ANNUALREPORT

2022-2023
For the year ended March 31, 2023

Railway Technical Research InstituteForeword
Ikuo WATANABE
President of the Railway Technical Research Institute

I am pleased to announce that the RTRI Annual Report 2022 is now available. This annual report,
first published in 2009 (the report on fiscal year 2008), outlines and introduces the organization andactivities of the Railway Technical Research Institute (RTRI), its master plan, research and development efforts, and other activities while referring to visualized data on various fields. We have totallyredesigned the layout for this fiscal 2022 issue to make it suitable for the integrated information sourceof RTRI and to provide high-quality information and faster and easier search experiences. It would
be our great pleasure if you could take this opportunity to see our progress in fiscal 2022 and deepen
your understanding of RTRI.
As many social and business activities were still restricted throughout the entire society due to the
influence of COVID-19, tough times continued in fiscal 2022 for railway businesses. Meanwhile, it wasalso the year when we saw positive signs of recovery in transport demand, such as the inaugurationof the West Kyushu Shinkansen and the resumed acceptance of inbound tourists.
Fiscal 2022 is the halfway point of our master plan, RESEARCH 2025, and the third year of our efforts
to accumulate the fruits of our research and development. RTRI revised its organizational structure
on April 1, 2022. This revision was intended to achieve research and development successes fasterand more efficiently and to make our business operations more efficient. By taking advantage of thefeatures of the new research divisions and new research structures and by increasing our researchresources, we focused on improving safety, especially in enhancing resilience to natural disasters.
We also concentrated on research and development for digital technology-based innovation in railway
systems, one of the urgent issues for all railway operators. Moreover, we set up new research anddevelopment projects in a new field, decarbonization of railway transport, and focused on promoting
the projects. As a result, a total of 273 research and development projects were implemented, and
114 of them were completed. For the heavy rainfall in August 2022, the researchers at RTRI set up a
cross-disciplinary team, quickly conducted damage surveys, and provided technical support.
The Japanese railway, celebrating its 150th anniversary since its opening in 1872 between Shimbashi
and Yokohama, has been contributing to society as the foundation of the state and a driving forceof economic development. RTRI also has been dedicated to playing its part in society for 115 years
while changing its name. Under the vision “-we will develop innovative technologies to enhance the railmode so that railways can contribute to the creation of a happier society”, we will continue to create
quality research outcomes by fully utilizing our capacity as an institute covering all the fields of railway
technologies, to meet expectations by railway operators and customers. Continued support and advice
from all the rail-related people will be greatly appreciated.

Annual Report 2022-2023

1

Overview

Contents

Organization

2 Overview
Results of
4 MajorResearch and Development

36 News Release

Board of Trustees

Board of Directors

Auditors

38 Data2

Annual Report 2022-2023

Railway Technical Research InstituteExecutive
Vice
Presidents

ChairmanPlanning Division

VehicleTechnology Division

Compliance Division

Structures
Technology Division

President
Executive
Directors

Administration Division
Miyazaki Test
CenterPower Supply
Technology Division
Track
Technology Division

Accounting Division

Disaster Prevention
Technology Division

Research &Development
Promotion Division

Signalling andOperation Systems
Technology DivisionInternational
Affairs

Information andCommunication
Technology Division

Wind Tunnel
Technical Center
Yamanashi
Maglev
Test Center
Marketing & Business
Development Division
Health Insurance
Systems Center

Materials
Technology Division
Railway Dynamics
Division
Environmental
Engineering Division
Human Science
Division

Railway Technology
Promotion Center

Maglev Systems
Technology Division

Railway International
Standards Center

Center for Railway
Earthquake Engineering
Research(As of June, 2022)

Annual Report 2022-2023

3

Major Results of
Research and Development
IMPROVEMENT OF SAFETY
1. Rapid seismic damage estimation tool for large bridges• We developed a preliminary estimation tool for evaluating seismic damage to large bridges within a
few minutes of an earthquake event.
• The tool can be used to quickly determine if it is safe to resume operations along railway routes withlarge bridges.
After an earthquake, it is important to be able to assess the extent of damage to all railway structures in order todetermine whether it is safe to resume operations. The Railway Technical Research Institute has previously developed
a nomogram for rapid assessment of seismic damage to standard bridges and viaducts following an earthquake.
However the nomogram is not able to accommodate the more complex behaviors of structures such as large bridges.
The Institute has now developed a preliminary estimation tool specifically for large bridges. The new tool (Figure 1)
breaks down the complex behavior of large bridges into multiple motions (modes of vibration) and estimates the extent
of motion (response value) for each mode of vibration. The previous nomogram, meanwhile, can be used to estimate
response values for each mode of vibration, allowing us to quickly determine the overall extent of damage.
A comprehensive analysis of seismic damage to the 790-meter cable-stayed bridge shown in Figure 2 would take several
hours, whereas the new tool can provide a comprehensive safety assessment in just a few minutes (see Figure 3). The
tool can also be used to reliably estimate damage to individual structural members, such as bearings and supports,
expressed as response values. In this way, the tool can be used to quickly determine if it is safe to resume operations
along railway routes with large bridges.
Estimate damage by comparing response and limit values of member

Use damage estimation nomogram to quickly estimate response
values by mode of vibration

Response values for member: → + ← + . . .
Response value (1) Response value (2)Response
value (1)
Motion of large bridge structure during
earthquake

・・・

Response
value (2)Mode of vibration (1)Add response values for
multiple modes of vibration

Mode of vibration (2)

790 m

Tower

Pier

Bearing
2 2
BearingNo.No.
Bearing
1 1
BearingNo.No.
Bearing No. 0

Figure 2 Cable-stayed bridge

4

Verification value(response value/limit value)

Figure 1 Seismic damage estimation tool for large bridges

Input: maximum expected earthquake motion

0.5

Comprehensive analysis (several hours/days per bridge)

0.4
0.3

Tool (a few minutes)
Calculations are hundreds oftimes faster

0.2
0.1
0

0

10

20

30

40

50

60

70

Bearing No.

Figure 3 Seismic damage estimation

Annual Report 2022-2023

Railway Technical Research Institute

2. Seismic diagnostic for existing pile foundations affected by
nearby excavation work
• We investigated the mechanism via which excavation work reduces the horizontal resistance of existing
pile foundations nearby.
• We developed a seismic diagnostic for existing pile foundations that takes into account changes inground resistance characteristics according to the separation distance between existing piles andexcavated soil retaining walls as well as the resistance width of retaining walls.
• We demonstrated an approximately 20% reduction in overall construction costs by eliminating theneed for large-scale works.
There are many railway projects currently underway in urban areas including building new lines, undergrounding
existing lines, and removing level crossings by relocating the road under the railway line. Most of these projects involve
construction of new underground structures in close proximity to existing railway structures, where retaining walls are
put in place prior to deep excavation. As such, there is a need for a diagnostic tool for existing pile foundations thattakes into account the impact of nearby excavation works (Figure 1). In particular, although it is known that excavation
work impacts the horizontal resistance of existing pile foundations, to date we have not had a good understanding of
the underlying mechanism, nor have we had a suitable diagnostic tool.

(1) During construction
Existing pile foundations (piers)

Retaining walls for excavation

Retaining walls for
excavation

Separation
distance

Diagnosis

Horizontal load due toearthquake motion (or similar)

Soil
improvement
Earthquake
motionWales

Spring constant overdesign factor

We used a combination of numerical analysis and large-scale model testing to investigate the mechanism throughwhich excavation can affect the horizontal resistance of existing pile foundations in the vicinity. We also developed astructural analysis model for use in seismic diagnosis (Figure 2). This allows us to perform seismic diagnosis of existing
pile foundations that takes into account the distance between the piles and the excavation retaining walls as well as theresistance width of the retaining walls. As a result, we can now determine the dimensions of retaining walls needed inorder to provide the necessary seismic performance when excavating near to existing pile foundations. This approachis estimated to reduce overall construction costs by around 20%, since there is no need for large-scale works such as
soil improvement.

Spring constant model for
separation distance

4
3
2
1
0

0

1

2

3

4

5

Separation distance ÷ pile diameter

Model

Ground resistancecharacteristics(2) Completed
Existing pile foundations (piers)
Open cut tunnel

Depth model for separation distance⇨ Interaction springmodel

Resistance widthof retaining walls⇨ Depth model

8
Depth ÷ pile diameter

Pile
Large-scale remedial works(typically soil improvement)

6With wales

420

No wales

0

1

2

3

4

5

Separation distance ÷ pile diameter

Horizontal
Pile
load

6

Depth

Mutualreactive force
Retaining walls for excavation

Figure 1 Excavation near toexisting pile foundation

Annual Report 2022-2023

Figure 2 Seismic diagnostic tool5

Major Results of Research and Development

3. Earthquake countermeasure for poles on viaduct using stays
and overhead wires• We developed an earthquake countermeasure method involving a combination of stays and overhead
wires that effectively reduces longitudinal vibration of poles on viaduct (in train direction) during anearthquake.
• This earthquake countermeasure method, when used together with portal structures, is faster andcheaper than conventional reinforcement or other solutions such as pole replacement.
Portal structures, with a beam connecting two poles on either side of the railway track, are often used for seismic
reinforcement of poles on viaduct. Although portal structures have been shown to effectively reduce transverse vibration(perpendicular to track direction), the additional mass of the beam can extend the natural period in the track direction andlead to resonance with the viaduct itself. In the worst-case scenario, the portal structure may collapse. In combination
with the stays, we have established that rigid connectors, which are attached to poles to prevent lightning damage tooverhead wires, help to reduce the natural period of the poles, in turn suppressing resonance with the viaduct (seeFigure 1).
We used full-scale tests to demonstrate the validity of our analysis model (see Figure 2). We also showed that the stays
effectively reduce the natural period of the poles in the track direction, and that the rigid connectors allow for longer
intervals between stays without affecting performance (see Figure 3). The shorter natural period of the poles effectively
reduces pole vibration associated with L2 earthquake motion (see Figure 4).
In combination with portal structures that mitigate transverse vibration (perpendicular to train direction), this approachproves to be both faster and cheaper than conventional reinforcement or other solutions such as pole replacement.

Stays only

Stays on every second span

(using rigid connectors on overhead wires)

Overhead wire

Beam

Pole
Overhead wire

Stay
Stay

Rigid connector

Pole

0
-1

0

5
Time (s)(a) Test data

10

No stays

0.4
0

0

5
Interval between stays(number of spans)

10Figure 3 Reducing the natural period of poles

0
-1

0.8

(stays only)

1

0

5Time (s)(b) Analysis data

Figure 2 Verifying analysis accuracy(pole response waveform
caused by earthquake motion)

6

Natural period (s)110

Acceleration ratio(no stays)(m/s2)

Acceleration(m/s2)

Acceleration

Figure 1 Earthquake countermeasure using stays and overhead wires

1.0
0.5
0.0

No stays

Stays only

Stays on every
second span

Figure 4 Pole vibration during L2 earthquake motion

Annual Report 2022-2023

Railway Technical Research Institute

4. Rainfall-damaged embankment safety assessment tool
• We developed a tool to assess rainfall-damaged embankments to determine whether it is safe toresume operations, and to help choose the best emergency repair structure.
• The tool simplifies the decision-making process around whether to conduct emergency repairs ona damaged embankment and the extent of emergency repairs required. This encourages the use ofless complex repair structures, thereby facilitating the speedy resumption of train services.
In recent years, heavy rainfall events have been causing severe damage to railway embankments. The initial emergency
response to resuming operations following embankment damage along rail lines typically involves the use of large
sandbags and train operation at reduced speeds. In the absence of proper structural standards or criteria, this meansthat major repairs are often needlessly deployed on relatively minor instances of embankment damage.
We conducted a number of rainfall and load tests to evaluate the impact of resuming train operations on damaged
embankments and embankments fortified with large sandbags. We used the findings to create a flowchart (see Figure1) tool for deciding whether it is safe to resume operations when an embankment has been damaged by rainfall, anddetermining the most appropriate repair structure. The tool uses a nomogram (see Figure 2) to assess the damage tothe embankment (the scale and angle of rupture) and determine whether train operations can be safely resumed without
the need for emergency repairs. Where repairs are deemed necessary, the tool includes a handy selection table (seeFigure 3) for choosing the required repair structure. Both the nomogram and the selection table are based on stability
calculations predicated on the minimum required safety standard for damaged and repaired embankments, indicatedby the blue arrows in Figure 1. Where circumstances allow for preliminary testing and/or site surveys of embankment
strength and groundwater conditions, this information is incorporated into the nomogram and selection table. In somecases it may be determined that there is no need for emergency repair work on an embankment that has been declared
unstable, or that a simpler approach can be used (see red arrows in Figure 1).
This tool can be used to quickly determine whether a rainfall-damaged embankment requires emergency repairs and,if so, the most appropriate structure. The tool facilitates speedy resumption of operations and also helps to reduce thenumber of sandbags deployed in emergency repairs.
START

Evaluate stability to support fully
loaded trains according to type andextent of damage

Evaluate repairs based on nomogram(minimum value)

Is embankment strength and groundwater data
available?

NO

YES
StableEvaluate repairs based on nomogram(site conditions)
Unstable

Choose repair structure from selection table (number of sandbag levels/rows)
Emergency repairs not required

Emergency repairs with large sandbags(embankment height = 3 m,strength = low, water level = high)

END

ResistanceAngle of rupture

100

Expected scale ofembankment rupture80

Stability requirements notsatisfied ⇨ Consider
sandbags or similar

60
40
Sufficiently stable⇨ Safe to resume
operations without
emergency repairs

20
0
30

35

40

45

50

55

60

Angle of rupture (°)

Figure 1 Embankment safety assessment tool
Stability calculation
for large sandbags

Scale of rupture(relative to slope face length)

Sliding force

Nomogram

Preliminary site survey/testing

Unstable

Proportion of slope face affectedby rupture (%)

StableDamaged embankment stability
calculation

Figure 2 Nomogram for safe resumption of operations

Emergency repair selection table (assuming 100% slope rupture)
Height = 3 m

Height = 6 m

Height = 6 m, water level = 0 (or below)

Simpler repairs

Conventional approachLargesandbags

6,000

5
1:0.

6,000

5
1:0.
2,000

Resistance

3,000

Slip plane

5
1:0.
1,000

Sliding
force

Figure 3 Choosing the type of emergency repairs

Annual Report 2022-2023

7

Major Results of Research and Development

5. Creation of time-varying dynamic wind maps
• We have developed a method for creating dynamic wind speed maps that vary in time according to
the update interval of the wind speed observed by an anemometer.
• This method can map wind speeds equivalent to instantaneous wind speeds with an error of
approximately 5 m/s or less.
• This wind map can be used to monitor strong winds and determine operation controls for railway
sections where anemometers have not yet been installed.
In light of the increasing intensity of typhoons in recent years, with stronger winds observaed over wider areas, there
is a need to augment wind data observed by discretely installed anemometers. In order to realize low-cost, wide-area,and detailed monitoring of strong winds, we have developed a method to create dynamic wind speed maps that changewith time by combining “point” wind speeds observed from time to time by anemometers and “surface” wind speed
distributions obtained by CFD (Computational Fluid Dynamics).
First, CFD is performed for each of the 16 wind directions over an approximately 30 km x 30 km analysis domain (upper
left of Figure 1) that includes the location of one or more anemometers. Next, using the results of CFD, the rate ofincrease or decrease of wind speed (hereafter, the rate of increase or decrease of wind speed is denoted as the windspeed maltipliers.) at each analysis grid point within the domain (bottom left of Figure 1) is calculated based on thewind speed at the analysis grid point where the anemometer is located. The wind speed multipliers take into accountthe average wind intensity at each analysis grid point relative to the entire domain, as well as the effect of local windspeed increases at each analysis grid point. By multiplying the wind speed observed by an anemometer (upper rightof Figure 1) by the wind speed multipliers, the wind speed distribution over the surface (wind speed map, lower rightof Figure 1) is obtained. When the wind speed observed by the anemometer is updated, the wind speed map is also
automatically updated. In addition to anemometers owned by railway companies, the Japan Meteorological Agency’s
AMeDAS can be used as anemometers to obtain wind speed data.
Verification with 14 cases of strong winds obtained with anemometers at six locations (Figure 2 upper) confirmed that the
10-minute maximum instantaneous wind speed can be evaluated with an error of approximately 5 m/s (Figure 2 lower).
This wind map can be used to monitor strong winds and determine operation controls for railway sections whereanemometers have not yet been installed.
CFD (Computational Fluid Dynamics)

Observed wind speed (averaged)
Average wind
speed

10 km

30

km

km

Wind speed multiplier(Conversion from average wind speed
to instantaneous wind speed.)Wind speed multipliers are calculated foreach of the 16 wind directions.

Anemometer
AMeDAS wind speed and direction data is updated
every ten minutes

Observed wind speed x wind speed
multiplier = wind speed map equivalent toinstantaneous wind speed

Station A

0.9 0.9 1.2 1.1 1.1
1.1 1.0 0.9 0.8 0.7
1.0 1.1 1.1 0.8 0.7

Station B
Station C

1.2 1.0 0.9 0.8 0.6
1.3 1.2 1.1 0.7 0.5

Wind speed multipilers take into account the
local increase in wind speed corresponding to
the instantaneous wind speed.

Station D
Station E(4)(1)(3)(2)(6)

Wind speed
(m/s)

35
30
25
20
15
10
5
0

5

5.7
4.9
4.1

0

(1)

(2)

5.5
4.4

3.4

(3)

(4)

(5)

(6)

Verification anemometers

When the wind speed observed by the anemometeris updated, the wind speed map is also automatically
updated.

Figure 1 Procedure for creating a wind speed map

8(5)Verification anemometers (1) to (6)Wind speed map

Anemometer

Railway line

Wind speed estimation error
RMSE (m/s)

30

Average winddirection

Figure 2 Margin of error in wind speed
map for 14 strong wind events

Annual Report 2022-2023

Railway Technical Research Institute

6. Braking performance improvement under snow conditions
by controlling wheel temperature using snowproof brakesystems• It is clarified that higher wheel temperatures increase the coefficient of friction of tread brake blocks
in snow conditions.
• Management of wheel temperatures by setting appropriately the air pressure of snowproof brakes canlead to the improved braking performance with no adverse impact on the wheels.
Snow can affect the performance stability of tread brakes, sometimes causing trains to stop out of position at stations.
We conducted bench tests to investigate the causes and mechanisms of the reduction in the friction coefficient of thebrake blocks. We found that for certain brake block materials, under a mild pushing force higher wheel tread temperatures
lead to an increase in the coefficient of friction in the presence of cold water (see Figure 1). This was found to be related
to the pressure of the water entering the interface between the brake block and the wheel tread.
Snowproof brake systems that keep the brake blocks very lightly applied to the wheels during normal motion for theprevention of ingress of snow into the contact interface are implemented, but their potential influence on wheel temperature has not been considered. If snowproof brakes make the wheels warmer, then we can expect to see improved
braking performance due to the higher coefficient of friction of the brake blocks. However this is true only up to a certain
point—wheel temperatures in excess of 150°C can cause problems such as hollow wear on the wheel tread.

High

We designed a model for estimating wheel temperatures during snowproof braking operation with greater accuracy using
the finite element method (FEM). The validity of this methodology was confirmed by our observations (see Figure 2).
Using FEM, we showed that by adjusting the air pressure of the snowproof brake we can keep wheel temperatures at alevel that increases the coefficient of friction on the brake blocks and improves braking performance in snow conditions
without any adverse impact on the wheels.

In-situ observation

Average coefficient of friction (distance based)

10 kN
15 kN
30 kN

0.3

Wheel temperature has no effectunder strong pushing force (30 kN)

0.2

0.1
Coefficient of friction increases withtemperature

0

0

20

40

60

80

100

120

Wheel temperature (°C)

Figure 1 Wheel temperature and coefficient of friction

Annual Report 2022-2023

FEM

Low

Coefficient of friction with water present
0.4

Infrared temperature observation
140
Wheel rim surface temperature (°C)

Stationary test

Temperature

Cold water applied

FEM temperature estimate

120
100
80
60

Wheel temperature when using snowproof brakes
for approximately two hours

40
20
0

0

2000

4000

6000

8000

Elapsed time (s)Figure 2 Observed vs estimated temperature on wheel rims

9

Major Results of Research and Development

7. Platform safety support device using side-mounted cameras• A system for checking platform safety using cameras mounted on the side of the rolling stock.
• Designed to detect passengers close to the train and alert the train operators accordingly. Can also
recognize wheelchairs, baby strollers and white canes.
• Utilizes a high-speed AI model with 20 msec response time using only CPUs, specifically trained forthe railway environment.
On one-person operated (OPO) trains, it is the responsibility of the train operator to ensure platform safety during
departure. In recent years, these trains have been equipped with cameras on the sides of the vehicles, enabling operators
to check the platform from the driver’s seat. We have developed a platform safety support device that utilizes theseside-mounted cameras to provide assistance for the train operators.
For training an AI model, we employed dedicated datasets tailored to the railway station environment, enabling it torecognize both the full body and head of approaching passengers, along with identifying objects such as wheelchairs,
baby strollers, and white canes(see Figure 1). Our device comprises a high-speed AI model developed in-house andpaired with a CPU. The AI implementation prioritizes processing speed while maintaining performance, resulting in
effective real-time operation with a 20-millisecond response time, as illustrated in Figure 2. Notably, our device offers
a cost-effective solution, as it omits expensive components such as GPUs and their associated image processing
semiconductors. Furthermore, its design, lacking a dedicated cooling fan, results in a smaller, quieter, and less vibrationprone system (complies with JIS E4031 Rolling Stock Equipment - Vibration and Shock Tests). The device can be
retrofitted to use the video feed from existing side-mounted cameras providing timely alerts to train operators regarding
approaching passengers (see Figure 3).

AI detection speed

PersonHead
Wheelchair
Baby stroller
White cane

200 msConventional
approach

30 ms

Proposed
approach

20 ms
0

CPU
GPU

Boosts CPU speed withoutcompromising performance

100

200

(ms)

✔ Trained on data set tailored to railway station environment✔ Features lightweight AI technology (knowledge distillation/pruning)✔ Inference engine optimized for CPU processing
Figure 1 Recognizing human form and key objects

Figure 2 AI model optimization

Driver alert

Complies with JIS E4031 vibration standard
Platform safety check system

Passenger
approaching carriage

Uses video feed from existing side-mounted cameras

Figure 3 Platform safety check device using side-mounted cameras

10

Annual Report 2022-2023

Railway Technical Research Institute

8. Silicon buffers on passenger cars provide longitudinal ride
comfort and improved safety in train breakdown operations• We developed a silicon buffer for passenger cars designed to replace existing rubber buffers.
• Internal running tests showed that the silicon buffer reduces longitudinal vibration by up to 6.5 dB.
• A numerical simulation of a breakdown operation involving two 8-car trains found that the silicon
buffer reduces the maximum coupler force on couplings at all points, and also helps to prevent car
body floating.
Most rubber buffers currently in use on passenger cars are designed with no initial pressure, in order to maximize ride
comfort. However, some initial pressure is needed during train breakdown operations so that the buffers can absorb
more energy, which reduces the risk of trainset buckling. Buffers are thus required to perform two conflicting functions:maximizing ride comfort while minimizing the trainset buckling risk. We developed a silicon buffer unit for passenger
cars (see Figure 1) that features an internal attenuating element designed to generate a measured level of attenuating
force in response to longitudinal movement between cars.
We installed the new silicon buffer on a two-car train at the Railway Technical Research Institute and performed a running test with the brakes disconnected on one of the cars (car T). The test shows immediate attenuation in longitudinal
vibration (see Figure 2). The silicon buffer was also found to improve the ride quality level, reducing longitudinal vibration
by up to 6.5 dB compared to conventional rubber buffers. In a numerical simulation of a breakdown operation involving
two 8-car trains, the longitudinal force (coupler force) acting on couplers was found to be lower at all points when rapid
deceleration equivalent to emergency braking was applied at a speed of 10 km/h. There was no indication of car body
floating (with associated risk of derailment) at any point. This indicates that train breakdown operations can be performed
safely without needing to expel air from the air springs (see Figure 3). Thus we showed that replacing existing rubber
buffers with the new silicon buffers will improve ride comfort in the longitudinal direction while also mitigating the riskof trainset buckling during breakdown operations.

Cross-section of buffer

Enlarged view of attenuation element

Cylinder internal wall

Pistondisplacement

Silicon flow

PistonFigure 1 Schematic of silicon buffer
Silicon buffer

Rubber buffer

Longitudinal
acceleration(car M)

Maximum coupler force (kN)

Silicon buffer

2 m/s2Longitudinal
acceleration(car T)

2 m/s

2

0

1

2

3

4

5

Time (s)Figure 2 Acceleration damping in longitudinal direction(brakes on car M, no brakes on car T)

Annual Report 2022-2023

Rubber buffer

Car body floating = yes

500
400
300

Car body floating = no200

Reduced coupler force at all points

100

0

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16

Measurement point

Figure 3 Using numerical simulation
to reduce maximum coupler force(for breakdown operation involving two 8-car trains)11

Major Results of Research and Development

COST REDUCTION

9. Integrated analytics platform for railways• We have developed an integrated analytics platform that aggregates maintenance data from multiple
systems and allows data sharing and analysis between systems.
• By aggregating observation data from different systems and supporting conversion of differing formats
for location information, the platform supports integrated analytical projects involving multiple systems.
Maintenance data such as rolling stock, tracks and power systems is currently held in individual systems. As railways
are systems where these are interrelated, cross-sectional analysis using data from other systems is expected toefficiently identify the root causes of harmful alterations and design appropriate remedial strategies. The problem has
been, however, that different systems use differing data formats and measuring systems (particularly in terms of data
precision and intervals between observations), not to mention different types of equipment and even different formats
for expressing location information.
Our integrated analytics platform for railways aggregates and unifies maintenance data from differing systems to enablecross-sectional analytical studies across multiple systems. The platform uses a standardized kilometrage format that
distinguishes between up/down lines and main lines/passing tracks, and also recognizes discontinuities associatedwith track works, in order to enable seamless conversion of differing location information formats used by different
systems. We have also developed an analytical technique that uses multiple indicators to investigate relationships
between different data sets. This technique can quickly and efficiently trawl through huge volumes of data aggregatedfrom multiple systems to identify the location of any potential changes in equipment status.
In order to verify the functionality of the platform, we built a prototype system of the platform for use on the test track at
the Institute (see Figure 1). Using the prototype system, we demonstrated how maintenance data from multiple systems
is automatically forwarded to the mirror server on the platform and converted into a common format for aggregation.
As an example, we set a simple scenario with one train and one substation, and demonstrated that data on current
supplied by the substation and current collected in the train (in differing formats) could be successfully collated andused to detect an earth fault of approximately 100 A within just a few minutes.

Detecting earth fault by comparing supplied current with collected current
Actual fault current (observed)
Projected difference between supplied current andcollected current (from analysis)

Current/voltage

TracksIntegrated analytics
platform

Distanceaxis
Rolling stock

Power supply

Time axis
GPS

Distance axis
Time axis

Mirror server

Data correlation
analysisStandardized
database

Specific analytics apps

140
120
100
80
60
40
20
0
11:09:00

Simulated earth fault
period: 17 sec

11:09:20
Time

11:09:40

Rolling stock +Power supply

Standardized
kilometrage
Conversion

Common analytics app

Current (A)Current/voltage

Standardizes
location
information

Readout app
Multi-field

Photograph of actual
prototype system

Figure 1 Prototype system of integrated analytics platform

12

Annual Report 2022-2023

Railway Technical Research Institute

10. On-board obstacle detection system suitable for lowlight conditions• An obstacle detection system that can be installed in the driver’s cab. The system utilizes a combinationof camera and LiDAR technology to identify objects on the railway tracks, even in low-light conditions.
• Achieved a detection success rate of over 90% for identifying the human form at distances of up to
400 meters under low-light nighttime conditions.
• The system and sensors can be configured to suit specific performance requirements.
Normally when a driver sees an obstacle on the railway tracks, they slow the train or stop altogether. Automatic detectionof objects in front of the train will reduce the driver workload and boost operational safety. Our obstacle detection system
can be used to assist drivers in spotting obstacles on the railway tracks both during the day and at night.
We have previously developed AI-based systems for the identification of the human form and other objects in video
images. However, these systems achieved a success rate of 95% or higher only at distances of up to 300 metersin daylight and performed less effectively at night. In contrast, our newly developed system also utilizes AI for object
identification in video images, but the incorporation of LiDAR data (laser reflections from objects) enables it to maintainperformance in low-light conditions, including nighttime scenarios.
Figure 1 illustrates the functioning of our system for nighttime obstacle detection on railway tracks. It is evident that the
LiDAR laser accurately identifies a person under conditions where a camera may fail. To assess the system’s nighttime
detection performance, we conducted tests using up to nine LiDAR units at different distances (see Figure 2).
Our findings indicate that when nine LiDAR units are connected, the system can identify individuals located 400 meters
ahead with an accuracy exceeding 90%. Furthermore, increasing the number of LiDAR units enhances resolution,
characterized by the number of laser points per unit area at the detection distance. With 14 units installed, the system
can reliably detect individuals at distances of up to 500 meters during nighttime conditions, achieving close to 100%
accuracy.
While our findings shown in Figure 2 are for distances of up to 500 meters, if newer LiDAR units with laser coverage
beyond 500 meters were to become available, it will deliver further improvements in detection distances. Given that
LiDAR units are compact (around 10 cm3) and relatively inexpensive (under ¥200,000), a system comprising ten or
more LiDAR units is considered feasible in terms of size and cost. The obstacle detection system offers design flexibility,
so that railway operators can choose the sensor configuration to suit their particular performance requirements.
100
Camera only

Not detected

Camera + LiDAR

80

Detected

Detected
position

Detection rate (%)

Enlarged view

14
(25 points/m2)
12(21 points/m2)

90%

60

9
(16 points/m2)

40

6(11 points/m2)

20
0

LiDAR laser points

Figure 1 Detection performance: person on tracks

Annual Report 2022-2023

Estimate basedon test results

Test results3(5.3 points/m2)

0

100

200

300

400

Distance from object (m)500
Number of LiDAR units(laser points per m 2 per 0.1s)

Figure 2 Number of LiDAR units vs detection rate

13

Major Results of Research and Development

11. Tool for estimating bridge deflection from on-board
track irregularity measurements• We developed a tool that estimates the amount of bridge deflection by calculating the difference
between track irregularities measured at the front and rear cars of the train set.
• At speeds of up to approximately 100 km/h, the tool is able to calculate bridge deflection with an errorof no more than around 10% relative to on-site measurements.
Bridge deflection associated with passing trains is a key indicator used in bridge maintenance work. Traditionally,
bridge deflection is measured using on-site instruments for each span in turn. This costly and time-consuming process
has effectively prevented ongoing monitoring of bridges through regular measurements of bridge deflection. Our tool
generates an estimate of bridge deflection by calculating the difference between track irregularities measured at thefront and rear cars of a moving train set.
In order to estimate bridge deflection using observations taken from a moving train set, we need to determine thecorrelation between track irregularity and bridge deflection, which varies in accordance with train movement. We alsoneed to isolate the bridge deflection component within track irregularity. To this end, we first established that bridge
deflection is theoretically proportional to the difference in observed track irregularity between the front and rear carsof the train set. We used this to extract the bridge deflection component by eliminating static track irregularity from thedifference, then multiplied by the conversion coefficient to produce an estimate for bridge deflection (see Figure 1).
Our tool requires track measuring devices to be installed on the front and rear cars of the train set. As Figure 2 shows,
trials on an in-service railway line indicate that the maximum bridge deflection estimated by the tool is accurate to withinapproximately 10% of standard on-site observations for train speeds of up to 100 km/h.
This tool can be used to identify bridges subject to significant bridge deflection along each route, and schedule repair
work on the basis of ongoing monitoring of bridges.

Track irregularity - front car

Track irregularity - rear car
Bridge A

Train speed = 100 km/h

Car load

Structure
Major deflection
crossing bridge

Minor deflection
crossing bridge
Bridge50 m
Difference between
front and rear

Conversion coefficient

Mounted on train body10

Difference between
front and rear
Conversion
coefficient

10

20

30

40

50

60

70

Bridge deflection

Span (m)Figure 1 Bridge deflection calculated from difference in trackirregularity between front and rear cars

14

Positive

Maximum
difference

Position

Mounted on bogie

0

Negative

Track irregularity
at front car

Track irregularity - at rear car
20

Difference
waveform

Conversion coefficient

6
Deflection (mm)Track irregularity

Front

Rear

Track
irregularity

5
4

On-site observation
Train-based estimate

3
2
1
10

Error = ±10%

20

30

40

50

60

70

Span (m)Figure 2 Evaluation of bridge deflection tool(train speed = 100 km/h)

Annual Report 2022-2023

Railway Technical Research Institute

12. Wear measuring apparatus for overhead conductor railusing light sectioning• We designed a contactless measuring apparatus that uses light-sectioning to measure wear depth
on various types of overhead conductor rail with an accuracy of 0.5 mm.
• The system can take measurements at 20-mm intervals from a maintenance car traveling at 30 km/h,
thus providing an efficient and reliable source of data for maintenance.
Overhead conductor rail that employs rail-shaped conductors or deformed contact wires in confined spaces such as
subways and narrow tunnels is subject to wear, yet the width of the contact surface does not change. For this reason,
the standard approach used on overhead contact wires, which involves estimating wear depth based on the width ofthe contact surface, is unsuitable and manual fixed-point inspection is used instead.
Our apparatus uses contactless light-sectioning, via a slit laser and camera, to provide continuous contour measurements that can be used to evaluate wear depth on overhead conductor rail (see Figure 1). By overlaying the contour
generated via light-sectioning onto the original profile of the rigid conductor equipment, the system measures theresidual height of the conductor, and by extension the wear depth (see Figure 2). It can be used on conductors of all
types and profiles. Stationary tests show that the system is accurate to within 0.5 mm irrespective of the type or profileof overhead conductor rail under measurement (see Figure 3).
The existing process for checking wear depth involves a maintenance worker conducting a preliminary inspection ofhigh-wear sites for evidence of wear, then following up with manual measurement if necessary. Our system, which isinstalled on a maintenance car or similar and takes continuous measurements at 20-mm intervals at a speed of 30
km/h, is both more reliable and more efficient than the current approach. The system has already been adopted by the
Tokyo Metropolitan Government Bureau of Transportation for use in measuring wear on rail-shaped conductors as partof maintenance operations on the Oedo subway line (see Figure 4).

Overhead conductor rail

CameraLaser light source

Overlay

Residual height

Original profile Current contour(measured via(from design
light-sectioning)drawing)

Wear depth

Figure 2 Wear depth algorithm
Rail-shaped conductors

0.5
0.25

π shapeT shape

Measuring apparatus

Rail-shaped conductor

0
-0.25
-0.5

Wear depth

Measurement error (mm)Figure 1 Overhead conductor rail wear depth measuringsystemContinuous observation
Value at given point

300 m

Figure 3 Wear depth measurement error for different
overhead conductor rail profiles

Annual Report 2022-2023

Figure 4 Measuring overhead conductor railwear depth in situ

15

Major Results of Research and Development

13. Imaging analysis method for detecting various
anomalies in overhead contact lines• We developed a method that can detect various anomalies in overhead contact line fittings such as
deformation and breaks, and also determine the level of corrosion of messenger wires.
• The tool boosts efficiency by providing preliminary screening for various anomalies.
Overhead contact line fittings are subject to various anomalies including general deformation, localized anomalies suchas dropper bar breaks that are traditionally harder to detect, and incorrect bolt configurations. We developed a newmachine learning algorithm capable of detecting both localized and combination anomalies via training on digitizeddata correlations as used in natural language processing (see Figure 1). Using images of overhead contact line fittings
captured outdoors during daylight hours, the tool successfully detects anomalies with an accuracy of 90% or better.
The tool screens for the four most common anomalies in overhead contact line fittings: deformation, bar breaks,
misalignment, and loose or missing bolts.
Shadows within wires such as messenger wires that comprise wire strands have traditionally made it difficult to detectchanges in color that indicate corrosion. But now, thanks to machine learning, we can isolate wire strand components
within the image, and create color and brightness distributions that can be used as the basis for an overall corrosion
score, as shown in Figure 2. As the example in Figure 3 shows, corrosion scores for messenger wires increase andthen decrease as the corrosion progresses. Using the corrosion score, it will be possible to track the chronologicalprogression to determine the extent of corrosion in the future.
By combining the tool with images of overhead contact lines captured from the top of an electric inspection car or
ordinary commercial car, we can screen the images, detect anomalies sites and assess the extent of corrosion. Thisis significantly more efficient than the current process of manual inspection for anomalies based on overhead contactline image.

Defect severity =highColor indicates defect severity
determined by algorithm

Simulated break in hanger wire

Defect severity =lowComponent identified as in good condition but
incorrectly configured(a) Bracket breakage detected(b) Incorrect bolt configuration detected

Figure 1 Deformation in overhead contact line bracket

Threshold

384
256
128
0
0

No red rustCorrosion score: 0.0

64

128

192

256

Brightness (R component)

No red rustThinning

1.0

384
256
128
0

Red rust
Corrosion score: 0.6

0

64

128

192

256

Brightness (R component)

Figure 2 Quantitative analysis of corrosion in messenger wire16

Red rust overentire surface

512

Corrosion score

512

Brightness (G & B components)

Brightness (G & B components)

Corrosion score is based on pixel distribution below the threshold

0.8
0.6
0.4
0.2
0.0

Figure 3 Typical sequence of corrosion scoresin messenger wire

Annual Report 2022-2023

Railway Technical Research Institute

14. Automated crew scheduling system• We developed an automated crew scheduling system for both drivers and conductors.
• The system takes approximately 30 minutes to generate a daily timetable for around 400 trains using
the least possible number of crews while satisfying all conditions and requirements.
Every timetable revision requires changes to train schedules as well as scheduling of train crews (drivers and conductors)
The aim of crew scheduling is to maintain train operations in accordance with the relevant conditions and requirements
while using the minimum possible number of drivers and conductors. At present, it takes an experienced schedulingplanner days or even weeks to produce a crew schedule for each section of a line, depending on length. Our system
automates this process.
Crew scheduling is a two-stage process. First, we design a duty for each crew member with trains assigned to themfor each shift. Then we draw up a roster that orders the various duties and incorporates rest breaks at suitable points.
Duties are subject to a variety of restrictions and requirements as per the relevant agreements and regulations, including
overall work hours, accumulated driving time, and rest and meal times. Rosters are likewise governed by restrictions
on hours spent at home and rest days. To date, mathematical optimization techniques have been of limited use, since
the number of combinations increases dramatically as more trains are added, making the computation process socomplex and time-consuming as to be unfeasible. We have addressed this by introducing network optimization basedon graph theory. By reducing the number of combinations involved, our system is able to generate crew schedules
with minimum staffing levels in rapid time. It even takes into account the different restrictions applicable to drivers andconductors (see Figure 1).
Our system, which runs on a standard consumer-level computer, takes no more than about 30 minutes to generate
separate crew schedules for driver and conductor sections of a line with around 400 trains per day. This includes duties
and rosters that are broadly equivalent to the conventional manually produced versions (see Figure 2). In addition toefficiency and labor-saving benefits for crew scheduling, the system also helps to reduce reliance on the capabilities
of individual workers.

Arc denotes transfer tonext assigned location

Duty generation network

Manual vs. automated schedulingLine
A(conductors)

Excluded from consideration
due to long working hours

AllowschangeoverB(drivers)

Trains371

431

MethodCrew

Calculation time

Automated

105

7 min. approx.

Manual107-

Automated

117

20 min. approx.

Manual120-

Automated crew schedule for line A

Reduce combinations bylimiting the number of arcs
Depot aDepot bEntrance of rollingstock depot

This arc can not be selected for driver
due to entrance of depot

Different arc settings for drivers and conductors

Figure 1 Duty produced with network optimization

Annual Report 2022-2023

Confirm that requirements are satisfied
for all duties

Figure 2 Automatically generated crew schedule17

Major Results of Research and Development

15. Fast-track embankment structure for confined settings• We have developed a fast-track embankment structure suitable for deployment in confined settings,
featuring a combination of fluidized soil that does not require compaction and a protective layer of rubble.
• Sandbag formwork is combined with anchoring materials to make steeply inclined embankments.
• The structure can be erected around 20% faster than conventional embankment works with around
10% less labor input.
Major projects such as track doubling in urban settings frequently involve construction of embankments in confined
spaces, using small-scale construction machinery as well as compaction by hand. This is both time-consuming andlabor-intensive, and a better solution is needed. Our solution uses fluidized soil, which is made by mixing cement andwater into soil from construction works. Fluidized soil has excellent fluidity characteristics suitable for earthwork anddoes not require compaction, in contrast to conventional embankment construction. To date, fluidized soil has notbeen used in train embankments due to a lack of data on stability performance when subjected to train loads, and also
because it has less strength when dry.
Our proposal adds a protective layer of rubble over the surface to prevent the fluidized soil from drying out (see Figure1). Taking into account dispersion of train load by the rubble layer, we have demonstrated the performance requirements of the fluidized soil including minimum required strength of 600 kPa. Fluidized soil offers good fluidity during
the construction phase and good strength when hardened. Our proposed embankment structure for confined settings
uses a combination of sandbag formwork and anchoring materials, as shown in Figure 2. This provides sufficientstability both during construction and once in service, as shown in Figure 3. The construction phase is shorter than forconventional embankments, and the design allows for steeper slope gradients, thereby reducing the space required
to accommodate the embankment.
Railway embankments in confined settings are traditionally made with precast retaining walls or similar. We have shownthat for an embankment of height 3 m and crown width 7.5 m, our proposed structure can be constructed 20% quicker
and using 10% less labor (see Table 1).

Rubbleprotective layer
Sprayedconcrete• Compaction not required• Minimum strength 600 kPa when hardened
BallastFluidized soil

Steep gradient formwork

Test site

Sandbag formwork
Anchoring materials

Height 2 m

Sandbag
formwork

Track bed
Anchoringmaterials

Sandbag
formwork

Guide bars

Fluidized soil

Pour tube

Enlarged view

OutletFluidized soil

2m

Figure 1 Embankment structure
Construction phase

In service

Lateral (fluid) pressure during
fluidized soil works

Collapse of steep formwork
caused by earthquake or similar

Figure 2 Construction design

Height = 3 m, crown width = 7.5 m
Conventional L type retaining wall
7.5 m
Ballast

Track bed

Resistance: dead load of steep
formwork

Fluidized soil (hardened)

Resistance: anchoring materials

Figure 3 Steeply inclined formwork

18

Single pour to 2 m height

Embankment

Precast retaining
wall

Construction
period

Labor

Cost

1.0

1.0

1.0

0.8

0.9

0.4

3m

Fluidized soil (proposed structure)
7.5 m
Protective
Track bed Ballast
layer
Sandbag
formwork
3m
Anchoringmaterials

Fluidized soil

Sprayedconcrete

Table 1 Proposed structure vs conventional embankment

Annual Report 2022-2023

Railway Technical Research Institute

16. Numerical analysis method for predicting interaction
impacts on closely adjacent tunnels• We developed a numerical analysis method for predicting the potential impacts of excavating a newshield tunnel very close to an existing tunnel, such as subsidence of the existing tunnel and deformationof the new tunnel.
• By providing quantitative predictions tailored to ground conditions, the numerical analysis method canbe used to show that soil improvement works are not required in cases where the ground is alreadyin suitable condition.
Demand for new subways in major cities is making for increasingly congested underground spaces, with adjacenttunnels being built ever closer together (see Figure 1). When a new tunnel is excavated next to an existing tunnel andthe separation between the two is less than the diameter of the new tunnel, excavation work can potentially impact
the existing tunnel, typically by causing deformation. Technical Regulatory Standards state the special condition isrequired. In the absence of any formal methodology in this area, a blanket approach to soil improvement has generally
been applied, regardless of the actual ground conditions, and this can add unnecessary costs. We have developed
a numerical analysis method for predicting the impact of excavating a new tunnel very close to an existing one. The
numerical analysis method is based on new understandings about the behavior mechanisms of closely adjacent tunnels.
The numerical analysis uses a 3-D tunnel model, and mutual interaction between the tunnel and surrounding groundis expressed using springs. We derived a calculation methodology for decreasing the spring modulus of the interaction
spring as ground stability declines (see Figure 2). In trapdoor tests designed to simulate tunnels located very close
together, we were able to the tendency for the spring modulus to decrease closer to the intersection point, as well as
distortion characteristics in the existing tunnel (see Figure 3). Since it takes ground conditions into account, the method
can be used to demonstrate that soil improvement may not be required in cases where the ground is already in suitable(see Figure 4).
Existing tunnel

Existing tunnel

Subsidence associated withdecrease in spring reaction force

Spring reaction force acting on existing tunnel

Separation

Springmodulus = 0
Force acting on ground andnew tunnel

Diameter of
new tunnelD

New shield tunnel

Incorporating groundstability calculations

New tunnel

Spring modulus

Figure 1 New tunnel close to existing tunnel

Stablei st i

ng

tun

ne

Upthrustl

Points =test observations

0.0

Existing tunnel

Distortion

Points = test observations

0

New tunnel
Line = analysis
prediction

0

2D

Horizontal
separation

4D

Horizontal distance from point of intersection

Figure 3 Investigation using interaction spring

Annual Report 2022-2023

0.20
0.00

30
Distortion in existingtunnel (μ)

Reducedstability

Figure 2 Mechanism of interaction and spring parametersLine = analysis
prediction

0.5

-30

UnstableEx

Decreases

Spring modulus ofgroundLooseness zone

Longitudinal deformation
as load decreases

Closely adjacent tunnels: Separation between
tunnels is less than diameter D of new tunnel

1.0

Gradualincrease inspring modulus

Spring modulus
decreases relative tostability

Ne

w

ie
sh

ld

tun

ne

0.25

l

Subsidence

0.50
(mm)

Ground strength (N value)N < 40N ≥ 40Analysis prediction

x Soil improvement
requiredO Soil improvement
not required

Figure 4 Adjacent tunnel analysis using the method19

Major Results of Research and Development

17. Method for evaluating girder vibration characteristics withconsideration for track rigidity• We have developed a simple yet highly accurate method for evaluating the flexural rigidity of girders.
Our method takes into consideration the rigidity of tracks and other components that have traditionallynot been included in bridge design.
• The method demonstrates that the girder vibration amplification factor when a train passes the bridge
may be up to 50% less than assumed. This knowledge allows us to reduce the quantity of reinforcing
steel members required in the design.
Some Shinkansen bridges exhibit significant resonance vibration as trains pass over. In order to accurately predict thevibration characteristics of a bridge, we need to calculate the flexural rigidity of its girders. Conventional girder design
does not consider the rigidity contribution of the tracks and other above-deck components such as roadbed concrete,
track slabs and wheel guards, simply because we do not have a proper understanding of their respective rigidity
contributions. As a result, girder designs are often uneconomical. We have developed a method for calculating girder
flexural rigidity that incorporates consideration for the rigidity of tracks and other above-deck components (Figure 1
upper left). The method even takes into account loss of rigidity associated with cracks in concrete members, in orderto provide a more realistic picture of overall structural integrity.
Tracks and other components used on bridges vary widely in terms of type, shape and design. In addition, cracking
behavior is different depending on structural types such as concrete and composite girders. Our method incorporates
these differences into the girder flexural rigidity calculation by multiplying the cross-sectional rigidity of each component
by the effectiveness factor, a simply used indicator of combined structural integrity (the degree of structural integration
with the main structure). Through a combination of finite-element method analysis and observation of different girder
structures, we have quantified individual effectiveness factors for the rigidity of tracks and other above-deck components(Figure 1 upper right). In addition, we have used fatigue testing to develop a dedicated formula for the effectivenessfactor of concrete girders with bending cracks, which can be used to estimate the progress of cracking under repetition
and the associated decline in flexural rigidity (Figure 1 lower right). The accuracy of the method has been validated
via actual bridge measurements (Figure 1 lower left). The rigidity of reinforced concrete (RC), pre-stressed concrete(PC) and composite girders has conventionally been underestimated. Our method strips up to 50% off the vibration
amplitude factor during train passages for such girders. In the example of the RC girder in Figure 1, rigidity is shownto increase by 80% while the vibration amplitude factor drops by 50%, leading to an overall 10% saving in the quantityof steel used in the bridge design.
Proposed tool
Girder flexural rigidity

Rigidity contribution of above-deckcomponents-effectiveness factor= Σ cross-sectional rigidity of components x
effectiveness factor
Effectiveness factor: indicates combined structural integrity(where 1.0 denotes fully integrated with main structure,
0 indicates adds no rigidity)

Concrete slope for water runoff = 1.0
Roadbed concrete = 0.9
Cement-asphaltmortar/track slab = 0.5

Trough = 0.3
Wheel
guards = 0.5

Rails = 0.5

Tracks and otherabove-deck components
Main girders

Flexural rigidity calculations
Loss of rigidity
due to cracks

Composite girder Rigidity contribution of RC girder
Rigidity

tracks and otherabove-deck components

1.00

1.42

Conventional Proposed
approach
tool

1.45

Observed
valuesConsistent
resultConsistent
result1.00

1.82

Conventional Proposed
approach
tool

1.97

Observed
values

Effectiveness factor for flexural rigidity

Main structure

Loss of rigidity in main structure due to cracks
Onset of cracking
Total cross-section
effective

1.0

Rigidity declines
due to cracks

Predicted
curvature

Steel reinforcement
yieldsLoad

Figure 1 Girder flexural rigidity calculation and application

20

Annual Report 2022-2023

Railway Technical Research Institute

18. Concrete repair methodology based on crack width andlocation• We developed a new concrete repair methodology tailored to the width and location of cracks, basedon the findings of a dedicated study of water penetration through cracks.
• Water seldom penetrates into concrete via undersurface cracks, regardless of width, so repairs should
focus on preventing flaking. Repairs to cracks on the top surface, meanwhile, should prioritize preventing
the ingress of water.
Water ingress via cracks in concrete can lead to significant degradation in reinforced concrete structures. It has
traditionally been assumed that damage is proportional to crack width. As such, repair work has sought to prevent water
ingress via a combination of grouting and surface coating. Repairing undersurface cracks, particularly on the undersideof bridge structures, usually requires scaffolding and is more labor-intensive and time-consuming.
We used an internal imaging system to investigate water penetration in concrete. We found there was less water ingress
via cracks on the undersurface compared to cracks on the top and side surfaces. Interestingly, when we compared
undersurface cracks of width 1 mm and 0.2 mm, we found less evidence of water ingress through the wider crack, aswell as limited penetration (see Figure 1). We also found that applying a coating to the undersurface hinders naturalevaporation and actually leads to an increase in internal moisture levels, with the associated risk of steel corrosion(see Figure 2).

Water penetration
through cracks (g)50.2 mm crack

3.98

4

3.00

3
2
1
0

1.19
Top

1.09
Side

1 mm crack

Significantly less

0.41

0.06

Undersurface

Figure 1 Crack location and water penetration level

Evaporation from undersurface(mg/cm 2)

In light of the above, we conclude that repairs to concrete cracks on the undersurface should give priority to suppressing
concrete flaking rather than preventing water ingress as such. Repairs to top surface cracks, on the other hand, should
be designed to prioritize preventing water penetration (see Figure 3).

20
15

Uncoated

105Inorganic coating

0-5

Organic coating

0

7

14

21

28

Test period (days)

Figure 2 Impact of undersurface coating on suppressing
evaporation of internal moisture◎ Prioritizing preventing water ingress from top surface

Currently: spot repairs as necessary

New repair
methodology◎ Rain strip

Currently: surface-coat underside to prevent flaking◎ Preventing concrete flaking vs. preventing rebar corrosion

Figure 3 New location-specific approach to repairing concrete cracks

Annual Report 2022-2023

21

Major Results of Research and Development

19. Using expansive concrete in track slabs to minimize
reinforcement• We have developed a new track slab formulation and structural design that utilizes the volumetric
expansion of expansive concrete to generate compressive force constrained by internal reinforcement.
• The expansive concrete track slab uses a third less reinforcement by volume and is 5% cheaper
to produce, yet delivers crack resistance and frost resistance characteristics broadly equivalent toconventional track slabs.
Track slabs are a key component of ballastless track (also known as slab track), which has been carrying train loads formore than 50 years. The reinforcement quantities currently specified for track slabs provide little margin for cracking.
Meanwhile, it is effectively impossible to cut back on reinforcement as a cost-saving measure, because the maximum
spacing between rebars is strictly defined in the specification (see Figure 1). Our track slab design requires lessreinforcement thanks to the use of expansive concrete, which expands as it hardens. The volumetric expansion of the
expansive concrete is constrained by the reinforcing bars, generating compressive force that acts to suppress cracking.
This allows us to reduce the quantity of reinforcement used. Meanwhile, excessive volumetric expansion can itself cause
damage, which may impact on frost resistance.
We investigated a number of different formulations and conducted a series of freezing/thawing tests in order to better
understand the impact of the concrete mix proportion (including expansive additives) on volumetric expansion andfrost resistance characteristics. We found that the well balance of concrete strength and volumetric expansion can beachieved by introducing the expansive additive at the expense of cement rather than sand, with only a relatively small
quantity of additive needed to obtain the required expansion. Frost resistance is also equivalent to conventional trackslabs (see Figure 2). Designed to comply with the existing limit for crack width, our track slab requires one-third lessreinforcement and delivers a 5% saving in manufacturing costs (see Figure 3).

RailRail fastener

Rail longitudinal direction

Circular stopper

Track slab

Reinforcement
Maximum allowable spacing between rebars of
200 mm means large quantities of reinforcement
are required

Circular stopper
Filling layer

Figure 1 Slab track design(a) Reinforcement for conventional concrete

Rate of decline in modulus of elasticity (%)

Sand reduced to 64 kg/m 3, with expansive additive

Falling80
60

No significant
deterioration

40
20

Delamination

Maximum spacing between rebars = 312 mm

Surface of 100 mm square test piece post test

0

30

60

90

120

150

180

210

240

270

Freezing/thawing cycles

Figure 2 Freezing/thawing test results (equivalent expansion)

22

Circular stopper

No change

1000Rail longitudinal direction

Cement reduced to 54 kg/m 3, with expansive additive120

Maximum spacing between rebars = 200 mm

300(b) Rebar arrangement for expansive concrete

Figure 3 Track slab manufacturing process

Annual Report 2022-2023

Railway Technical Research Institute

20. U sing temperature sensors to help predict service lifeof electronic signallingequipment• Temperature is a key factor governing the service life of electronic signalling equipment. We havedeveloped a system that can accurately evaluate current and future deterioration levels and predict
service life.
• The system can be used to determine equipment replacement intervals tailored to the operating
environment and usage conditions.
Given the difficulty of evaluating deterioration in signalling equipment, to date a blanket approach has been adopted
whereby all equipment is replaced at defined intervals, irrespective of factors such as equipment type, operating
environment and manner of use. A more tailored approach is required.
We have upgraded the existing approach for predicting service life, which uses acceleration models for electronic
components and solder in signalling equipment, by adding environmental sensors to provide better information about
equipment degradation and enable more accurate prediction of future degradation. Temperature is considered a key
factor governing the service life of electronic signalling equipment. Our acceleration models link actual equipment
operating temperatures (from sensor data) with the temperatures quoted by device and component manufacturers inreliability tests to generate more accurate predictions of degradation and service life for the equivalent period under
test conditions per day (see Figure 1). The existing approach defines a specific temperature as the basis for predicting
service life, then adds an error margin to reflect potential temperature variation associated with factors such as exposure
to direct sunlight and relocation or reconfiguration of equipment. Our system uses data from temperature sensors and isthus able to deliver predictions that reflect actual usage conditions. Trials conducted on electronic signalling equipment
located next to rail lines demonstrated that the system generates more accurate predictions than the existing approachin relation to the temperature margin (see Figure 2). Equipment service life predictions can also be based on weather
data from the Meteorological Agency, but this is less accurate.

Convert via acceleration modelService life under
test conditions L0

Operatingtemperature

Service life under
actual operating
conditions L1

Calculated from temperature sensor
data per unit time (here, per day)Sensortemperature

ActualPredicted

L0

Equivalent
period under
test
conditions,
per dayDays
elapsedActualPredicted

Current level of degradation
based on L0
Estimated future degradation based on predicted temperature (which is basedon repetition data for last 365 days plus climate change modifier)Figure 1 Using temperature sensors to predict service life

Annual Report 2022-2023

Conventional
approach

70
60

Now

50
40
30

100
90
80
70
60
50
40
30
20
10
0

Sensor data

Remaining service life (%)

Testtemperature

Temperature (°C)

Our system can be used to determine equipment replacement intervals that better reflect the actual operating environment and usage conditions.

Predicted
Remaining service life = 56%
Replace in 8.5 years from now

Service life predictedby conventional
methodAccuracy of temperature margin15.7 years

12.7 years

0

5

10
Elapsed time (years)

15

20

Figure 2 Testing on electronic device close to rails(aluminum electrolytic capacitor)

23

Major Results of Research and Development

21. Effective and efficient steam weeding technique• We have developed a weed control technique using steam that is both effective and easier to use.
• Field tests found that regrowth of a large variety of weed one year after steam weeding fell by 70%.
• The tests also showed that steam weeding is 44% faster than conventional slashing using the bush
cutter and requires only three workers instead of five.
Slashing using the bush cutter is generally considered a faster way to remove weeds growing on railway field between
the tracks and the side boundaries. During summer, however, weeds can grow back quickly, while workers can only use
bush cutters for relatively short periods due to the health impacts of vibration. In addition, signaling and communication
cables near to tracks have to be protected from potential damage by the bush cutter. Steam spraying is an effective andefficient weeding technique that is used in the agricultural sector. The heat of the steam causes thermal denaturationof key proteins in weeds and effectively withers them . We have adapted this process for weed control on railway field.
Conventional steam weeding requires considerable quantities of water as well as a dedicated large boiler designed formains water. Our technique uses a general purpose steam cleaner fitted with a high-efficiency hand-held nozzle arrayspecifically developed for use on railway field, where access to water may be limited. Compared to conventional steam
weeding, the nozzle array uses less than one-tenth as much water (72 l/h vs. 1,000 l/h), while still providing sufficientheat to wither weeds and also be easier to use (see Figure 1). In addition no requirement need to protect signaling andcommunication cables since steam weeding does not involve a slashing blade.
Trials conducted at a test site covered in thick growth of large weeds found that weed regrowth three months after steam
weeding was no more than around 10%, while the number of plants reappearing after one year fell by 70% (see Figure 2).
Steam weeding was also found to be 44% faster per unit area. It would take an estimated 50 minutes to cover an areaof 300 m2 including preparation to finish, some 30% faster than slashing using the bush cutter, which takes 72 minutes.
Steam weeding also delivers a 60% labor saving, since it requires only three workers rather than five.

Hand-held nozzle
array

General purpose steam cleaner(Water consumption 72 L/h)

Slashing using the bush cutter

Steam weeding
BeforeGenerator

Test site

Thick growth oflarge weeds overentire test site

Test site(a) Equipment
Spray gunAfter 3 months

Unique nozzle configuration withers
weeds in five seconds

Hose

Regrowth
area

Cover helps to disperse steam
more effectively

Regrowth
area

Slashing using the bush cutter
Regrowth over entire test site,
some weeds flowering
Steam weeding
Regrowth confined toaround 10% of site area,
no flowering

Regrowth after one year
89 plants per m 2

25 plants per m 2

70% less regrowth(b) Nozzle array

Figure 1 Steam weeding equipment

24

Figure 2 Removing large weeds (Solidago altissima)

Annual Report 2022-2023

Railway Technical Research Institute

HARMONIZATION WITH THE ENVIRONMENT

22. High-efficiency generator design for diesel electric cars
• We developed a technique for boosting power generation efficiency in diesel electric cars using detailed
thermal efficiency mapping to modify the engine working points.
• We estimate that the combination of a high-efficiency generator and energy-saving modifications to
the engine working points could reduce fuel consumption by around 10%.
Hybrid diesel and diesel electric railcars for non-electrified lines, introduced in a bid to save energy and reduce
maintenance workloads, have poor mechanical power transmission efficiency compared to standard diesel hydraulicrailcars, which translates to higher fuel consumption. We have designed an improved high-efficiency power generation
system centered on the engine and generator.
Two factors are critical to improving the fuel consumption of the power generation system: running the engine in thehigh-efficiency zone, and minimizing generator losses. Engine efficiency characteristics are not widely known, and todate have not been incorporated into system design. We used detailed thermal efficiency mapping generated fromunit tests to derive an efficiency map of the power generation system. We used the efficiency map to identify the mostefficient working points in the engine as the basis for our generator design (see Figure 1). Engine output is proportional
to the product of engine speed and torque. Our technique involves finding the optimum combination of speed and torqueto deliver maximum power generation efficiency without affecting engine output.
We investigated the power generation capacity and efficiency characteristics of the induction machine and brushlesssynchronous machine, which have been widely used over the years, as well as the more recent permanent magnet
synchronous machine. We found that the permanent magnet synchronous machine has the highest efficiency.
We demonstrated that the combination of a high-efficiency permanent magnet synchronous machine together withenergy-saving modifications to the engine working points can reduce the fuel consumption of a diesel hybrid railcar byan estimated 10% (see Figure 2).

0.375
1600

0.300

Torque (Nm)

1200

0.275

1000

0.250

800

0.225

600

200
1200

0.200

Fixed output

400

80%

0.325

Most efficient working points

Fuel consumption

1400

100%

0.350

60%
40%
20%

0.175
1400

1600

1800

Speed (rpm)

2000

0.150
Efficiency

Figure 1 Energy map for power generation system and mostefficient working points

Annual Report 2022-2023

0%Conventional working points(induction machine)

Optimized working points(permanent magnet
synchronous machine)

Figure 2 Calculating energy savings of high-efficiency
generator in combination with modified engine
working points

25

Major Results of Research and Development

23.Driver advisory system for long-distance trains by speedtransition estimation• We developed a driver advisory system for long-distance train stopping at limited stations that generates
real-time estimations of recommended train speed transition to improve punctuality and save energy
consumption, and displays recommended driving operation to the station to be passed.
• In trial operations by several drivers with several trains, the system provided useful driving instruction.
• We also demonstrated that the system achieved energy savings in the range 4% - 14%.
Driving operations of train drivers are various. We developed a driver advisory system that reduces the variance, improvepunctuality, and save energy consumption. The system combines train speed and location (from Global Navigation
Satellite System data) with route data such as stations and gradients to provide real-time estimation of train speedtransitions. The system also recommends driving operations to the stations to be passed thorough. A tablet PC in thedriver’s cab estimates several train speed transitions for different driving instructions based on the speed and positionof the train at the present time, and selects a driving instruction to improve for punctuality and save energy consumption(see Figure 1). And, the system shows the recommended driving instruction to the driver, and shows the time that thetrain is expected to pass through the station (see Figure 2). The system also provides audio prompts at the appropriate
timing. The system saves energy consumption by eliminating unnecessary acceleration and deceleration and reducing
losses associated with running resistance and traction equipment.
We used the system as trials on several freight trains and several drivers. We then interviewed the drivers and also
asked them to fill in a survey. The responses were generally positive, indicated that the system is easy to use. We
investigated the difference in energy consumption when using the system, and found that it delivers energy savings in
the range 4% - 14%, depending on factors such as route, gradient and travel time (see Figure 3).

station to be passed

Ahead of time
Punctual

Speed

Behind time

Energy consumption per unit train set mass (Wh/t)

0

200

400

600

800

1000

“Accelerate to 70 km/h then coast”
Declining
gradient

Route A (loaded)

Location

Figure 1 Estimated train speed transition
Route B (loaded)
Station B

Pass at 13:31:00

Station C

Pass at 13:35:00

Recommendation:
5 sec delay
Recommendation:
on timeTime predicted topass through station
Recommended
driving technique

Recommendation: coast

Route C (empty)

3.0 km remaining at constant speed of 62 km/h
Station A - Station B
Coast

62 km/h constant speed

Route D (empty)

Entry

Station A
Exit

Estimated train speed transition
Down 3

Down 2

Down 1Figure 2 System screenshot

26

Entry

Station B
Exit

System off

System on

Figure 3 Comparisons of Energy consumption

Annual Report 2022-2023

Railway Technical Research Institute

24. Energy-saving driving operation to minimize the energy
consumption during small scale delays• We have developed an energy-saving driving operation for use during small scale delays that can
reduce powering energy and promote regenerative power interchange by coordinating the timing of
powering and braking.
• The energy-saving driving operation integrates seamlessly with automatic train operation and helps
drive the decarbonization of railways.
In the event of a small scale delay lasting a few minutes, trains are sometimes required to be suspended at stations. The
aim is to prevent spikes in passenger numbers and also to avoid having trains slow down or come to a stop between
stations. When a train proceeds slowly to the next station (instead of stopping at the previous station), we can reduce
powering energy (see Figure 1). Meanwhile, by adjusting the suspension time at stations and coordinating the timingof powering and braking by the affected trains, we can increase regenerative energy (see Figure 1). To date, we havenot been able to reconcile these two seemingly conflicting objectives. But our proposed energy-saving driving operation
uses mathematical optimization and simulation to achieve both objectives simultaneously. We begin by defining theregenerative power interchange index, the potential for regenerative power interchange during periods when poweringand braking coincide for two separate trains (see Figure 2). We then use an existing Train Operation Power Simulator
to calculate, for each train in turn, how travel time between stations affects powering energy. Mathematical optimization
tries to minimize the difference between total powering energy and the sum of the regenerative power interchange
indices multiplied by the coefficient W. A larger W value indicates higher priority to regenerative power interchange.

Powering

Braking

Train A

Powering

Station B

Time

Adjusted stopping time
Adjusted running time

Suspendedat station Reduced powering
energyTrain B

More regenerative energy

TimeMore regenerative
energyRegenerative energy

W = 0 W = 0.05 W = 0.1 W = 0.15 W = 0.2 W = 0.25

Train 2Train 1

Time

200
180
160
140
120
100

Figure 3 Powering energy vs. regenerative energy

Annual Report 2022-2023

Train 3

Station A to B

Station C to B

2.5%
Energy savings

Powering energy

Train 2

(period when powering and braking coincide)

Adjusted running time

Regenerative energy (kWh)Powering energy (kWh)Train 3Train 1

Regenerative power interchange index

Adjusted stopping time

Adjusted running time

Mathematical optimization minimizes energy consumption
by reallocating suspension times at stations across runningtimes and stopping times

Station D

Time

Figure 1 Basic principle of energy-saving driving operation during small scaledelays
760
740
720
700
680
660

Braking

Station C
Suspended
Reduced powering at station Reduced powering
energyenergySum total is the same
Train Operationx
Power
Simulator

Coasting

Station A

Speed

Adjusted operatingintervals due todelay of trains(energy-saving
driving operation)

Coasting

Speed

Adjusted operatingintervals due todelay of trains(Currently)

Speed

Normal operations

Speed

We analyzed the train timetable over a period of one hour following a small scale delay, which includes the time taken
to restore normal operations. We looked at two scenarios: trains suspended at stations, with no consideration of energy
consumption; and using our optimization approach. The analysis found that both powering and regenerative energy
increased in line with the W value (see Figure 3), and that the optimized energy-saving driving operation delivers energy
savings of around 2% (see Figure 4). The energy-saving driving operation is readily incorporated into automatic train
operation systems because it updates train arrival and departure times, and speed profiles in real time. It can also be
used to build energy-saving considerations into timetable planning.

Train 3 braking
Regenerative power interchange
index
exchange index
for trains 2 and 3interchange
index
Regenerative power exchange
index
for trains 1 and 3

Station C to D

Time

Figure 2 Regenerative powerinterchange index

2.0%
1.5%
1.0%
0.5%
0.0%

W = 0 W = 0.05 W = 0.1 W = 0.15

W = 0.2 W = 0.25

Figure 4 Projected energy savings

27

Major Results of Research and Development

25. Modular sound insulating barriers designed to replace
existing noise barriers on viaducts• We have developed a modular sound insulating barrier designed to replace older noise barriers.
• The modular barriers can be assembled and installed by hand, during daylight hours, without needfor heavy machinery.
• The barriers feature a laminated sound absorption material that has been tailored specifically to thesound of passing trains, so as to mitigate noise levels along train lines.
Future large-scale renovations on Shinkansen (bullet train) viaducts are expected to include replacement of aging noisebarriers. This requires a new type of barrier that is easier to install and also more effective at reducing noise levels. We
have developed a modular sound insulating barrier intended as a replacement for existing noise barriers (see Figure 1).
The modular units are essentially tall posts of identical width, which are linked together to form a contiguous wallalongside the railway tracks. Structural strength is provided by corrosion-resistant steel panels treated with an anti-rust
coating that weigh about a third as much as traditional concrete noise barriers while still being sufficiently sturdy. Thanks
to the lightweight design, our barriers can be assembled and erected manually by workers, without the need for heavymachinery, provided that sufficient measures for safety considerations can be made.
Soundproofing is provided by layers of laminated polyester fiber sound absorption material with differing characteristics
on the side facing the tracks (see Figure 1). Sound absorption is equivalent to or better than glass wool, used extensively
in noise barriers for many years (see Figure 2). By integrating sound insulating barriers with sound absorption panels,
we can reduce noise levels along the entire polyester surface facing the tracks, eliminating the need for additionalsound absorbing work. We trialed the modular barrier system on a commercial line, using a directional microphone tomeasure noise levels. The equivalent continuous A-weighted sound pressure level averaged from nine passing trains(single train sets) was 2.4 dB lower compared to conventional concrete noise barriers (see Figure 3).

Fixed-width poles joined
together to create
contiguous barrier

Noise testing on modular barrier

2.6 m

0.3 m

Layer 1
(facing noise source)

Modular sound insulating barrier

Layer 2Laminated sound absorption material

2.6 m

Designed to absorb the specific frequencies
generated by vehicle undercarriage

Directional microphone

Sound absorption coefficient

Figure 1 Modular sound insulating barrier
1.2
1.0
0.8
0.6

Conventional glass wool

0.4

Laminated sound absorption material (isolated)

0.2

Modular sound insulating barrier(steel panel + laminated sound absorption material)

0.0
100

500

1k

2k

A-weighted sound pressure level

Power collector

5 dB

2.4 dB reduction inequivalent continuous
sound pressure level*(Average for nine trains)

Conventional concrete noise barrier (2.6 m)
Modular sound insulating barrier (2.6 m)* Time-average sound pressure level for 16-car trains measured at 25 observation pointon ballast section, train speed = 300 km/h

Time

3k 4k 5k

Frequency (Hz)Figure 2 Results of sound absorption coefficient by
reverberation room method

28

Figure 3 Sound pressure level time history waveform comparison(using directional microphone at 25 m observation point on different noise barrier sections, for identical passing trains)

Annual Report 2022-2023

Railway Technical Research Institute

26. Reducing aerodynamic noise and pressure fluctuationfrom bogies on high-speed trains• We developed several strategies for mitigating aerodynamic noise and pressure fluctuation generated
by bogies on high-speed trains, and validated their efficacy through wind tunnel tests.
• We then tabulated the wind test data in table form to illustrate the level of mitigation at different
frequencies.
• Further tests conducted on real trains showed that these strategies are effective in mitigating both
aerodynamic noise and pressure fluctuation.
Reducing both noise and low-frequency pressure fluctuation on open sections is key to improving amenity in the vicinityof rail lines. Noise generated by high-speed trains can be broken down into a number of constituent elements: wheel
noise, noise from structures such as bridges, and aerodynamic sound from train bogies, which is the single largest
noise source. Past research has shown that bogies are also the primary source of pressure fluctuation from passing
trains. We developed several strategies for mitigating both aerodynamic sound and pressure fluctuation from bogies
on high-speed trains, and used wind tunnel tests to validate their efficacy (see Figure 1).
We adopted a two-pronged approach to reducing aerodynamic sound and pressure fluctuation: (1) keep internal partsof the bogie away from high-speed airflows, and (2) suppress noise propagation by adding sound insulating materials
inside the bogie storage area to mitigate multipath sound reflection. We conducted a series of wind tunnel tests toassess the efficacy of different strategies in reducing aerodynamic sound and pressure fluctuation (see Figure 2).
Table 1 shows how the cumulative effect of multiple strategies used in combination reduces pressure fluctuation andaerodynamic sound at key frequencies. In relation to rounding of corners, considered the most easily applicable solution,
we measured noise and pressure fluctuation levels on an operational train where the railway operator had already done
something similar, and we found that this had proven effective.
In the future, we plan to conduct running tests on low-noise train models and use our findings to inform the developmentof enhanced practical solutions suitable for deployment on commercial train services.
(1) Noise sources(2) Noise propagationSound
absorption
material

Baffle plate

Front and rear cover plates

Rounded corners

Undercover(with sound absorption
material on upper face)

Porousmaterial
In corner sections

Figure 2 Strategies to reduce aerodynamic soundand pressure fluctuation from train bogies
Unidimensional microphone array for pressure variation

Table 1 Effectiveness by frequency

Wind direction

Bogie

Frequency (Hz)Two-dimensional microphone array for
aerodynamic soundFigure 1 Wind tunnel testing to evaluate
aerodynamic sound and pressure fluctuationfrom train bogies

Annual Report 2022-2023

Pressure variation(low frequencies)

Aerodynamic sound(mid frequencies)

<16

25

50

250

500

1000

2000

Baffle plate

-0.8

-0.4

0.2

0.4

-1.2

-1.3

-2.2

Rounded corners

-0.8

-0.1

-0.3

-0.3

-0.4

-0.3

0.1

Porous material

0.0

-1.0

-1.4

-0.6

-0.4

0.1

0.8

Sound absorption
material

0.0

-0.1

-1.0

-0.7

-1.1

-1.0

-1.2

Undercover

-0.8

-0.3

-0.3

-1.1

-1.7

-0.4

-0.9

Blue indicates impact (dark blue = strong impact), all figures in dB

29

Major Results of Research and Development

IMPROVEMENT OF CONVENIENCE
27. Automatic interim timetabling algorithms in case ofdisasters and train crew/rolling stock shortages• We developed an automatic timetable generator that produces interim timetables for use in emergency
situations such as natural disasters and in the event of train crew or rolling stock shortages.
• The generator produces interim timetables that are tailored to constraints around crew and rolling stock
availability, and also designed to accommodate compounding factors such as longer travel times due
to speed restrictions and congestion.
In the event that trains cannot operate along with the base timetable because of crew and/or rolling stock shortages,
for instance due to pandemic or natural disasters, an interim timetable with fewer trains is required. It has to take into
account crew and rolling stock availability as well as other constraints such as longer travel times due to speed restrictions on some sections. Making a such timetable involves as much work for timetable planners as making a fully new
timetable (see Figure 1). The interim timetable has to be renewed when the line section in operation is expanded in
the restoration process of the disaster. For a busy commuter line in a big city, it takes five days or more to produce anupdated timetable. We have developed a fast timetabling algorithm which can generate an interim timetable for a busy
commuter line in a big city, by reducing the minimum necessary number of trains reflecting any operating restrictions.
Our timetabling algorithm calculates the frequency of trains that can be delivered, subject to constraints around crew androlling stock availability, as the basis for a workable timetable. Where the imposition of speed restrictions causes longer
travel times and reduced rolling stock allocation efficiency, the timetabling algorithm calculates the impact on service
frequency by applying a multiplier to represent the increased duration of each return trip. The timetable generator also
optimizes train intervals and dwell time to break the snowball effects between reduced services (which lead to longer
intervals between services, more congestion and extended dwell times) and extended dwell times (which in turn leadto longer intervals between services). Thus, the interim timetable is able to provide transportation services tailored tomultiple constraints with minimal delays.
In trials conducted on an operational railway line, the timetabling algorithm was able to generate an interim timetable
in a matter of seconds (see Figure 2). This will greatly assist with business continuity planning (BCP) in the event of
natural disasters or pandemics.

Station A(1) Rolling stock
Closedavailability
section Number of trains
Station B

Base timetable

StationBase timetable

ClosedsectionStation C(2) Crew availability
Total train travel time
Sum of train travel time
under interim timetable

StationStationStationStationStationStationStationStationStationStationStationStationStationStation(3) Speed restrictions,extended dwell times
Multiplier for return train train trip

Interim timetable

StationStationStationStationStationStation

Closedsections

Constraints

Station E

Average train interval = 4:31

StationStation D• Rolling stock: 87% availability

Interim timetable• Crew: 75% availability• Train travel time: 50% increase• Dwell time: 95 sec longer

StationStation F

StationReturn train trip duration - base timetable

StationStationStationStationReturn train trip duration - interim timetable
Both rolling stock allocation efficiency and service frequency decline in proportion to
duration of return train trip

Figure 1 Key constraints and considerations

30

StationStation* Timetable generated within
seconds

Equal train intervals of 6:25

Figure 2 Base vs. Interim timetable

Annual Report 2022-2023

Railway Technical Research Institute

28. Tool for evaluating congestion in terms of passenger
discomfort in station• We developed a tool for evaluating congestion in a station environment in terms of passenger
discomfort, based on a combination of passenger flow simulation and modeling of the level andduration of congestion tolerated by different passenger cohorts.
• Passenger discomfort level represents a handy indicator for comparing congestion levels in different
sections of a station and also for comparing stations. This information can then be used to prioritize
planning and improvements at railway stations.
Queue time — the duration of continuous congestion in a specific location — is currently the main indicator for evaluatingcongestion in station premises. However, there can be considerable variance between stations with respect to the spatial
design and configuration of congested locations (such as staircase width) as well as how they are utilized (such as therelative proportion of those traveling up versus down). Further, the willingness of passengers to tolerate congestion ina station environment differs between cohorts. Queue time alone is therefore not considered an adequate measure ofcongestion for the purpose of comparison.
We developed a tool for evaluating congestion in a station environment based on the notion of passenger discomfort.
The tool combines passenger flow analysis with modeling of the level and duration of congestion tolerated by different
cohorts of passengers, as determined through a survey of passengers on congestion (or density) tolerance (seeFigure 1). By calculating passenger discomfort, which represents a constantly changing dynamic, the tool augments
the traditional measure of queue time to enable quantitative evaluation of congestion levels in different parts of station
premises. Passenger discomfort in congested locations is calculated taking into account factors such as passenger
attributes and the spatial configuration of the station, as determined via passenger flow analysis. This allows us to derivea common indicator of discomfort that can be used to make direct comparisons with congested parts of other stations.
By enabling direct comparison of discomfort levels at different congestion locations within station premises, and also
between stations, the tool can be used to prioritize planning and improvements at railway stations.

Congestion evaluation tool
Perceived separation from others(= congestion level)
Acceptable

Unacceptable

X

Acceptable duration of congestion(= allowable duration)
Acceptable
00:15

Unacceptable
00:30 ... 2:45

3:00+

Outcome
Passenger cohort

Acceptable acceptable level of congestion for allowable duration
Tolerable - tolerable level of congestion for allowable duration
Unacceptable - all other situations
Discomfort level on stairs = 5%(color denotes passenger cohort)

Passenger flow simulation withmodeling based on passenger cohort

5% 8%

87%

Figure 1 Evaluating congestion at station premises

Annual Report 2022-2023

31

Major Results of Research and Development

BASIC RESEARCH

29. Investigation of vehicle behavior during earthquakewith focus on on-rail safety limit• We have developed a simulator for evaluating vehicle behavior during an earthquake that extends
beyond the safety limit of 70 mm relative lateral displacement between wheels and rails traditionallyused in simulations.
• Our simulator accurately reproduced the results of excitation tests on a 1/10 scale model of aconventional line train.
To date, analysis of vehicle behavior during an earthquake has traditionally employed a safety limit of 70 mm relative
lateral displacement between wheel and rail (hereinafter referred to as “70mm safety limit”). We know little about vehicle
behavior outside of the 70 mm safety limit.
We developed a simulator for evaluating vehicle dynamic behavior during an earthquake outside of the 70mm safety
limit for conventional simulations (see Figure 1). We conducted excitation tests on a 1/10 scale model vehicle of aconventional line train using the large-scale shaking table at the Railway Technical Research Institute. Figure 2 showsthe excitation amplitude at which derailment occurs for various excitation frequencies. The graph shows the predictions
from our vehicle dynamics simulator to be highly consistent with the test results of the model vehicle.
We also performed a simulation of a conventional line train to determine the limit point beyond the 70mm safety limit atwhich the vehicle finally cannot stay on the rails anymore. Figure 3 compares this limit point (we call this new criterion“on-rail safety limit”) with the 70mm safety limit. In the 0.4 - 0.6 Hz range, the on-rail safety limit exists at excitation
amplitudes of up to 100 mm beyond the 70mm safety limit.
Our findings can be used to inform in-depth studies of vehicle running safety during an earthquake, as well as improved
vehicle designs that offer better resistance to derailment.

Relative lateraldisplacement
between wheel
and rail in excess
of 70 mm

Wheel returns on rail(no derailment)

Figure 1 Simulation outcome of returning on the rail behavior
after displacement of more than 70 mm.

1000
800
700
600
500
400
300
200

0.2

0.4

0.6

0.8

1

Frequency (Hz)Figure 2 Simulation results vs. derailment tests on model vehicle(model test, simulation results, excitation frequency and amplitude scaled up to actual car size)

32

400
350
300
250
Lateral displacement
between wheel and rail
exceeded 70mm, thevehicle finally stayed on rail

200
150

100
0

70mm safety limit
On-rail safety limit

450
Excitation amplitude (mm)Excitation amplitude (mm)500

Test results
Simulator

900

100

0.2

0.4

0.6

0.8

1

Frequency (Hz)Figure 3 70mm safety limit vs. on-rail limit

Annual Report 2022-2023

Railway Technical Research Institute

30. Numerical simulation tool for RTRI Large-Scale LowNoise Wind Tunnel• We developed a numerical wind tunnel simulation tool that reproduces air flows in the Large-Scale
Low-Noise Wind Tunnel at the Railway Technical Research Institute (RTRI).
• The tool includes a complete system incorporating processes from geometric definition of specimens
through to computation.
• Used in combination with real-life wind tunnel testing, the tool enhances efficiency and allows tests
to be tailored more closely to the requirements of railway operators and other bodies.
The Maibara Large-Scale Low-Noise Wind Tunnel, a key aerodynamics research facility at the Railway TechnicalResearch Institute, is utilized more than 200 days per year for a variety of wind tunnel tests. By faithfully reproducing
the wind tunnel testing environment, our numerical simulation tool helps to boost the efficiency and accuracy of windtunnel testing at this facility.
The tool was developed through observation and numerical reproduction of airflows in the Maibara Wind Tunnel (seeFigure 1). The numerical reproduction phase involved a series of complex flow calculations performed on the powerful in-house airflow simulator at the RTRI. We used a 3D laser scanner for geometric modeling of specimens, andconstructed a system that encompasses all processes from geometric definitions through to computation (see Figure2). The findings generated by the numerical simulation tool were found to be highly consistent with test results (Figure
1 right, Figure 2 right).

Average speed in main flow direction

Our numerical wind tunnel simulation tool is designed to augment and enhance conventional wind tunnel testing, primarily
by allowing researchers to use a narrower set of test conditions and fewer measurement points. Used in combination
with conventional wind tunnel testing, the tool helps to speed up the testing phase, which means that research projects
can be completed sooner. The tool also provides railway operators and others with an additional option for investigating
issues as they arise, and one that can be used in combination with (rather than relying solely on) wind tunnel testing.

Simulation tool
Visualized vortex structure
Airflowx = 3.5 m
x = 0.1 m(height of nozzle center)

1.2
1.0
0.8
0.6

x = 0.1 m (test)

0.4

x = 3.5 m (test)
x = 0.1 m (simulation)

0.2
0.0

x = 0.1 m (simulation)

-0.5

0.0

0.5

1.0

1.5

Position (positive = facing nozzle center axis) (m)Figure 1 Numerical simulation of airflows in the Large-Scale Low-Noise Wind Tunnel

Airflow

2.0
Height above ground (m)Simulation vs.
test results

Geometric model fed into numericalsimulation tool

Flow speed
Geometric modeling with laser scanner

Flow calculations, visualization/interpretation of results

1.5

Test
Simulation

1.0
0.5
0.0

0.0

0.5

1.0

Average speed in main flow direction
Validation of numerical simulation tool
via wind tunnel tests

Figure 2 Integrated system from geometric definition of specimens through to computation

Annual Report 2022-2023

33

Major Results of Research and Development

31. Snowplowing simulator coupled with vehicle dynamics
simulator• We developed a snowplowing simulator that calculates the force acting on a snowplow during selfsnowplowing running operations, and verified reproducibility in tests on a 1/10 scale model vehicle.
• When coupled with vehicle dynamics simulator, our simulator can be used to evaluate vehicle runningsafety during self-snowplowing running operations.

Speed controls are sometimes imposed on Shinkansen (bullet train) services at high snow depths on the tracks, basedon snowplowing tests performed on actual vehicle. Actual vehicle tests are costly and time-consuming, and are also
hampered by availability constraints. We have developed a snowplowing simulator as an analytical tool to supplement
actual vehicle tests. The simulator is a modified version of the particle simulator currently under development at the
Railway Technical Research Institute (RTRI).
The snowplowing simulator was developed in parallel with a series of snowplowing tests performed on a scale model
vehicle. The tests were conducted at the RTRI snow testing station in Shiozawa. A 1/10 scale model vehicle fittedwith a snowplow was mounted on a truck and driven along a 60-m guide rail track into a snow bed (see Figure 1).
The snowplowing simulator successfully modeled the properties of snow and demonstrated resistance to deformation
up to the point where fluidity begins to occur. Figure 2 shows the force acting on the snowplow when it hits the snow
bed, as observed in the 1/10 scale model tests and as predicted by the simulator. It can be seen that the simulator
accurately reproduces the Shiozawa test results (see Figure 3). We also developed a coupling methodology that inputs
the snowplowing force estimated by the simulator into the vehicle dynamics simulator in order to derive the car attitude
and movement amount (see Figure 4). The coupling methodology is useful in several scenarios: preliminary analysis
of snowplowing conditions for tests on actual vehicle; assessing the safety of self-snowplowing running; and in new
snowplow designs.

Front

Scale model vehicle
SnowplowSnowplow

Snow bed

Force on snowplow upon contact (N)Figure 1 Testing at Shiozawa
600

Test

Relative error
13%

400

Simulator

Speed

High

Figure 2 Simulator graphic

Vehicle dynamics simulator

Car attitude and movement

200
0

Speed
30 m/sSnowplow
depth
15 mm

Speed
30 m/sSnowplow
depth
10 mm

Speed
20 m/sSnowplow
depth
15 mm

Speed
20 m/sSnowplow
depth
10 mm

Speed
20 m/sSnowplow
depth
5 mm

Figure 3 Force acting on snowplow - simulator vs.
test results

34

Low

Rear

Force acting on snowplow

Snowplowing simulator

Figure 4 Snowplowing simulator coupled with vehicle dynamics simulator

Annual Report 2022-2023

Railway Technical Research Institute

Notes
IMPROVEMENT OF SAFETY• 1 was undertaken in conjunction with the Honshu-Shikoku Bridge Expressway Company Limited.
• 4 was funded in part by a rail technology development grant from the Ministry of Land, Infrastructure,
Transport and Tourism.
• 5 was undertaken in conjunction with Toshiba Energy Systems & Solutions Corporation.
• 8 was undertaken in conjunction with Japan Steel Works (JSW).
COST REDUCTION• 12 was undertaken in conjunction with Nippon Densetsu Kogyo.
• 13 was undertaken in conjunction with National University Corporation Shizuoka University andMeidensha Corporation (Meiden).
HARMONIZATION WITH THE ENVIRONMENT• 25 was undertaken in conjunction with Nippon Steel Metal Products.

Annual Report 2022-2023

35

News
Release
R&D

36

M/D/Y

Title

Jul 11, 2022

RTRI Develops Ballast Inspection System Using
transmitted Sounds

link

Sep 09,2022

RTRI Develops a New Method to Prevent Fouled
Ballasted Track from Subsidinglink

Sep 16,2022

Japan-Led International Standards for Driving
Simulator Issued by ISO

link

Oct 25, 2022

RTRI Develops Slope Soil Testing Machinelink

Nov 30, 2022

Japan-Led International Standard on AC Power
Compensator Issuedlink

Dec 07, 2022

RTRI Develops Acid-Resistant Geopolymer Mortar
Emitting Less Carbon Dioxidelink

Dec 14, 2022

RTRI and Nippon Densetsu Kogyo Develop a
Device to Measure Wear of Overhead Conductor
Rails Using a Light-Sectioning Methodlink

Dec 22, 2022

Japan-Led International Standard on Train Running
Time Calculation Issuedlink

Mar 10, 2023

RTRI Develops a Program to Predict Frost
Formation on the Contact Wirelink

Mar 16, 2023

RTRI Develops a Device to Prevent Bridge Collapse

link

Annual Report 2022-2023

Railway Technical Research Institute

Award
M/D/Y

Title

May 25, 2022

RTRI’s Researcher Receives Young Scientist
Award

link

Oct 28, 2022

RTRI’s Researchers Commended for Their
Contribution to Developing Industrial Standards
and for Achievement in IEC/TC9 Activities

link

Event
M/D/Y

Title

Jun 21, 2022

WCRR 2022 Held in Birminghamlink

Dec 23, 2022

The 10th SNCF-RTRI Collaborative Research
Seminar Takes Placelink

Annual Report 2022-2023

37

Data
Quarterly Report (QR)
QR is an electronic quarterly journal published in English to present RTRI’s research anddevelopment achievements to overseas readers.

Vol. / No.

M/YVol.63 / No.2

May / 2022

Vol.63 / No.3

Aug / 2022

Vol.63 / No.4

Nov / 2022

Vol.64 / No.1

Feb / 2023

World Railway Technology (WRT)WRT is an electronic quarterly magazine that timely provides the latest railway technicalinformation from overseas.
NOTE: WRT ceased publication with the issue of January 2023 (No. 1 issue of Vol. 14).

Vol. / No.

M/YVol.13 / No.2

Apr / 2022

Vol.13 / No.3

Jul / 2022

Vol.13 / No.4

Oct / 2022

Vol.14 / No.1

Jan / 2023

AscentAscent, an English-language electronic public relations journal published at twice a year,
introduces RTRI’s research and development efforts to overseas readers.

38

No.

M/Y

special feature

No.11

Oct / 2022

Research and Development at RTRI toward
the Achievement of Net-Zero by 2050

No.12

Mar / 2023

Technology to Enhance the Safety of Railway
Structures During Earthquakes

Annual Report 2022-2023

Railway Technical Research InstituteCollaboration with Other Organizations
Collaboration is in progress with the following railway operators, research institutes,
universities, and other organizations for joint research, commission research, technicalcollaboration, and the like.

Organisation Name
University of Birmingham
Rail Safety and Standards Board (RSSB)International Union of Railways (UIC)
Gustave Eiffel University (UGE)
French National Railways (SNCF)
Polytechnic University of Milan
German Aerospace Center (DLR)
DB Systemtechnik (DBST)High Speed Railways Innovation Centre (HSRIC)
China Academy of Railway Sciences (CARS)
Korea Railroad Research Institute (KRRI)
Taiwan Railways Administration (TRA)
National Science and Technology Development Agency (NSTDA)

Annual Report 2022-2023

39

YEAR 2022-2023: April 1, 2022 - March 31, 2023
Copyright© 2023 Railway Technical Research Institute.
All rights reserved.
International Affairs, Research and Development Promotion Division,
Railway Technical Research Institute
Hikari-cho 2-8-38, Kokubunji-shi, Tokyo 185-8540 JAPANFind us :WEB: https: //www.rtri.or.jp/eng/index.html

For download :Brochure & Annual Report: https://www.rtri.or.jp/eng/rtri/brochure.html
Other Publications: https://www.rtri.or.jp/eng/publish/