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Data analysis in performance management

This document discusses how Transport for London (TfL) uses data to analyze transportation performance, measure passenger demand, and plan network capability. TfL collects vast amounts of data from sources like road sensors, bus tracking, automatic license plate recognition, train tracking, and Wi-Fi connections. They use this data to measure performance metrics, understand passenger demand, and evaluate the current and future capability of the transportation network. Key goals are ensuring safe, reliable, high-quality service and using data to prioritize projects that improve the passenger experience and support the priorities in the Mayor's Transport Strategy.

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Data Analysis for Performance Management and Service Development David Winslett and Howard Wong Transport for London 1 8TH NOVEMBER 201 9
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
130 million Wi-Fi data connections But data itself is not enough....
Our job is to make this data useful...
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
We use the data in these key areas: Measuring Passenger Demand Evaluating Performance Planning Network Capability
v 7 Evaluating performance
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?
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
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
v 11 Service quantity and quality Service quantity is about how many Capacity Provision Capacity Utilisation
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
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
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
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 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:
v 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
34 TfL must ensure future services meet future demand We must consider: - Infrastructure capability - Operational processes - Service patterns and frequency 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
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 v 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
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
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
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 00:00 01:00 02:00 03:00 Train 1 Train 2 Train 3 Train 4 Train 5 Train 6 Minimum Headway : 1m34s ~ 38.3 tph Maximum Headway : 2m43s ~ 22.1 tph 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 Average Headway : 1m54s ~ 31.6 tph
v 44 Capability Planning future capability TfL use models to predict future demand and test future service designs Our models: Calculate Benefits Test Feasibility
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
Network simplifiedCapability
Picc Met Picc Met
2 1 1 1 2 1 1 2 2112199 142 140 141 Picc Met Picc Met Direction 2 links Link -530 Link -533 Link -538 Link -535 Link -235 Link -564
2 1 1 1 2 1 1 2 2112199 142 140 141 Picc Met Picc Met Layout complete Picc Met Picc MetLink 529 Link 534 Link 532 Link 536 Link 235 Link 564Link -530 Link -533 Link -538 Link -535 Link -235 Link -564
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
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
60 Bakerloo Line - Oxford Circus Central Line – Liverpool Street Piccadilly Line - Holborn Waterloo & City Line - Waterloo 00:46 03:38 00:53 00:00 01:00 02:00 03:00 04:00 05:00 00:44 00:54 00:48 00:54 00:42 00:54 00:40 00:00 01:00 02:00 03:00 04:00 05:00 00:43 01:53 00:47 01:42 00:32 00:00 01:00 02:00 03:00 04:00 05:00 00:43 02:13 00:46 01:11 00:31 00:00 01:00 02:00 03:00 04:00 05:00 22 tph (2m43) 27 tph (2m13) 32 tph (1m53) 33 tph (1m49) 24 tph (2m30) 36 tph (1m40) 22 tph (2m43) 27 tph (2m13)
61 A static calculation
62 TSM output – Central Line Achieved TPHLeytonstone Leyton Stratford MileEnd BethnalGreen LiverpoolStreet Bank StPauls ChanceryLane Holborn TottenhamCourtRoad OxfordCircus BondStreet MarbleArch LancasterGate Queensway NottingHillgate HollandPark ShepherdsBush WhiteCity EastActon NorthActon WestActon EalingBroadway HangerLane Perivale Greenford Northolt SouthRuislip RuislipGardens WestRuislip Required TPH 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 18 18 18 18 18 18 18 18 18 07:00:00 07:15:00 07:30:00 07:45:00 08:00:00 08:15:00 08:30:00 08:45:00 09:00:00 09:15:00 09:30:00 09:45:00 Leytonstone Leyton Stratford MileEnd BethnalGreen LiverpoolStreet Bank StPauls ChanceryLane Holborn TottenhamCourtRoad OxfordCircus BondStreet MarbleArch LancasterGate Queensway NottingHillgate HollandPark ShepherdsBush WhiteCity EastActon NorthActon WestActon EalingBroadway HangerLane Perivale Greenford Northolt SouthRuislip RuislipGardens WestRuislip Required TPH 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 18 18 18 18 18 18 18 18 18 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 07:30:00 07:45:00 08:00:00 08:15:00 08:30:00 08:45:00 09:00:00 09:15:00 09:30:00 09:45:00 Leytonstone Leyton Stratford MileEnd BethnalGreen LiverpoolStreet Bank StPauls ChanceryLane Holborn TottenhamCourtRoad OxfordCircus BondStreet MarbleArch LancasterGate Queensway NottingHillgate HollandPark ShepherdsBush WhiteCity EastActon NorthActon WestActon EalingBroadway HangerLane Perivale Greenford Northolt SouthRuislip RuislipGardens WestRuislip Required TPH 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 18 18 18 18 18 18 18 18 18 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 08:15:00 08:30:00 08:45:00 09:00:00 09:15:00 09:30:00 09:45:00 Leytonstone Leyton Stratford MileEnd BethnalGreen LiverpoolStreet Bank StPauls ChanceryLane Holborn TottenhamCourtRoad OxfordCircus BondStreet MarbleArch LancasterGate Queensway NottingHillgate HollandPark ShepherdsBush WhiteCity EastActon NorthActon WestActon EalingBroadway HangerLane Perivale Greenford Northolt SouthRuislip RuislipGardens WestRuislip Required TPH 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 18 18 18 18 18 18 18 18 18 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 23.4mins Reduced waiting times Reduced journey times Reduced crowdingMore than 100,000 additional journeys per day
64 v 64 Future developments
Use of Wifi data
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
v 67 Thank You
Howard Wong David Winslett 1 0th Floor, Palestra 1 97 Blackfriars Road London, SE1 8NJ howard.wong@tube.tfl.gov.uk davidwinslett@tfl.gov.uk 020 3054 8674

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Data analysis in performance management

  • 1.
    Data Analysis forPerformance Management and Service Development David Winslett and Howard Wong Transport for London 1 8TH NOVEMBER 201 9
  • 2.
    TfL are datarich 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
  • 3.
    130 million Wi-Fi dataconnections But data itself is not enough....
  • 4.
    Our job isto make this data useful...
  • 5.
    Healthy Streets and HealthyPeople 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 thedata in these key areas: Measuring Passenger Demand Evaluating Performance Planning Network Capability
  • 7.
  • 8.
    v 8 ▪ What getsmeasured 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: expectationsof 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
  • 11.
    v 11 Service quantity and qualityService quantity is about how many Capacity Provision Capacity Utilisation
  • 12.
    v 12 ▪ % ofscheduled 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 differencesbetween 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 ofprovision ▪ Excess waiting time ▪ Turn up and go services can be reliably unreliable Service quantity and quality Reliability of service quantity provision
  • 15.
    v 15 UK rail UK NationalRail performance measures
  • 16.
    v 16 UK rail National Railpunctuality measures ▪ PPM (Public Performance Measure) ▪ Right Time ▪ CaSL (Cancellations and Significant Lateness)
  • 17.
    v 17 Service quantity and qualityTime based measures ▪ Time lost as a result of poor performance ▪ Primary delays and secondary delays ▪ Can be demand weighted
  • 18.
    v 18 Demand-based metrics More complexdemand-based measures ▪ London Underground Lost Customer Hours ▪ London Underground Excess Journey Time
  • 19.
    v 19 Demand-based metrics LU LostCustomer 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 ExcessJourney 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
  • 21.
    v 21 Demand-based metrics LU EJTand customer satisfaction ▪ Reducing EJT increases customer satisfaction
  • 22.
    v 22 Demand-based metrics Considerations andconstraints ▪ 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
  • 23.
  • 24.
    v 24 Measuring demand Why dowe 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 crowdinglocations ▪ 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:
  • 26.
    v 27 Measuring demand Analysing demand- example Why are Fridays busier? Analyse purpose, origin / destination, distance, time of journeys
  • 27.
    v 28 Measuring demand Baselining toforecast the future Use current demand to create a baseline in order to forecast future travel demand
  • 28.
    v 29 Measuring demand Developing ademand 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 ademand 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 datasetoutputs ▪ 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 thedataset ▪ 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
  • 32.
  • 33.
    34 TfL must ensurefuture services meet future demand We must consider: - Infrastructure capability - Operational processes - Service patterns and frequency 34
  • 34.
    35 v 35 Capability Understanding current capability Tounderstand 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 Understandinghow 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
  • 36.
    37 v 37 Capability Understanding current capability TfLhold a record of every train movement for the past 1 5 years It is stored at detailed signal berth level
  • 37.
    Capability Network performanceassessment 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 Thelead 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 Dwelltime 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
  • 40.
    43 00:00 01:00 02:00 03:00 Train 1 Train2 Train 3 Train 4 Train 5 Train 6 Minimum Headway : 1m34s ~ 38.3 tph Maximum Headway : 2m43s ~ 22.1 tph 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 Average Headway : 1m54s ~ 31.6 tph
  • 41.
    v 44 Capability Planning future capability TfLuse models to predict future demand and test future service designs Our models: Calculate Benefits Test Feasibility
  • 42.
    The Train ServiceModel 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
  • 44.
  • 48.
  • 49.
    2 1 1 1 2 1 1 2 2112199 142 140 141 Picc Met Picc Met Direction 2links Link -530 Link -533 Link -538 Link -535 Link -235 Link -564
  • 50.
    2 1 1 1 2 1 1 2 2112199 142 140 141 Picc Met Picc Met Layout complete Picc Met Picc MetLink529 Link 534 Link 532 Link 536 Link 235 Link 564Link -530 Link -533 Link -538 Link -535 Link -235 Link -564
  • 51.
    58 Example New Tube forLondon 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 ) Whattrain 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
  • 53.
    60 Bakerloo Line -Oxford Circus Central Line – Liverpool Street Piccadilly Line - Holborn Waterloo & City Line - Waterloo 00:46 03:38 00:53 00:00 01:00 02:00 03:00 04:00 05:00 00:44 00:54 00:48 00:54 00:42 00:54 00:40 00:00 01:00 02:00 03:00 04:00 05:00 00:43 01:53 00:47 01:42 00:32 00:00 01:00 02:00 03:00 04:00 05:00 00:43 02:13 00:46 01:11 00:31 00:00 01:00 02:00 03:00 04:00 05:00 22 tph (2m43) 27 tph (2m13) 32 tph (1m53) 33 tph (1m49) 24 tph (2m30) 36 tph (1m40) 22 tph (2m43) 27 tph (2m13)
  • 54.
  • 55.
    62 TSM output –Central Line Achieved TPHLeytonstone Leyton Stratford MileEnd BethnalGreen LiverpoolStreet Bank StPauls ChanceryLane Holborn TottenhamCourtRoad OxfordCircus BondStreet MarbleArch LancasterGate Queensway NottingHillgate HollandPark ShepherdsBush WhiteCity EastActon NorthActon WestActon EalingBroadway HangerLane Perivale Greenford Northolt SouthRuislip RuislipGardens WestRuislip Required TPH 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 18 18 18 18 18 18 18 18 18 07:00:00 07:15:00 07:30:00 07:45:00 08:00:00 08:15:00 08:30:00 08:45:00 09:00:00 09:15:00 09:30:00 09:45:00 Leytonstone Leyton Stratford MileEnd BethnalGreen LiverpoolStreet Bank StPauls ChanceryLane Holborn TottenhamCourtRoad OxfordCircus BondStreet MarbleArch LancasterGate Queensway NottingHillgate HollandPark ShepherdsBush WhiteCity EastActon NorthActon WestActon EalingBroadway HangerLane Perivale Greenford Northolt SouthRuislip RuislipGardens WestRuislip Required TPH 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 18 18 18 18 18 18 18 18 18 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 07:30:00 07:45:00 08:00:00 08:15:00 08:30:00 08:45:00 09:00:00 09:15:00 09:30:00 09:45:00 Leytonstone Leyton Stratford MileEnd BethnalGreen LiverpoolStreet Bank StPauls ChanceryLane Holborn TottenhamCourtRoad OxfordCircus BondStreet MarbleArch LancasterGate Queensway NottingHillgate HollandPark ShepherdsBush WhiteCity EastActon NorthActon WestActon EalingBroadway HangerLane Perivale Greenford Northolt SouthRuislip RuislipGardens WestRuislip Required TPH 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 18 18 18 18 18 18 18 18 18 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 08:15:00 08:30:00 08:45:00 09:00:00 09:15:00 09:30:00 09:45:00 Leytonstone Leyton Stratford MileEnd BethnalGreen LiverpoolStreet Bank StPauls ChanceryLane Holborn TottenhamCourtRoad OxfordCircus BondStreet MarbleArch LancasterGate Queensway NottingHillgate HollandPark ShepherdsBush WhiteCity EastActon NorthActon WestActon EalingBroadway HangerLane Perivale Greenford Northolt SouthRuislip RuislipGardens WestRuislip Required TPH 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 18 18 18 18 18 18 18 18 18 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
  • 56.
    63 Impact on CentralLine Passengers 26.6mins 23.4mins Reduced waiting times Reduced journey times Reduced crowdingMore than 100,000 additional journeys per day
  • 57.
  • 58.
  • 59.
    TfL have developed systemsto analysis Oyster data and identify individuals paying the incorrect fare for their journey Used to protect revenue Understanding travel patterns to identify unpaid fares
  • 60.
  • 61.
    Howard Wong David Winslett 10th Floor, Palestra 1 97 Blackfriars Road London, SE1 8NJ howard.wong@tube.tfl.gov.uk davidwinslett@tfl.gov.uk 020 3054 8674