Building the Rail Network Digital Twin at CSX

1. © 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
CSX Technology:
Building the Rail Network Digital
Twin
Dean Schaefer,
Software Engineer
Nicholas Jones,
Software Engineer

2. © 2022 Neo4j, Inc. All rights reserved.
2
20,000
route miles of track
$12.5 B
in yearly revenue
194
years in operation

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3
Assets and Equipment in Neo4j
24,000
Locomotives
CSX and others
6,700
CSX Train
Crew Members
263,000
Railcars
CSX and others
1,100
CSX Mileposts
1,600
CSX Trains
per day

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4
The Goal
Create a centralized repository of
shipment data represented through a
near real-time graph in order to provide
more accurate and granular asset
tracking.
• Locomotives
• Railcars
• Customers
• Shipping Orders
• Mileposts
Image Placeholder

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5
Graph Model

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6
Graph Model

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7
Basic Architecture Overview

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Train Movement Events
represent various activities
8
Arrival
At
Milepost
Departure
From
Milepost

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Table of Contents
• Overview
◦ CSX
• Assets
◦ Digital Twin
• Domain
 We Are here
• Graph Business Rules
• Challenges
◦ Scenario 1
• Solution
◦ Scenario 2
9

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© 2022 Neo4j, Inc. All rights reserved.
10
1
Business Rule
LOCATED_AT
• Start Date = Arrival Time
• End Date = Depart Time
L
Equipment
• Initial
• Number
ASSOCIATED_TO LOCATED_AT
Arrival @ Milepost
Trip
• startDate
• endDate
• loadEmpty
Milepost
• milepostId
startDate
endDate

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© 2022 Neo4j, Inc. All rights reserved.
11
2
Business Rule
When Load Empty Status changes…
• End Date Existing Trip
• Create a new one
Departure @ Milepost
L
Equipment
• Initial
• Number
ASSOCIATED_TO LOCATED_AT
Trip
• startDate
• endDate
• loadEmpty
Milepost
• milepostId
E
startDate
endDate

12. © 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
12
Challenges
1 Race
Conditions
• doesEquipmentExist() ? use() : create();
• Concurrent events duplicate equipment
2 Event
Veracity
• Missing expected events
• Out of Order Events
◦ Delayed Manual Reports
◦ Multiple Reporting Systems

13. © 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
Scenario
with Out Of Order Events
Order Time Type Milepost Load Empty
1 9:00 Arrival A Empty
2 11:00 Arrival B Loaded
3 10:00 Departure A Empty

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14
:Equipment
E
:Trip
A
:Milepost
equipmentInitial = CSXT
equipmentNumber = 123
startDate = 9:00
endDate =
loadEmptyStatus = E
ASSOCIATED_TO LOCATED_AT
stationMilepost = A
Order Time Type Milepost Load Empty
1 9:00 Arrival A Empty 1) Expected
• Active Trip associated

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15
stationMilepost = A
Order Time Type Milepost Load Empty
1 9:00 Arrival A Empty
2 11:00 Arrival B Loaded
1) Expected
• Active Trip associated
2) Expected
• New Trip Created
• Prior Trip Terminated
:Equipment
E
:Trip
A
:Milepost
equipmentInitial = CSXT
equipmentNumber = 123
startDate = 9:00
endDate = 11:00
loadEmptyStatus = E
ASSOCIATED_TO LOCATED_AT
LOCATED_AT
B
L
startDate = 11:00
endDate =
loadEmptyStatus = L

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16
:Equipment
E
:Trip
A
:Milepost
equipmentInitial = CSXT
equipmentNumber = 123
startDate = 9:00
endDate = 11:00
loadEmptyStatus = E
ASSOCIATED_TO LOCATED_AT
stationMilepost = A
Order Time Type Milepost Load Empty
1 9:00 Arrival A Empty
2 11:00 Arrival B Loaded
3 10:00 Departure A Empty
LOCATED_AT
B
L
startDate = 11:00
endDate = 10:00
loadEmptyStatus = L
E
startDate = 10:00
endDate =
loadEmptyStatus = E
1) Expected
• Active Trip associated
2) Expected
• New Trip Created
• Prior Trip Terminated
3) Invalid State
• Late event tramples prior
• Active Trip not latest
• Split Location relationships

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17
The Solution
Redesigning for Immutability
• Remove “state dependent” insertions
• Leverage MERGE Clause
• Pivot Graph Strategy
• New Trip node for each event
• Event specific relationships
• Don’t end date relations or nodes

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18
Scenario
with MERGE

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19
MERGE
:Equipment
E
:Trip
A
:Milepost
equipmentInitial = CSXT
EquipmentNumber = 123
eventDate = 12/12/12 9:00
loadEmptyStatus = E
ARRIVED_AT
9:00
Arrival at A
ASSOCIATED_TO
E
v
e
n
t
A
r
r
i
v
a
l
T
i
m
e
Scenario with MERGE

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20
MERGE
MERGE
:Equipment
E
:Trip
A
:Milepost
equipmentInitial = CSXT
EquipmentNumber = 123
eventDate = 12/12/12 9:00
loadEmptyStatus = E
ASSOCIATED_TO ARRIVED_AT
B
L
eventDate = 12/12/12 11:00
loadEmptyStatus = L
ARRIVED_AT
9:00
11:00
Arrival at A
Arrival at B
E
v
e
n
t
A
r
r
i
v
a
l
T
i
m
e
Scenario with MERGE

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21
:Equipment
E
:Trip
A
:Milepost
equipmentInitial = CSXT
EquipmentNumber = 123
eventDate = 12/12/12 9:00
loadEmptyStatus = E
ARRIVED_AT
E
eventDate = 12/12/12 10:00
loadEmptyStatus = E
Arrival at A
MERGE
Arrival at B
MERGE
Departure from A
MERGE
9:00
11:00
10:00
ASSOCIATED_TO
B
L
eventDate = 12/12/12 11:00
loadEmptyStatus = L
ARRIVED_AT
E
v
e
n
t
A
r
r
i
v
a
l
T
i
m
e
Scenario with MERGE

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22
Lessons Learned
• Be deliberate with your graph model
• Minimize baked-in business logic
• Choose the right abstraction to model reality
• Consider leveraging immutability
• MERGE is a powerful tool for stateful operations

23. © 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
Fin.