LDM PLD Comparison: Preliminary Information Specification (Phase 0: LDM) Technical Note Ref: HS2/019

Transcription

1 LDM PLD Comparison: Preliminary Information Specification (Phase 0: LDM) Technical Note Ref: HS2/019 October 2011 Prepared for

2 Revision Schedule Tender October 2011 Rev Date Details Prepared by Reviewed by Approved by 01 25/10/11 Draft Pawel Kucharski Senior Consultant Elena Golovenko Technical Director Bryan Whittaker Technical Director 02 28/10/11 Final Blake Gravenor Consultant Bryan Whittaker Technical Director Bryan Whittaker Technical Director 03 28/10/11 Final James Fox Rand Europe Bryan Whittaker Technical Director Bryan Whittaker Technical Director 04 16/11/11 Final Client Comments James Fox Rand Europe Bryan Whittaker Technical Director Bryan Whittaker Technical Director This document has been prepared in accordance with the scope of Scott Wilson's appointment with its client and is subject to the terms of that appointment. It is addressed to and for the sole and confidential use and reliance of Scott Wilson's client. Scott Wilson accepts no liability for any use of this document other than by its client and only for the purposes for which it was prepared and provided. No person other than the client may copy (in whole or in part) use or rely on the contents of this document, without the prior written permission of the Company Secretary of Scott Wilson Ltd. Any advice, opinions, or recommendations within this document should be read and relied upon only in the context of the document as a whole. The contents of this document do not provide legal or tax advice or opinion. Scott Wilson Ltd 2010 URS Scott Wilson Scott House Alençon Link Basingstoke Hants RG21 7PP Tel Fax

3 Table of Contents 1 Introduction Background Scenarios and Deliverables Structure of This Note Programming of LDM Output Introduction Model changes Base Year Run Production of Base Matrices Base Year Run Description Base Year Run Deliverables Standardisation of Future Year Assumptions Future Year Assumptions Incorporation of Land-Use and GDP Assumptions Introduction Population Model Inputs Specification of HSR Scheme Introduction HSR Routes, Frequency and Run-Times Other HSR Assumptions Base Year LDM Runs with HSR Base Year Run with HSR Description Base Year Run with HSR Deliverables Additional LDM Runs With Reduced Segmentation Introduction Current Segmentation Changes to LDM Segmentation Reduced Segmentation Deliverables Future Year Runs Future Year Runs Description Future Year Runs Deliverables APPENDIX A APPENDIX B APPENDIX C APPENDIX D APPENDIX E... 37

4 1 Introduction 1.1 Background URS Scott Wilson has been commissioned by HS2 Ltd. to undertake Phase 0 of the LDM PLD Comparison: Preliminary Information Specification from the Long Distance Model. The key elements of the scope of work are the implementation of the reduction in the number of the demand segments in the Long Distance Model (LDM) and the derivation of demand and levelof-service outputs from LDM for a range of Base Year and Future Year scenarios defined in the scope. These deliverables will facilitate the comparison of the LDM and PLD forecast growth rates and demand model structures, to be undertaken in the subsequent Phases 1 and 2. Phase 1 will focus on the comparison of growth in demand between 2008 Base Year and 2021 Future Year runs (for the total of four future year tests) and will require the demand matrices from all these scenarios. Phase 2 will focus on comparisons between the LDM and PLD demand models. The comparisons will be undertaken on the basis of outputs from the Base Year and the Base Year with HSR tests (from both full and reduced segmentation runs). Phase 2 will require the demand and level-of-service matrices from these runs. A summary of all scenarios and respective deliverables is presented in Section 1.2 below. 1.2 Scenarios and Deliverables The agreed Phase 0 scenarios and deliverables, together with the indication of their applicability as inputs into the subsequent phases, are summarised in Table 1.1. Table 1.1 Phase 0 Test Scenarios and Deliverables Scenario Name Scenario Description Deliverable Number Base Year Run Full segmentation Deliverable 1 Base Year Run with HSR Full segmentation Deliverable 2 Deliverable Description Demand and Level-ofservice Matrices. Demand and Level-ofservice Matrices. Input Into Phase 1 Phase 2 Phase 2 Base Year Run Reduced segmentation Deliverable 3 Demand Matrices. Phase 2 Base Year Run with HSR Future Year Run 1 Future Year Run 2 Future Year Run 3 Future Year Run 4 Reduced segmentation Deliverable 3 Demand Matrices. Phase 2 Full segmentation; Standard GDP growth; Real fare and fuel increase Full segmentation; Standard GDP growth; No fare and fuel increase Full segmentation; Alternative GDP growth; Real fare and fuel increase Full segmentation; Alternative GDP growth; No fare and fuel increase Deliverable 4 Demand Matrices. Phase 1 Deliverable 4 Demand Matrices. Phase 1 Deliverable 4 Demand Matrices. Phase 1 Deliverable 4 Demand Matrices. Phase 1 4

5 The agreed scenario specification is identical to that implemented in parallel in the PLD model. All matrices will be supplied in a zipped format on a data CD. We will also supply a summary spreadsheet containing all matrix totals by mode, purpose and segment. The spreadsheet will contain modelled tour rates as specified in Task (vii): Additional runs of LDM using reduced segmentation. Further details of the scenarios and respective sets of deliverables are described in the subsequent sections of this note in the order specified in the Phase 0 scope of work. 1.3 Structure of This Note This technical note summarises the implementation of the scope of work and deliverables required from LDM and specified in Section 5 of the tender document LDM PLD comparison: Preliminary information specification (Phase 0: LDM) (ref: HS2/019). The main body of the note presents the steps undertaken during the implementation of the requirements. The deliverables are presented in Appendices. This technical note is structured as follows: Section 2 summarises the implementation of changes to the LDM model code required to produce the required deliverables (Task (i): Programming of LDM Output); Section 3 summarises the Base Year LDM run (Task (ii): Base year run Production of Base Matrices); Section 4 summarises the standardised future year assumptions (Task (iii): Standardisation of Future Year assumptions); Section 5 describes the implementation of land-use and GDP assumptions in the LDM Population Model (Task (iv): Incorporating land-use and GDP assumptions in LDM Population Model); Section 6 summarises the agreed specification of HSR services for input into LDM (Task (v): Specification of the HSR scheme to be tested with the demand model); Section 7 describes the Base Year runs of LDM with the inclusion of HSR services (Task(vi): Run LDM in base year with HSR); Section 8 describes the additional LDM runs undertaken in the version of LDM with reduced demand segmentation (Task (vii): Additional runs of LDM using reduced segmentation) Section 9 describes the 2021 future year LDM runs and specifies the deliverables (Task (x): Future year runs of (full) LDM) 5

6 2 Programming of LDM Output 2.1 Introduction Currently the LDM demand model is run for 3 journey purposes (with up to 4 choices of mode): Commute Highway, Rail or Coach; Business Highway, Air or Rail; and Other Highway, Air, Rail or Coach. The model outputs a 406 zone 406 zone production-attraction matrix for each mode-purpose combination, giving a total of 10 matrices (13 if HSR is available). These fully synthetic matrices are combined with Base Year observed data via a pivoting process to produce the final LDM forecast matrices. In order to better analyse the operation of the demand model it has been decided that the synthetic matrices shall be outputted at a higher level of disaggregation. Some recoding of the demand model was necessary to achieve this. 2.2 Model changes The synthetic matrices were output at the level of segmentation used in the PLD model. For a description of the differences between the segmentation used in the two models please see Section 8. Additional code was added to the input files for each purpose to define the output segmentation being used. The factors in Table 8.4 were also incorporated into the choice model and code added to give the user the option to output matrices by segment. Finally the demand model was recoded to perform two passes through the choice model: one to print the full synthetic matrices, one to print the matrices disaggregated by segment. Running through the choice model twice for each purpose has the effect of increasing total model runtime by approximately 20 minutes. Extra space is also required to store the expanded outputs with each set of segmented matrices occupying around 140MB. 6

7 3 Base Year Run Production of Base Matrices 3.1 Base Year Run Description The 2008 Base Case run has been based on the original version of the LDM model including full model segmentation and the original demand model coefficients. 3.2 Base Year Run Deliverables The required deliverables from the Base Year run are 100 demand matrices disaggregated by mode, purpose, car ownership category and income band summarised in Table 3.1 below. We note that Table 3.1 presents reduced demand segmentation consistent with that used in PLD. However, this segmentation is only applied to the model outputs to enable consistent comparisons (as specified in the scope of work); the model runs themselves have been undertaken with the use of the original fully segmented demand model. Table 3.1 Segmentation of Demand Outputs, Base Year Run Segmentation Type Mode Purpose Car ownership Income band Highway (HW); Air; Rail; and Coach. Commute (W); Business (EB); and Other (O). No car (0 cars); and One or more cars (1+ cars) Description Less than 20k pa (<20k); Between 20k and 40k (20-40k); Between 40k and 50k (40-50k); Between 50k and 75k (50-75k); and Greater than 75k (>75k). The demand matrices output from this run, in conjunction with demand matrices output from future year runs described later in this note, will form the basis for the comparisons of demand growth rates between LDM and PLD required in Phase 1. A detailed list of the output matrices is presented in Appendix A. 7

8 4 Standardisation of Future Year Assumptions Future Year Assumptions The future year assumptions have been derived and agreed through liaison with HS2 Ltd and the PLD model developers. Standardising the assumptions facilitates a fair comparison of the growth rates forecast by the two demand model structures, as required for the subsequent Phases 1 and 2. The assumptions have been agreed for the future year 2021 and form an input into the future year LDM runs described in Section 9 of this note. These include: 2021 Land-Use; Standard and alternative GDP growth assumptions; and Real fuel price and fare growth assumptions. A summary of the full set of agreed assumptions used in future year runs is presented in Table 4.1. Table 4.1 Forecasting Assumptions and Inputs Input / Assumption Source Description Land-use TEMPRO 5.4 Zonal target data used in the population model. Estimated attraction factors based on the number of jobs and population by attraction zone in Real GDP per capita Agreed by HS2 Ltd 16% growth between 2008 and 2021 Real GDP per capita (alternative growth scenario) Real fare increases Real fare increases (alternative scenario) Agreed by HS2 Ltd 24% growth between 2008 and 2021 Current DfT policy (confirmed by HS2 Ltd) Real fuel price increases PLD (from DfT Feb 2011 forecast) Real fuel price increases (alternative scenario) VAT changes underlying fuel cost changes (separation of VAT is required for forecasting business demand) HM Treasury, (consistent with PLD) RPI+1 until 2012; RPI+3 until 2015; and RPI+1 thereafter. - No increase. - No increase. 25% increase between 2008 and 2021 Change from 17.5% to 15% in 2009; Change from 15% to 17.5% in 2010; Change from 17.5% to 20% in 2011; and 20% onwards. 8

9 5 Incorporation of Land-Use and GDP Assumptions 5.1 Introduction A description of the entire population to be sampled is required before the travel choice model can be run to determine the frequency, mode choice and destination of long-distance journeys. RAND Europe has developed the LDM population model in order to predict the size, distribution and socio-economic makeup of the population. As the LDM has not been run for the year 2021 before, new population model forecasts were required for Phase 0. These draw upon assumptions of future land-use and include two GDP growth scenarios. GDP growth is also used in modelling changes in users Value of Time. The assumptions used in the main model mirror those used in the population model. 5.2 Population Model Inputs The 2021 Population Model runs use zonal target variables that are taken from the NATCOP2 implementation used in TEMPRO 5.4. The first step in the process was to aggregate the 2021 target variables from the NATCOP2 implementation defined at the 2496 NTEM zone level into the 406 zones system in the LDM. The Population Model has been run for two sets of GDP assumptions, a standard GDP growth assumption, and an alternative GDP growth assumption. To forecast growth in GDP from 2008 to 2021, the following set of GDP growth factors has been used, taken from the April 2011 Model Development and Baseline Report by MVA and Mott MacDonald. Table 5.1 Standard GDP growth assumptions from 2008 to 2021 Year Real GDP 2008/9-1.40% 2009/ % 2010/ % 2011/ % 2012/ % 2013/ % 2014/ % 2015/ % 2016/ % 2017/ % 2018/ % 2019/ % 2020/ % % This defines the standard GDP growth assumption. The alternative GDP growth assumption is calculated by multiplying the real GDP growth from 2008 to 2021 by a factor of 1.5 to give 36.5% growth and is shown in Figure 5.2 below. 9

10 Table 5.2 Summary of GDP Growth Assumptions from 2008 to 2021 Total GDP Standard GDP growth assumption 24.3 % Alternative GDP growth assumption 36.5 % To apply the GDP growth assumptions, the following approach is used Assume the population in each zone increases by a factor x, and that household size increases by a factor y. The factors x and y can be defined for each model zone by the base and future targets taken from the NATCOP2 implementation within TEMPRO V5.4. Example 5.3 illustrates how the GDP growth assumptions are applied in the model to forecast changes in household income. Example 5.3 Implementing GDP growth assumptions Consider an example zone where the population increases by a factor of x (e.g. 1.10) and the household size by a factor of y (e.g. 0.97). The number of households will then increase by a factor of x/y (e.g ) If in a zone average household income in the Base Year is H b (e.g. 40k) for n b (e.g. 10k) households, then total income is H b.n b (e.g. 400m). If in the future year total GDP increases by a factor of z (e.g ) then the new total income in that zone is H b.n b.z. (e.g m). This income is spread over n b.x/y households, so future average household income is: H f = H b.z.y/x (about 43.8k). To calculate the zonal income target in the base year, we enter base households n b and average household income H b to get total income H b. n b. which forms the income target for the zone. To calculate the zonal income target in the future year, we first calculate the future household average income accounting for changes in household size and population in the zone: H f = H b.z.y/x The zonal income target for the zone is then calculated as H f.n f where n f is the future number of households in the zone. To apply the Population Model for a future year, the process is run iteratively to determine the appropriate value for the welfare factor, which defines the proportion of the total income increase that is due to general increase in welfare applied to all incomes, with the remainder of income growth taking place due to shifting between income bands. The welfare factors selected for both the standard GDP growth and alternative GDP growth assumptions were those that gave the best overall fit to the zonal targets. In Table 5.4 the welfare factors obtained for the two 2021 runs are compared to the growth in total GDP. Table 5.4 Growth in GDP, zonal income and welfare from 2008 to

11 Total GDP Welfare Factor Standard GDP growth assumption 24.3% 1.14 Alternative GDP growth assumption 36.5% 1.24 Under the standard GDP growth assumption, the welfare increase represents 58% of the growth in total GDP, under the alternative GDP growth assumption, the welfare factor represents 66% of the growth in total GDP. The final stage is to calculate factors that rescale the 2021 Populations to normalise to the total population and workers given by TEMPRO 5.4, and apply the welfare factors to specify growth in trips due to the component of income growth that results from the welfare increase. These steps are summarised in the following tables. Table 5.5 Calculation of population adjustment factors Year TEMPRO v5.4 Predicted by QUAD Factor ,631,368 59,755, Normal GDP 64,123,492 63,327, Alternative GDP 64,123,492 63,605, The population factors are used to adjust the population weights used for the VFR/other model. Table 5.6 Calculation of worker adjustment factors Year TEMPRO v5.4 Predicted by QUAD Factor ,060,404 29,722, Normal GDP 29,360,741 29,970, Alternative GDP 29,360,741 30,318, The worker adjustment factors are used to adjust the population weights used for the commute/education and business models. The income growth factors apply the elasticity of trips to income growth to the welfare factors. As most income growth is due to the welfare increase, most of the growth in trips for future years comes about as a result of the application of these income growth factors, and only a small amount of growth is predicted in the LDM due to re-distribution between income bands. Table 5.7 Calculation of income growth factors Year Welfare Factor Com/Edu Factor Business Factor VFR/Other Factor Elasticity to welfare factor Normal GDP Alternative GDP The overall adjustment factors for each purpose are the product of the population or worker adjustment factor and the income growth factor. 11

12 Table 5.9 Calculation of overall frequency adjustment factors Year Commute/ Education Business VFR/Other Normal GDP Alternative GDP

13 6 Specification of HSR Scheme 6.1 Introduction A definition of HSR services for input into the Base Year LDM was required to undertake the runs described in Section 7 and produce the deliverables required as inputs into Phase 2. The HSR scheme definition has been agreed with HS2 Ltd and covered the following assumptions: HSR routes; Frequency of services; and Run-times between stations. In addition to the set of assumptions listed above, a set of general assumptions not relating to route, frequency and run-time has been agreed. These include: Treatment of fare premium; Applicability of HS2 to HS2 interchanges; Applicability of classic to HS2 interchanges; Treatment of the Old Oak Common station; Changes to classic rail timetable; and Treatment of rail reliability. Section 6.2 summarises the former set of assumptions. The latter set is presented in Section

14 6.2 HSR Routes, Frequency and Run-Times Tables 6.1, 6.2 and 6.3 below summarise the agreed HSR service specification supplied by the PLD. Table 6.1 describes HSR routes and frequencies. Table 6.2 provides information on runtimes of all HSR services from/to London. Table 6.3 specifies run times for all HSR services other than the London services (these include the services between Birmingham and the northern cities and have been grouped under the name cross-country HSR services. This name has been retained for consistency with the PLD descriptions). Table 6.1 Agreed HSR Routes and Frequencies Service Numbers (for ref. with Tables 3.2, 3.3) Route 1, 2, 3, 4 London Euston Birmingham Interchange Birmingham Fazeley Street Frequency 3 tph (4 tph in peak) 5, 6, 8 London Euston Manchester Piccadilly 2 tph (3 tph in peak) 7 London Euston Birmingham Interchange Manchester Piccadilly 9 London Euston Warrington Bank Quay Liverpool Lime Street 10 London Euston Stafford Crewe Runcorn Liverpool Lime Street 1 tph 1 tph 1 tph 18 London Euston Nottingham - Sheffield 1 tph 13 London Euston Leeds 1 tph 14 London Euston Birmingham Interchange - Leeds 1 tph 15 London Euston Nottingham Sheffield Leeds 1 tph 16 London Euston York Darlington Newcastle 1 tph 17 London Euston Birmingham International York Darlington Newcastle 11, 12 London Euston Warrington Bank Quay Wigan North Western Preston Glasgow Central 1 tph 2 tph 19, 20, 21 Birmingham Fazeley Street Manchester Piccadilly 3 tph 22, 23 Birmingham Fazeley Street Nottingham Sheffield Leeds 2 tph 24 Birmingham Fazeley Street Nottingham Sheffield York Darlington Newcastle 1 tph Source: PLD 14

15 Table 6.2 Agreed HSR Run-Times (London Services) All Day LONDON SERVICES London Euston Old Oak Common Birmingham Interchange Birmingham Fazeley Street 48 Stafford 2 17 Crewe Warrington Bank Quay Runcorn Liverpool Lime Street Nottingham Sheffield 2 Manchester Airport Manchester Piccadilly Leeds York Teesside Airport/Darlington Newcastle 9 9 Wigan North Western Preston 2 2 Carstairs Glasgow Central Edinburgh 0:49 0:49 0:49 0:49 1:20 1:20 1:26 1:20 1:37 1:50 1:15 1:20 1:26 1:35 2:22 2:28 3:47 3:47 Source: PLD 15

16 Table 6.3 Agreed HSR Run-Times (Cross-Country Services) All Day CROSSCOUNTRY Birmingham Fazeley Street Stafford Crewe Warrington Bank Quay Runcorn Liverpool Lime Street Nottingham Sheffield Manchester Airport Manchester Piccadilly Leeds 31 York 2 28 Teesside Airport/Darlington 2 30 Newcastle Wigan North Western Preston Carstairs Glasgow Central Edinburgh Source: PLD 0:54 0:54 0:54 1:05 1:05 2:20 16

17 6.3 Other HSR Assumptions Table 6.4 below summarises the general HSR assumptions agreed with HS2 Ltd and not related to route, frequency and run-times. These include treatment of fare premium, interchanges, rail reliability and the approach to the classic rail timetable changes associated with the implementation of HSR. Table 6.4 Other Standardised HSR Assumptions Input / Assumption Source Description Fare premium HS2 Ltd No fare premium. HS2 to HS2 interchanges HS2 Ltd No such interchanges assumed. Classic to HS2 interchanges HS2 Ltd. All stations apart from Birmingham Interchange. Old Oak Common HS2 Ltd. Not modelled as stop. Crossrail not included in the Base Year therefore no interchange is possible. However, the additional time representing the stop is included in the run-times. Changes not classic rail services HS2 Ltd. No changes. Reliability HS2 Ltd. / PLD Reliability cushion is not included. 17

18 7 Base Year LDM Runs with HSR 7.1 Base Year Run with HSR Description The Base Year run with HSR is based on the Base Year scenario described in Section 3 (2008 Base Case) with the only difference that it also includes the HSR services. The specification of HSR services has been agreed with HS2 Ltd and is described in Section Base Year Run with HSR Deliverables The required deliverables are 130 demand matrices for each mode, purpose, car ownership category and income band (summarised in Table 7.1 below) and level-of-service matrices for classic rail and HSR (summarised in Table 7.2). We note that, similarly to Base Year run described in Section 3, Table 7.1 presents reduced demand segmentation consistent with that used in PLD. However, this segmentation is only applied to the model outputs to enable consistent comparisons (as specified in the scope of work); the model runs themselves have been undertaken with the use of the original fully segmented demand model. Table 7.1 Segmentation of Demand Outputs, Base Year with HSR Run Segmentation Type Mode Purpose Car ownership Income band Highway (HW); Air; Rail; Coach; and High-Speed Rail (HSR). Commute (W); Business (EB); and Other (O). No car (0 cars); and One or more cars (1+ cars) Description Less than 20k pa (<20k); Between 20k and 40k (20-40k); Between 40k and 50k (40-50k); Between 50k and 75k (50-75k); and Greater than 75k (>75k). The level-of-service (LoS) matrices requested by HS2 Ltd. are intended to be those required to recreate the model calculations for use in Phase 2. We note that since value of time (VoT) varies by user income, it is impossible to supply a single generalised time matrix that is valid for all users. Instead separate journey time and cost matrices have been supplied that can be combined using an appropriate VoT for each demand segment. A crowding matrix was also requested for the commute purpose for trips lasting less than 2 hours. Since this component has in fact been superseded, the matrix has not been supplied. Note that the effects of crowding are still included for classic rail as part of the cost matrix. During estimation of the Other demand model, a term was included that conferred a 64 minute bonus on any journeys (by any mode) taking less than 6 hours. This bonus has been included in the Rail and HSR journey time matrices. Note that this may result in some rail times less than 0, although in all cases these are not classified as long distance journeys. 18

19 Table 7.2 Segmentation of LoS Outputs, Base Year with HSR Run Mode Purpose Description Rail Commute (W) Journey time (including access and PDFH penalties). Rail Commute (W) Cost (including crowding penalty). Rail Business (EB) In-vehicle time; Frequency (trains per hour); Access and egress time; Number of interchanges; and Cost (including crowding penalty). Rail Other (O) Journey time (including access, PDFH and return journey penalties). Rail Other (O) Cost (including crowding penalty). HSR Commute (W) Journey time (including access and PDFH penalties). HSR Commute (W) Cost (no crowding is modelled on HSR) HSR Business (EB) In-vehicle time; Frequency (trains per hour); Access and egress time; and Cost (no crowding is modelled on HSR). HSR Other (O) Journey time (including access and PDFH and return journey penalties). HSR Other (O) Cost (no crowding is modelled on HSR) Note: Those journeys that cannot be made using HSR have entries of 0 in the matrices The outputs from this run will form the basis for the comparative analysis of the LDM and PLD demand models required in Phase 2. A detailed list of the output demand matrices is presented in Appendix B. The list of LoS matrices is presented in Appendix C. 19

20 8 Additional LDM Runs With Reduced Segmentation 8.1 Introduction A major difference between the LDM and PLD is the level of segmentation used in the two demand models. Segmenting the population introduces extra variables into the model calibration process, increasing explanatory power at the cost of greater computational expense. In order to compare the two models, the more detailed LDM segmentation system needs to be collapsed to the PLD system. This was done for the six sets of base and future matrices described in Sections 3, 7 and 9. However, a further comparison was made by reducing the segmentation in the LDM demand model itself to see what effect this would have on results. The reduced segmentation model was run in the Base Year with and without the full HSR network. 8.2 Current Segmentation The level of segmentation used in the LDM varies by purpose: Commute 144 segments; Business 144 segments; and Other 504 segments. It is our understanding that the PLD operates over 10 demand segments for all journey purposes. Tables 8.1 and 8.2 below show the attributes used to segment total demand and the number of levels each may take. Table 8.1 LDM segmentation Household Attribute Commute Levels Business Levels Other Levels Cars Available Income Band Gender Age Work Status Household Status (Children) Table 8.2 PLD segmentation Household Attribute Levels (all purposes) Cars Available 2 Income Band 5 20

21 Notice that in the LDM commute model there are only 4 income bands. One of these contains all households with incomes between 40k and 75k per annum. This is less detailed than the PLD system where there are 2 corresponding bands: 40k- 50k and 50k- 75k, in fact it is the only place where such an overlap arises. To split the band in two, mean proportions have been calculated by RAND Europe from expanded NTS data containing the samples of long-distance commute tours used in model estimation. The mean proportions in Table 8.3 have been split by car ownership and mode to account for variations in the income split across these segments. Rail and coach have been aggregated as the sample sizes for coach users are small. Table k- 50k income band demand as mean proportion of 40k- 75k band Cars available Highway demand PT demand 0 cars 30.6% 74.7% 1 or more cars 41.5% 40.5% 8.3 Changes to LDM Segmentation In the LDM demand model each segment is associated with one or more constants that are added to (or subtracted from) the utility of using either a specific mode or to the utility of not travelling at all (the so called no tour utility). Reducing the segmentation requires a corresponding decrease in the number of constants. This was achieved by taking averages of related constants weighted by mean proportions of users (calculated by RAND Europe). For mode constants the mean proportions were derived from weighted samples of the NTS long-distance tours used in model estimation. For frequency constants the mean proportions were taken from the Base Year population outputs and therefore provided an exact match. Example 8.4 shows how the two mode constants were combined. Example 8.4 Cars available constant (Commute) The LDM commute model has 3 levels of car availability: 0, 1 or 2+ cars available. In the full model the following values are added to the utility of travelling by car: 0 cars available = 0 1 car available = or more cars available = Since the second two segments are combined in the reduced model, the two constants are weighted using the appropriate NTS data (1 car available = 2,355; 2 car available = 6,143) to give: Revised constant for 1+ cars = (2355* *7.59)/( ) = The complete set of mean proportions and resulting average constants can be found in the Appendix D. As suggested by HS2 Ltd. the constants were used across multiple segments in the original model, thereby removing the distinctions between the segments and hence avoiding the need to reprogram the model. Making such large scale changes to the model form invariably leads to a change in the total number of trips generated for each purpose shown in Table 8.5. Therefore after running the 21

22 model with the new constants, the no tour utility constant was recalibrated in order to control total trips back to the original Base Year levels. This is a standard process undertaken whenever the LDM demand model has been re-estimated. Table 8.5 Reduced segmentation total trips before recalibration Purpose Original base year demand Demand with reduced segmentation Commute 370, ,754 Business 507, ,988 Other 2,191,847 2,282, Reduced Segmentation Deliverables The following deliverables from the reduced segmentation runs have been specified: 100 demand matrices from the Base Year run (for each mode, purpose, car ownership category and income); 130 demand matrices from the Base Year run With HSR (for each mode, purpose, car ownership category and income band); The segmentation of the output demand matrices is summarised in Table 8.6 below. Table 8.6 Segmentation of Output Demand, Reduced Segmentation Runs Segmentation Type Mode Purpose Car ownership Income band Description Highway (HW); Air; Rail; Coach; and High-Speed Rail (HSR) Base Year with HSR only Commute (W); Business (EB); and Other (O). No car (0 cars); and One or more cars (1+ cars) Less than 20k pa (<20k); Between 20k and 40k (20-40k); Between 40k and 50k (40-50k); Between 50k and 75k (50-75k); and Greater than 75k (>75k). The outputs from these runs will form the basis for the comparative analysis of the LDM and PLD demand models required in Phase 2. The same set of demand matrices are output as for the base year (Appendices A and B). Note that since the reduction in segmentation has no effect on the level-of-service matrices used by the choice model, the exact same matrices are used as those specified in Table 7.2 and Appendix C. 22

23 9 Future Year Runs 9.1 Future Year Runs Description Four Future Year Runs have been specified in the scope of work required in Phase 0 of the LDM PLD Comparison Project. The future year test scenarios are summarised in Section 1 of this note and again in Table 9.1 below. Table 9.1 Future Year Test Scenarios Real Fare and Fuel Increase No Fare and Fuel Increase Standard GDP Growth FY Test 1 FY Test 2 Alternative GDP Growth FY Test 3 FY Test 4 The Future Year Runs have been based on the original version of the LDM model including full model segmentation, the original demand model coefficients and do not include HSR. The deliverables from these runs are described in Section 9.2 below. 9.2 Future Year Runs Deliverables The required deliverables for each of the Future Year Runs are 100 demand matrices for each mode, purpose, car ownership category and income band summarised in Table 9.2 below. We note that Table 9.2 presents reduced demand segmentation consistent with that used in PLD. However, this segmentation is only applied to the model outputs to enable consistent comparisons (as specified in the scope of work); the model runs themselves have been undertaken with the use of the original fully segmented demand model. Table 9.2 Segmentation of Output Demand, Future Year Runs Segmentation Type Mode Purpose Car ownership Income band Highway (HW); Air; Rail; Coach; and Commute (W); Business (EB); and Other (O). No car (0 cars); and One or more cars (1+ cars) Description Less than 20k pa (<20k); Between 20k and 40k (20-40k); Between 40k and 50k (40-50k); Between 50k and 75k (50-75k); and Greater than 75k (>75k). 23

24 The outputs from these four runs, together with the Base Year Run outputs described in Section 3, will form the basis for the analysis of demand growth rate in LDM and PLD required in Phase 1. A detailed list of the output demand matrices is presented in Appendix E. 24

25 APPENDIX A The table below lists the 2008 base demand matrices. They can be found on the data CD in the following folder \Demand\2008\Full_segmentation\Base. Table A1 Base Year Run (full segmentation) Deliverables: Demand Matrices Mode Purpose Car ownership Income Segment Name Matrix name HW W 0 cars <20k seg1 2008BLM_HW_W_seg1.csv HW W 0 cars 20k-40k seg2 2008BLM_HW_W_seg2.csv HW W 0 cars 40k-50k seg3 2008BLM_HW_W_seg3.csv HW W 0 cars 50k-75k seg4 2008BLM_HW_W_seg4.csv HW W 0 cars >75k seg5 2008BLM_HW_W_seg5.csv HW W 1+ cars <20k seg6 2008BLM_HW_W_seg6.csv HW W 1+ cars 20k-40k seg7 2008BLM_HW_W_seg7.csv HW W 1+ cars 40k-50k seg8 2008BLM_HW_W_seg8.csv HW W 1+ cars 50k-75k seg9 2008BLM_HW_W_seg9.csv HW W 1+ cars >75k seg BLM_HW_W_seg10.csv HW EB 0 cars <20k seg1 2008BLM_HW_EB_seg1.csv HW EB 0 cars 20k-40k seg2 2008BLM_HW_EB_seg2.csv HW EB 0 cars 40k-50k seg3 2008BLM_HW_EB_seg3.csv HW EB 0 cars 50k-75k seg4 2008BLM_HW_EB_seg4.csv HW EB 0 cars >75k seg5 2008BLM_HW_EB_seg5.csv HW EB 1+ cars <20k seg6 2008BLM_HW_EB_seg6.csv HW EB 1+ cars 20k-40k seg7 2008BLM_HW_EB_seg7.csv HW EB 1+ cars 40k-50k seg8 2008BLM_HW_EB_seg8.csv HW EB 1+ cars 50k-75k seg9 2008BLM_HW_EB_seg9.csv HW EB 1+ cars >75k seg BLM_HW_EB_seg10.csv HW O 0 cars <20k seg1 2008BLM_HW_O_seg1.csv HW O 0 cars 20k-40k seg2 2008BLM_HW_O_seg2.csv HW O 0 cars 40k-50k seg3 2008BLM_HW_O_seg3.csv HW O 0 cars 50k-75k seg4 2008BLM_HW_O_seg4.csv HW O 0 cars >75k seg5 2008BLM_HW_O_seg5.csv HW O 1+ cars <20k seg6 2008BLM_HW_O_seg6.csv HW O 1+ cars 20k-40k seg7 2008BLM_HW_O_seg7.csv HW O 1+ cars 40k-50k seg8 2008BLM_HW_O_seg8.csv HW O 1+ cars 50k-75k seg9 2008BLM_HW_O_seg9.csv HW O 1+ cars >75k seg BLM_HW_O_seg10.csv Air W 0 cars <20k seg1 2008BLM_Air_W_seg1.csv Air W 0 cars 20k-40k seg2 2008BLM_Air_W_seg2.csv 25

26 Air W 0 cars 40k-50k seg3 2008BLM_Air_W_seg3.csv Air W 0 cars 50k-75k seg4 2008BLM_Air_W_seg4.csv Air W 0 cars >75k seg5 2008BLM_Air_W_seg5.csv Air W 1+ cars <20k seg6 2008BLM_Air_W_seg6.csv Air W 1+ cars 20k-40k seg7 2008BLM_Air_W_seg7.csv Air W 1+ cars 40k-50k seg8 2008BLM_Air_W_seg8.csv Air W 1+ cars 50k-75k seg9 2008BLM_Air_W_seg9.csv Air W 1+ cars >75k seg BLM_Air_W_seg10.csv Air EB 0 cars <20k seg1 2008BLM_Air_EB_seg1.csv Air EB 0 cars 20k-40k seg2 2008BLM_Air_EB_seg2.csv Air EB 0 cars 40k-50k seg3 2008BLM_Air_EB_seg3.csv Air EB 0 cars 50k-75k seg4 2008BLM_Air_EB_seg4.csv Air EB 0 cars >75k seg5 2008BLM_Air_EB_seg5.csv Air EB 1+ cars <20k seg6 2008BLM_Air_EB_seg6.csv Air EB 1+ cars 20k-40k seg7 2008BLM_Air_EB_seg7.csv Air EB 1+ cars 40k-50k seg8 2008BLM_Air_EB_seg8.csv Air EB 1+ cars 50k-75k seg9 2008BLM_Air_EB_seg9.csv Air EB 1+ cars >75k seg BLM_Air_EB_seg10.csv Rail W 0 cars <20k seg1 2008BLM_Rail_W_seg1.csv Rail W 0 cars 20k-40k seg2 2008BLM_Rail_W_seg2.csv Rail W 0 cars 40k-50k seg3 2008BLM_Rail_W_seg3.csv Rail W 0 cars 50k-75k seg4 2008BLM_Rail_W_seg4.csv Rail W 0 cars >75k seg5 2008BLM_Rail_W_seg5.csv Rail W 1+ cars <20k seg6 2008BLM_Rail_W_seg6.csv Rail W 1+ cars 20k-40k seg7 2008BLM_Rail_W_seg7.csv Rail W 1+ cars 40k-50k seg8 2008BLM_Rail_W_seg8.csv Rail W 1+ cars 50k-75k seg9 2008BLM_Rail_W_seg9.csv Rail W 1+ cars >75k seg BLM_Rail_W_seg10.csv Rail EB 0 cars <20k seg1 2008BLM_Rail_EB_seg1.csv Rail EB 0 cars 20k-40k seg2 2008BLM_Rail_EB_seg2.csv Rail EB 0 cars 40k-50k seg3 2008BLM_Rail_EB_seg3.csv Rail EB 0 cars 50k-75k seg4 2008BLM_Rail_EB_seg4.csv Rail EB 0 cars >75k seg5 2008BLM_Rail_EB_seg5.csv Rail EB 1+ cars <20k seg6 2008BLM_Rail_EB_seg6.csv Rail EB 1+ cars 20k-40k seg7 2008BLM_Rail_EB_seg7.csv Rail EB 1+ cars 40k-50k seg8 2008BLM_Rail_EB_seg8.csv Rail EB 1+ cars 50k-75k seg9 2008BLM_Rail_EB_seg9.csv Rail EB 1+ cars >75k seg BLM_Rail_EB_seg10.csv 26

27 Rail O 0 cars <20k seg1 2008BLM_Rail_O_seg1.csv Rail O 0 cars 20k-40k seg2 2008BLM_Rail_O_seg2.csv Rail O 0 cars 40k-50k seg3 2008BLM_Rail_O_seg3.csv Rail O 0 cars 50k-75k seg4 2008BLM_Rail_O_seg4.csv Rail O 0 cars >75k seg5 2008BLM_Rail_O_seg5.csv Rail O 1+ cars <20k seg6 2008BLM_Rail_O_seg6.csv Rail O 1+ cars 20k-40k seg7 2008BLM_Rail_O_seg7.csv Rail O 1+ cars 40k-50k seg8 2008BLM_Rail_O_seg8.csv Rail O 1+ cars 50k-75k seg9 2008BLM_Rail_O_seg9.csv Rail O 1+ cars >75k seg BLM_Rail_O_seg10.csv Coach W 0 cars <20k seg1 2008BLM_Coach_W_seg1.csv Coach W 0 cars 20k-40k seg2 2008BLM_Coach_W_seg2.csv Coach W 0 cars 40k-50k seg3 2008BLM_Coach_W_seg3.csv Coach W 0 cars 50k-75k seg4 2008BLM_Coach_W_seg4.csv Coach W 0 cars >75k seg5 2008BLM_Coach_W_seg5.csv Coach W 1+ cars <20k seg6 2008BLM_Coach_W_seg6.csv Coach W 1+ cars 20k-40k seg7 2008BLM_Coach_W_seg7.csv Coach W 1+ cars 40k-50k seg8 2008BLM_Coach_W_seg8.csv Coach W 1+ cars 50k-75k seg9 2008BLM_Coach_W_seg9.csv Coach W 1+ cars >75k seg BLM_Coach_W_seg10.csv Coach O 0 cars <20k seg1 2008BLM_Coach_O_seg1.csv Coach O 0 cars 20k-40k seg2 2008BLM_Coach_O_seg2.csv Coach O 0 cars 40k-50k seg3 2008BLM_Coach_O_seg3.csv Coach O 0 cars 50k-75k seg4 2008BLM_Coach_O_seg4.csv Coach O 0 cars >75k seg5 2008BLM_Coach_O_seg5.csv Coach O 1+ cars <20k seg6 2008BLM_Coach_O_seg6.csv Coach O 1+ cars 20k-40k seg7 2008BLM_Coach_O_seg7.csv Coach O 1+ cars 40k-50k seg8 2008BLM_Coach_O_seg8.csv Coach O 1+ cars 50k-75k seg9 2008BLM_Coach_O_seg9.csv Coach O 1+ cars >75k seg BLM_Coach_O_seg10.csv 27

28 APPENDIX B The table below lists the 2008 HSR Test demand matrices. We note that these names refer to the outputs from both reduced and full segmentation versions of the model. However, the reduced and full segmentation versions are stored in separate, clearly identified folders on the data CD: \Demand\2008\Full_segmentation\HSR and \Demand\2008\Reduced_segmentation\HSR. Table B1 Base Year Run with HSR (full segmentation) Deliverables: Demand Matrices Mode Purpose Car ownership Income Segment Name Matrix name HW W 0 cars <20k seg1 2008DSLM_HW_W_seg1.csv HW W 0 cars 20k-40k seg2 2008DSLM_HW_W_seg2.csv HW W 0 cars 40k-50k seg3 2008DSLM_HW_W_seg3.csv HW W 0 cars 50k-75k seg4 2008DSLM_HW_W_seg4.csv HW W 0 cars >75k seg5 2008DSLM_HW_W_seg5.csv HW W 1+ cars <20k seg6 2008DSLM_HW_W_seg6.csv HW W 1+ cars 20k-40k seg7 2008DSLM_HW_W_seg7.csv HW W 1+ cars 40k-50k seg8 2008DSLM_HW_W_seg8.csv HW W 1+ cars 50k-75k seg9 2008DSLM_HW_W_seg9.csv HW W 1+ cars >75k seg DSLM_HW_W_seg10.csv HW EB 0 cars <20k seg1 2008DSLM_HW_EB_seg1.csv HW EB 0 cars 20k-40k seg2 2008DSLM_HW_EB_seg2.csv HW EB 0 cars 40k-50k seg3 2008DSLM_HW_EB_seg3.csv HW EB 0 cars 50k-75k seg4 2008DSLM_HW_EB_seg4.csv HW EB 0 cars >75k seg5 2008DSLM_HW_EB_seg5.csv HW EB 1+ cars <20k seg6 2008DSLM_HW_EB_seg6.csv HW EB 1+ cars 20k-40k seg7 2008DSLM_HW_EB_seg7.csv HW EB 1+ cars 40k-50k seg8 2008DSLM_HW_EB_seg8.csv HW EB 1+ cars 50k-75k seg9 2008DSLM_HW_EB_seg9.csv HW EB 1+ cars >75k seg DSLM_HW_EB_seg10.csv HW O 0 cars <20k seg1 2008DSLM_HW_O_seg1.csv HW O 0 cars 20k-40k seg2 2008DSLM_HW_O_seg2.csv HW O 0 cars 40k-50k seg3 2008DSLM_HW_O_seg3.csv HW O 0 cars 50k-75k seg4 2008DSLM_HW_O_seg4.csv HW O 0 cars >75k seg5 2008DSLM_HW_O_seg5.csv HW O 1+ cars <20k seg6 2008DSLM_HW_O_seg6.csv 28

29 HW O 1+ cars 20k-40k seg7 2008DSLM_HW_O_seg7.csv HW O 1+ cars 40k-50k seg8 2008DSLM_HW_O_seg8.csv HW O 1+ cars 50k-75k seg9 2008DSLM_HW_O_seg9.csv HW O 1+ cars >75k seg DSLM_HW_O_seg10.csv Air W 0 cars <20k seg1 2008DSLM_Air_W_seg1.csv Air W 0 cars 20k-40k seg2 2008DSLM_Air_W_seg2.csv Air W 0 cars 40k-50k seg3 2008DSLM_Air_W_seg3.csv Air W 0 cars 50k-75k seg4 2008DSLM_Air_W_seg4.csv Air W 0 cars >75k seg5 2008DSLM_Air_W_seg5.csv Air W 1+ cars <20k seg6 2008DSLM_Air_W_seg6.csv Air W 1+ cars 20k-40k seg7 2008DSLM_Air_W_seg7.csv Air W 1+ cars 40k-50k seg8 2008DSLM_Air_W_seg8.csv Air W 1+ cars 50k-75k seg9 2008DSLM_Air_W_seg9.csv Air W 1+ cars >75k seg DSLM_Air_W_seg10.csv Air EB 0 cars <20k seg1 2008DSLM_Air_EB_seg1.csv Air EB 0 cars 20k-40k seg2 2008DSLM_Air_EB_seg2.csv Air EB 0 cars 40k-50k seg3 2008DSLM_Air_EB_seg3.csv Air EB 0 cars 50k-75k seg4 2008DSLM_Air_EB_seg4.csv Air EB 0 cars >75k seg5 2008DSLM_Air_EB_seg5.csv Air EB 1+ cars <20k seg6 2008DSLM_Air_EB_seg6.csv Air EB 1+ cars 20k-40k seg7 2008DSLM_Air_EB_seg7.csv Air EB 1+ cars 40k-50k seg8 2008DSLM_Air_EB_seg8.csv Air EB 1+ cars 50k-75k seg9 2008DSLM_Air_EB_seg9.csv Air EB 1+ cars >75k seg DSLM_Air_EB_seg10.csv Rail W 0 cars <20k seg1 2008DSLM_Rail_W_seg1.csv Rail W 0 cars 20k-40k seg2 2008DSLM_Rail_W_seg2.csv Rail W 0 cars 40k-50k seg3 2008DSLM_Rail_W_seg3.csv Rail W 0 cars 50k-75k seg4 2008DSLM_Rail_W_seg4.csv Rail W 0 cars >75k seg5 2008DSLM_Rail_W_seg5.csv Rail W 1+ cars <20k seg6 2008DSLM_Rail_W_seg6.csv Rail W 1+ cars 20k-40k seg7 2008DSLM_Rail_W_seg7.csv Rail W 1+ cars 40k-50k seg8 2008DSLM_Rail_W_seg8.csv Rail W 1+ cars 50k-75k seg9 2008DSLM_Rail_W_seg9.csv Rail W 1+ cars >75k seg DSLM_Rail_W_seg10.csv Rail EB 0 cars <20k seg1 2008DSLM_Rail_EB_seg1.csv 29

30 Rail EB 0 cars 20k-40k seg2 2008DSLM_Rail_EB_seg2.csv Rail EB 0 cars 40k-50k seg3 2008DSLM_Rail_EB_seg3.csv Rail EB 0 cars 50k-75k seg4 2008DSLM_Rail_EB_seg4.csv Rail EB 0 cars >75k seg5 2008DSLM_Rail_EB_seg5.csv Rail EB 1+ cars <20k seg6 2008DSLM_Rail_EB_seg6.csv Rail EB 1+ cars 20k-40k seg7 2008DSLM_Rail_EB_seg7.csv Rail EB 1+ cars 40k-50k seg8 2008DSLM_Rail_EB_seg8.csv Rail EB 1+ cars 50k-75k seg9 2008DSLM_Rail_EB_seg9.csv Rail EB 1+ cars >75k seg DSLM_Rail_EB_seg10.csv Rail O 0 cars <20k seg1 2008DSLM_Rail_O_seg1.csv Rail O 0 cars 20k-40k seg2 2008DSLM_Rail_O_seg2.csv Rail O 0 cars 40k-50k seg3 2008DSLM_Rail_O_seg3.csv Rail O 0 cars 50k-75k seg4 2008DSLM_Rail_O_seg4.csv Rail O 0 cars >75k seg5 2008DSLM_Rail_O_seg5.csv Rail O 1+ cars <20k seg6 2008DSLM_Rail_O_seg6.csv Rail O 1+ cars 20k-40k seg7 2008DSLM_Rail_O_seg7.csv Rail O 1+ cars 40k-50k seg8 2008DSLM_Rail_O_seg8.csv Rail O 1+ cars 50k-75k seg9 2008DSLM_Rail_O_seg9.csv Rail O 1+ cars >75k seg DSLM_Rail_O_seg10.csv Coach W 0 cars <20k seg1 2008DSLM_Coach_W_seg1.csv Coach W 0 cars 20k-40k seg2 2008DSLM_Coach_W_seg2.csv Coach W 0 cars 40k-50k seg3 2008DSLM_Coach_W_seg3.csv Coach W 0 cars 50k-75k seg4 2008DSLM_Coach_W_seg4.csv Coach W 0 cars >75k seg5 2008DSLM_Coach_W_seg5.csv Coach W 1+ cars <20k seg6 2008DSLM_Coach_W_seg6.csv Coach W 1+ cars 20k-40k seg7 2008DSLM_Coach_W_seg7.csv Coach W 1+ cars 40k-50k seg8 2008DSLM_Coach_W_seg8.csv Coach W 1+ cars 50k-75k seg9 2008DSLM_Coach_W_seg9.csv Coach W 1+ cars >75k seg DSLM_Coach_W_seg10.csv Coach O 0 cars <20k seg1 2008DSLM_Coach_O_seg1.csv Coach O 0 cars 20k-40k seg2 2008DSLM_Coach_O_seg2.csv Coach O 0 cars 40k-50k seg3 2008DSLM_Coach_O_seg3.csv Coach O 0 cars 50k-75k seg4 2008DSLM_Coach_O_seg4.csv Coach O 0 cars >75k seg5 2008DSLM_Coach_O_seg5.csv Coach O 1+ cars <20k seg6 2008DSLM_Coach_O_seg6.csv 30

31 Coach O 1+ cars 20k-40k seg7 2008DSLM_Coach_O_seg7.csv Coach O 1+ cars 40k-50k seg8 2008DSLM_Coach_O_seg8.csv Coach O 1+ cars 50k-75k seg9 2008DSLM_Coach_O_seg9.csv Coach O 1+ cars >75k seg DSLM_Coach_O_seg10.csv HSR W 0 cars <20k seg1 2008DSLM_HSR_W_seg1.csv HSR W 0 cars 20k-40k seg2 2008DSLM_HSR_W_seg2.csv HSR W 0 cars 40k-50k seg3 2008DSLM_HSR_W_seg3.csv HSR W 0 cars 50k-75k seg4 2008DSLM_HSR_W_seg4.csv HSR W 0 cars >75k seg5 2008DSLM_HSR_W_seg5.csv HSR W 1+ cars <20k seg6 2008DSLM_HSR_W_seg6.csv HSR W 1+ cars 20k-40k seg7 2008DSLM_HSR_W_seg7.csv HSR W 1+ cars 40k-50k seg8 2008DSLM_HSR_W_seg8.csv HSR W 1+ cars 50k-75k seg9 2008DSLM_HSR_W_seg9.csv HSR W 1+ cars >75k seg DSLM_HSR_W_seg10.csv HSR EB 0 cars <20k seg1 2008DSLM_HSR_EB_seg1.csv HSR EB 0 cars 20k-40k seg2 2008DSLM_HSR_EB_seg2.csv HSR EB 0 cars 40k-50k seg3 2008DSLM_HSR_EB_seg3.csv HSR EB 0 cars 50k-75k seg4 2008DSLM_HSR_EB_seg4.csv HSR EB 0 cars >75k seg5 2008DSLM_HSR_EB_seg5.csv HSR EB 1+ cars <20k seg6 2008DSLM_HSR_EB_seg6.csv HSR EB 1+ cars 20k-40k seg7 2008DSLM_HSR_EB_seg7.csv HSR EB 1+ cars 40k-50k seg8 2008DSLM_HSR_EB_seg8.csv HSR EB 1+ cars 50k-75k seg9 2008DSLM_HSR_EB_seg9.csv HSR EB 1+ cars >75k seg DSLM_HSR_EB_seg10.csv HSR O 0 cars <20k seg1 2008DSLM_HSR_O_seg1.csv HSR O 0 cars 20k-40k seg2 2008DSLM_HSR_O_seg2.csv HSR O 0 cars 40k-50k seg3 2008DSLM_HSR_O_seg3.csv HSR O 0 cars 50k-75k seg4 2008DSLM_HSR_O_seg4.csv HSR O 0 cars >75k seg5 2008DSLM_HSR_O_seg5.csv HSR O 1+ cars <20k seg6 2008DSLM_HSR_O_seg6.csv HSR O 1+ cars 20k-40k seg7 2008DSLM_HSR_O_seg7.csv HSR O 1+ cars 40k-50k seg8 2008DSLM_HSR_O_seg8.csv HSR O 1+ cars 50k-75k seg9 2008DSLM_HSR_O_seg9.csv HSR O 1+ cars >75k seg DSLM_HSR_O_seg10.csv 31

32 APPENDIX C The table below lists the 2008 Rail and HSR level-of-service matrices. The same matrices are used with both reduced and full segmentation versions of the model. They can be found in the parent folder \LoS\2008. Table C1 Base Year Run with HSR Deliverables: Level-of-service Matrices Mode Purpose Description Matrix name Rail W Journey time 2008B_Rail_W_Tour_JT.csv Rail W Cost 2008B_Rail_W_Tour_Cost.csv Rail EB In-vehicle time 2008B_Rail_EB_Tour_Time.csv Rail EB Frequency 2008B_Rail_EB_Tour_Freq.csv Rail EB Access/Egress 2008B_Rail_EB_Tour_Access.csv Rail EB Interchanges 2008B_Rail_EB_Tour_Inters.csv Rail EB Cost 2008B_Rail_EB_Tour_Cost.csv Rail O Journey time 2008B_Rail_O_Tour_JT.csv Rail O Cost 2008B_Rail_O_Tour_Cost.csv HSR W Journey time 2008DS_HSL_W_Tour_JT.csv HSR W Cost 2008DS_HSL_W_Tour_Cost.csv HSR EB In-vehicle time 2008DS_HSL_EB_Tour_Time.csv HSR EB Frequency 2008DS_HSL_EB_Tour_Freq.csv HSR EB Access/Egress 2008DS_HSL_EB_Tour_Access.csv HSR EB Cost 2008DS_HSL_EB_Tour_Cost.csv HSR O Journey time 2008DS_HSL_O_Tour_JT.csv HSR O Cost 2008DS_HSL_O_Tour_Cost.csv 32

33 APPENDIX D The following tables provided by RAND Europe detail the mean proportions that have been calculated to implement those frequency model parameters not defined by the reduced segmentation. Table D1 Commute/education, average parameters for frequency model Segmentation Term Parameter Value Mean Proportion Average Effect Gender Fmale Age Fage Table D2 Business, average parameters for frequency model Segmentation Term Parameter Value Mean Proportion Average Effect Gender Fmale Age Fage Table D3 VFR/other, average parameters for frequency model Segmentation Term Parameter Value Mean Proportion Average Effect Work Status Fretired Work Status Funemply Household Status Fhhwcld n/a Fagelt Note that the Fagelt29 term is not defined by the full segmentation and therefore mean proportions cannot be calculated from the 2008 Population Model outputs for VFR/other. Instead, the mean proportion of this term used in the full segmentation implementation has been retained. This value was determined from analysis of the sample of persons in the NTS data used for estimation of the VFR/other frequency model. The Business and Other frequency models contain income terms defined for six and seven segments respectively. These are reduced to 5 segments using the parameters calculated in the following tables. Table D4 Business, average income parameters for frequency model Segmentation Term 6 full Segments Mean Proportion 5 reduced Segments Household Income (inc020) inc inc inc inc incgt

34 Table D5 VFR/other, average income parameters for frequency model Segmentation Term 7 full Segments Mean Proportion 5 reduced Segments Household Income (inc010) inc inc inc inc inc incgt The full segmentation models distinguish three car ownership segments, 0, 1 and 2+ cars per household. In the reduced segmentation version of the model, this is reduced to 0 and 1+ cars per household segments. In all three of the models, car ownership terms are defined for the 1 and 2+ cars segments, and so average effects for these terms have been calculated which are applied to the 1+ cars segment in the reduced segmentation version of the model. Table D6 Commute/education, average parameters for mode-destination model Segmentation Term Mode Parameter Value Mean Proportion Average Effect Sex Car_male Car Part-time workers Bus_ptwkr Coach Student Bus_stud Coach Car ownership onecar Car Car ownership gt2cars Car Table D7 Business, average parameters for mode-destination model Segmentation Term Mode Parameter Value Mean Proportion Average Effect Sex Car_male Car Part-time workers car_ptwkr Car Car ownership RL_gt2cars Rail Car ownership onecar Car Car ownership gt2cars Car Table D8 VFR/other, average parameters for mode-destination model Segmentation Term Mode Parameter Value Mean Proportion Average Effect Sex RL_male Rail Sex Bus_male Coach Car ownership onecar Car Car ownership gt2cars Car

35 The three tables below combine the average mode and frequency constants derived by RAND and present them in terms of the reduced segmentation system. Table D9 Commute constants Cars Available Income Band Highway Air Rail (Classic & HSR) Coach No Tour 0 <20k k-40k k-50k k-75k >75k <20k k-40k k-50k k-75k >75k Table D10 Business constants Cars Available Income Band Highway Air Rail (Classic & HSR) Coach No Tour 0 <20k k-40k k-50k k-75k >75k <20k k-40k k-50k k-75k >75k

36 Table D11 Other constants Cars Available Income Band Highway Air Rail (Classic & HSR) Coach No Tour 0 <20k k-40k k-50k k-75k >75k <20k k-40k k-50k k-75k >75k

37 APPENDIX E The table below lists the 2021 future year demand matrices (with full segmentation). The matrix names included in the table refer to the 2021 Reference Case test. The names of the outputs from the remaining three alternative 2021 scenarios (Alternative growth; No real fare and fuel increase; and Growth + no fare and fuel increase) have the same naming convention with the only difference that the they are indexed DS (i.e. 2021DSLM*.csv). All results are found in clearly labelled subfolders of \Demand\2021. Table E1 Future Year Runs (full segmentation) Deliverables: Demand Matrices Mode Purpose Car ownership Income Segment Name Matrix name * HW W 0 cars <20k seg1 2021DMLM_HW_W_seg1.csv HW W 0 cars 20k-40k seg2 2021DMLM_HW_W_seg2.csv HW W 0 cars 40k-50k seg3 2021DMLM_HW_W_seg3.csv HW W 0 cars 50k-75k seg4 2021DMLM_HW_W_seg4.csv HW W 0 cars >75k seg5 2021DMLM_HW_W_seg5.csv HW W 1+ cars <20k seg6 2021DMLM_HW_W_seg6.csv HW W 1+ cars 20k-40k seg7 2021DMLM_HW_W_seg7.csv HW W 1+ cars 40k-50k seg8 2021DMLM_HW_W_seg8.csv HW W 1+ cars 50k-75k seg9 2021DMLM_HW_W_seg9.csv HW W 1+ cars >75k seg DMLM_HW_W_seg10.csv HW EB 0 cars <20k seg1 2021DMLM_HW_EB_seg1.csv HW EB 0 cars 20k-40k seg2 2021DMLM_HW_EB_seg2.csv HW EB 0 cars 40k-50k seg3 2021DMLM_HW_EB_seg3.csv HW EB 0 cars 50k-75k seg4 2021DMLM_HW_EB_seg4.csv HW EB 0 cars >75k seg5 2021DMLM_HW_EB_seg5.csv HW EB 1+ cars <20k seg6 2021DMLM_HW_EB_seg6.csv HW EB 1+ cars 20k-40k seg7 2021DMLM_HW_EB_seg7.csv HW EB 1+ cars 40k-50k seg8 2021DMLM_HW_EB_seg8.csv HW EB 1+ cars 50k-75k seg9 2021DMLM_HW_EB_seg9.csv HW EB 1+ cars >75k seg DMLM_HW_EB_seg10.csv HW O 0 cars <20k seg1 2021DMLM_HW_O_seg1.csv HW O 0 cars 20k-40k seg2 2021DMLM_HW_O_seg2.csv HW O 0 cars 40k-50k seg3 2021DMLM_HW_O_seg3.csv HW O 0 cars 50k-75k seg4 2021DMLM_HW_O_seg4.csv HW O 0 cars >75k seg5 2021DMLM_HW_O_seg5.csv HW O 1+ cars <20k seg6 2021DMLM_HW_O_seg6.csv HW O 1+ cars 20k-40k seg7 2021DMLM_HW_O_seg7.csv HW O 1+ cars 40k-50k seg8 2021DMLM_HW_O_seg8.csv 37

38 HW O 1+ cars 50k-75k seg9 2021DMLM_HW_O_seg9.csv HW O 1+ cars >75k seg DMLM_HW_O_seg10.csv Air W 0 cars <20k seg1 2021DMLM_Air_W_seg1.csv Air W 0 cars 20k-40k seg2 2021DMLM_Air_W_seg2.csv Air W 0 cars 40k-50k seg3 2021DMLM_Air_W_seg3.csv Air W 0 cars 50k-75k seg4 2021DMLM_Air_W_seg4.csv Air W 0 cars >75k seg5 2021DMLM_Air_W_seg5.csv Air W 1+ cars <20k seg6 2021DMLM_Air_W_seg6.csv Air W 1+ cars 20k-40k seg7 2021DMLM_Air_W_seg7.csv Air W 1+ cars 40k-50k seg8 2021DMLM_Air_W_seg8.csv Air W 1+ cars 50k-75k seg9 2021DMLM_Air_W_seg9.csv Air W 1+ cars >75k seg DMLM_Air_W_seg10.csv Air EB 0 cars <20k seg1 2021DMLM_Air_EB_seg1.csv Air EB 0 cars 20k-40k seg2 2021DMLM_Air_EB_seg2.csv Air EB 0 cars 40k-50k seg3 2021DMLM_Air_EB_seg3.csv Air EB 0 cars 50k-75k seg4 2021DMLM_Air_EB_seg4.csv Air EB 0 cars >75k seg5 2021DMLM_Air_EB_seg5.csv Air EB 1+ cars <20k seg6 2021DMLM_Air_EB_seg6.csv Air EB 1+ cars 20k-40k seg7 2021DMLM_Air_EB_seg7.csv Air EB 1+ cars 40k-50k seg8 2021DMLM_Air_EB_seg8.csv Air EB 1+ cars 50k-75k seg9 2021DMLM_Air_EB_seg9.csv Air EB 1+ cars >75k seg DMLM_Air_EB_seg10.csv Rail W 0 cars <20k seg1 2021DMLM_Rail_W_seg1.csv Rail W 0 cars 20k-40k seg2 2021DMLM_Rail_W_seg2.csv Rail W 0 cars 40k-50k seg3 2021DMLM_Rail_W_seg3.csv Rail W 0 cars 50k-75k seg4 2021DMLM_Rail_W_seg4.csv Rail W 0 cars >75k seg5 2021DMLM_Rail_W_seg5.csv Rail W 1+ cars <20k seg6 2021DMLM_Rail_W_seg6.csv Rail W 1+ cars 20k-40k seg7 2021DMLM_Rail_W_seg7.csv Rail W 1+ cars 40k-50k seg8 2021DMLM_Rail_W_seg8.csv Rail W 1+ cars 50k-75k seg9 2021DMLM_Rail_W_seg9.csv Rail W 1+ cars >75k seg DMLM_Rail_W_seg10.csv Rail EB 0 cars <20k seg1 2021DMLM_Rail_EB_seg1.csv Rail EB 0 cars 20k-40k seg2 2021DMLM_Rail_EB_seg2.csv Rail EB 0 cars 40k-50k seg3 2021DMLM_Rail_EB_seg3.csv Rail EB 0 cars 50k-75k seg4 2021DMLM_Rail_EB_seg4.csv Rail EB 0 cars >75k seg5 2021DMLM_Rail_EB_seg5.csv Rail EB 1+ cars <20k seg6 2021DMLM_Rail_EB_seg6.csv 38

39 Rail EB 1+ cars 20k-40k seg7 2021DMLM_Rail_EB_seg7.csv Rail EB 1+ cars 40k-50k seg8 2021DMLM_Rail_EB_seg8.csv Rail EB 1+ cars 50k-75k seg9 2021DMLM_Rail_EB_seg9.csv Rail EB 1+ cars >75k seg DMLM_Rail_EB_seg10.csv Rail O 0 cars <20k seg1 2021DMLM_Rail_O_seg1.csv Rail O 0 cars 20k-40k seg2 2021DMLM_Rail_O_seg2.csv Rail O 0 cars 40k-50k seg3 2021DMLM_Rail_O_seg3.csv Rail O 0 cars 50k-75k seg4 2021DMLM_Rail_O_seg4.csv Rail O 0 cars >75k seg5 2021DMLM_Rail_O_seg5.csv Rail O 1+ cars <20k seg6 2021DMLM_Rail_O_seg6.csv Rail O 1+ cars 20k-40k seg7 2021DMLM_Rail_O_seg7.csv Rail O 1+ cars 40k-50k seg8 2021DMLM_Rail_O_seg8.csv Rail O 1+ cars 50k-75k seg9 2021DMLM_Rail_O_seg9.csv Rail O 1+ cars >75k seg DMLM_Rail_O_seg10.csv Coach W 0 cars <20k seg1 2021DMLM_Coach_W_seg1.csv Coach W 0 cars 20k-40k seg2 2021DMLM_Coach_W_seg2.csv Coach W 0 cars 40k-50k seg3 2021DMLM_Coach_W_seg3.csv Coach W 0 cars 50k-75k seg4 2021DMLM_Coach_W_seg4.csv Coach W 0 cars >75k seg5 2021DMLM_Coach_W_seg5.csv Coach W 1+ cars <20k seg6 2021DMLM_Coach_W_seg6.csv Coach W 1+ cars 20k-40k seg7 2021DMLM_Coach_W_seg7.csv Coach W 1+ cars 40k-50k seg8 2021DMLM_Coach_W_seg8.csv Coach W 1+ cars 50k-75k seg9 2021DMLM_Coach_W_seg9.csv Coach W 1+ cars >75k seg DMLM_Coach_W_seg10.csv Coach O 0 cars <20k seg1 2021DMLM_Coach_O_seg1.csv Coach O 0 cars 20k-40k seg2 2021DMLM_Coach_O_seg2.csv Coach O 0 cars 40k-50k seg3 2021DMLM_Coach_O_seg3.csv Coach O 0 cars 50k-75k seg4 2021DMLM_Coach_O_seg4.csv Coach O 0 cars >75k seg5 2021DMLM_Coach_O_seg5.csv Coach O 1+ cars <20k seg6 2021DMLM_Coach_O_seg6.csv Coach O 1+ cars 20k-40k seg7 2021DMLM_Coach_O_seg7.csv Coach O 1+ cars 40k-50k seg8 2021DMLM_Coach_O_seg8.csv Coach O 1+ cars 50k-75k seg9 2021DMLM_Coach_O_seg9.csv Coach O 1+ cars >75k seg DMLM_Coach_O_seg10.csv HSR W 0 cars <20k seg1 2021DMLM_HSR_W_seg1.csv HSR W 0 cars 20k-40k seg2 2021DMLM_HSR_W_seg2.csv HSR W 0 cars 40k-50k seg3 2021DMLM_HSR_W_seg3.csv HSR W 0 cars 50k-75k seg4 2021DMLM_HSR_W_seg4.csv 39

40 HSR W 0 cars >75k seg5 2021DMLM_HSR_W_seg5.csv HSR W 1+ cars <20k seg6 2021DMLM_HSR_W_seg6.csv HSR W 1+ cars 20k-40k seg7 2021DMLM_HSR_W_seg7.csv HSR W 1+ cars 40k-50k seg8 2021DMLM_HSR_W_seg8.csv HSR W 1+ cars 50k-75k seg9 2021DMLM_HSR_W_seg9.csv HSR W 1+ cars >75k seg DMLM_HSR_W_seg10.csv HSR EB 0 cars <20k seg1 2021DMLM_HSR_EB_seg1.csv HSR EB 0 cars 20k-40k seg2 2021DMLM_HSR_EB_seg2.csv HSR EB 0 cars 40k-50k seg3 2021DMLM_HSR_EB_seg3.csv HSR EB 0 cars 50k-75k seg4 2021DMLM_HSR_EB_seg4.csv HSR EB 0 cars >75k seg5 2021DMLM_HSR_EB_seg5.csv HSR EB 1+ cars <20k seg6 2021DMLM_HSR_EB_seg6.csv HSR EB 1+ cars 20k-40k seg7 2021DMLM_HSR_EB_seg7.csv HSR EB 1+ cars 40k-50k seg8 2021DMLM_HSR_EB_seg8.csv HSR EB 1+ cars 50k-75k seg9 2021DMLM_HSR_EB_seg9.csv HSR EB 1+ cars >75k seg DMLM_HSR_EB_seg10.csv HSR O 0 cars <20k seg1 2021DMLM_HSR_O_seg1.csv HSR O 0 cars 20k-40k seg2 2021DMLM_HSR_O_seg2.csv HSR O 0 cars 40k-50k seg3 2021DMLM_HSR_O_seg3.csv HSR O 0 cars 50k-75k seg4 2021DMLM_HSR_O_seg4.csv HSR O 0 cars >75k seg5 2021DMLM_HSR_O_seg5.csv HSR O 1+ cars <20k seg6 2021DMLM_HSR_O_seg6.csv HSR O 1+ cars 20k-40k seg7 2021DMLM_HSR_O_seg7.csv HSR O 1+ cars 40k-50k seg8 2021DMLM_HSR_O_seg8.csv HSR O 1+ cars 50k-75k seg9 2021DMLM_HSR_O_seg9.csv HSR O 1+ cars >75k seg DMLM_HSR_O_seg10.csv Note: Names of matrices from alternative scenarios start with 2021DSLM*.csv 40