality occurs or periodic
provision of information once every few months would be
sufficient.

3

Issues to Address to Promote Practical
Application

3.1. Issues in Practical Application of the FOTs’ Concept
As noted at the beginning of this paper, each use case is awork in progress, and there are various issues to be addressed in
the future for their practical application.
The main issues are as follows.

3.1.1. Ascertaining the Occurrence of Waiting Timeand Information Sharing
It is necessary to create a person in charge of organizing
basic data for analysis and preparing data analysis materials.

Regarding the analysis of waiting time, it is necessary to matchthe basic operation management data with other related data inorder to correct the required time and occurrence location, but
it seems that there are many carriers who find it difficult toperform such work and create analysis materials for theoccurrence of waiting time. For this reason, it is necessary to
promote the creation of a business operator (assuming a paid
service) that can perform the task of organizing and analyzing
data on behalf of carriers. It is also necessary to create anenvironment in which carriers can request such data analysis
providers to prepare analytical data on the actual status of
waiting time occurrence.

3.1.2. Checking Daily Inspection Items Using Vehicle Data
As mentioned above, JAMA has standardized the "data items
provided by vehicle and probe data" and the "production rules
for the API used to acquire data from the back-end of heavyduty truck manufacturers" and progressing the establishment ofa system that allows data users to acquire and use common data
items from each truck manufacturer using the same API if themenu is the same for that user. This use case is based on theassumption that this mechanism will be established, and it isexpected that this mechanism will be materialized first. It isstrongly hoped that a wide range of vehicle signal data items
related to daily inspection items, including items not includedin these FOTs, will be selected as common data items provided
by all truck manufacturers.
In addition, daily inspections must be conducted inaccordance with the "Vehicle Inspection Standards" and "Guide
for the Inspection and Maintenance of Motor Vehicles"
established by the Ministry of Land, Infrastructure, Transport
and Tourism. After confirming the consistency of the methodof checking daily inspection items using vehicle data signals
with these regulations, it is desirable to also discuss the revisionof these regulations as necessary.
It is also desirable to establish an algorithm that can
automatically determine the appropriateness of the results ofdaily inspections and to electronically record the results of suchdeterminations in the future. Although these are long-term
technological development topics, it is hoped that procedures
for acquiring truck vehicle signal data will be established andput into practical use, and that the Vehicle Inspection Standards
will be revised in the future, in order to create an environment
in which each company can engage in research and development.

3.1.3. Measuring Load Weight and Tire Data to Ensure Legal Compliance and Safety
Regarding both of these, there is a strong demand fromcarriers to be able to obtain information centrally from digitaltachometers and to be able to link it with other data, rather than216

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﻿3)Research to Realize More Effective Logistics System with Probe Vehicle Dataobtaining the information independently. In this case, it isdesired that it be possible to acquire and link data from thedigital tachographs made by any company in Japan, and not justthose made by a specific manufacturer.
In addition, regarding tire data, research and development
initiatives are being performed to obtain information on tire
wear and wheel breakage risks through linkage with truck
vehicle signal data. The establishment and practical applicationof procedures for acquiring truck vehicle signal data by JAMA,
as described in the previous section, is also an important issue
for this research and development topic.

3.2. Necessity to Create Future Promoters Towards
Practical Application
Regarding "ascertaining the occurrence of waiting time andinformation sharing", it is planned to consider ways to create abusiness entity responsible for organizing basic data andcreating analysis materials, and to request the cooperation oftruck carrier business associations in promoting awareness of
this use case. Regarding "checking daily inspection items" and"measurement of load weight data and tire data", the exchangeof opinions with related companies is being conducted so that it
can be taken up as part of the specific items being considered inthe "Logistics MaaS" promoted by the Ministry of Economy,
Trade and Industry.
It is expected that both the private sector and businessorganizations, as well as the government and other publicagencies, will support the implementation of all use cases.

[Contacts]
Department, NX Logistics Research Institute and Consulting, Inc., 2 Kanda
Izumi-cho, Chiyoda-ku, Tokyo 101-0024
Research & Consulting Unit 1, (General, Tire Data Measurement) 03-5829-1423,
Contact: Yukihiko Akao
Research & Consulting Unit 2, (ascertaining waiting time and information sharing)
03-5829-1405, Contact: Masaaki Kanazawa
Research & Consulting Unit 3, (Confirmation of daily inspection items,
measurement of loading weight) 03-5829-1420, Contact: Daijiro Sato

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4)Utilization and Application of Probe Data
to Road Maintenance and Management
Hirokazu Ichikawa, Atsushi Takenouchi, Naohiro Uchiyama, Shinichi Nedu, Kazuki Tokunaga (Pacific Consultants Co., Ltd.)(Abstract) This project was conducted for the purpose of studying potential generation of data that can be utilized inroad maintenance and management, as well as information useful in automated vehicle driving, such as information
related to poor weather and road conditions, based on probe vehicle data. As probe vehicle data used for studies, wetake up data that can currently be acquired, such as ABS, traction control systems, Electronic Stability Controls, andwindshield wiper operation data, and data that may be acquired in the future, such as road grip level data and water
film thickness estimates. By grasping the correlation between probe vehicle data and various meteorological data thatcan grasp the precipitation and organizing in along the same units for each category, we plan to consider the potential
for data generation and the effectiveness of this data in automated driving support and road maintenance.
Keywords: Probe vehicle data, road management, water film thickness, road conditions, vehicle control log

1

Purpose of project

During our researches in Section 3 3); Technological
Development for Lane-specific Road Traffic Information Using
Vehicle Probes, we improved the detection accuracy of thecongestion tail for each lane by utilizing direction indicator
information. As such, it may be possible to further utilize probevehicle data. On the other hand, data on road conditions during
rain and precipitation information may hold the key for use inroad management operations support and information delivery
for automated vehicles to issue a take-over request in enoughtime, as well as automated driving decisions in poor weather.
Meteorological data provided in real-time has a resolution of
250 meter mesh units in High-resolution Precipitation
Nowcasts. If it is possible to grasp road conditions andprecipitation data from probe vehicle data received fromcommercial vehicles (OEM, etc.), it may be possible to utilize
more detailed information.
This project aims to explore potential generation of data thatcan be utilized in road maintenance and management, as wellas information useful in automated vehicle driving, such as
information related to poor weather and road conditions, basedon probe vehicle data.

2

Overview of deliberations

We extracted and studied probe vehicle data that may be able
to detect status of water film due to rain and precipitation
conditions, degree of danger in driving due to water films, andflooding to / roughness of, paved roads. Specifically, thefollowing data was examined as probe vehicle data for our
researches: data that can currently be acquired, such as ABS(Anti-lock Braking System), Traction Control System (TCS),
Electronic Stability Control (ESC), and wiper operations
information, and data that can be acquired in the future, such as
road grip level data and water film thickness estimate value. We
also studied potential utilization by testing the correlation ofthis probe vehicle data and various meteorological data androad conditions where the status of precipitation could begrasped. (Fig.1)
1. Gather probe vehicle data
Data A: Gather ABS data, etc. (ABS, TCS, ESC, windshield wiper data)
Data B: Gather water film thickness, road grip level data (conduct test
vehicle drives in rain)

2. Compare evaluation of probe data and testing data
(1) Confirm correlation between grip level and water film thickness and ABS data, etc.
(2) Confirm correlation between grip level information and water film thickness andthe status of precipitation(3) Confirm correlation between ABS activation frequency and precipitation status

3. Deliberate use for automated driving supportand road management
Fig.1: Research procedures

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﻿4)Utilization and Application of Probe Data to Road Maintenance and Management

Further, the scope for gathering probe vehicle data, etc. andtesting its potential utilization was No. 1 Haneda Line andWangan Line (Fig.2) as expressway areas and National RouteNo. 357 (Shinkiba, Edo Ward to Funbashi City) (Fig.3) as a
general road.
As a use case, support for road maintenance and management
and automated vehicle driving is expected, as shown in Table 1.
Shiodome

Tokyo Waterfront
City area

techniques, and data organization examples.

3.1. Probe vehicle data
(1) Vehicle control records (ABS, TCS, etc.)
We gathered information (Table 2) on windshield wipers,
ABS, TCS, and ESC activation, which are thought to change
based on precipitation and the status of road water film, grasp
the correlation between this data and precipitation amount, etc.
and consider potential utilization for road management. We
gathered the following data in the below period and locations.
・Data acquisition period: Four months from August to
November 2022
・Data acquisition area: Two lines on Shuto Expressway(Haneda line, Wangan line)・Data acquired: Number of passing vehicles that data can beobtained from, number of activations of wipers, ABS, TCS,
and ESC in a 100 meter zone
Table 2: Overview of vehicle control devices

HanedaHaneda Airport

Source: Added to ⒸNTT InfraNet

ABS

Quickly controls the degree of braking and prevents slips andspins on slippery roads due to rain or snow

TCS

Prevents tire slip that occurs at sudden acceleration on slippery
roads

ESC

Prevent vehicle from going outside or inside lane when turning
around curves

Fig.2: Target line (expressway area)

National Route No. 357(Shinkiba to Funabashi)

Source: Added to ⒸNTT InfraNet

Fig.3: Target line (general road area)
Table 1: Expectation of Use Cases
・G rasp poor weather and road conditions early on, determine
Automated
whether in or outside Operational Design Domain (ODD), anddriving
implement safety mechanism (to decelerate, implement earlysupportmanual assistance requests, etc.)
・D eliberate necessity of asphalt repairs (to prevent splashingRoadpedestrians, extract areas that are easy to slip, such as sudden curves,management
etc.)
support・Urge caution of flooding, etc. based on information boards, etc.

(2) Road grip level information
By analyzing the tire rotation signal, etc., it is possible toidentify the status of grip with the road. The relationshipbetween the slip rate and force differ based on the degree ofslipperiness of the road (Fig.4). This slope is found from the tire
rotation signal, etc. to detect the slipperiness of the road (roadgrip level). If a water film appears on the road due to rain, theroad becomes more slippery. This data enables us to grasp thedanger of potential hydroplaning. We have considered theusefulness of this information.
We acquired the data in the following period and locations
by the following data acquisition method.
・Data acquisition period: Two months from August to
September 2022
・Data acquisition area: Two lines on Shuto Expressway
Tire μ (friction coefficient) -s (slip rate) characteristic
Driving force

Maximum driving force

Regular driving area

Slip rate
High μ3

Gather probe vehicle data and meteorological data

Following is an overview of data needed to test theeffectiveness of probe vehicle data utilization, data gathering

Maximum
control force

Medium μ
Low μ

Control force"Slope" changes
based on road

Source: Materials provided by Sumitomo Rubber Industries, Ltd.

Fig.4: Road grip level estimate

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﻿4)Utilization and Application of Probe Data to Road Maintenance and Management(3) Water film thickness estimates
We use the information of driving force of vehicles driving
on roads to estimate the water film thickness. In addition to
verifying estimate potential, we have grasped the correlation
between estimated water film thickness and precipitation
volume, etc. We acquired the data in the following period andlocations by the following data acquisition method.
・Data acquisition period: Two months from August to
September 2022
・Data acquisition area: Two lines on Shuto Expressway(Haneda line, Wangan line) and National Route No. 357
・Data acquisition method: Estimate water film thickness
through driving vehicles in the rain with devices on-boardthat can acquire necessary data, such as driving force andresistance, etc.

4

After organizing the gathered meteorological data and probevehicle data so that units match in each zone, we have grasped
their correlation. Specifically, we have conducted the following
testing. (Fig. 5)(1)Verify the correlation between the grip level information,
the water film thickness, and the frequency of activation ofABS, TCS, and ESC(2)Verify the correlation between grip level information, thewater film thickness, and the status of precipitation(3)Verify the correlation between frequency of activation ofABS, TCS, and ESC and the status of precipitation
In addition to the above, we used dashboard cameras and
CCTV images to confirm the status of water film generationand grasp the appropriateness of water film estimate for thegeneration of actual water film.
Information source

3.2. Meteorological information

ABS, TCS,
ESC activation
frequency(1)
Probe vehicle data B(Road grip level
information)
High-resolution
Precipitation Nowcasts,
rain gauge(precipitation)(3)

Water film thickness(2)
Precipitation status

Probe vehicle data A(windshield wiper information)

Dashboard cameras/CCTV
images, etc.
(Road/water film status)

Fig.5: Information source and generated information

Fig.6 shows the relationship of the wiper activation detection
rate every ten minutes of vehicles driving on the No.1 Haneda
line and Wangan line and the precipitation during the rain on
August 13, 2022. In this case, we can see a degree of correlation
between the high-speed activation of windshield wiper
100％
80％
60％
40％

Wiper activation detection rate/precipitation volume (per ten minutes)

Weather on Saturday, August 13, 2022:
Rain (typhoon)
10

Other
Intermittent
activation
Low speed
activation
High speed
activation
Precipitation
volume (mm)

8
6
4

20％

2

0％

0

18:00
17:30
17:00
16:30
16:00
15:30
15:00
14:30
14:00
13:30
13:00
12:30
12:00
11:30
11:00
10:30
10:00
9:30
9:00
8:30
8:00
7:30
7:00
6:30

(2) Roadside rain gauges
We gathered precipitation data acquired by rain gauges set
up by road managers to ensure safe and smooth road traffic andgrasp the relationship between it and probe vehicle data. The
following data will be acquired in the following period andlocations.
・Data acquisition period: Four months from August to
November 2022
・Data acquisition points: Rain volume gauges in National
Route No. 357 zone (six areas)・Data details: Hourly precipitation (mm/h), Accumulated
precipitation (mm)Generated information

Probe vehicle data A(ABS, TCS, ESC data)

Wiper activation detection rate(1) High-resolution Precipitation Nowcasts
The
High-resolution
Precipitation
Nowcastsis
meteorological information provided by the Weather Service
Support Center that aims to strengthen monitoring and forecast
ability of localized heavy rain storms. It provides high precisionand detailed radar images and precipitation volume forecasts.
The data of High-resolution Precipitation Nowcasts include
both rain rate intensity and five-minute rainfall rate. Utilizing
both, we have grasped the relationship with the probe vehicledata.
・Data acquisition points: Four months from August to
November 2022
・Data acquisition points: Rain volume gauges in National
Route No. 357 zone (six areas)・Data details: Rainfall rate intensity (mm/h), five-minute
rainfall rate (mm)Probe vehicle data and meteorological
data comparison and evaluation

Precipitation volume (per ten minutes)(Haneda line, Wangan line) and National Route No. 357
・Data acquisition method: Acquire road grip level throughdriving vehicles in the rain with devices on-board that canacquire necessary data, such as tire speed signals, etc.

Fig.6: Windshield wiper activation detection rate/precipitation volume
relationship example (Saturday, August 13, 2022)

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activation detection rate and precipitation. However, wiper
activation detection rate is a figure that applies to the entire
target zone, while precipitation is a value observed in the
Amadeus Haneda observation area. Potential correlation
between windshield wiper activation detection rate andprecipitation within a certain range has been presented. In the
future, we will verify the localized correlation of other probe
data using high resolution precipitation nowcasting, etc. shownin Fig.7.
Saturday, August 13 11:30 Rain rate intensity(mm/h)

Saturday, August 13 11:30 Five minute rainfall
rate (mm/5 minutes)

Source: Ⓒ OpenStreetMap contributors
Add data of Weather Service Support Center and High-resolution Precipitation Nowcasts inbase map of https://www.openstreetmap.org/copyrightFig.7: Examples of analysis datafrom High-resolution Precipitation Nowcasts(Saturday, August 13, 2022, 11:30 a.m., Left: Rain rate intensity,
Right: Five minute interval of precipitation volume)

5

Conclusion

We plan to organize the acquired probe vehicle data andmeteorological data for the 100 meter zone unit. After that, we
will grasp the difference in the data acquisition status of probevehicle data during rain and sun, verify the correlation withobserved meteorological data, and attempt to grasp feasibilityof information generation, detectable phenomenon by probevehicle data, detection accuracy, and resolution. Based on theseresults, we are planning to organize data that can be utilized in
automated driving support and road management.

[References]
(1) Japan Automobile Manufacturers Association: "Producing safer cars...
Preventative safety techniques to prevent accidents..." https://www.jama.or.jp/
library/children/encyclopedia/encyclopedia5.html (accessed 2022.08.30)(2) Forecast Department, Japan Meteorological Agency: Technological information
related to delivered materials No. 507 - Improvements to High-resolution
Precipitation Nowcasts
High-resolution Precipitation Nowcasts-, February 27, 2019(3) Japan Meteorological Agency: Manual on Codes ( in Japanese based on
International Codes, Volume I.2, Annex II to the WMO Technical Regulations)8, No. 23 March 1990 (revised December 12, 2018)(4) Japan Meteorological Agency: Manual on Codes ( in Japanese based on
International Codes, Volume I.2, Annex II to the WMO Technical Regulations)
Appendix No. 36, March 1997 (revised January 12, 2022)[Contacts]
Pacific Consultants Co., Ltd. Digital Services Division, DX Promotion Division,
Traffic System Office, 3-22 Kanda Nishiki-cho, Chiyoda-ku, Tokyo, 03-6777-4476,
Contact: Naohiro Uchiyama

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