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Data analysis in performance management
1. Data Analysis for Performance Management
and Service Development
David Winslett and Howard Wong
Transport for London
1 8TH NOVEMBER 201 9
2. TfL are data rich
15,000 Road User
detectors creating
5.2bn records
650 million iBus
events
On a typical day 12 million ANPR registration
plates from the 1600
cameras across our road
network
540 Trains running
24hrs a day on
some lines
5 millions LU
journeys per day
5. Healthy Streets and
Healthy People Planning for new homes
and jobs
A good public transport
experience
TfL priorities are set out in the Mayors Transport Strategy
Mayor’s Transport Strategy
MARCH 2018
Prioritising the work we do
6. We use the data in these key areas:
Measuring
Passenger
Demand
Evaluating
Performance
Planning
Network
Capability
8. v
8
▪ What gets measured gets done
▪ To establish and explain the current position
▪ To understand the scale of problems
▪ To support planning decisions
Stakeholders Why do we measure performance?
9. v
9
▪ Funders: expectations of value for money
▪ Operators: contractual requirements
▪ Customers: expect to go from A to B on time
Stakeholders Stakeholders have different expectations
Time
Cost Quality
Customers
Funders /
service specifiers
Operators
10. v
10
Balancing between:
Safety / security Delivery
Cost People
Service quantity
and quality Service quantity and service quality
Quantity
• The amount of
something
• Objective
Quality
• The characteristics
of something
• Subjective
12. v
12
▪ % of scheduled services operated
▪ % of scheduled km operated
▪ Asset availability
▪ Benchmarking, historical trends, comparability
between operators
▪ Need data and time to collect and analyse
Service quantity
and quality Examples of quantity measures
13. v
13
▪ Big differences between urban railways and inter-
urban railways
– eg express services, skip-stopping, alternatives during
disruptions, trips purposes
▪ Require different measures for high frequency,
high capacity urban railway
Service quantity
and quality
Differences between urban and inter-
urban railways
14. v
14
▪ Variability of provision
▪ Excess waiting time
▪ Turn up and go services can be reliably unreliable
Service quantity
and quality Reliability of service quantity provision
16. v
16
UK rail
National Rail punctuality measures
▪ PPM (Public Performance Measure)
▪ Right Time
▪ CaSL (Cancellations and Significant Lateness)
17. v
17
Service quantity
and quality Time based measures
▪ Time lost as a result of poor performance
▪ Primary delays and secondary delays
▪ Can be demand weighted
19. v
19
Demand-based
metrics LU Lost Customer Hours
Lost Customer Hours are an estimate of the impact that an
incident may have on the journey time of all passengers
expected to be travelling at the time of the incident
The measure allows TfL to prioritise the investigation and
resolution of system failures on those that have the highest
impact on the travelling public
An incident in central London at 08:00AM on Wednesday is much worse
than one in outer London on a Sunday afternoon
20. v
20
Demand-based
metrics LU Excess Journey Time
▪ Excess Journey Time measures the reliability of
the service
▪ Journey Time weighted by perceived time weights for different
components of a journey, and also weighted by expected demand
▪ Excess Journey Time is the additional time on top of scheduled time
22. v
22
Demand-based
metrics Considerations and constraints
▪ Performance against capability
– eg rolling stock, track, signalling, staff
▪ Financial performance
– eg fare per trip, cost per pax-km, cost recovery
▪ Financial performance linked to operations,
– eg lost revenue due to incident
▪ Optimisation between asset utilisation and performance
– eg availability of hot spares for redundancy
▪ Service optimisation
– eg run time, scheduling, maintenance strategy
▪ Capability analysis, constraints / bottleneck analysis
▪ Relationship between specifiers, operators and customers
▪ Data availability and capability to analyse, model and forecast
▪ Automatic fare collection / electronic ticketing
24. v
24
Measuring demand
Why do we measure demand?
▪ Safety - how many passengers in the system?
▪ Who / where / why / how passengers are
travelling?
– Understand the market characteristics
▪ Measuring the current performance
▪ Plan for the future
– Scheme appraisals
25. v
26
Measuring demand
Monitoring crowding locations
▪ Understanding where / when
the on-train crowding is and
the level of discomfort
▪ Subsequent impact on
station movement and dwell
time
▪ Investigate how to improve
the services to alleviate
crowding
Wood Lane
Shepherd’s Bush
Market
Goldhawk Road
Terminal 4
Terminal 5
Terminals
1,2,3
Piccadilly
Circus
St. James’s
Park
Charing
Cross
Green
Park
Mansion
House
Leicester
Square
Cannon Street
Covent Garden
Chancery
Lane
Holborn
Russell
Square
Moorgate
St. Paul’s
Bank
Barbican
Farringdon Old Street
Tottenham
Court Road
Goodge
Street
Oxford
Circus
Warren Street Euston
Square
Euston AngelBaker Street
King’s Cross
St. Pancras
Mornington
Crescent
Great
Portland
Street
Regent’s
Park
Marble
Arch
Bond
Street
St. John’s Wood
Swiss Cottage
Finchley Road
Edgware
Road
West
Hampstead
Camden Town
Chalk Farm
Belsize Park
Kentish Town
Caledonian Road
Holloway Road
Arsenal
Tufnell Park
Archway
Manor House
Turnpike Lane
Wood Green
Highgate
Hampstead
Golders Green
Brent Cross
East Finchley
Finchley Central
West Finchley
Woodside Park
Totteridge & Whetstone
Mill Hill East
Hendon Central
Colindale
Burnt Oak
Edgware
High Barnet
Highbu
Islingto
Liverpool
Street
Bounds Green
Arnos Grove
Southgate
Oakwood
Cockfosters
Aldgate
Monument Tower
Hill
Blackfriars
Temple
Embankment
Southwark
London Bridge
Bermond
Waterloo
Lambeth
North Borough
Elephant & Castle
Kennington
Brixton
Oval
Pimlico
Stockwell
Clapham North
Clapham Common
Clapham South
Balham
Tooting Bec
Tooting Broadway
Colliers Wood
South Wimbledon
Morden
Vauxhall
Wimbledon
Wimbledon Park
Southfields
East Putney
Putney Bridge
Parsons Green
Fulham Broadway
West Brompton
Earl’s
Court
South
Kensington
Victoria Westminster
Sloane
Square
Gloucester
Road
Knightsbridge
Hyde Park Corner
High Street
Kensington
Notting
Hill Gate
Lancaster
Gate
Queensway
Kensington
(Olympia)
Shepherd’s
Bush
Holland
Park
Bayswater
Paddington
Paddington
Marylebone
Edgware
Road
East
Acton
White
City
Latimer Road
Ladbroke Grove
Westbourne Park
Royal Oak
Warwick Avenue
Maida Vale
Kilburn Park
Queen’s Park
Kensal Green
West
Acton
North
Acton
Willesden Junction
Harlesden
Stonebridge Park
Wembley Central
North Wembley
South Kenton
Northwick
Park Wembley
Park
Kilburn
Willesden Green
Dollis Hill
Neasden
Kingsbury
Queensbury
Canons Park
Preston
Road
Kenton
Harrow-
on-the-Hill
North Harrow
Pinner
Northwood Hills
Northwood
Harrow &
Wealdstone
StanmoreMoor Park
Croxley
Watford
Chesham
Chalfont
& Latimer
Amersham
Chorleywood
Rickmansworth
West Ruislip
Hillingdon
Uxbridge Ickenham
Ruislip
Ruislip Manor
Eastcote
Rayners Lane
Ruislip Gardens
South Ruislip
Northolt
South Harrow
West Harrow
Greenford
Perivale
Hanger Lane
Sudbury Hill
Sudbury Town
Alperton
Park Royal
North Ealing
Ealing Broadway
Ealing Common
Acton
Town
Hammersmith
Barons
Court
West
Kensington
South Ealing
Northfields
Boston Manor
Osterley
Hounslow East
Hounslow Central
Chiswick
Park
Turnham
Green
Stamford
Brook
Ravenscourt
Park
Gunnersbury
Hounslow West
Hatton Cross Kew Gardens
Richmond
zero to 50% of seats taken
50% to 100% seats taken
0 to 1 passengers/sq metre
1 to 2 passengers/sq metre
2 to 3 passengers/sq metre
3 to 4 passengers/sq metre
4 + passengers/sq metre
Key to lines
Seats Free:
Seats Taken:
Some Standing:
Busy:
Crowded:
Very Crowded:
Maximal:
28. v
29
Measuring demand
Developing a demand dataset for London
▪ Need a comprehensive demand dataset:
– Easy to reference, to use and to understand
– Covers typical weekday, Friday, Saturday and Sunday
– Covers London Underground, London Overground,
Docklands Light Railway, Elizabeth Line
▪ Need a consistent data series
– Trend analysis and explain phenomena
– To replace the old data series that lasted 20 years
29. v
30
Measuring demand
Creating a demand dataset
▪ Demand from smartcards, gatelines and
automatic passenger counters
▪ Services from timetables
▪ Model used to assign journeys to routes using
generalised journey time
30. v
31
Measuring demand
The dataset outputs
▪ The dataset provides
– Journeys per day by all rail modes in London
– Values for each 1 5minute period of the day
– Provides information on number of interchanging
passengers at key stations
31. v
32
Measuring demand
Accessing the dataset
▪ The dataset is available across TfL and published on our
website
▪ It is available for all users
– Everyone is making the same assumptions
– Reduce the need for ad hoc surveys for individual projects
▪ Make it available for the public
– Official demand dataset
– Nice and clean, ready to use
▪ crowding.data.tfl.gov.uk
33. 34
TfL must ensure future
services meet future
demand
We must consider:
- Infrastructure capability
- Operational processes
- Service patterns and
frequency
34
34. 35
v
35
Capability
Understanding current capability
To understand changes required to meet future
demand, first we must understand todays
capability
Capability/Capacity of
the Signalling System
Capability/Capacity of
the Trains
Operational Rules and
Regulations
35. 36
v
36
Capability
Understanding current capability
Understanding how a Signalling System works is not
the same as understanding the performance of the
service.
We review actual train movement data to assess
the service performance and the capability of the
system
37. Capability Network performance assessment
The data is analysed to assess
actual performance, including the
variation
Runtimes impact the Customer
Journey Time, Resource
Utilisation
Runtime variation impacts
performance
Managing station dwell times is
key to achieving the high
frequencies required in the
capital
Station Dwell Times
Inter Station Run Times
38. Network performance assessment
The lead train is in the platform with a green signal and is ready to leave;
a following train is waiting at the ‘home’ signal;
our REOCCUPATION stopwatch is at 00:00
The following train has berthed in the platform;
our REOCCUPATION I stopwatch shows that 50s elapsed – our REOCCUPATION is therefore 50s 00:0000:2500:50
If we freeze the action at this point we can see that:
the lead train has departed, and is a section clear of the platform;
the following train has been given a green signal to enter the platform;
our REOCCUPATION stopwatch shows that 25s has elapsed
A key measure of system
capability is the platformre-
occupation time
Must be minimised to achieve
the high service frequencies
39. 40
Dwell Time factors
Dwell time is highly variable
and there are many complex
interacting factors that
influence the time
TfL have researched many
areas and our models
incorporate many to gain an
understanding of:
a) the variability at each
station by time of day
b) how the variability affects
the overall performance of
the line
42. The Train Service Model
A simulation model that
calculates the journey time and
assesses crowding effects
Used to test changing
infrastructure and train services
Includes feedback loops in
measures such as dwell times
and lateness.
45
51. 58
Example
New Tube for London
58
Train Replacement and Signalling
Upgrade to 4 Lines
• Piccadilly, Central, Bakerloo and
Waterloo & City
• Higher Frequencies
• More comfortable, faster,
higher capacity trains
• Automated operation
• Improved Reliability
• Improved Safety
52. 59
1 ) What train frequency target should be set to
optimise passenger benefit?
2) Given the predicted system capability:
• where will the operational constraints be?
• what would be the benefit of removing
them?
Key Questions
56. 63
Impact on Central Line Passengers
26.6mins
23.4mins
Reduced waiting times
Reduced journey times
Reduced crowdingMore than
100,000
additional
journeys per day
59. TfL have developed
systems to analysis Oyster
data and identify individuals
paying the incorrect fare for
their journey
Used to protect revenue
Understanding
travel patterns to
identify unpaid
fares