This document summarizes a presentation about analyzing passenger data to understand passenger choice in boarding metro cars. It discusses using loadweight data from London Underground trains to analyze which cars passengers board at different stations. The analysis finds that approximately 55% of passengers make boarding choices based on their destination station, aiming to board cars closer to exits, while the other 45% base choices on boarding the car nearest their point of entrance. The document advocates developing frameworks to systematically analyze data and create value from understanding passenger behavior.

1. Data Analytics for Metro
Performance Management
Taku Fujiyama
Associate Professor
University College London
Railway Research Group
Department of Civil, Environmental and Geomatic Engineering

2. About myself
• 5.5 years of work experience at East Japan Railway
Company as a railway civil engineer, before joining UCL
• Designed stations, including Shinjyuku, Ikebukuro, etc.
• Main research topics
• Passenger/pedestrian movements within railway systems
• Railway traffic management
2

3. UCL and Railways
• Richard Trevithick (1771 – 1833)
• Mining Engineer, Pioneer of high-
pressure steam
• First person to invent steam
locomotives
• Developed first passenger-carrying
train (‘Catch me who can’) in 1808
3
At the location of UCL Civil Engineering Building

4. OVERVIEW
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5. Data and Performance evaluation
• Data can be an enabler
• We could extra valuable information that
• Generally improve our understanding
• Identify problems and issues
• Etc
• However, it is not all simple
• There is no perfect data
• It could find/address some issues, but usually it does not go further.
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unless you have frameworks to systematically exploit it

6. Framework?
Example
• Railway tunnel
• Traditionally, visual inspection by people
(cracks, water, sound, etc)
• New measurement systems: Strain gauge,
video images, fibre optic cables, etc
• A big question. How to see the data?
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You need to have
Right evaluation frameworks (or models) that are built on
• Understanding of the nature of the problem of your interest
• Right data collection and processing methods
• Right evaluation criteria

7. Framework
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Collection Processing Materialising
Action in
practice
Knowledge,
service, product
• A framework directs Materialisation and Action
• It also suggests how data be collected and processed
In other words, this creates a value chain, where better
outcomes of each element leads to better final outcomes

8. Different types of Framework
8
Collection Processing Materialising
Action in
practice
Example 1
• Use an existing framework. Use
big data for precise and better
information
• Use smartcard data for departure
time distribution
Example 2
• Exploiting (existing) data for
your different question
• e.g. smart card data for
dynamic train control
Value creation by data

9. Data usability for value creation
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AFC Tikcet
Barrier
Train load Footfall
counter
CCTV Wi-fi GPS + app Web-use Shopping
Coverage • • • • • • • • • • • • •
Accuracy • • • • • • • • • • •
Exploitability • • • • • • • • • • • • • • •
Availability
(Privacy
sensitivity)
• • • • • •
Technical
maturity • • • • • • • • • • • • • •
Observation MobileRail-related User-activity
• There is no perfect dataset that directly answers
any important question
• Current trend is to combine different datasets

10. Case Study 1: Project Evaluation
• Framework: Generalised Journey Time
• Based on much theoretical and implementation work in
academia and at London Underground
• Assumption
• Journey time is the factor that determines passenger choice/behaviour
• Quality of time is not equal between different actions
• Sitting on a seat on train 15 minutes = standing 10 minutes
• Big data is essential to calibrate the models
• (UCL/London Underground can offer this as a course) 10

11. Case Study 2: Journey Planner
• Transport for London makes
journey planning data
available to businesses
• Some companies create
new business opportunities
by providing private journey
planners with additional
functions
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12. UNDERSTANDING PASSENGER
CHOICE OF CAR
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13. • Passenger choice of boarding cars affects station
platform congestion
• Aim of the project:
to understand how passengers choose the cars to
board
e.g. which car do people choose to board?
Background
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14. Why could the outcome be useful?
• Uneven crowding across the cars of the whole train means
‘capacity not fully utilised’.
• If there were factors that 1) influence passenger choice and
2) are controllable by metro operators, then by changing
the factors we can improve capacity utilisation.
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Our ambition is that controlling occupancy of each car (not
each train) becomes a new norm for metro operations

15. Loadweigh data
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§ London Underground S stock
(2010-) can record the weight of
each car after the departure at
each station
§ The weight can be converted to
the number of passengers
§ 7-car trains (for H&C, Circle lines)

16. Hypotheses for passenger choice of
boarding cars
• Main factors
1. Some passengers choose cars close to the exits
of their destination (Destination-based choice)
2. Other passengers choose the car next to the
entrance (Origin-based choice)
• Other potential factors
• Seat availability, crowding on train, service headway, etc
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17. Example:
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§ Hammersmith (HMS) and Goldhawk Road (GHR)
End of Hammersmith&City,
and Circle Lines
Data: July&August, 2016
Weekday AM Peak
(7-10am)

18. • % of passengers by destinations (at Hammersmith)
based on an OD survey
Destination-based Choice (1)
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4.0%
3.8%
9.6%
2.9%
7.1%
3.2%
4.6%
14.3%
4.0%
7.2%
5.6%
4.7%
5.7%
6.4%
3.7%
8.0%
4.1%
0.8% 0.4%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
Hammersmith
GoldhawkRoad
Shepherd'sBush…
WoodLane
LatimerRoad
LadbrokeGrove
WestbournePark
RoyalOak
Paddington
EdgwareRoad
BakerStreet
reatPortlandStreet
EustonSquare
King'sCrossSt.…
Farringdon
Barbican
Moorgate
LiverpoolStreet
AldgateEast
Whitechapel
StepneyGreen
MileEnd
BowRoad
Bromley-by-Bow
WestHam
Plaistow
UptonPark
EastHam
Barking

19. • Location of exits at destinations
Destination-based Choice (2)
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Car 7 Car 6 Car 5 Car 4 Car 3 Car 2 Car 1
Shepherds Bush
Market
Car 7 Car 6 Car 5 Car 4 Car 3 Car 2 Car 1
Wood Lane
···
• Location of the exit is next to Car 6
• ‘Destination-based choice’
passengers travelling to this station
are assigned to Car 6

20. • Location of entrance at the origin
Origin-based Choice
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Car 7 Car 6 Car 5 Car 4 Car 3 Car 2 Car 1Hammersmith
• Location of the entrance
is next to Car 7
• Assumption that the
further the car, the
fewer people would takeAllocation of
‘origin-based
choice’ passengers

21. This presentation
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• Assuming that
• trains are not crowded (have empty seats),
• α % of people choose their boarding cars based on their
Destination (Destination-based choice)
• β % of people choose their boarding cars based on their Origin
(Origin-based choice)
• α + β = 100%
• What would be α (as β=100- α) ?
Finding α that suits the real Loadweigh data best

22. Result 1: Hammersmith (HMS)
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α%=55.5%
Average GEH=0.29

23. Result 2: Goldhawk Road (GHR)
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α%=55.4%
Average GEH=0.17

24. Summary of this research
• Model development/calibration is in progress.
• A summary of investigation:
• If we assume
• 100- α % of people make Destination-based choice
• α % of people make Origin-based choice
• Result:
• Hammersmith: α = 55.5%
• Goldhawk Road: α = 55.4%
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25. Other UCL research
• Dwell time
• Relationship between passenger loading and dwell time
• Impact of door and carriage designs
• Passenger movements within stations
• Railway Traffic Control
• Railway Business management
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26. CONCLUSION
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27. As Concluding Marks
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• No data is perfect, but our understanding can be better
• Active data collection (and more collaboration between
data collection technique developers and data users) rather
than passively waiting for data!
• New value could be created through collaboration between
data collectors, processors, service/product developers,
and users.