SlideShare une entreprise Scribd logo
1  sur  54
Télécharger pour lire hors ligne
Class 5 - GS1 Railway
Internet of Trains
CS632/SEP564, Fall 2018
Daeyoung Kim
Professor, School of Computing, KAIST
• kimd@kaist.ac.kr, http://oliot.org, http://autoidlab.kaist.ac.kr, http://resl.kaist.ac.kr http://autoidlabs.org http://gs1.org
Introduction
Motivation & challenge
New GS1 application standard for visibility in rail
GS1 EPCIS for Rail Vehicle Visibility Application Standard
© Auto-ID Lab Korea / KAIST
Slide 3
Introduction
• Problem
• Due to limited visibility and information about rail vehicles, it’s
difficult for rail operators to plan and meet customer demands for
timely deliveries and updates.
© Auto-ID Lab Korea / KAIST
Slide 4
Introduction
• Objective
• Provides roadmap for rail stakeholders to gain visibility of rolling
stock and access to real-time information
© Auto-ID Lab Korea / KAIST
Slide 5
Introduction
• The standard provides significant business benefits for all
players
• Improved safety
• Facilitation of preventative maintenance
• Reduced environmental impact of transport
• Improved customer service
• Greater process efficiencies
• Reduced costs
Rail Vehicle Visibility
© Auto-ID Lab Korea / KAIST
Slide 7
Rail Vehicle Visibility
• Business need
• The needs for information sharing:
• A need for tracking of vehicles as they travel within countries and across different countries
• A need to associate the vehicle data with the Wayside Train Monitoring System (WTMS) data
about vehicles and vehicle components to enhance preventive maintenance
• The use of RFID for railway vehicles becomes more and more
popular. -> More applications for vehicle and train visibility
information will emerge.
• These two applications are the basis for the initial EPCIS application
in rail.
© Auto-ID Lab Korea / KAIST
Slide 8
Rail Vehicle Visibility
• Asset tracking applications
• Normal operative functions where the location of specific vehicles
needs to be known
• Vehicle level management
• Tracking the location and status of each vehicle as they travel
• Independent of the train
• Potential future applications:
• Real-time cargo tracking
• Estimating the vehicle distance travelled for planning preventive maintenance
• Planning vehicle availability
© Auto-ID Lab Korea / KAIST
Slide 9
Rail Vehicle Visibility
• Wayside Train Monitoring System (WTMS)
• An integrated system of measurement devices that monitor the
condition of rolling stock travelling on the railway tracks
• Reduce accidents, enable preventive maintenance and improving rail
system reliability
• Typical WTMS device types:
• Hot Axle-Box Detectors (HABD) or hot-box detectors
• Wheel impact load detectors (WILD)
• Acoustic Axle Bearing Monitoring (AABM)
• Automatic pantograph monitoring systems (APMS)
• RFID allows for automatic collection of all measurement results
and creating statistical data of measurement results per vehicle.
• Applying standard EPC/RFID protocols will facilitate the exchange
of data across WTMS systems and between railway operators.
© Auto-ID Lab Korea / KAIST
Slide 10
Rail Vehicle Visibility
• RFID enabled Automatic Vehicle Identification (AVI) systems
• Fixed readers and wheel sensors (at trackside)
• The fixed trackside readers identify the vehicles of the passing train.
• Identify all tagged vehicles and their order in the train
• Detect the presence of vehicles with missing or broken tags and
their relative location in the train
• Important for the WTMS use case, since it enables the measurement results to
be linked to the correct vehicles in a train set
• Determine the travel direction, the orientation, axle count, speed,
and length of each vehicle
• Enable train level information exchanges
• Ex) A train entering or leaving a yard and the composition/formation of the
train
© Auto-ID Lab Korea / KAIST
Slide 11
Rail Vehicle Visibility
• RFID enabled Automatic Vehicle Identification (AVI) systems
Fixed trackside RFID configuration
© Auto-ID Lab Korea / KAIST
Slide 12
Rail Vehicle Visibility
• Train Management System (TMS)
• A system used to control railway operations
• Detect and control movement of trains on a track
• The information from the TMS can be used to generate additional
event data.
• Ex) The train entering or leaving an area can be deduced by combining data
from a TMS and data from previously read points provided by the AVI system.
Vehicle Identification
© Auto-ID Lab Korea / KAIST
Slide 14
Vehicle Identification
• Vehicle identification with “master” GIAI
• Identify each rail vehicle as an asset
• EPC URI is used to represent rail vehicles which are included in
EPCIS events.
• Ex) urn:epc:id:giai:4012345.98765432198765432
• Unambiguously determine static information about the rail vehicle
• Rail vehicle type, axle count, vehicle owner, etc.
• Communicate information as master data
• Not physical, deduced by proxy GIAI
© Auto-ID Lab Korea / KAIST
Slide 15
Vehicle Identification
• AIDC device identification with “proxy” GIAI
• Multiple EPC/RFID tags
• to enable the automatic identification of the side or end of the wagon
• Placing two tags on opposite sides of the wagon at opposing corners
• Ex) Vehicle end/side indicator as per GS1 in Europe’s “RFID in Rail” guideline
• Vehicle orientation can be inferred from the identity of the tag
observed.
© Auto-ID Lab Korea / KAIST
Slide 16
Vehicle Identification
• AIDC device identification with “proxy” GIAI
• Each of these tags is identified by a unique GIAI
• Ex) tag 1 of 2: urn:epc:tag:giai-96:1.4012345.18765432198765432
tag 2 of 2: urn:epc:tag:giai-96:1.4012345.28765432198765432
• These “device” GIAIs serve as “proxy” representation
Rail vehicle with multiple tags - top views Rail vehicle with multiple tags - side
views
Read Point and Business
Location Identification
How locations can be identified in a rail context
© Auto-ID Lab Korea / KAIST
Slide 18
Read Point and Business Location
Identification
• Read Points
• A location that is meant to identify the most specific place at which
an EPCIS event took place
• Identified using the SGLN
• Unique Read Point – Unambiguously determine the read point’s
physical location, line name/ID, and track name/ID
Example of Read Point
© Auto-ID Lab Korea / KAIST
Slide 19
Read Point and Business Location
Identification
• Identifying a track using EPC/RFID
• A single track: A one-to-one relation between reader and read point
• Multiple tracks: apply separate read points with distinct SGLNs for
each track
AVI system monitoring a single track AVI system monitoring multiple tracks
© Auto-ID Lab Korea / KAIST
Slide 20
Read Point and Business Location
Identification
• Business location
• The location where the rail vehicle is assumed to be following the
event
• Assumed to be valid until superseded by the business location of a
subsequent event pertaining to the rail vehicle
• Used to tell the location where the vehicles or trains are found after
the event took place
• Ex) track section, station, shunting yard, or specific shunting yard location
• Used to serve asset tracking needs
• While the read point is more directly related to the measurement values
received in conjunction with an EPCIS event
Business location
Determining vehicle and train
visibility data
© Auto-ID Lab Korea / KAIST
Slide 22
Determining vehicle and train visibility data
• Determining the train direction
• Available from the AVI system
• Expressed in terms of the ‘railroad direction’ of the track
• Using the direction indicator
• 0 : the direction was not detected
• 1 : one direction in the rail network
• 2 : the opposite direction in the rail network
• The compass direction indicating the actual direction of the train at
the read point
• Expressed using a cardinal (N, S, W, E) or intermediate cardinal (NW, NE, SE,
SW) value
© Auto-ID Lab Korea / KAIST
Slide 23
Determining vehicle and train visibility data
• Determining the train direction
Determining Train Direction
© Auto-ID Lab Korea / KAIST
Slide 24
Determining vehicle and train visibility data
• Determining the orientation of the rail vehicle
• The correct assignment of measurement values
requires the direction of travel of the vehicle.
• The orientation of a rail vehicle is determined by:
• The observed tag
• The train direction
• Ex)
Train direction indicator = 2 (compass direction = NE)
t1: Vehicle 2 – tag 2, vehicle end 2 passed first
t2: Vehicle 1 – tag 1, vehicle end 1 passed first
© Auto-ID Lab Korea / KAIST
Slide 25
Determining vehicle and train visibility data
• Determining Source and Destination
• Information on the origin of the train and its ultimate destination
• Parties with access to the railroad plan – can derive this based on
the provided read points and direction information
• Parties that do not have access – utilize the Source and Destination
elements in EPCIS
• Identified with SGLN
• Not necessary, may additional
© Auto-ID Lab Korea / KAIST
Slide 26
Determining vehicle and train visibility data
• Determining a train passage
• The AVI can detect whether observed vehicles are connected in the
same train-set.
• A separate EPCIS event for each vehicle observation
• To specify which vehicles were observed as part of the same passage,
a passage identifier should be added to the object events.
• Passage identifier should be included as an EPCIS Business
Transaction.
• A Transaction Event will be used to list all observed rail vehicles.
• The train number can be used to link to information in other train
management systems.
• Include the vehicle level information in the EPCIS transaction event
for a passage
• To avoid use of “dummy” EPCs for hidden vehicles
Sharing vehicle and train
visibility data with EPCIS
Special issues when applying Epcis functions to railway vehicle visibility
© Auto-ID Lab Korea / KAIST
Slide 28
Sharing vehicle and train visibility data with
EPCIS
• Vehicle and train visibility – critical tracking events
Standard Rail Journey Diagram
© Auto-ID Lab Korea / KAIST
Slide 29
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• ObjectEvent (action OBSERVE) – serves as an observation of a uniquely
identified rail vehicle in passage along its journey, or upon its arrival
at or departure from a terminus
• TransactionEvent (action ADD) – serves as a “summary” event following
the observation of a passing train’s trailing vehicle, reiterating the
proxy GIAIs of positively identified vehicles, as well as relevant totals
for all vehicles
© Auto-ID Lab Korea / KAIST
Slide 30
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• What
• Indicates the objects to which the EPCIS event pertains
• Each observed rail vehicle should be captured in a separate ObjectEvent.
• The epcList element should contain only the master GIAI of the observed
vehicle.
• Ex)
• A passage should be defined using a TransactionEvent.
• The epcList element includes the master GIAIs of all observed, positively
identified rail vehicles.
• Ex)
© Auto-ID Lab Korea / KAIST
Slide 31
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• When
• eventTime – the time at which the vehicle was observed (Object events), the
time at which the first (leading) vehicle of a passing trainset was observed
(Transaction events)
• recordTime – the date and time at which this event was recorded by an EPCIS
Repository
• Ex)
© Auto-ID Lab Korea / KAIST
Slide 32
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• Where
• Indicates the location at which the EPCIS event was observed, as well as the
whereabouts of the object subsequent to the event
• readPoint – the SGLN corresponding to the event’s location
• bizLocation – the SGLN corresponding to the object’s whereabouts subsequent
to the event
• For all object events and transaction events, either the readPoint or the
bizLocation or both should be populated.
• Ex)
© Auto-ID Lab Korea / KAIST
Slide 33
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• Why
• Reflects the business context (“Business Step”) of the EPCIS event, as well as
the status (“Disposition”) of the object subsequent to the event
• Business Step
• Specifies the business process linked to the EPCIS event
• Ex)
• Disposition
• Denotes the status of an object subsequent to the EPCIS event
• Ex)
© Auto-ID Lab Korea / KAIST
Slide 34
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• Why
• Source/Destination
• Use the urn:epcglobal:cbv:sdt:location source/destination type identifier with SGLN
• Ex)
© Auto-ID Lab Korea / KAIST
Slide 35
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• Why
• Business Transactions
• EPCIS Business Transactions are defined using a combination of Business
Transaction Type and Business Transaction ID
• Rail sector-specific vehicle visibility applications should use HTTP URLs for business
transaction identifiers
• To share information about a passage, the bizTransaction element can be used in
two ways:
• as a transaction reference in a rail visibility ObjectEvent, to indicate which events
belong to the same ‘passage’
• as a transaction type in a rail visibility TransactionEvent, to specify totals and tag
IDs for a particular ‘passage’
• Ex) transaction reference
© Auto-ID Lab Korea / KAIST
Slide 36
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• Extension elements
• All of these elements should be specified using the namespace
urn:gs1:epcisapp:rail
• Direction elements (for object and transaction events)
• directionIndicator
• 0 : the direction was not detected
• 1 : the first direction in the rail network
• 2 : the second direction in the rail network
• compassDirection
• Using a cardinal (N, S, W, E) or inter-cardinal (NW, NE, SE, SW)
• Ex)
© Auto-ID Lab Korea / KAIST
Slide 37
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• Extension elements
• Object event elements
• vehicleOrientation
• 1 : vehicle end one is leading, relative to the direction of travel
• 2 : vehicle end two is leading, relative to the direction of travel
• 0 : leading vehicle end not determined
• vehiclePosition – A number identifying the relative position of the rail vehicle within
the passage
• vehicleAxleCount – The number of axles of the vehicle
• proxyGIAI – the GIAI(s) of the observed tag(s)
• Ex)
© Auto-ID Lab Korea / KAIST
Slide 38
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• Transaction event elements (optional)
• trainAxleCount – the total number of axles observed for the passage of the entire trainset
• trainVehicleCount – the total number of vehicles observed for the passage of the entire trainset
• Vehicle – information on each of the observed vehicles
• vehiclePosition – the relative position of the vehicle in the passage
• vehicleAxleCount – the number of axles of the observed vehicle
• vehicleUniqueIdentified – indicates whether the ID of the observed rail vehicle was captured
• vehicleMasterGIAI – optional element specifying the ID of the rail vehicle
• Ex)
Examples of Rail Visibility
Events
© Auto-ID Lab Korea / KAIST
Slide 40
Examples of Rail Visibility Events
• Rail vehicle observations
• Two tags were observed
• First tag 2 of vehicle 676, after that tag 1 of vehicle 070
• The passage ID for both observed vehicles is the same
• Part of the same train set
070 676
Read point
1 2
12
© Auto-ID Lab Korea / KAIST
Slide 41
Examples of Rail Visibility Events
• Rail vehicle observations
Rail vehicle observation – Object event 1
© Auto-ID Lab Korea / KAIST
Slide 42
Examples of Rail Visibility Events
• Rail vehicle observations
Rail vehicle observation – Object event 2
© Auto-ID Lab Korea / KAIST
Slide 43
Examples of Rail Visibility Events
• Rail vehicle changing direction
Illustration of direction change – Object event 1
© Auto-ID Lab Korea / KAIST
Slide 44
Examples of Rail Visibility Events
• Rail vehicle changing direction
Illustration of direction change – Object event 2
© Auto-ID Lab Korea / KAIST
Slide 45
Examples of Rail Visibility Events
• Rail vehicle changing direction
Illustration of direction change – Object event 3
© Auto-ID Lab Korea / KAIST
Slide 46
Examples of Rail Visibility Events
• Train passage transaction event (including untagged vehicle)
• How a train passage can be expressed using a transaction event
• A train passage which consists of three rail vehicles
Train passage
© Auto-ID Lab Korea / KAIST
Slide 47
Examples of Rail Visibility Events
• Train passage transaction event (including untagged vehicle)
Train passage transaction event
© Auto-ID Lab Korea / KAIST
Slide 48
Examples of Rail Visibility Events
• Train passage transaction event (including untagged vehicle)
Train passage transaction event
EPCIS Query examples for
rail vehicle visibility
© Auto-ID Lab Korea / KAIST
Slide 50
EPCIS Query examples for rail vehicle
visibility
• EPCIS Query Control Interface
• On-demand (synchronous) – a client makes a request through the
EPCIS Query Control Interface and receives a response immediately
• Standing request (asynchronous) – a client establishes a subscription
for a periodic query. Each time the periodic query is executed, the
results are delivered asynchronously to a recipient via the EPCIS
Query Callback Interface
© Auto-ID Lab Korea / KAIST
Slide 51
EPCIS Query examples for rail vehicle
visibility
• On-demand queries via EPCIS Query Control Interface
• Observations of a specified vehicle since a specified date/time
• Example of on-demand query by vehicle
• Observations of all vehicles at a specified read point in a specified
window of time
• Example of on-demand query by read point
© Auto-ID Lab Korea / KAIST
Slide 52
EPCIS Query examples for rail vehicle
visibility
• On-demand queries via EPCIS Query Control Interface
• Events for a given passage
• Example of on-demand query by passage ID
• Passage-level queries
• Example of on-demand query for passage events
© Auto-ID Lab Korea / KAIST
Slide 53
EPCIS Query examples for rail vehicle
visibility
• Standing queries (Subscriptions) via EPCIS Query Call-back
Interface
• Notification whenever a specified vehicle is observed at any read
point
• Example of standing query by vehicle
• Notification whenever any uniquely identified vehicle is observed at
specified read point
• Example of standing query by read point
Thank you

Contenu connexe

Similaire à GS1 Railway Visibility Standard

Version 3 by raymond (active)
Version 3 by raymond (active)Version 3 by raymond (active)
Version 3 by raymond (active)wmyip3
 
Generation of Autonomous Vehicle Validation Scenarios Using Crash Data
Generation of Autonomous Vehicle Validation Scenarios Using Crash DataGeneration of Autonomous Vehicle Validation Scenarios Using Crash Data
Generation of Autonomous Vehicle Validation Scenarios Using Crash DataM. Ilhan Akbas
 
Presentation on Open Toll & Toll Systems
Presentation on Open Toll & Toll SystemsPresentation on Open Toll & Toll Systems
Presentation on Open Toll & Toll Systemsshanmuga sundaram
 
Presentation on Open Toll & Toll Systems
Presentation on Open Toll & Toll SystemsPresentation on Open Toll & Toll Systems
Presentation on Open Toll & Toll Systemsshanmuga sundaram
 
Intelligent transport system (its) [autosaved]
Intelligent transport system (its) [autosaved]Intelligent transport system (its) [autosaved]
Intelligent transport system (its) [autosaved]Krishna Bhola
 
Vehicular ad hoc network - VANET
Vehicular ad hoc network - VANETVehicular ad hoc network - VANET
Vehicular ad hoc network - VANETSarah Baras
 
TRAFFIC SIMULATION AT TOLL ROAD SECTION USING VISSIM SOFTWARE
TRAFFIC SIMULATION AT TOLL ROAD SECTION USING VISSIM SOFTWARETRAFFIC SIMULATION AT TOLL ROAD SECTION USING VISSIM SOFTWARE
TRAFFIC SIMULATION AT TOLL ROAD SECTION USING VISSIM SOFTWAREshrikrishna kesharwani
 
Intelligent Transportation Systems .pptx
Intelligent Transportation Systems .pptxIntelligent Transportation Systems .pptx
Intelligent Transportation Systems .pptxTheConqueror2
 
Development of a Validation Regime for an Autonomous Campus Shuttle
Development of a Validation Regime for an Autonomous Campus ShuttleDevelopment of a Validation Regime for an Autonomous Campus Shuttle
Development of a Validation Regime for an Autonomous Campus ShuttleM. Ilhan Akbas
 
Intelligent transportation system
Intelligent transportation systemIntelligent transportation system
Intelligent transportation systemKunalPolkundwar
 
Vehicle Identification and Classification System
Vehicle Identification and Classification SystemVehicle Identification and Classification System
Vehicle Identification and Classification SystemVishal Polley
 
Safety Argumentation And Using Simulation To Solve Long Tail Problem Of Autom...
Safety Argumentation And Using Simulation To Solve Long Tail Problem Of Autom...Safety Argumentation And Using Simulation To Solve Long Tail Problem Of Autom...
Safety Argumentation And Using Simulation To Solve Long Tail Problem Of Autom...FengLiu90
 
Congestion Control System Using Machine Learning
Congestion Control System Using Machine LearningCongestion Control System Using Machine Learning
Congestion Control System Using Machine LearningIRJET Journal
 

Similaire à GS1 Railway Visibility Standard (20)

IOT
IOTIOT
IOT
 
IOT
IOTIOT
IOT
 
Electronic toll system
Electronic toll systemElectronic toll system
Electronic toll system
 
Version 3 by raymond (active)
Version 3 by raymond (active)Version 3 by raymond (active)
Version 3 by raymond (active)
 
Generation of Autonomous Vehicle Validation Scenarios Using Crash Data
Generation of Autonomous Vehicle Validation Scenarios Using Crash DataGeneration of Autonomous Vehicle Validation Scenarios Using Crash Data
Generation of Autonomous Vehicle Validation Scenarios Using Crash Data
 
Presentation on Open Toll & Toll Systems
Presentation on Open Toll & Toll SystemsPresentation on Open Toll & Toll Systems
Presentation on Open Toll & Toll Systems
 
Presentation on Open Toll & Toll Systems
Presentation on Open Toll & Toll SystemsPresentation on Open Toll & Toll Systems
Presentation on Open Toll & Toll Systems
 
Intelligent transport system (its) [autosaved]
Intelligent transport system (its) [autosaved]Intelligent transport system (its) [autosaved]
Intelligent transport system (its) [autosaved]
 
Vehicular ad hoc network - VANET
Vehicular ad hoc network - VANETVehicular ad hoc network - VANET
Vehicular ad hoc network - VANET
 
TRAFFIC SIMULATION AT TOLL ROAD SECTION USING VISSIM SOFTWARE
TRAFFIC SIMULATION AT TOLL ROAD SECTION USING VISSIM SOFTWARETRAFFIC SIMULATION AT TOLL ROAD SECTION USING VISSIM SOFTWARE
TRAFFIC SIMULATION AT TOLL ROAD SECTION USING VISSIM SOFTWARE
 
Intelligent Transportation Systems .pptx
Intelligent Transportation Systems .pptxIntelligent Transportation Systems .pptx
Intelligent Transportation Systems .pptx
 
Development of a Validation Regime for an Autonomous Campus Shuttle
Development of a Validation Regime for an Autonomous Campus ShuttleDevelopment of a Validation Regime for an Autonomous Campus Shuttle
Development of a Validation Regime for an Autonomous Campus Shuttle
 
FinalReport
FinalReportFinalReport
FinalReport
 
Intelligent transportation system
Intelligent transportation systemIntelligent transportation system
Intelligent transportation system
 
Final report
Final reportFinal report
Final report
 
QSI RIDS
QSI RIDSQSI RIDS
QSI RIDS
 
Vehicle Identification and Classification System
Vehicle Identification and Classification SystemVehicle Identification and Classification System
Vehicle Identification and Classification System
 
Safety Argumentation And Using Simulation To Solve Long Tail Problem Of Autom...
Safety Argumentation And Using Simulation To Solve Long Tail Problem Of Autom...Safety Argumentation And Using Simulation To Solve Long Tail Problem Of Autom...
Safety Argumentation And Using Simulation To Solve Long Tail Problem Of Autom...
 
MATC Fall Lecture Series: Robert Kollmar
MATC Fall Lecture Series: Robert KollmarMATC Fall Lecture Series: Robert Kollmar
MATC Fall Lecture Series: Robert Kollmar
 
Congestion Control System Using Machine Learning
Congestion Control System Using Machine LearningCongestion Control System Using Machine Learning
Congestion Control System Using Machine Learning
 

Plus de Daeyoung Kim

주소기반혁신성장 산업 - 주소가 바꿀 미래 사회와 산업 - 행정안전부와 주소포럼
주소기반혁신성장 산업 - 주소가 바꿀 미래 사회와 산업 - 행정안전부와 주소포럼주소기반혁신성장 산업 - 주소가 바꿀 미래 사회와 산업 - 행정안전부와 주소포럼
주소기반혁신성장 산업 - 주소가 바꿀 미래 사회와 산업 - 행정안전부와 주소포럼Daeyoung Kim
 
Standards and AI Transformation (SAX) 국제표준과 인공지능 기반의 철도산업 디지털 전환
Standards and AI Transformation (SAX) 국제표준과 인공지능 기반의 철도산업 디지털 전환Standards and AI Transformation (SAX) 국제표준과 인공지능 기반의 철도산업 디지털 전환
Standards and AI Transformation (SAX) 국제표준과 인공지능 기반의 철도산업 디지털 전환Daeyoung Kim
 
기후대응을 위한 EU 디지털제품여권법 동향과 GS1 국제표준 적용 방안 소개
기후대응을 위한 EU 디지털제품여권법 동향과 GS1 국제표준 적용 방안 소개기후대응을 위한 EU 디지털제품여권법 동향과 GS1 국제표준 적용 방안 소개
기후대응을 위한 EU 디지털제품여권법 동향과 GS1 국제표준 적용 방안 소개Daeyoung Kim
 
데이터공유 농축산식품-GS1적용(김대영)
데이터공유 농축산식품-GS1적용(김대영)데이터공유 농축산식품-GS1적용(김대영)
데이터공유 농축산식품-GS1적용(김대영)Daeyoung Kim
 
gs1 standards in building smart cities
gs1 standards in building smart citiesgs1 standards in building smart cities
gs1 standards in building smart citiesDaeyoung Kim
 
Smartship in GS1's perspective
Smartship in GS1's perspectiveSmartship in GS1's perspective
Smartship in GS1's perspectiveDaeyoung Kim
 
GS1 standards in agriculture - Jan. 2017
GS1 standards in agriculture - Jan. 2017GS1 standards in agriculture - Jan. 2017
GS1 standards in agriculture - Jan. 2017Daeyoung Kim
 
GS1 standards - Jan. 2017
GS1 standards - Jan. 2017GS1 standards - Jan. 2017
GS1 standards - Jan. 2017Daeyoung Kim
 
Gs1au newsletter-building-march-2021
Gs1au newsletter-building-march-2021Gs1au newsletter-building-march-2021
Gs1au newsletter-building-march-2021Daeyoung Kim
 
GS1 Data Revolution Series #3 Healthcare
GS1 Data Revolution Series #3 HealthcareGS1 Data Revolution Series #3 Healthcare
GS1 Data Revolution Series #3 HealthcareDaeyoung Kim
 
GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)Daeyoung Kim
 
GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)Daeyoung Kim
 
Smart city position paper - GS1 standards perspective
Smart city position paper - GS1 standards perspectiveSmart city position paper - GS1 standards perspective
Smart city position paper - GS1 standards perspectiveDaeyoung Kim
 
GS1 Tutorial (Korean) by Daeyoung Kim, Auto-ID Labs, KAIST
GS1 Tutorial (Korean) by Daeyoung Kim, Auto-ID Labs, KAISTGS1 Tutorial (Korean) by Daeyoung Kim, Auto-ID Labs, KAIST
GS1 Tutorial (Korean) by Daeyoung Kim, Auto-ID Labs, KAISTDaeyoung Kim
 
Global Seafood Traceability System
Global Seafood Traceability SystemGlobal Seafood Traceability System
Global Seafood Traceability SystemDaeyoung Kim
 
GS1 standards and Blockchain Technology for Traceability in Agriculture and S...
GS1 standards and Blockchain Technology for Traceability in Agriculture and S...GS1 standards and Blockchain Technology for Traceability in Agriculture and S...
GS1 standards and Blockchain Technology for Traceability in Agriculture and S...Daeyoung Kim
 
Soscon2019 oliot-auto-id-labs-kaist
Soscon2019 oliot-auto-id-labs-kaistSoscon2019 oliot-auto-id-labs-kaist
Soscon2019 oliot-auto-id-labs-kaistDaeyoung Kim
 
Lh iot-bigdata-20181023
Lh iot-bigdata-20181023Lh iot-bigdata-20181023
Lh iot-bigdata-20181023Daeyoung Kim
 
(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...
(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...
(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...Daeyoung Kim
 
Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...
Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...
Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...Daeyoung Kim
 

Plus de Daeyoung Kim (20)

주소기반혁신성장 산업 - 주소가 바꿀 미래 사회와 산업 - 행정안전부와 주소포럼
주소기반혁신성장 산업 - 주소가 바꿀 미래 사회와 산업 - 행정안전부와 주소포럼주소기반혁신성장 산업 - 주소가 바꿀 미래 사회와 산업 - 행정안전부와 주소포럼
주소기반혁신성장 산업 - 주소가 바꿀 미래 사회와 산업 - 행정안전부와 주소포럼
 
Standards and AI Transformation (SAX) 국제표준과 인공지능 기반의 철도산업 디지털 전환
Standards and AI Transformation (SAX) 국제표준과 인공지능 기반의 철도산업 디지털 전환Standards and AI Transformation (SAX) 국제표준과 인공지능 기반의 철도산업 디지털 전환
Standards and AI Transformation (SAX) 국제표준과 인공지능 기반의 철도산업 디지털 전환
 
기후대응을 위한 EU 디지털제품여권법 동향과 GS1 국제표준 적용 방안 소개
기후대응을 위한 EU 디지털제품여권법 동향과 GS1 국제표준 적용 방안 소개기후대응을 위한 EU 디지털제품여권법 동향과 GS1 국제표준 적용 방안 소개
기후대응을 위한 EU 디지털제품여권법 동향과 GS1 국제표준 적용 방안 소개
 
데이터공유 농축산식품-GS1적용(김대영)
데이터공유 농축산식품-GS1적용(김대영)데이터공유 농축산식품-GS1적용(김대영)
데이터공유 농축산식품-GS1적용(김대영)
 
gs1 standards in building smart cities
gs1 standards in building smart citiesgs1 standards in building smart cities
gs1 standards in building smart cities
 
Smartship in GS1's perspective
Smartship in GS1's perspectiveSmartship in GS1's perspective
Smartship in GS1's perspective
 
GS1 standards in agriculture - Jan. 2017
GS1 standards in agriculture - Jan. 2017GS1 standards in agriculture - Jan. 2017
GS1 standards in agriculture - Jan. 2017
 
GS1 standards - Jan. 2017
GS1 standards - Jan. 2017GS1 standards - Jan. 2017
GS1 standards - Jan. 2017
 
Gs1au newsletter-building-march-2021
Gs1au newsletter-building-march-2021Gs1au newsletter-building-march-2021
Gs1au newsletter-building-march-2021
 
GS1 Data Revolution Series #3 Healthcare
GS1 Data Revolution Series #3 HealthcareGS1 Data Revolution Series #3 Healthcare
GS1 Data Revolution Series #3 Healthcare
 
GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
 
GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
 
Smart city position paper - GS1 standards perspective
Smart city position paper - GS1 standards perspectiveSmart city position paper - GS1 standards perspective
Smart city position paper - GS1 standards perspective
 
GS1 Tutorial (Korean) by Daeyoung Kim, Auto-ID Labs, KAIST
GS1 Tutorial (Korean) by Daeyoung Kim, Auto-ID Labs, KAISTGS1 Tutorial (Korean) by Daeyoung Kim, Auto-ID Labs, KAIST
GS1 Tutorial (Korean) by Daeyoung Kim, Auto-ID Labs, KAIST
 
Global Seafood Traceability System
Global Seafood Traceability SystemGlobal Seafood Traceability System
Global Seafood Traceability System
 
GS1 standards and Blockchain Technology for Traceability in Agriculture and S...
GS1 standards and Blockchain Technology for Traceability in Agriculture and S...GS1 standards and Blockchain Technology for Traceability in Agriculture and S...
GS1 standards and Blockchain Technology for Traceability in Agriculture and S...
 
Soscon2019 oliot-auto-id-labs-kaist
Soscon2019 oliot-auto-id-labs-kaistSoscon2019 oliot-auto-id-labs-kaist
Soscon2019 oliot-auto-id-labs-kaist
 
Lh iot-bigdata-20181023
Lh iot-bigdata-20181023Lh iot-bigdata-20181023
Lh iot-bigdata-20181023
 
(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...
(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...
(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...
 
Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...
Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...
Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...
 

Dernier

GOING AOT WITH GRAALVM – DEVOXX GREECE.pdf
GOING AOT WITH GRAALVM – DEVOXX GREECE.pdfGOING AOT WITH GRAALVM – DEVOXX GREECE.pdf
GOING AOT WITH GRAALVM – DEVOXX GREECE.pdfAlina Yurenko
 
What is Fashion PLM and Why Do You Need It
What is Fashion PLM and Why Do You Need ItWhat is Fashion PLM and Why Do You Need It
What is Fashion PLM and Why Do You Need ItWave PLM
 
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...confluent
 
Ahmed Motair CV April 2024 (Senior SW Developer)
Ahmed Motair CV April 2024 (Senior SW Developer)Ahmed Motair CV April 2024 (Senior SW Developer)
Ahmed Motair CV April 2024 (Senior SW Developer)Ahmed Mater
 
How to submit a standout Adobe Champion Application
How to submit a standout Adobe Champion ApplicationHow to submit a standout Adobe Champion Application
How to submit a standout Adobe Champion ApplicationBradBedford3
 
CRM Contender Series: HubSpot vs. Salesforce
CRM Contender Series: HubSpot vs. SalesforceCRM Contender Series: HubSpot vs. Salesforce
CRM Contender Series: HubSpot vs. SalesforceBrainSell Technologies
 
Software Coding for software engineering
Software Coding for software engineeringSoftware Coding for software engineering
Software Coding for software engineeringssuserb3a23b
 
What is Advanced Excel and what are some best practices for designing and cre...
What is Advanced Excel and what are some best practices for designing and cre...What is Advanced Excel and what are some best practices for designing and cre...
What is Advanced Excel and what are some best practices for designing and cre...Technogeeks
 
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...Cizo Technology Services
 
Introduction Computer Science - Software Design.pdf
Introduction Computer Science - Software Design.pdfIntroduction Computer Science - Software Design.pdf
Introduction Computer Science - Software Design.pdfFerryKemperman
 
Machine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their EngineeringMachine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their EngineeringHironori Washizaki
 
PREDICTING RIVER WATER QUALITY ppt presentation
PREDICTING  RIVER  WATER QUALITY  ppt presentationPREDICTING  RIVER  WATER QUALITY  ppt presentation
PREDICTING RIVER WATER QUALITY ppt presentationvaddepallysandeep122
 
KnowAPIs-UnknownPerf-jaxMainz-2024 (1).pptx
KnowAPIs-UnknownPerf-jaxMainz-2024 (1).pptxKnowAPIs-UnknownPerf-jaxMainz-2024 (1).pptx
KnowAPIs-UnknownPerf-jaxMainz-2024 (1).pptxTier1 app
 
Precise and Complete Requirements? An Elusive Goal
Precise and Complete Requirements? An Elusive GoalPrecise and Complete Requirements? An Elusive Goal
Precise and Complete Requirements? An Elusive GoalLionel Briand
 
Maximizing Efficiency and Profitability with OnePlan’s Professional Service A...
Maximizing Efficiency and Profitability with OnePlan’s Professional Service A...Maximizing Efficiency and Profitability with OnePlan’s Professional Service A...
Maximizing Efficiency and Profitability with OnePlan’s Professional Service A...OnePlan Solutions
 
Recruitment Management Software Benefits (Infographic)
Recruitment Management Software Benefits (Infographic)Recruitment Management Software Benefits (Infographic)
Recruitment Management Software Benefits (Infographic)Hr365.us smith
 
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样umasea
 
Odoo 14 - eLearning Module In Odoo 14 Enterprise
Odoo 14 - eLearning Module In Odoo 14 EnterpriseOdoo 14 - eLearning Module In Odoo 14 Enterprise
Odoo 14 - eLearning Module In Odoo 14 Enterprisepreethippts
 
SpotFlow: Tracking Method Calls and States at Runtime
SpotFlow: Tracking Method Calls and States at RuntimeSpotFlow: Tracking Method Calls and States at Runtime
SpotFlow: Tracking Method Calls and States at Runtimeandrehoraa
 

Dernier (20)

GOING AOT WITH GRAALVM – DEVOXX GREECE.pdf
GOING AOT WITH GRAALVM – DEVOXX GREECE.pdfGOING AOT WITH GRAALVM – DEVOXX GREECE.pdf
GOING AOT WITH GRAALVM – DEVOXX GREECE.pdf
 
What is Fashion PLM and Why Do You Need It
What is Fashion PLM and Why Do You Need ItWhat is Fashion PLM and Why Do You Need It
What is Fashion PLM and Why Do You Need It
 
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
 
Ahmed Motair CV April 2024 (Senior SW Developer)
Ahmed Motair CV April 2024 (Senior SW Developer)Ahmed Motair CV April 2024 (Senior SW Developer)
Ahmed Motair CV April 2024 (Senior SW Developer)
 
How to submit a standout Adobe Champion Application
How to submit a standout Adobe Champion ApplicationHow to submit a standout Adobe Champion Application
How to submit a standout Adobe Champion Application
 
CRM Contender Series: HubSpot vs. Salesforce
CRM Contender Series: HubSpot vs. SalesforceCRM Contender Series: HubSpot vs. Salesforce
CRM Contender Series: HubSpot vs. Salesforce
 
Software Coding for software engineering
Software Coding for software engineeringSoftware Coding for software engineering
Software Coding for software engineering
 
What is Advanced Excel and what are some best practices for designing and cre...
What is Advanced Excel and what are some best practices for designing and cre...What is Advanced Excel and what are some best practices for designing and cre...
What is Advanced Excel and what are some best practices for designing and cre...
 
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...
 
Introduction Computer Science - Software Design.pdf
Introduction Computer Science - Software Design.pdfIntroduction Computer Science - Software Design.pdf
Introduction Computer Science - Software Design.pdf
 
Machine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their EngineeringMachine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their Engineering
 
PREDICTING RIVER WATER QUALITY ppt presentation
PREDICTING  RIVER  WATER QUALITY  ppt presentationPREDICTING  RIVER  WATER QUALITY  ppt presentation
PREDICTING RIVER WATER QUALITY ppt presentation
 
KnowAPIs-UnknownPerf-jaxMainz-2024 (1).pptx
KnowAPIs-UnknownPerf-jaxMainz-2024 (1).pptxKnowAPIs-UnknownPerf-jaxMainz-2024 (1).pptx
KnowAPIs-UnknownPerf-jaxMainz-2024 (1).pptx
 
Precise and Complete Requirements? An Elusive Goal
Precise and Complete Requirements? An Elusive GoalPrecise and Complete Requirements? An Elusive Goal
Precise and Complete Requirements? An Elusive Goal
 
Maximizing Efficiency and Profitability with OnePlan’s Professional Service A...
Maximizing Efficiency and Profitability with OnePlan’s Professional Service A...Maximizing Efficiency and Profitability with OnePlan’s Professional Service A...
Maximizing Efficiency and Profitability with OnePlan’s Professional Service A...
 
Recruitment Management Software Benefits (Infographic)
Recruitment Management Software Benefits (Infographic)Recruitment Management Software Benefits (Infographic)
Recruitment Management Software Benefits (Infographic)
 
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
 
Advantages of Odoo ERP 17 for Your Business
Advantages of Odoo ERP 17 for Your BusinessAdvantages of Odoo ERP 17 for Your Business
Advantages of Odoo ERP 17 for Your Business
 
Odoo 14 - eLearning Module In Odoo 14 Enterprise
Odoo 14 - eLearning Module In Odoo 14 EnterpriseOdoo 14 - eLearning Module In Odoo 14 Enterprise
Odoo 14 - eLearning Module In Odoo 14 Enterprise
 
SpotFlow: Tracking Method Calls and States at Runtime
SpotFlow: Tracking Method Calls and States at RuntimeSpotFlow: Tracking Method Calls and States at Runtime
SpotFlow: Tracking Method Calls and States at Runtime
 

GS1 Railway Visibility Standard

  • 1. Class 5 - GS1 Railway Internet of Trains CS632/SEP564, Fall 2018 Daeyoung Kim Professor, School of Computing, KAIST • kimd@kaist.ac.kr, http://oliot.org, http://autoidlab.kaist.ac.kr, http://resl.kaist.ac.kr http://autoidlabs.org http://gs1.org
  • 2. Introduction Motivation & challenge New GS1 application standard for visibility in rail GS1 EPCIS for Rail Vehicle Visibility Application Standard
  • 3. © Auto-ID Lab Korea / KAIST Slide 3 Introduction • Problem • Due to limited visibility and information about rail vehicles, it’s difficult for rail operators to plan and meet customer demands for timely deliveries and updates.
  • 4. © Auto-ID Lab Korea / KAIST Slide 4 Introduction • Objective • Provides roadmap for rail stakeholders to gain visibility of rolling stock and access to real-time information
  • 5. © Auto-ID Lab Korea / KAIST Slide 5 Introduction • The standard provides significant business benefits for all players • Improved safety • Facilitation of preventative maintenance • Reduced environmental impact of transport • Improved customer service • Greater process efficiencies • Reduced costs
  • 7. © Auto-ID Lab Korea / KAIST Slide 7 Rail Vehicle Visibility • Business need • The needs for information sharing: • A need for tracking of vehicles as they travel within countries and across different countries • A need to associate the vehicle data with the Wayside Train Monitoring System (WTMS) data about vehicles and vehicle components to enhance preventive maintenance • The use of RFID for railway vehicles becomes more and more popular. -> More applications for vehicle and train visibility information will emerge. • These two applications are the basis for the initial EPCIS application in rail.
  • 8. © Auto-ID Lab Korea / KAIST Slide 8 Rail Vehicle Visibility • Asset tracking applications • Normal operative functions where the location of specific vehicles needs to be known • Vehicle level management • Tracking the location and status of each vehicle as they travel • Independent of the train • Potential future applications: • Real-time cargo tracking • Estimating the vehicle distance travelled for planning preventive maintenance • Planning vehicle availability
  • 9. © Auto-ID Lab Korea / KAIST Slide 9 Rail Vehicle Visibility • Wayside Train Monitoring System (WTMS) • An integrated system of measurement devices that monitor the condition of rolling stock travelling on the railway tracks • Reduce accidents, enable preventive maintenance and improving rail system reliability • Typical WTMS device types: • Hot Axle-Box Detectors (HABD) or hot-box detectors • Wheel impact load detectors (WILD) • Acoustic Axle Bearing Monitoring (AABM) • Automatic pantograph monitoring systems (APMS) • RFID allows for automatic collection of all measurement results and creating statistical data of measurement results per vehicle. • Applying standard EPC/RFID protocols will facilitate the exchange of data across WTMS systems and between railway operators.
  • 10. © Auto-ID Lab Korea / KAIST Slide 10 Rail Vehicle Visibility • RFID enabled Automatic Vehicle Identification (AVI) systems • Fixed readers and wheel sensors (at trackside) • The fixed trackside readers identify the vehicles of the passing train. • Identify all tagged vehicles and their order in the train • Detect the presence of vehicles with missing or broken tags and their relative location in the train • Important for the WTMS use case, since it enables the measurement results to be linked to the correct vehicles in a train set • Determine the travel direction, the orientation, axle count, speed, and length of each vehicle • Enable train level information exchanges • Ex) A train entering or leaving a yard and the composition/formation of the train
  • 11. © Auto-ID Lab Korea / KAIST Slide 11 Rail Vehicle Visibility • RFID enabled Automatic Vehicle Identification (AVI) systems Fixed trackside RFID configuration
  • 12. © Auto-ID Lab Korea / KAIST Slide 12 Rail Vehicle Visibility • Train Management System (TMS) • A system used to control railway operations • Detect and control movement of trains on a track • The information from the TMS can be used to generate additional event data. • Ex) The train entering or leaving an area can be deduced by combining data from a TMS and data from previously read points provided by the AVI system.
  • 14. © Auto-ID Lab Korea / KAIST Slide 14 Vehicle Identification • Vehicle identification with “master” GIAI • Identify each rail vehicle as an asset • EPC URI is used to represent rail vehicles which are included in EPCIS events. • Ex) urn:epc:id:giai:4012345.98765432198765432 • Unambiguously determine static information about the rail vehicle • Rail vehicle type, axle count, vehicle owner, etc. • Communicate information as master data • Not physical, deduced by proxy GIAI
  • 15. © Auto-ID Lab Korea / KAIST Slide 15 Vehicle Identification • AIDC device identification with “proxy” GIAI • Multiple EPC/RFID tags • to enable the automatic identification of the side or end of the wagon • Placing two tags on opposite sides of the wagon at opposing corners • Ex) Vehicle end/side indicator as per GS1 in Europe’s “RFID in Rail” guideline • Vehicle orientation can be inferred from the identity of the tag observed.
  • 16. © Auto-ID Lab Korea / KAIST Slide 16 Vehicle Identification • AIDC device identification with “proxy” GIAI • Each of these tags is identified by a unique GIAI • Ex) tag 1 of 2: urn:epc:tag:giai-96:1.4012345.18765432198765432 tag 2 of 2: urn:epc:tag:giai-96:1.4012345.28765432198765432 • These “device” GIAIs serve as “proxy” representation Rail vehicle with multiple tags - top views Rail vehicle with multiple tags - side views
  • 17. Read Point and Business Location Identification How locations can be identified in a rail context
  • 18. © Auto-ID Lab Korea / KAIST Slide 18 Read Point and Business Location Identification • Read Points • A location that is meant to identify the most specific place at which an EPCIS event took place • Identified using the SGLN • Unique Read Point – Unambiguously determine the read point’s physical location, line name/ID, and track name/ID Example of Read Point
  • 19. © Auto-ID Lab Korea / KAIST Slide 19 Read Point and Business Location Identification • Identifying a track using EPC/RFID • A single track: A one-to-one relation between reader and read point • Multiple tracks: apply separate read points with distinct SGLNs for each track AVI system monitoring a single track AVI system monitoring multiple tracks
  • 20. © Auto-ID Lab Korea / KAIST Slide 20 Read Point and Business Location Identification • Business location • The location where the rail vehicle is assumed to be following the event • Assumed to be valid until superseded by the business location of a subsequent event pertaining to the rail vehicle • Used to tell the location where the vehicles or trains are found after the event took place • Ex) track section, station, shunting yard, or specific shunting yard location • Used to serve asset tracking needs • While the read point is more directly related to the measurement values received in conjunction with an EPCIS event Business location
  • 21. Determining vehicle and train visibility data
  • 22. © Auto-ID Lab Korea / KAIST Slide 22 Determining vehicle and train visibility data • Determining the train direction • Available from the AVI system • Expressed in terms of the ‘railroad direction’ of the track • Using the direction indicator • 0 : the direction was not detected • 1 : one direction in the rail network • 2 : the opposite direction in the rail network • The compass direction indicating the actual direction of the train at the read point • Expressed using a cardinal (N, S, W, E) or intermediate cardinal (NW, NE, SE, SW) value
  • 23. © Auto-ID Lab Korea / KAIST Slide 23 Determining vehicle and train visibility data • Determining the train direction Determining Train Direction
  • 24. © Auto-ID Lab Korea / KAIST Slide 24 Determining vehicle and train visibility data • Determining the orientation of the rail vehicle • The correct assignment of measurement values requires the direction of travel of the vehicle. • The orientation of a rail vehicle is determined by: • The observed tag • The train direction • Ex) Train direction indicator = 2 (compass direction = NE) t1: Vehicle 2 – tag 2, vehicle end 2 passed first t2: Vehicle 1 – tag 1, vehicle end 1 passed first
  • 25. © Auto-ID Lab Korea / KAIST Slide 25 Determining vehicle and train visibility data • Determining Source and Destination • Information on the origin of the train and its ultimate destination • Parties with access to the railroad plan – can derive this based on the provided read points and direction information • Parties that do not have access – utilize the Source and Destination elements in EPCIS • Identified with SGLN • Not necessary, may additional
  • 26. © Auto-ID Lab Korea / KAIST Slide 26 Determining vehicle and train visibility data • Determining a train passage • The AVI can detect whether observed vehicles are connected in the same train-set. • A separate EPCIS event for each vehicle observation • To specify which vehicles were observed as part of the same passage, a passage identifier should be added to the object events. • Passage identifier should be included as an EPCIS Business Transaction. • A Transaction Event will be used to list all observed rail vehicles. • The train number can be used to link to information in other train management systems. • Include the vehicle level information in the EPCIS transaction event for a passage • To avoid use of “dummy” EPCs for hidden vehicles
  • 27. Sharing vehicle and train visibility data with EPCIS Special issues when applying Epcis functions to railway vehicle visibility
  • 28. © Auto-ID Lab Korea / KAIST Slide 28 Sharing vehicle and train visibility data with EPCIS • Vehicle and train visibility – critical tracking events Standard Rail Journey Diagram
  • 29. © Auto-ID Lab Korea / KAIST Slide 29 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • ObjectEvent (action OBSERVE) – serves as an observation of a uniquely identified rail vehicle in passage along its journey, or upon its arrival at or departure from a terminus • TransactionEvent (action ADD) – serves as a “summary” event following the observation of a passing train’s trailing vehicle, reiterating the proxy GIAIs of positively identified vehicles, as well as relevant totals for all vehicles
  • 30. © Auto-ID Lab Korea / KAIST Slide 30 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • What • Indicates the objects to which the EPCIS event pertains • Each observed rail vehicle should be captured in a separate ObjectEvent. • The epcList element should contain only the master GIAI of the observed vehicle. • Ex) • A passage should be defined using a TransactionEvent. • The epcList element includes the master GIAIs of all observed, positively identified rail vehicles. • Ex)
  • 31. © Auto-ID Lab Korea / KAIST Slide 31 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • When • eventTime – the time at which the vehicle was observed (Object events), the time at which the first (leading) vehicle of a passing trainset was observed (Transaction events) • recordTime – the date and time at which this event was recorded by an EPCIS Repository • Ex)
  • 32. © Auto-ID Lab Korea / KAIST Slide 32 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • Where • Indicates the location at which the EPCIS event was observed, as well as the whereabouts of the object subsequent to the event • readPoint – the SGLN corresponding to the event’s location • bizLocation – the SGLN corresponding to the object’s whereabouts subsequent to the event • For all object events and transaction events, either the readPoint or the bizLocation or both should be populated. • Ex)
  • 33. © Auto-ID Lab Korea / KAIST Slide 33 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • Why • Reflects the business context (“Business Step”) of the EPCIS event, as well as the status (“Disposition”) of the object subsequent to the event • Business Step • Specifies the business process linked to the EPCIS event • Ex) • Disposition • Denotes the status of an object subsequent to the EPCIS event • Ex)
  • 34. © Auto-ID Lab Korea / KAIST Slide 34 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • Why • Source/Destination • Use the urn:epcglobal:cbv:sdt:location source/destination type identifier with SGLN • Ex)
  • 35. © Auto-ID Lab Korea / KAIST Slide 35 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • Why • Business Transactions • EPCIS Business Transactions are defined using a combination of Business Transaction Type and Business Transaction ID • Rail sector-specific vehicle visibility applications should use HTTP URLs for business transaction identifiers • To share information about a passage, the bizTransaction element can be used in two ways: • as a transaction reference in a rail visibility ObjectEvent, to indicate which events belong to the same ‘passage’ • as a transaction type in a rail visibility TransactionEvent, to specify totals and tag IDs for a particular ‘passage’ • Ex) transaction reference
  • 36. © Auto-ID Lab Korea / KAIST Slide 36 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • Extension elements • All of these elements should be specified using the namespace urn:gs1:epcisapp:rail • Direction elements (for object and transaction events) • directionIndicator • 0 : the direction was not detected • 1 : the first direction in the rail network • 2 : the second direction in the rail network • compassDirection • Using a cardinal (N, S, W, E) or inter-cardinal (NW, NE, SE, SW) • Ex)
  • 37. © Auto-ID Lab Korea / KAIST Slide 37 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • Extension elements • Object event elements • vehicleOrientation • 1 : vehicle end one is leading, relative to the direction of travel • 2 : vehicle end two is leading, relative to the direction of travel • 0 : leading vehicle end not determined • vehiclePosition – A number identifying the relative position of the rail vehicle within the passage • vehicleAxleCount – The number of axles of the vehicle • proxyGIAI – the GIAI(s) of the observed tag(s) • Ex)
  • 38. © Auto-ID Lab Korea / KAIST Slide 38 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • Transaction event elements (optional) • trainAxleCount – the total number of axles observed for the passage of the entire trainset • trainVehicleCount – the total number of vehicles observed for the passage of the entire trainset • Vehicle – information on each of the observed vehicles • vehiclePosition – the relative position of the vehicle in the passage • vehicleAxleCount – the number of axles of the observed vehicle • vehicleUniqueIdentified – indicates whether the ID of the observed rail vehicle was captured • vehicleMasterGIAI – optional element specifying the ID of the rail vehicle • Ex)
  • 39. Examples of Rail Visibility Events
  • 40. © Auto-ID Lab Korea / KAIST Slide 40 Examples of Rail Visibility Events • Rail vehicle observations • Two tags were observed • First tag 2 of vehicle 676, after that tag 1 of vehicle 070 • The passage ID for both observed vehicles is the same • Part of the same train set 070 676 Read point 1 2 12
  • 41. © Auto-ID Lab Korea / KAIST Slide 41 Examples of Rail Visibility Events • Rail vehicle observations Rail vehicle observation – Object event 1
  • 42. © Auto-ID Lab Korea / KAIST Slide 42 Examples of Rail Visibility Events • Rail vehicle observations Rail vehicle observation – Object event 2
  • 43. © Auto-ID Lab Korea / KAIST Slide 43 Examples of Rail Visibility Events • Rail vehicle changing direction Illustration of direction change – Object event 1
  • 44. © Auto-ID Lab Korea / KAIST Slide 44 Examples of Rail Visibility Events • Rail vehicle changing direction Illustration of direction change – Object event 2
  • 45. © Auto-ID Lab Korea / KAIST Slide 45 Examples of Rail Visibility Events • Rail vehicle changing direction Illustration of direction change – Object event 3
  • 46. © Auto-ID Lab Korea / KAIST Slide 46 Examples of Rail Visibility Events • Train passage transaction event (including untagged vehicle) • How a train passage can be expressed using a transaction event • A train passage which consists of three rail vehicles Train passage
  • 47. © Auto-ID Lab Korea / KAIST Slide 47 Examples of Rail Visibility Events • Train passage transaction event (including untagged vehicle) Train passage transaction event
  • 48. © Auto-ID Lab Korea / KAIST Slide 48 Examples of Rail Visibility Events • Train passage transaction event (including untagged vehicle) Train passage transaction event
  • 49. EPCIS Query examples for rail vehicle visibility
  • 50. © Auto-ID Lab Korea / KAIST Slide 50 EPCIS Query examples for rail vehicle visibility • EPCIS Query Control Interface • On-demand (synchronous) – a client makes a request through the EPCIS Query Control Interface and receives a response immediately • Standing request (asynchronous) – a client establishes a subscription for a periodic query. Each time the periodic query is executed, the results are delivered asynchronously to a recipient via the EPCIS Query Callback Interface
  • 51. © Auto-ID Lab Korea / KAIST Slide 51 EPCIS Query examples for rail vehicle visibility • On-demand queries via EPCIS Query Control Interface • Observations of a specified vehicle since a specified date/time • Example of on-demand query by vehicle • Observations of all vehicles at a specified read point in a specified window of time • Example of on-demand query by read point
  • 52. © Auto-ID Lab Korea / KAIST Slide 52 EPCIS Query examples for rail vehicle visibility • On-demand queries via EPCIS Query Control Interface • Events for a given passage • Example of on-demand query by passage ID • Passage-level queries • Example of on-demand query for passage events
  • 53. © Auto-ID Lab Korea / KAIST Slide 53 EPCIS Query examples for rail vehicle visibility • Standing queries (Subscriptions) via EPCIS Query Call-back Interface • Notification whenever a specified vehicle is observed at any read point • Example of standing query by vehicle • Notification whenever any uniquely identified vehicle is observed at specified read point • Example of standing query by read point