Demand forecasting technical note

Transcription

1 Planning and Regulation Route Planning New Lines Programme Demand forecasting technical note

2 Contents Contents 1 NTRODUCTON 1 Overview of Approach 1 This Report 3 2 NTERCTY MODEL 5 ntroduction 5 GJT Calculator 5 nput Data 7 Calculating Trunk Costs 7 Calculating Trunk Access Times 8 Summing Trunk and Access GJTs 9 Mode Specific Assumptions and Methods 10 Values of Time and Modal Preference 10 Crowding and Network Topology 13 Mode Split Calculations 17 Running the Model 21 Model Outputs 22 llustrative Model Results 23 Model Discussion 30 Potential Model Refinements 32 3 COMMUTER MODEL 33 ntroduction 33 Scope of Model 33 Methodology Overview 35 Data nputs 37 Detailed Review of Methodology 38 Model Outputs 44 4 REGONAL MODEL 47 ntroduction 47 Scope of the Model 47 Methodology Overview 48 Data nputs 48 Detailed Review of Methodology 48 Model Outputs 53 5 HEATHROW ACCESS MODEL 57 2

3 ntroduction 57 Model Development and Calibration 57 Model Forecasting 58 Model Outputs 60 6 NEAR EUROPE MODEL 61 ntroduction 61 Scope of the Model 61 Methodology Overview 62 Model Outputs 1 FGURES Figure 1.1 New Lines Modelling Suite 2 Figure 2.1 Rail Trunk With Feeder Catchment Zones 6 Figure 2.2 Distribution of Virgin West Coast Load Factors (London End) 17 Figure 2.3 Hierachical Logit Model Structure 18 Figure 2.4 Option MB Figure 2.5 London Do-Minimum Catchment 25 Figure 2.6 Manchester Do-Minimum (Classic Rail) Catchment 25 Figure 2.7 Manchester Do-Something (New Lines) Catchment 26 Figure 2.8 Do Something GJT breakdown 29 Figure 3.1 Commuter Model Methodology Flow Diagram 35 Figure 4.1 Example of Station Groupings 49 Figure 4.2 Example of Outputs produced by the Regional Model 53 Figure 4.3 Distribution of Demand ncreases Greater Than 10,000 Pax Per Year 54 Figure 4.4 Distribution of Demand And Revenue ncreases By Station 54 TABLES Table 2.1 ntercity VoT (2007 prices / 2030 values) 11 Table 2.2 Table 2.3 llustrative Perceived Journey Time Benefit for a 240 Minute Classic Rail Journey Switching to New Lines 12 llustrative Perceived Journey Time Benefit for a 120 Minute Classic Rail Journey Switching to New Lines 13 Table 2.4 mplied Journey Time Equivalent Crowding Penalties 15 Table 2.5 Proposed Time Equivalence Penalties ndividual Train 16

4 Contents Table 2.6 Scaling Factors 19 Table 2.7 Do-Minimum Crowding Levels 26 Table 2.8 New Line Crowding 27 Table 2.9 London-Manchester Journeys Results Summary 27 Table 2.10 Top 30 Flows London Manchester 28 Table 2.11 London - Stockport detail 29 Table 2.12 Total Revenue for Option Table 2.13 Relative market size for the North West and Scotland in Table 3.1 NMF Group Station Zones 34 Table 3.2 PDFH Based Elasticity Methodology Example 36 Table 3.3 Time Period Definitions 36 Table 3.4 GJT Overlays 40 Table 3.5 Crowding Example Watford Junction to Bushey Link 42 Table 3.6 Absolute Annual Demand and Revenue Change by Flow 44 Table 3.7 Do-Minimum Constrained vs Unconstrained Demand 45 Table 4.1 Lookup Table Used To Calculate Fast-Slow Multipliers 50 Table 4.2 Lookup Table Used to Calculate Add-on Generalised Journey Times 50 Table 4.3 Wait Time Penalties by Ticket Type (mins) 51 Table 4.4 nterchange Penalties by Ticket Type (mins) 52 Table Base Year Do Minimum % of Demand by Mode for Scotland 60 Table 5.2 GJT Costs (Perceived Mins) by Mode for Scotland 60 Table 6.1 Mode constants and Scaling Parameters for Near Europe model 1 4

5 1 ntroduction Overview of Approach 1.1 The aim of the New Lines project is to evaluate the potential benefits of the construction of new, potentially high speed, rail lines in the UK. These benefits would primarily accrue to the demand using the New Lines (transferring from other rail services or other modes, and new trips), but also from demand generated by improved services on the classic (existing) rail network facilitated by the transfer of services to the New Line. 1.2 The demand forecasting framework has therefore been designed to consider the impacts on a range of markets that could be affected by New Lines: Long distance ntercity markets served by any New Line; The impact on commuter and regional markets as the classic network is commensurately recast and improved as the New Line removes the ntercity services; The improvement in the attractiveness of using rail to access the near continent (notably Paris and Brussels), through interchange onto HS1; and The improvement in the attractiveness of using rail to access Heathrow, especially if direct services are provided. 1.3 The framework comprises a suite of five, spreadsheet based, models (decision support tools), designed to capture the impact of New Lines on each of the aforementioned markets: An ntercity model, designed to forecast the demand impacts of New Lines on the demand for inter city travel, considering how demand may switch from other modes (classic rail, air and car) and how the New Line may generate new demand on the New Line corridor (either through changes in destination or through changes in trip frequency). This model has been based on the PLANET Strategic model (PSM) developed for the SRA s High Speed Line Study in 2002, with rebased demand and model parameters. A Commuter model, focusing on the London commuter market and how a recast network would benefit travellers through improvements in journey time, frequency and crowding A Regional model, which focuses on the remaining parts of the network not captured in the ntercity and Commuter models and considers how changes in journeys times and frequencies afforded by a classic rail network recast would improve the rail offer for regional flows (including to and from London for flows other than the major cities). A Near Europe model, a simple mode choice model considering how the improved connections to London from the regional cities would affect demand to Paris and Brussels via HS1 through model shift from air. A Heathrow Access model, which considers how indirect or direct New Lines services improving accessibility to Heathrow may affect the choices between surface access modes and between air interlining and surface access. 1

6 1.4 Although these models all use the same demand and service specification data and output results on a consistent basis, the methodology of each model is tailored to the specific characteristics of the particular market segment for which it is designed. This means that the scope of each model cannot be considered in isolation, and if the scope of one is changed then the scope of the others have to be updated accordingly to avoid omitting or double counting results. 1.5 There is the potential for overlap between the first three of these models (ntercity, Commuter and Regional), which collectively deal with wholly domestic travel. To avoid double counting results, a methodical approach was adopted to assign each flow to one of the three models. Firstly, a commuter corridor was defined, and all flows lying within this corridor were assigned to the Commuter model; it was then decided which of the remaining flows should belong to the ntercity model, and all the leftover flows were then assigned to the Regional model. The Heathrow Access Model and Near Europe models deal with travel beyond the mainland United Kingdom and as such are mutually exclusive markets, with no potential overlap. The scope of the models as applied to an illustrative New Lines option centred on the West Midlands and North West corridor is illustrated in Figure 1.1. FGURE 1.1 NEW LNES MODELLNG SUTE Glasgow and Edinburgh Preston Liverpool Manchester Stockport Warrington ntercity model Crewe Stafford Stoke on Trent Near Europe model Regional model Birmingham New Street Birmingham nternational Coventry Rugby To Paris/ Brussels Huntingdon Northampton Commuter model Heathrow Access Model Milton Keynes Central Berkhamsted Stevenage Watford London and SE 1.6 No primary research has been undertaken for demand data, rather full use has been made of datasets of existing and forecast demand. Of note, use has been made of the demand data originally collected for the SRA study into high speed lines undertaken in 2002, CAA air demand data and RFF/LENNON rail ticket sales data. These have been used to derive estimates of Base 2007 demand by mode. 2

7 1.7 Forecasts of (Do-Minimum) demand in 2030 before any New Lines are introduced are based on DfT forecasts of changes in rail, air and road demand and are therefore consistent with national policy. These forecasts reflect expected changes in transport infrastructure, pricing policies by the respective market players and changes in the socio-economic drivers of demand (such as the spatial distribution and level of population and employment, car ownership and GDP growth). 1.8 The decision support tools are exactly that; at this stage, the key requirement is that the tools enable relative differences between options to be estimated and assessed with confidence. The tools are not designed, at this stage, to provide absolute forecasts with the certainty required to make a robust decision to proceed with the implementation of New Lines per se. Clearly, Steer Davies Gleave has applied best practice techniques to all of the modelling analysis and has used all its experience and judgement to ensure that the forecast estimates produced are as robust as possible. However, the model outputs are designed to support the Strategic Business Case and inform the development of train service specifications the tools are not designed, nor would be appropriate at this stage, to provide inputs to a detailed timetabling exercise. 1.9 n addition to the aforementioned models, an additional Model converts service specifications data into a format usable by the demand models The modelling framework has been developed to a level commensurate with the overall study, namely that of establishing if a case for New Lines exists. Further development of the modelling suite, including enhanced data, would be required should the case be considered in more detail. This Report 1.11 This report sets out at an overview of the respective models, what they do, how they operate and examples of outputs. This report is not a detailed model development report akin to a Local Model Validation Report (LMVR) or similar used to set out in detail the model development and application Each model is dealt with in a separate Chapter, as follows: The ntercity model is dealt with in Chapter 2; Chapter 3 deals with the Commuter model; Chapter 4 covers the Regional Model; The Heathrow Access Model is covered in Chapter 5; and Chapter 6 details the Near Europe Model. 3

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9 2 ntercity Model ntroduction 2.1 The ntercity model is essentially a simplified, spreadsheet based version of the Planet Strategic Model (PSM) developed for the Strategic Rail Authority in 2002 as part of a study considering the case for new high speed rail lines in the UK. 2.2 For relevant origin-destination (OD) pairs (based on the same 235 zoning system as in PSM) the model assigns demand data across four modes: Classic Rail (CR) representing the existing rail network; New Rail (NR) the proposed new rail network which will compete with CR; Air; and Car. 2.3 For CR, NR and air, the model uses a trunk and feeder approach to defining the transport network. For key trunk journeys, the model determines which OD flows are in scope to travel on that trunk and assigns them to that journey. 2.4 The ntercity model is split into three parts, a Generalised Journey Time (GJT) calculator, a mode split model and a crowding process. The GJT calculator determines the GJT for in-scope zones and the mode split model uses these costs to determine forecast demand. The crowding process then takes the forecast demand and determines the crowding level that this causes. This result is used to vary the GJT, in turn varying the forecast demand. The crowding process controls this iterative process. GJT Calculator 2.5 Generalised journey time (GJT) is a measure of the overall temporal cost of a journey and is made up of a number of component costs. These are in-vehicle time (VT), the journey fare and the service interval penalty. Additionally, other components may be included, for example interchange penalties, parking costs and waiting time. GJT is measured in minutes and therefore the component parts of the GJT must be converted into minutes. For those components not already measured in minutes, this means applying a Value of Time (VoT) to the component. The VoT is based on research and may vary by journey purpose. The values used are discussed in section The GJT calculator module models the rail and air transport networks as a series of trunk links which are accessible via Trunk Access Points (TAPs). These TAPs are accessible from any of the zones which are included in the model. This concept is illustrated in Figure 2.1 5

10 FGURE 2.1 RAL TRUNK WTH FEEDER CATCHMENT ZONES TAP Trunk (rail or air) TAP 2.7 By setting a parameter, the catchment area of each TAP can be adjusted. The results of the catchment scoping require manual checking to ensure that only zones that would realistically travel via that trunk are in scope. This process simplifies the network model approach that PSM takes by quickly removing journeys that are determined not in scope for a given trunk. 2.8 TAP access times can be varied by each TAP, allowing the user to tighten the catchment where multiple travel options exist. The access time is specified in GJT terms since access time inputs are measured in this unit. Note that whilst the catchments can be varied by TAP, the resulting catchment is then applied to all flows to and from that TAP i.e. the catchment for a London TAP is constant, irrespective of the flow and the associated options for access. 6

11 nput Data New Lines Programme: Demand forecasting technical note Demand Data 2.9 The demand data input into the ntercity model is based on data used in PSM. The 2000 PSM data is uplifted to 2007 and then DfT national forecasts are used to grow the rail, air and car demand to 2030 levels. To keep the spreadsheet model to a manageable size only zones deemed relevant to the modelling process are included. For purposes of comparison, 2007 demand data is included as a model input. Network Data 2.10 DfT s Network Modelling Framework (NMF) model is the source for rail GJTs between various zones and is used to determine the access times to TAPs. t also provides average fare data PSM data is used for car journey times, both for access to TAPs and for zone to zone journeys. Alongside this data is a crow-flies distance value, used to calculate car fuel costs Trunk journey times used to calculate trunk GJT for air and rail is entered by the user. These are based on 2007, 2030 Do-Minimum or 2030 Do-Something data as appropriate Where a TAP allows rail or car access, the minimum of the two access times is chosen. Parameters 2.14 Mode choice parameters such as scaling factors, Alternative Specific Constant (ASC), and generation factor can be set. A discussion of VoT and ASC parameters can be found in section Generation factors have been reduced from those used in PSM for business users. Trunk nput 2.15 Trunk fares can be specified for trunk journeys in three categories business, commuter and other (i.e. leisure). For rail these inputs will normally be calculated from NMF fares data, which are held by ticket type (full, reduced and seasons). A conversion process to journey purpose is undertaken, in effect, yield by journey purpose information is entered into the mode choice model The service interval input is in minutes. Note that the feasibility of a trunk journey is determined by the service interval input As well as specifying the journey time for trunk journeys, journey time information must be input for all parts of the journey where the journey passes over multiple crowding links. Crowding penalties are applied on a link by link basis according to the crowding on a link and the journey time over that link. Calculating Trunk Costs Journey Time 2.18 The journey time, in minutes, makes up a significant portion of the GJT for a trip. t is weighted for each mode to account for a traveller s preferences for a mode. For example, if the measured VT value of time is higher for air than for classic rail, this shows that people are willing to pay more to save a minute travelling by air. n other words, they prefer one minute in the air less than on classic rail. Therefore, a 7

12 minute by air is actually longer (i.e. it is more costly to the traveller) than a minute by classic rail Weightings are calculated by the ratio of the VoT for each mode. Classic rail is defined as the reference mode, meaning that all of the GJTs calculated for the trunk routes are effectively in units of classic-rail equivalent minutes. For example: VoT ( car _ VT) Weight ( car) = VoT ( ClassicRail _ VT) 2.20 Journey times are subsequently multiplied by the weightings to give a weighted trunk cost. Fare 2.21 The fare for a journey is another significant cost in the GJT calculation. The fare is divided by the VoT to generate the generalised minutes equivalent. This is done for each journey purpose type. Since business users tend to have a higher VoT than leisure users, this means that the fare is a larger portion of a leisure traveller s GJT than a business traveller s even if the same fare applies. Service nterval 2.22 Since waiting at a station is not typically considered useful time, travellers perceive a disbenefit with longer services. The penalty added to the GJT is calculated using the headways and relevant value of time Note that a service nterval penalty is applied to classic rail, high-speed rail and also to air. t is assumed that values for air are the same as for rail. Crowding 2.24 Crowding penalties are added on to the GJT. For a discussion of calculating crowding penalties, see section Calculating Trunk Access Times Access Time Threshold 2.25 An access time threshold parameter can be specified to enable the user to filter out certain OD flows in the overall results by saying, for example, that travellers can access the trunk network only if they are within a certain access time from a TAP The type of access allowable at each TAP is designated by the user. This is designed to prevent unrealistic car access journey times to city centre stations and to allow car access only to any easily accessible stations Using the access time threshold and access type, a list of zones accessible from each TAP is compiled. For rail access, the minimum access time for the three ticket types (F/R/S) is chosen for comparison against the threshold. Where both car and rail access are permitted at a station, the minimum of the car and the rail access time is used For each zone the closest of any accessible New Line TAP is chosen. t is important to bear this in mind when interpreting the results as choosing the closest TAP to a particular zone may mean removing the feasibility of certain OD pairs since the 8

13 closest TAP may not have all trunk journeys available. This is not a significant issue given the New Line service patterns under consideration in the New Lines programme. An alternative TAP choice methodology is employed for Air as described in section f a zone contains a TAP there is a parameter describing an intra-zonal access time that represents how long it takes to get to the TAP Note that for car access, travellers with a car available will have it available at one end only. n this model, this complexity is ignored and car available is assumed to mean car available at either end. Since OD pairs are bi-directional (that is to say that, for example, OD includes all those travelling from 45 to 67 and 67 to 45) this is a necessary assumption. Rail Access Time Features 2.31 Whilst the access time for car is the point to point journey time most rail journeys are not usually as direct. For this reason, there is an additional parameter for a secondary rail access time. This is the time taken for a user to access their local station, from which they catch a train to access the TAP. This number represents walking or driving to the station, for example Additionally, if using rail to access a TAP, an interchange penalty representing the inconvenience of the arrival and departure times of services not being aligned, is calculated and added to the access time. This occurs at both the start and end TAP Note that there is no interchange penalty for car users transferring onto the trunk and this is assumed to be zero, even though in reality, there may be a penalty (probably smaller than rail) When determining the closest TAP for a zone, the secondary rail access time is included but the interchange penalty is not. When calculating the overall GJT both additional costs are included Note that access times are weighted according to the ratio of the access time VoT compared to classic rail VT VoT. Summing Trunk and Access GJTs Rail 2.36 The total GJT for all in-scope OD zone pairs is calculated by adding the trunk GJT to the relevant access times. n-scope is defined by the O and D zones having accessible TAPs and there being a feasible trunk journey between the TAPs For any infeasible journey, the GJT is set to the infeasible journey length constant. This should be a large number showing that the journey would not be made in reality (eg 9999). For zone pairs where the O and D are the same, the GJT is set to zero. Air 2.38 Rather than choosing the closest TAP, in reality, people would choose to access a trunk network not by how long it takes to get to the nearest TAP but by selecting a TAP that gives them the best overall journey. Although, for rail, the nearest TAP assumption is made for simplicity, it is not satisfactory for air since different flights are available from different airports. 9

14 2.39 For example, someone living in North London, wishing to travel to Glasgow Prestwick may be closest to Luton airport. However, there is no service from Luton to Prestwick. There is however a service from Stansted which they may be willing to take, which should be available to the mode choice model For air, therefore, the top 3 closest airports are chosen for each zone. The flow GJT is then calculated for all 9 combinations of access points (3 at the origin, 3 at the destination). The access points giving the lowest GJT are then selected as the travellers preferred. Mode Specific Assumptions and Methods Car 2.41 When calculating the GJT for a car journey the monetary cost of the journey is measured in terms of cost of fuel. t is assumed that there is no wear and tear cost. Fuel cost per kilometre is calculated using methodology set out in the DfT s WebTAG. This is multiplied by the distance travelled and then divided by a parameter giving the average number of occupants in a car since the mode split model is comparing the cost against public transport options where the cost is per person Distance: The PSM model includes a straight line crow-flies distance for each zone to zone OD. This data is imported into the model and multiplied by a parameter used to account for the fact that no journey by car between zones would actually be a straight line Fuel cost: The WebTAG methodology calculates the fuel efficiency of the average car using set assumptions for various years. Fuel cost is estimated and duty and VAT is applied (VAT at 17.5%). t is assumed that the average speed of traffic is 90kph, although this can be varied by the user. The 2007 fuel cost is then used in model calculations (this can also be varied by the user). Air 2.44 Car parking is set as a parameter. This is set with an assumption on how much and how long people park at the airport park for Note that air users are assumed to always have a car available and that no one commutes by air. New Rail and Classic Rail 2.46 Rather than using PDFH interchange penalties, which vary by distance, there is a single parameter for interchange penalty. The PDFH penalty is more relevant when comparing direct journeys against journeys with multiple legs. n this model, it is more likely that the interchange penalty will relate to the frequency of local rail services rather than distance of the entire journey. Additionally, the penalty is not specified by ticket type of journey purpose, since the interchange penalty is weighted in the access time calculations. Values of Time and Modal Preference 2.47 Since trunk GJTs are weighted in the ntercity model, it is necessary to ensure that the correct combination of Values of Time (VoT)s are used; these are shown in Table 2.1. PSM VoTs are taken as a starting point. These VoTs are uprated to 2030 values (the forecasting year) and modified to a 2007 price base (the forecast price base). 10

15 Furthermore, there is a modification to the New Line n-vehicle Time (VT) VoT as described below. TABLE 2.1 NTERCTY VOT (2007 PRCES / 2030 VALUES) Business Commuting CA Commuting NCA Other CA Other NCA Air VT n/a n/a 40.9 n/a Air Headway 36.7 n/a n/a 5.3 n/a Air Access n/a n/a 40.9 n/a Car VT n/a 30.3 n/a Car Access n/a 40.7 n/a CR VT CR Headway CR Access NR VT NR Headway NR Access The PSM model used a higher value of time than the ntercity model, but used a fixed time advantage (mode constant) to produce an overall perceived journey time benefit. However, for long journeys this meant that the perceived journey time saving may actually be less than the real journey time saving, whereas for short journeys the opposite occurred where the perceived journey time saving could be well in excess of that actually obtained. For very short journeys this could lead to a negative journey time for new rail trips. As a counter factor passengers on high speed rail were assumed to have a higher value of time than classic rail passengers. t appears that the air value of time was adopted for new rail passengers The Stated Preference (SP) survey undertaken by SDG tested the model form used in the PSM model, of a fixed mode specific constant and a higher value of time for new rail journeys. The SP analysis found that this formulation was not statistically significant. However it did find that a model with a mode specific constant that varied by journey time (applied by reducing the value of time for passengers on high speed rail) was statistically significant Therefore approach adopted in the ntercity model and informed by the Stated Preference work, was to utilise a lower modal value of time for New Lines and dispense with any mode constant; this created a perceived benefit always in excess of the actual journey time saving. And also ensured that the mode specific preference would vary with journey length. 11

16 2.51 This is illustrated in Figure 2.2 and 2.3.for a classic rail journey of 240 minutes and 120 minutes respectively transferring to New Lines. For the former the PSM model has a lower perceived journey time where the New Line offers journey times more than 120 minutes; conversely, the approach used in the ntercity model has perceived time benefits consistently above the actual time saving. For the latter, classic rail journey of 120 minutes, the PSM model has relatively large perceived time benefits compared to the ntercity model used here TABLE 2.2 LLUSTRATVE PERCEVED JOURNEY TME BENEFT FOR A 240 MNUTE CLASSC RAL JOURNEY SWTCHNG TO NEW LNES Actual Time saving PSM Business time saving ntercity Model Business time saving PSM Lesiure time saving ntercity Model Leisure time saving Time benefit New Line time 12

17 TABLE 2.3 LLUSTRATVE PERCEVED JOURNEY TME BENEFT FOR A 120 MNUTE Time benefit Actual Time saving PSM Business time saving ntercity Model Business time saving PSM Lesiure time saving ntercity Model Leisure time saving New Line time CLASSC RAL JOURNEY SWTCHNG TO NEW LNES 2.52 Therefore on the basis of the SP results and review of the treatment of modal preference, the ntercity model adopted a factor of 75% of classic rail value of New Line value of time for Business travellers. For Leisure travellers, the ratio is 92%. Commuters are assumed to place no preference on classic rail or New Lines Due to two factors the SP results were not used to replace all parameters. Changing the parameters that exist in the present day would have required model recalibration. Furthermore the SP analysis produced a value of time for leisure passengers that was deemed too high and not appropriate for use. Crowding and Network Topology 2.54 Crowding can be an important factor (and therefore an addition to the generalised cost) when someone is choosing how to travel. n order to take crowding into account, it is necessary to include a cost for it in the GJT module The crowding process in the ntercity model works on the New Lines mode of the model only. For a discussion on crowding assumptions and how to use the model to calculate classic rail crowding in the Do-Minimum, see paragraphs 2.94 to Crowding is calculated on a link basis. This means that for a given rail link between two Trunk Access Points the entire demand and the entire capacity over that link is used to determine the level of crowding over the link The network topology is used to relate the network links and the services and associated demand that run over them. This takes the form of a matrix for each link (up to 20 links are allowed). The matrix is made up of 20x20 TAP origins and destinations. For each link a flag is set if a train for any particular O to D would run over that link. By adding this information for each link the network topology is defined. This information can then be used to determine how many services run over each link. A further input defining the train stopping pattern, frequency and capacity is required. This therefore enables the total capacity on each link to be calculated. 13

18 2.58 Note that there is only space for one network topology input per spreadsheet. This means that a single spreadsheet must be used for any particular network topology. t is, however, possible to add all possible links to the network topology and refer to only some of the TAPs in any given scenario Demand on each link is determined by totalling the demand for each OD flow that uses a particular link and a crowding cost for each link is calculated. Crowding costs are calculated on an all day basis using crowding penalty curves derived for this purpose. These curves define the crowding penalty as a proportion of the in-vehicle time; the relevant multiplier is thus applied to the journey time for each link to determine a crowding cost for that link. Note that this process infers that all services over each link are available to all of the demand; clearly there will be instances where this is not the case. Careful design of the TAP and link system can be used to mitigate this effect. This issue should not materially affect the result of the current New Line options given the service patterns modelled The crowding penalty is then fed back into the GJT cost for each trunk, which in turn alters the demand. A macro is used to iterate through the process up to ten times, with the resultant fed-back crowding cost being an average of previous runs to ensure that the process converges. Valuing Crowding 2.61 The incorporation of crowding into the ntercity model required a relatively simple, robust way of estimating the impact of crowding on demand, consistent with best practice. The exact requirement was for a function to convert average load factor on each TAP to TAP link to a crowding time penalty The methodology used is drawn heavily from that used in the NMF and comprises two key steps: Establishment of crowding time penalties by load factor for an individual train based on PDFH valuations; and Application of distributions representing the variation of demand over an appropriate time period to give average crowding time penalties over a number of services. Crowding Time Penalties 2.63 Whilst PDFH 4.1 expresses crowding penalties in terms of pence per minute, the NMF reflects current DfT guidance and uses an approach where the penalties are represented in terms of minutes of penalty per minute of journey time (as does the PSM model). Previous versions of the PDFH presented crowding penalties in both formats, and whilst it now only quotes monetary valuations, the time equivalent values can be derived by going back to the source studies as explained in the documentation. One of the reasons for using this approach is that it is difficult to apply the monetary approach at a link level as fares are not defined at that level. Table 2.4 details the resulting table of crowding penalties. 14

19 TABLE 2.4 New Lines Programme: Demand forecasting technical note MPLED JOURNEY TME EQUVALENT CROWDNG PENALTES Sitting Penalties (time penalties) London-based Services Non-London Based Services Business Commuting Load Factor Leisure Standard First Class Outer nner Leisure Business Commuting 60% % % % % % % % % % % 0.55 Standing Penalties (time penalties) London-based Services Non-London Based Services Business Commuting Load Factor Leisure Standard First Class Outer nner Leisure Business Commuting Below 100% % % % % % % % These were simplified to a set of recommended values for a single train service, which has been reduced to penalties for Commuting (split London and non-london based) and non-commuting. This simplification reflects uncertainty in the original valuations, the need for internal consistency and consistency with the standard GJT approach The penalties shown in Table 2.5 were adopted for use in NMF, and have been adopted for this work. The penalties are applied to in-vehicle times at the load factors shown and are additional to actual journey time. Values at 300% load factors are derived by extrapolation from the values at 120% and 140%. 15

20 TABLE 2.5 New Lines Programme: Demand forecasting technical note PROPOSED TME EQUVALENCE PENALTES NDVDUAL TRAN Load Factor Condition London-Based Commuting 1 Non- London Commuting 2 Non- Commuting 3 60% and below Sitting % Sitting % Sitting % Sitting % Sitting % Sitting % Sitting Below 100% Standing % Standing % Standing % Standing % Standing Using a Distribution to Estimate Average Crowding Costs for a Link 2.66 Load factors are available for each link within the model. n general this means that the resulting load factors will be averages over a number of train services over an average day The consequence of this is that, if there is any variability in load factors across services, then crowding off will start at an average load factor of less than the 80% shown in Table 2.5. This is because there will be some trains with higher load factors than the average, which incur crowding penalties, and this is not offset by those trains at lower load factors than the average which have zero crowding penalties. n general, crowding penalties for average load factors will be higher than for individual trains with the same load factor because of this asymmetry The construction of crowding curves assumes that distributions of load factors can be modelled using a Gamma distribution with an assumed distribution between the standard deviation and mean load factor. The ratio of the standard deviation to the mean load factor is assumed to remain constant as the mean load factor varies. 1 Arithmetic average of London based Commuting Outer and nner from Table Standing penalties as per Non-London based Commuting services, sitting penalties constrained to non- Commuting values from Table Arithmetic average of London based Leisure and Standard Business and Non-London based Leisure and Business values from Table

21 2.69 The Gamma distribution form is used because it approximates to a Normal distribution for certain parameter values, except that it is (usefully) non-negative. These distributions allowed us to infer a revised table of crowding penalties based on average load factors The NMF employed a process for fitting Gamma distributions for individual TOCs and time periods and this been adopted for application in the New Lines study, using the distribution parameters from the most appropriate TOCs for the whole day, rather than individual time period, derived by returning to the raw data The main source of data that has been used is a database of loadings on Virgin West Coast between 2 April and 30 June This has been analysed to understand the variation of weekday load factors on the final link into and out of London; this gives a sd/mean ratio of 0.40, which is consistent with the values used in the NMF. Figure 2.2 illustrates the fit of the distribution to the data. FGURE 2.2 DSTRBUTON OF VRGN WEST COAST LOAD FACTORS (LONDON END) All weekday flows starting or ending in London Analytically derived distribution 100% 90% 80% % of distribution less than load factor 70% 60% 50% 40% 30% 20% 10% 0% Load factor 2.72 The VWC data is the most up to date and most comprehensive and analysis of the other NMF TOC data showed that the other TOCs results appear to be distributed around VWC s. Given that VWC is comparable in character to a new High Speed Line and serves similar markets, the value of 0.40 has therefore been recommended for the sd/mean parameter in the New Lines crowding approach. Mode Split Calculations 2.73 The mode split model is based on the same hierarchical structure and parameters as the PSM model. Figure 2.3 shows the model structure with New Rail offered as an alternative mode to classic rail at the bottom of a 3 level hierarchy. 17

22 FGURE 2.3 New Lines Programme: Demand forecasting technical note HERACHCAL LOGT MODEL STRUCTURE All modes PT modes Rail modes Classic Rail New Rail Air Car 2.74 After classic rail, new rail, air and car generalised costs have been calculated, these costs are used to calculate estimates of the future modal share between the four modes. This is achieved using an incremental nested, or hierarchical, logit model, which is a statistical model commonly used to forecast the choices made by people faced with a series of discrete options. The inputs to the model are the Do- Minimum modal share and both Do-Minimum and Do-Something generalised costs, along with a number of parameters that influence how sensitive the model is to differences in the generalised costs. The output of the model is a number of conditional probabilities, which are interpreted as being the percentage of travellers choosing each mode when faced with the choice At the top of the nest, costs are aggregated to reflect the overall improvement in transport and this is used to obtain an estimate of generated demand. Parameters 2.76 The PSM mode choice and generation scaling parameters were employed, but modified to take account of the change in the modelled price base. The PSM model has a base year of 2000 and all monetary values are expressed in a price base of that year; the ntercity model has an equivalent (price) base year of This esults in, all other things being equal, a bigger monetary cost since the price base is higher. On that basis, the scaling parameters are reduced in compensation. RP data for 2000 and 2007 was used to inflate values of time, with a corresponding reduction in the scaling parameters. The resulting scaling parameters are set out in Table

23 TABLE 2.6 New Lines Programme: Demand forecasting technical note SCALNG FACTORS Scaling factor λ Business Commuti ng (CA) Commuti ng (NCA) Other (CA) Other (NCA) Classic rail versus new rail Rail versus air Public transport versus car Generation The generation scaling parameters are based on a factor (thetas strictly speaking ratios of scaling parameters) applied to the scaling parameter of the public transport versus car top nest. The default PSM value is 1/3 for all purposes, but this was reduced for Business to 0.15, based on review and analysis of model results and comparison and benchmarking against modelled and empirical results obtained elsewhere. t must be noted that the value of the generation scaling parameters (and hence the factor or theta applied) is a critical parameter and has a very large influence on the level of demand on New Lines. The values chosen are considered to give plausible forecasts, consistent with other forecasting methodologies and benchmarking. Application of the Mode Choice Model 2.78 The model was applied on an O-D basis and comprises five main steps. n all cases, where New Lines is identified as not competing with classic rail on a particular flow, the Do-Minimum mode split and demand is taken as the default result. Since crowding is modelled, the following process is done on an iterative process, with ten iterations being undertaken and a dampening process implemented to force convergence. Step 1: Classic Rail versus New Rail mode choice 2.79 This stage models the choice between classic rail and new rail. At this stage the model operates on an absolute basis for the Do-Something (i.e. with new rail) scenario only, since new rail does not exist in the Do-Minimum scenario and there is therefore no mode choice to be made Generalised costs are fed into the model in units of pence and modal share is calculated using the formulae: λ P( A GCNR e NR rail) = and GC λa GCNR λ A CR ( e + e ) λa GCCR e P( CR rail) = GC λa GCNR λa CR ( e + e ) where λ is the scaling factor and GC NR and GC CR are the generalised costs of new rail and classic rail respectively. 19

24 2.81 Composite costs for the rail mode were calculated using the formula, GC rail = λ 1 λa GCNR λa GCCR A ln( e + e ) 2.82 The change in composite cost between the Do-Minimum and Do-Something was calculated as, ΔGC rail = GC rail GC CR Step 2: Rail versus air mode choice 2.83 At this stage the change in cost calculated in the lower nest is compared with the generalised cost of air travel, using the incremental logit formula, λ P( B ΔGCrail Rail PT) = GC rail air and P e rail λb ΔGCrail λb Δ air ( P e + P e ) λ P( B ΔGCair P Air PT) = GC rail air e air λb ΔGCrail λb Δ air ( P e + P e ) where P air and P rail are the Do-Minimum percentage mode share of the air and rail modes respectively Composite costs are again calculated and fed to the public transport vs car mode choice model. The formula used is Δ GC PT = 1 λb ΔGCair λb ΔGCrail ln( Paire + Praile λb 2.85 f no Do-Minimum air demand exists for a flow, rail is assumed to capture 100% of the modal share and the rail composite cost is fed directly to the next nest in the model hierarchy. Step 3: Public transport versus car mode choice 2.86 The change in cost calculated in the preceding nest is compared with the generalised cost of car travel. The same incremental logit formulae are used as in the preceding level, with different costs and parameters as appropriate. Composite costs are again calculated and used to calculate generated demand. Step 4: Generated demand 2.87 With composite costs calculated at the top nest in the model structure, the generated demand is calculated, with Do-Something demand equating to: ) D DS = D DM e 1 λ C ΔGCall 3 Step 5: Do-Something demand 2.88 n the final stage the generated demand is added to the Do-Minimum demand and the total figure is apportioned using the probabilities calculated at each stage in the mode choice model For a number of flows the difference in GJT between NR and CR is fairly large. This is particularly true of flows with large access times to a NR TAP. The NR mode share parameter can be set to ensure that demand from flows with mode share less than 20

25 the parameter threshold is not assigned to New Rail. This ensures that material revenue generated from lots of small revenue gains is not claimed. Running the Model 2.90 The model needs Do-Minimum costs for classic rail, air and car and Do-Something costs for classic rail, new rail, air and car. Usually when running the model, car and air costs are unchanged from Do-Minimum to Do-Something The model is designed to be able to store and run multiple, but broadly similar, Do- Something scenarios (where a scenario is a set of fares, journey times and service intervals). Each scenario can be run simply by switching the scenario selector in the parameter sheet and rerunning the crowding process However, since the mode choice between new rail and classic rail will only be calculated if the relevant new rail flow is also in scope in the Do-Minimum, it is important to ensure that every scenario run is tested against the relevant Do- Minimum costs. This means that the correct set of Do-Minimum costs must be input into the model and that separate versions of the model will be needed when different sets of Do-Minimum costs are needed (for example when testing options that serve different corridors) Furthermore, it is important to note that crowding costs must be calculated for both the Do-Minimum classic rail and the Do-Something new rail costs. t is assumed that there is no crowding on classic rail flows where a New Line flow is introduced on the basis that the majority of the flow will transfer to New Rail, with most flows on the classic network not being subject to crowding (since for example travellers will have used the classic network in order to minimise the fare and hence used advanced booking with a seat). To keep the model size operable, the crowding function is only applicable to the New Rail GJTs and therefore to run crowding on CR in the Do- Minimum, a separate model is used and the CR Do-Minimum scenario run through the NR flow, including the crowding process. Do-Minimum 2.94 The process for running the CR DM is described below: Set up Air information (TAPs, access radii, journey time, service interval and fares) Set up CR information (TAPs, access radii, journey time, service interval and fares) in the NR inputs Set up the network topology this will included as many major TAPs as needed to ensure a realistic demand on trains to ensure crowding is correctly calculated Set up train service spec to determine train capacity for crowding Switch Use GJT weighting to NO. This ensures that the NR JT weighting is switched off (to ensure that CR GJTs are calculated). Also changing the switch forces the given demand results to be the results of the crowding process (rather than the outputs of the mode split model). Set up other parameters Clear then run the crowding macro 21

26 New Lines Programme: Demand forecasting technical note The figures in the sheet DM Results for DS can then be copied to the DS version of the model into the DM Results nputs sheet t is important to check that the right zones are in scope for all TAPs. t might be necessary to modify the VT manual adjustment parameters to allow certain zones to be assigned as in-scope To ensure correct working of the mode split model, the CR DM should include feasible journeys that are expected to be in scope in the NR DS. This is to ensure there is a competitive CR option for the model split mode to perform its calculations on. Do-Something 2.97 The process for running the DS is described below: Ensure correct DM results have been copied into the DM Results nputs sheet Set up Air information (typically unchanged from DM) nput NR TAPs and access radii into NR TAP definition nput fares, service interval and JT into NR inputs Set up Classic rail DS into CR inputs. The same TAPs and access radii must be used as in the DM. Fares, service interval and journey times can be modified. Set up NR network topology this would be point point links for high speed nonstop rail service Set up train service specification to determine train capacity for crowding Ensure Switch Use GJT weighting is YES Set up other parameters Run crowding Further NR scenarios can be run by selecting the relevant scenario in the GJT Parameters sheet This process is outlined in the Model Flow sheet in the model. Model Outputs 2.99 A number of model outputs are available to the user. These are described below. Appraisal Results As well as the demand and revenue results, the model produces outputs for appraisal related information such as journey time savings per mode and crowding and fares benefits and disbenefits. These benefits are derived from the calculated logsum results for New Rail vs Classic Rail nest in the mode split model and the results for the air and car directly. The detail of these calculations is discussed in the appraisal document. Top 30 & Summary The summary sheet displays the demand, revenue and passenger miles for the relevant link selected (in the Top 30 sheet). The Top 30 results sheet lists the 30 largest individual flows that make up the results in the summary sheet. This list is 22