PRESS RELEASE

May 30, 2024
Geotechnologies, Inc.

To the Press

GeoTechnologies Develops Intersection Risk Estimation Model
Based on People Flow Data
AI calculates the risk of traffic accidents at daily accessible intersections
and makes it possible to ascertain the risk level.
GeoTechnologies, Inc. (headquartered in Bunkyo-ku, Tokyo; Hiroshige Sugihara,Chairman and Representative Director; Yoichiro Yatsurugi, President and CEO; hereinafter“GeoTechnologies”), an ESG metaverse company, has successfully developed anintersection risk estimation model that calculates accident risk at familiar intersections suchas those in residential areas by combining our people flow data with AI technology.
With this development, it is now possible to calculate accident risks at small intersections
consisting of residential roads, which have been considered difficult to calculate in the past.
This makes it possible to identify in advance dangerous intersections hidden in daily
accessible roads that people usually use for commuting to work or school.
By identifying dangerous intersections, the system can be expected to contribute toaccident prevention efforts, such as presenting safer transportation routes that avoid theintersections in question, alerting drivers and residents in the vicinity, and improving roadmaintenance to enhance safety and security.

■ Social Background of Development
According to statistics from the Ministry of Land, Infrastructure, Transport and Tourism,
the number of traffic fatalities in Japan has been steadily decreasing since 1995*, reachinga record low of 2,610 in 2022. On the other hand, the decrease in the number of traffic
accidents on residential roads has been very small, with one out of every four accidentsoccurring on major roads in 2022. Therefore, the most important issue for further reductionof traffic accidents in the future is how to reduce the number of traffic accidents on roads
used for daily life.
*Ministry of Land, Infrastructure, Transport and Tourism, Traffic Accidents
https://www.mlit.go.jp/road/road/traffic/sesaku/jiko.html■ Features of the Intersection Risk Estimation Model© 2024 GeoTechnologies, Inc.
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The most distinctive feature of the intersection risk estimation model we havedeveloped is that it identifies traffic patterns (straight, right and left turns) atintersections by generating all users’ traffic trajectories based on human flow data , andincorporates cross-traffic and merge-traffic as risk estimation factors.
For example, straight traffic facing each other is unlikely to come into contact witheach other as shown in the blue figure, while cross traffic and merge traffic may comeinto contact with each other as shown in the red figure. Thus, by incorporating theestimation factors of cross-traffic and merge-traffic, we can make more effective
estimation than by simply estimating traffic volume.

Since our people flow data includes information not only on vehicles but also on
pedestrians, it is possible to create pedestrian-specific traffic trajectories. By utilizing
the characteristics of pedestrians, it is possible to estimate the risk of accidents between
vehicles and pedestrians, in addition to estimating the risk of accidents between vehicles.

■ Outline of Research and Development
Based on the characteristics of each intersection, such as the volume of vehicle andpedestrian traffic and the structure of the intersection, taking into account traffic
patterns calculated from our human flow data, and using traffic accident information*
published by the National Police Agency as the correct data, we created a model toestimate traffic accident risk at intersections. Accuracy was verified by comparing theaccident information with the results of risk estimation in a specific area.
*Open data of traffic accident statistics, National Police Agency
https://www.npa.go.jp/publications/statistics/koutsuu/opendata/index_opendata.html■Use Data
The following data are used in each model constructed in this study.

© 2024 GeoTechnologies, Inc.
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・Traffic Accident Information
Period4 years from January 2019 to December 2022

subject

Accidents between vehicles, and between vehicles and pedestrians

・People flow data
Period1 month from June 1, 2022 to June 30, 2022

■ Surveyed subject
The risk of accidents between vehicles and between vehicles and pedestrians was estimatedand evaluated for each target area.
subject

Unsignalized intersections consisting of residential roads

Target Area

4 areas Urban area (more/less accidents), rural urban area
(more/less accidents) **Calculated based on statistical tables from the National Police Agency:
https://www.npa.go.jp/publications/statistics/koutsuu/toukeihyo.html■ Results of the experimentAmong the results of risk estimation, the points that exceeded a certain risk value were set
as dangerous intersections, and their accuracy was verified by comparing them withintersections where accidents actually occurred based on traffic accident statistics.
As a result, it was confirmed that although the number of intersections identified as dangerous
was 115, a very small number compared to the total of 13,787, the actual number of accidents
among these intersections was 70, a high percentage that was consistent with the total
number of accidents that had occurred.
This means that we were able to identify with a high degree of accuracy the dangerous
intersections where accidents had actually occurred, and also that the 45 intersections wemissed were potentially dangerous intersections with similar characteristics to those whereaccidents had occurred, although no accidents had occurred.

© 2024 GeoTechnologies, Inc.
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PRESS RELEASE■ Contribution of features in intersection risk estimation model
In the risk estimation of each type of accident in the intersection risk estimation model, thecontributions of features were extracted in order of magnitude, with cross-traffic and merging
traffic contributing a high percentage in both vehicle-to-vehicle accidents and vehicle-topedestrian accidents. In addition, pedestrian-related features also contributed a high© 2024 GeoTechnologies, Inc.
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percentage to the risk estimation for both vehicles and pedestrians.
These indicate that among the extracted features, cross-traffic and merging traffic, inparticular, contribute significantly to the accuracy of risk estimation. Furthermore, whenestimating the risk of accidents between vehicles and pedestrians, the feature that contributes
the most is pedestrian cross-traffic, which means that pedestrian information is an essentialand critical element for improving the accuracy of the estimation.

■ Future Development
The results of the study confirm that the constructed intersection risk estimation model iscapable of estimating accident risk with high accuracy.
This time, we built a model to estimate the universal risk of an intersection without
considering the time of day. However, the amount of traffic at an intersection change frommoment to moment depending on the time of day, such as morning, noon, and evening,
and the risk is expected to change accordingly. Since our human flow data contains time
information, we are working hard to develop a model to estimate the risk at each time ofday.
We will also work toward commercialization by expanding the scope of target intersections,building a model to enable risk estimation including intersections with traffic signals, and© 2024 GeoTechnologies, Inc.
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then extending the area to the entire country for evaluation.

■ Contact Us
Companies wishing to utilize this "Intersection Risk Estimation Model" should contact us
at the URL below.
https://english.geot.jp/contact/support/■ About Geo Technologies, Inc.
Our mission is to "Fulfill the Earth with Pleasure." Founded in 1994 as a multimedia
software development company, we have been innovating since then, winning the Nikkei
Best Software Award for MapFan the following year, followed by the Nikkei Newspaper
Company Award for the first i-mode map in Japan. The company has further evolved and isleading the Japanese map industry as a major map company, providing not only map data
and location information solutions for corporate clients, but also high-precision 3D data mapsthat are indispensable for the realization of advanced automated driving.
In addition, "TRIMA," an M2E application that allows users to earn points just by movingaround, has been downloaded more than 18 million times* and is used by many people toenjoy daily transportation and various contents. We are a growing company that isexpanding globally by developing NFT, which is rooted in people's daily lives, using blockchain
technology both domestically and internationally.
As an ESG Metaverse Company, Geo Technologies will continue to create a predictableworld of "Geo-Prediction" by integrating various and enormous big data, including location
data and human flow data accumulated over the past 30 years. By integrating cutting-edge
technology with a variety of data related to the Earth (Geo), including location data, people
flow data, and other diverse and vast big data accumulated over the past 30 years, we will
create a world of "Geo-Prediction" and help solve various social issues surrounding the Earth.
*As of May 2024

Headquarters'
location:
Representative:
Founded:Business domains:

Bunkyo Green Court Center Office, 2-28-8 Honkomagome, Bunkyo-ku, Tokyo
Hiroshige Sugihara, Chairman & Representative Director
Yoichiro Yatsurugi, President & CEO
May 1, 1994
Automotive business (Development and edit of navigation data formats, Provision ofnavigation applications, Verification and evaluation through actual driving and desk check,
Sales of map update software for car navigation, Maintenance and development of contents
for business (rent-a-car, truck, etc.), Development, maintenance, and edit of high-definition
maps for AD/ADAS)
GIS business (Map application development kit, Digital map database, Map API for web and© 2024 GeoTechnologies, Inc.
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business systems, Map web portal "MapFan", Car navigation linkage application"MapFanAssist")
Application business (M2E application "TRIMA", "TRIMA Ads", "Geo-Research", Address
check service and address geocoding service, Analytics service of flow of people data, NFT
marketplace, Web3.0 Blockchain, Application for logistics)

URL：

https://geot.jp/

Datasets：

https://business.mapfan.com/en/

TRIMA

：

https://www.trip-mile.com/

TRIMA Ads：

https://ads.trip-mile.com/

Geo-Research：https://www.research.trip-mile.com/

SUGULOGI： https://www.sugulogi.com/© 2024 GeoTechnologies, Inc.
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