18TH NOVEMBER 201 9 Data Analysis for Performance Management and Service Development David Winslett and Howard Wong Transport for London On a typical day 12 million ANPR registration plates from the 1600 cameras across our road network
15,000 Road User detectors creating 650 million iBus 5.2bn records events
5 millions LU 540 Trains running journeys per day 24hrs a day on some lines
TfL are data rich But data itself is 130 million Wi-Fi not enough.... data connections Our job is to make this data useful... Prioritising the work we do
TfL priorities are set out in the Mayors Transport Strategy
A good public transport experience
Mayor’s Transport Strategy MARCH 2018
Healthy Streets and Healthy People Planning for new homes and jobs We use the data in these key areas:
Planning Evaluating Network Performance Capability
Measuring Passenger Demand 7
Evaluating performance
v 8 Stakeholders Why do we measure performance?
▪ What gets measured gets done
▪ To establish and explain the current position
▪ To understand the scale of problems
▪ To support planning decisions
v 9 Stakeholders Stakeholders have different expectations
▪ Funders: expectations of value for money ▪ Operators: contractual requirements ▪ Customers: expect to go from A to B on time
Funders / service specifiers Time
Cost Quality Customers Operators
v 10 Service quantity and quality Service quantity and service quality
Quantity Quality
• The amount of • The characteristics something of something • Objective • Subjective
Balancing between: Safety / security Delivery Cost People
v 11 Service quantity and quality Service quantity is about how many
Capacity Provision Capacity Utilisation
v 12 Service quantity and quality Examples of quantity measures
▪ % of scheduled services operated ▪ % of scheduled km operated ▪ Asset availability
▪ Benchmarking, historical trends, comparability between operators ▪ Need data and time to collect and analyse
v 13 Service quantity Differences between urban and inter- and quality urban railways
▪ 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
v 14 Service quantity and quality Reliability of service quantity provision
▪ Variability of provision
▪ Excess waiting time
▪ Turn up and go services can be reliably unreliable
v 15 UK rail UK National Rail performance measures
v 16 UK rail National Rail punctuality measures
▪ PPM (Public Performance Measure) ▪ Right Time ▪ CaSL (Cancellations and Significant Lateness)
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
v 18 Demand-based metrics More complex demand-based measures
▪ London Underground Lost Customer Hours ▪ London Underground Excess Journey Time
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
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
v 21 Demand-based metrics LU EJT and customer satisfaction
▪ Reducing EJT increases customer satisfaction
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
v 23
Measuring demand
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
v 26
Measuring demand Peak AM Hour Monitoring crowdingWatford locations High Barnet Cockfosters Epping Chesham Chalfont Theydon Bois C & Latimer Totteridge & Whetstone Oakwood Croxley Debden R Amersham Chorleywood Woodside Park Southgate Loughton Mill Hill East O Rickmansworth Stanmore Edgware Buckhurst Hill Moor Park Harrow & West Finchley Arnos Grove W Wealdstone Roding West Ruislip Northwood Burnt Oak Valley Chigwell Canons Park Finchley Central Bounds Green D Northwood Hills Hillingdon Ruislip Colindale Kenton East Finchley Grange Hill I Pinner Queensbury Wood Green ▪ Ruislip Manor Hendon Central Woodford Understanding where / when Hainault Uxbridge Ickenham North Harrow N Eastcote Highgate Turnpike Lane Tottenham Kingsbury Brent Cross Harrow- Preston Hale Walthamstow South Fairlop G on-the-Hill Road Archway Woodford Ruislip Gardens Golders Green Manor House Seven Blackhorse Central Barkingside Rayners Lane West Harrow Northwick Neasden Tufnell Park Sisters Road the on-train crowding is and Hampstead Newbury Park Park Wembley Finsbury Park South Ruislip Dollis Hill Snaresbrook Redbridge Upminster O South Kenton Park Arsenal Kentish Town South Harrow North Wembley Willesden Green Wanstead Gants Upminster Bridge V Belsize Park Holloway Road Northolt Hill Wembley Central Kilburn Leytonstone Hornchurch E the level of discomfort Stonebridge Park West Chalk Farm Caledonian Road Harlesden Hampstead Elm Park Greenford Sudbury Hill R Willesden Junction Finchley Road Camden Town Leyton Dagenham East V Kensal Green Mornington Dagenham Heathway Perivale Queen’s Park Swiss Cottage Highbury & Crescent King’s Cross I Sudbury Town Kilburn Park Islington St. John’s Wood St. Pancras Becontree Edgware Great E Maida Vale Upney Road Marylebone Baker Street Portland Euston Angel Stratford Warwick Avenue Street W Hanger Lane Alperton Royal Oak Barking Paddington Farringdon Old Street Westbourne Park Paddington Edgware Warren Street Euston East Ham Road Barbican Liverpool Bethnal ▪ Square Mile End Subsequent impact on Green Ladbroke Grove Regent’s Street Upton Park Park Royal Bayswater Park Goodge Russell Latimer Road Street Square Plaistow Moorgate North Ealing North White Holland Marble Oxford Bow Bromley- West Ham Holborn Acton City Park Queensway Arch Circus Road by-Bow station movement and dwell Aldgate Stepney Green Ealing Broadway St. Paul’s West East Shepherd’s Notting Lancaster Bond Tottenham Chancery East Whitechapel Acton Bush Hill Gate Gate Street Court Road Acton Lane Bank Wood Lane Covent Garden High Street Green Aldgate Kensington Kensington Park Cannon Street time Ealing Common Shepherd’s Bush (Olympia) Leicester Mansion Canning Town Market Hyde Park Corner Square Piccadilly House Monument Tower Knightsbridge Circus Charing Acton Goldhawk Road Hill Cross Town Barons Gloucester Blackfriars South Ealing Hammersmith Court Road Sloane St. James’s Square Park Temple Northfields London Bridge Canary Wharf North Boston Manor Chiswick Turnham Stamford Ravenscourt West Earl’s South Victoria Westminster Embankment Greenwich Osterley Park Green Brook Park Kensington Court Kensington Bermondsey Canada Water Hounslow East West Brompton Gunnersbury Waterloo ▪ Investigate how to improveHounslow Central Fulham Broadway Southwark Hounslow West Terminals Parsons Green Pimlico 1,2,3 Hatton Cross Kew Gardens Lambeth Putney Bridge Borough the services to alleviateTerminal 4 North Richmond Terminal 5 Elephant & Castle crowding East Putney Vauxhall Southfields Kennington Oval Stockwell Clapham North Wimbledon Park Clapham Common Brixton Clapham South Balham Wimbledon Key to lines Tooting Bec Tooting Broadway Seats Free: zero to 50% of seats taken Colliers Wood Seats Taken: 50% to 100% seats taken South Wimbledon Some Standing: 0 to 1 passengers/sq metre Morden Busy: 1 to 2 passengers/sqv metre Crowding with Scheduled Train Service - Crowded: 2 to 3 passengers/sq metre Very Crowded: 3 to 4 passengers/sq metre Autumn 2010 Weekday Peak AM Hour Maximal: 4 + passengers/sq metre Based on scheduled train services © Transport for London 27 Measuring demand Analysing demand - example
Why are Fridays busier? Analyse purpose, origin / destination, distance, time of journeys
v 28 Measuring demand Baselining to forecast the future
Use current demand to create a baseline in order to forecast future travel demand
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
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
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
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
v 33
Planning Capability
v 34 34
TfL must ensure future services meet future demand
We must consider:
- Infrastructure capability
- Operational processes
- Service patterns and frequency 35 35 Capability Understanding current capability
To understand changes required to meet future demand, first we must understand todays capability
Capability/Capacity of Capability/Capacity of Operational Rules and the Signalling System the Trains Regulations
v 36 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
v 37 37 Capability Understanding current capability
TfL hold a record of every train movement for the past 1 5 years
It is stored at detailed signal berth level
v Capability Network performance assessment
Inter Station Run Times The data is analysed to assess actual performance, including the variation
Runtimes impact the Customer Journey Time, Resource Utilisation Runtime variation impacts performance Station Dwell Times
Managing station dwell times is key to achieving the high frequencies required in the capital Network performance assessment
A key measure of system capability is the platform re- occupation time
Must be minimised to achieve the high service frequencies
TheIf we followinglead freeze train the trainis inaction thehas platform berthedat this point inwith the wea platform;green can see signal that: and is ready to leave; 00:00 ourathe following REOCCUPATION lead train train has is departed,waiting I stopwatch at andthe ‘home’ isshows a section signal;that 50sclear elapsed of the platform; – our REOCCUPATION is therefore 50s 00:5000:25 ourthe REOCCUPATION following train has stopwatch been given is ata green 00:00 signal to enter the platform; our REOCCUPATION stopwatch shows that 25s has elapsed 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 43 03:00
02:00
01:00
00:00 Train 1 Train 2 Train 3 Train 4 Train 5 Train 6
ReOcc 00:54 00:54 00:54 00:59 00:55 02:00 Dwell 00:48 00:42 00:40 01:08 00:46 00:43
Headway 01:42 01:36 01:34 02:07 01:41 02:43
Minimum Headway : 1m34s ~ 38.3 tph Maximum Headway : 2m43s ~ 22.1 tph Average Headway : 1m54s ~ 31.6 tph 44 Capability Planning future capability
TfL use models to predict future demand and test future service designs
Our models:
Calculate Benefits Test Feasibility
v 45
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.
Capability Network simplified
Met
Met Picc
Picc Met
Met Picc
Picc
1 1 1 142 140 199 1 112 2 Link -533 Link -235
2 2 Link -535 1 Link -530 141 Link -538 Direction 2 links 2 Link -564 Met
Met Picc
Picc
Link 529 Link 534 Met 1 1 1 Link 235 142 Link 536 140 Link 532 Met 199 1 112 2 Link -533 Link -235 Picc 2 2 Link -535 1 Link -530 Link 564 141 Link -538 Picc Layout complete 2 Link -564 58 58 Example New Tube for London
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 59 Key Questions
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? Bakerloo Line - Oxford Circus Central Line – Liverpool Street 60 22 tph (2m43) 32 tph (1m53)
27 tph (2m13) 33 tph (1m49) 00:46 00:53 00:44 00:48 00:42 00:40
03:38 00:54 00:54 00:54
00:00 01:00 02:00 03:00 04:00 05:00 00:00 01:00 02:00 03:00 04:00 05:00
Piccadilly Line - Holborn Waterloo & City Line - Waterloo 24 tph (2m30) 22 tph (2m43)
36 tph (1m40) 00:43 00:46 2700:43 tph (2m13)00:47 00:31 00:32
02:13 01:11 01:53 01:42
00:00 01:00 02:00 03:00 04:00 05:00 00:00 01:00 02:00 03:00 04:00 05:00 61 A static calculation 62
TSM output – Central Line Achieved TPH
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07:00:00 35.9 35.9 35.8 35.7 35.8 35.9 35.8 35.9 35.9 35.9 35.9 35.7 35.9 35.9 36.0 36.0 35.9 35.9 36.0 36.0 36.0 36.0 18.0 18.0 18.0 18.0 18.0 18.0 18.0 18.0 18.0 07:15:00 34.4 36.1 35.8 35.8 36.0 35.0 35.8 35.8 35.9 35.8 35.8 35.7 35.6 35.5 35.8 35.8 35.8 35.8 35.8 35.8 35.7 35.8 17.9 17.9 17.9 17.9 18.0 18.0 18.0 18.0 18.0 07:30:00 36.9 35.5 35.8 35.7 35.6 34.7 35.5 35.7 35.7 35.7 35.6 35.8 35.7 35.4 35.2 35.8 35.8 35.9 35.8 35.7 35.6 35.6 17.8 17.8 17.9 17.9 17.8 17.9 17.9 17.9 17.9 07:45:00 36.3 34.8 35.1 35.0 35.0 35.3 34.0 35.2 35.4 35.5 35.5 35.3 35.1 35.2 34.9 35.0 35.6 35.7 35.8 35.7 35.9 35.5 17.8 17.8 17.9 17.9 17.9 17.9 17.8 17.9 17.9 08:00:00 35.7 35.8 33.9 33.5 33.6 34.2 34.0 33.4 33.7 34.1 34.4 34.5 34.6 34.8 34.7 33.9 34.3 35.0 35.3 35.5 35.3 34.8 17.4 17.4 17.9 17.9 17.9 18.0 17.9 17.8 17.8 08:15:00 35.8 35.6 34.2 34.8 34.9 34.7 34.4 33.7 33.3 33.0 32.9 33.2 33.3 33.5 34.0 34.0 33.5 33.7 33.9 34.6 34.6 34.6 17.4 17.4 17.2 17.5 17.7 17.6 17.5 17.6 17.6 08:30:00 36.2 36.0 35.2 35.1 35.0 34.9 34.9 34.8 34.5 34.6 34.6 34.3 34.2 34.2 34.2 34.3 33.9 33.2 32.9 32.9 33.4 34.1 17.1 17.1 16.7 17.0 17.0 17.4 17.6 17.4 17.4 08:45:00 36.1 35.6 35.4 35.2 35.2 35.2 34.9 34.9 35.0 34.8 34.8 34.8 34.8 34.6 34.5 34.6 34.7 34.6 34.5 34.3 34.1 34.0 16.9 16.9 17.0 16.5 16.5 16.6 16.8 17.1 17.1 09:00:00 36.1 37.2 38.8 36.0 35.6 35.6 35.7 35.6 35.4 35.3 35.3 35.4 35.3 35.2 35.2 35.0 35.0 35.0 35.1 34.7 34.7 34.5 17.5 17.5 17.4 17.4 17.3 17.2 17.0 17.1 17.1 09:15:00 35.1 37.1 38.5 37.0 37.0 36.9 37.0 36.7 36.7 36.6 36.3 36.0 36.0 36.1 35.8 35.8 35.8 35.7 35.3 35.3 35.4 35.3 17.4 17.4 17.3 17.5 17.5 17.4 17.4 17.3 17.3 09:30:00 37.0 35.7 36.7 39.5 37.8 37.3 37.3 37.5 37.4 37.4 37.2 37.5 37.5 37.5 37.4 37.2 36.9 36.9 36.8 36.7 36.1 35.9 17.9 17.9 17.9 17.7 17.6 17.6 17.7 17.7 17.7 09:45:00 35.8 36.7 36.5 38.5 41.0 40.7 40.7 40.0 39.4 38.9 38.5 37.9 37.7 37.3 37.5 37.5 37.5 37.2 37.4 37.1 37.3 37.7 18.7 18.7 18.7 18.5 18.3 18.1 18.0 17.9 17.9 63 Impact on Central Line Passengers
26.6mins Reduced waiting times
23.4mins Reduced journey times
More than Reduced crowding 100,000 additional journeys per day 64 64
Future developments
v Use of Wifi data Understanding travel patterns to identify unpaid fares
TfL have developed systems to analysis Oyster data and identify individuals paying the incorrect fare for their journey
Used to protect revenue 67
Thank You
v Howard Wong David Winslett
10th Floor, Palestra 1 97 Blackfriars Road London, SE1 8NJ [email protected] [email protected] 020 3054 8674