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PLANET – A comprehensive tool for forecast and appraisal of public transport schemes in the UK. 12th European EMME/2 Users’ Conference Basel, Switzerland (22-23 May 2003). Peter Bartlett (Jacobs Consultancy). Presentation Summary. History of PLANET Interfaces Development of PS2002 models
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PLANET – A comprehensive tool for forecast and appraisal of public transport schemes in the UK 12th European EMME/2 Users’ Conference Basel, Switzerland (22-23 May 2003) Peter Bartlett (Jacobs Consultancy)
Presentation Summary • History of PLANET • Interfaces • Development of PS2002 models • Crowding and Assignment procedures • Validation • Application • Appraisal • Future Development
Jacobs Consultancy • Jacobs Consultancy UK • 275 staff worldwide • Transport Planning • Business Planning & Strategy • Development Planning • Jacobs Engineering Group Inc. • Turnover USD 5 billion • Total Staff 33,000 • Consulting, Design, Construction, Operations, Maintenance • Client Base: Industrial, Commercial, Government
Strategic Rail Authority (SRA) UK • Objectives of SRA • Promote use of the national rail network by letting rail passenger franchises andspecifying network enhancements • Sponsorship and Development of Infrastructure projects • Management of Freight Grants • All franchise changes and enhancements must satisfy value for money criteria and be appraised using a framework agreed with Department of Transport (DfT). www.sra.gov.uk
History of PLANET model development PLANET Model Future Development Development PLANET STRATEGIC PLANET PS2002 PLANET MM PLANET 99 PLANET 91 Time 1990 2000
PLANET 91 • 1991 National Rail Timetable • 1991 Demand • Geographical area covering former Network South East (NSE) rail services • Assignment Model • Early attempts at Distribution Model Split (DMS) model • No Underground or Bus network • Developed by British Rail (NSE)
PLANET 99 • 1997/98 National Rail timetable • 1997/98 Demand • Trip distribution based on 1991 data • Enhanced Crowding model • Elasticity approach for future year forecasts • Replaced early attempts at DMS model • Based on relative Generalised Time
PLANET MM • 1997/98 National Rail timetable • 1997/98 Demand • Geographical Extension • AM and Inter-Peak Time Periods • London Underground Network • London Bus Network • Highway Network
PLANET STRATEGIC • Geographical coverage • Northern and Southern England • 250 Zones • 16 Hour (all-day) model • Multi-Modal • Rail, Air, Car • Appropriate for Strategic tests • e.g. High Speed Line (London – NE England) • NOT appropriate for specific detail • i.e. Local flows, crowding at specific location • Developed by another UK consultancy
PLANET PS2002 • 2001/2002 National Rail timetable • Railtrack Train Service Database (TSDB) • 2001 Demand • CAPRI Ticket Sales data • Segmented by Journey purpose • North and South Models • Geographical split of UK • North – 1600 zones • South – 1400 zones • Split by Time Period • AM (07:00 – 09:59) • Inter Peak (10:00 – 15:59)
PLANET Interfaces • INPUTS • TSDB • Train Service Database • CAPRI • Computer Analysis of Passenger Revenue Information • ORCATS • Operational Research Computer Allocation of Tickets to Services
Demand matrices • Processing CAPRI data • October / November 2001 CAPRI data downloaded on detailed station-station basis • Period demand factored to average winter weekday • Annual season ticket demand “spread” to account for monthly sales patterns • Different factors applied to season, full and reduced tickets • Daily demand factored to AM / IP periods using ORCATS demand profiles, depending on • ticket type, • origin / destination station “types”, • journey length
Demand matrices • Assigning CAPRI data to zones • PLANET South station-zone distribution outside inner London based on previous PLANET models • PLANET South distribution inside inner London based on iterative approach • Start with previous (1991 LATS) distribution of London trip ends from surrounding zones • Inner London trip ends factored based on LUL station entries / exits counted Autumn 2001 • Surrounding zone trip ends based on CAPRI data • PLANET North station-zone distribution based on “zone trip generation” model • “Size” of zone (population / employment) • Distance of zone from station
Demand matrices • Segmentation by journey purpose Full fare Business Reduced fare Commuting Season tickets Leisure / Other Journey purpose split by ticket type Based on “dominant TOC” for each flow Derived from National Passenger Survey
Demand matrices (example) • Segmentation by journey purpose • South West Trains (TOC) 26% Full fare Business 40% 16% 2% 19% 34% Reduced fare Commuting 91% 65% Season tickets Leisure / Other 6%
Transit line / network details • Timetable details downloaded from “TSDB” (Train Service DataBase) file • More reliable than “PIF” file used previously • Contains details of “pick-up / set-down only” stops • Train route through network calculated by shortest path • Aided by “key passing points” identified in TSDB • Validation checks for correct links / nodes being used • Trains allocate to AM / IP periods by time at highest “hierarchy station” served • E.g. London terminus for London-based TOCs • Alternative main stations for non-London services (defined by TOC)
Transit line / network details • New transit line codes • Based on CAPRI codes • Easier identification of “service groups” 3rd Character = Key timing station; W = London Waterloo 1st Character = TOC; Y = South West Trains 5th/6th Character = Numerical list of trains YLWU03 1st/2nd Character = Service Group; YL = 6730 (Waterloo – Hampton Ct / Chessington / Dorking / Guildford 4th Character = Direction; U = Up
Assignment / Crowding Procedure Overall assignment process Business demand Commuter demand Leisure demand assign assign assign Scenario with Business costs Scenario with Commuter costs Scenario with Leisure costs Loading values accumulated in 6 iterations (50%,15%,10%, 10%,10%,5%) sum sum sum Scenario with total passenger volumes Calculation of Business costs Calculation of Commuter costs Calculation of Leisure costs
Crowding function For each segment and purpose (train / link combination in four sub-models) Modelled passenger volume (total over all journey purposes) Train capacity Service type (x5) Supply profile Calculation of ratio of volume to capacity (over 18 time slices) Journey purpose (x3) Demand profile Calculation of weighted average crowding cost factor for segment / purpose Crowd cost parameters Link type (x3)
Crowding function – Supply profiles • Profiles for North/South models + AM/ IP • Calculated from timed provision of train service capacity • As shown in passenger timetable • Different profiles reflect different types of service (in peak periods) • Flat profile: fixed formation, standard service interval • Long distance inter-urban service • Increasing profile : higher frequency services at end of peak period • Inter-urban service with business emphasis • Peaked profile: higher frequency and longer / higher-capacity train formations during peak period • Commuter services
Crowding function – Demand profiles • Profiles for North/South models + AM/ IP • Calculated on the basis of ORCATS demand profiles • Separate profiles for Business, Leisure, Commuter (based on profiles for Full, Reduced, Seasons) • Further refinement may be possible • But note that profiles are approximate anyway • Cover different flow “types” (to / from London, etc.) • Cover different flow lengths
Crowding function – cost estimates • Review of functionality of crowding model within PLANET • Concluded that form of the model appears capable of ensuring modelled train loads are limited to realistic levels • Higher crowding valuation would cap loads more effectively • Review of valuation of crowding • 1997 Passenger Demand Forecasting Handbook • SRA Research– “Valuation of crowding improvements on rail services” • Separate valuation for different service types • London-based inter-urban services • London commuter services • Non-London services
As outlined in the Outline Model Specification Technical Note, we will be implementing different crowding cost factors for different journey purpose segments. The discussions and conclusions above lead to the following recommended values to be applied in the PLANET model. These are most strongly based on the recent MVA work, but we have attempted to ensure a reasonable rate of cost increase as load factors approach crush capacity. Table 5‑A Suggested crowding penalties for PLANET Crowding function – cost estimates • Suggested crowding penalties for PLANET • Factors applied to in-vehicle journey time
Validation of PLANET PS2002 models • Comprehensive data collection • On-train passenger counts, by TOC, direction, and time of train • On station counts, by board/alight, and service time / TOC • Station entering / leaving counts, by time of day • Comparison between observed and modelled data • Observed data processed to provide totals by AM Peak / Inter-Peak, TOC, and model link / node
PLANET PS2002 Model Validation • Central London Termini Cordon • Inner London Screenlines • Underground • National Rail • National Rail Screenlines • TOC based (e.g. GNER, Great Western)
Application of PLANET • Thameslink 2000 • CrossRail Business Case • Strategic Plan 2003 • European Rail Traffic Management System (ERTMS) • Great Western Route modernisation • East-West Midlands Multi-Modal Study
PLANET Interfaces • OUTPUTS • ENIF • Thematic maps of time savings • Boarding/Alighting at nodes • MapInfo • Thematic maps etc • Spatial Analysis • Appraisal Template • Cost-Benefit Analysis
Appraisal Template: Overview • An architecture for consistent investment and policy appraisal • converts model outputs, appraisal parameters and cost inputs into value for money measures which can be changed transparently by manipulating the growth and other assumptions • Currently being updated for compatibility with the new SRA appraisal criteria • Final (Alpha version 5) will be available in early June 2003
Appraisal Template: Stages • Information • Assumptions • Costs • Road benefits • Planet (Interface with model output) • Growth factors • Growth • Cash flows • DCFs • Summary CBA
Future PLANET model development • Short Term • Crowding and assignment procedure (MSA) • Demand matrices (inc. LATS+CAPRI+other data sources) • Medium Term • Multi-modal modelling • Four-stage model (Generation, Distribution, Mode Split, Assignment) • Improved Appraisal • Long Term • Land-Use+Transport Interaction model