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SCOTTISH STRATEGIC RAIL STUDY

3. THE IMPLICATIONS OF DOING NOTHING

3.1 The starting point for the development of the strategy is to consider what the future would hold in the absence of the strategy and a key question to address is what level of demand for rail travel should we expect to have to cater for in the future? It is also necessary to define what changes to the infrastructure and services are expected to be in our do-nothing (or more strictly our 'do-minimum') future. This is set out in Table 3.1.

Do-Minimum Definition

Table 3.1 Do Minimum Definition

3.2 In addition to the schemes detailed in Table 3.1 the do-minimum also includes an upgrade to Edinburgh Waverley to accommodate the ECML upgrade. This has been described as Edinburgh Waverley Option 1, and whilst it is necessary to facilitate the upgrade it does not create any more capacity. As a consequence this assumption has no implication for the demand forecasts, however, it does have an implication for the incremental capital cost of the scenarios examined. This issue is referred to in more detail in Working Papers 6 and 7.

Do-Minimum Background Growth Forecasts

3.3 When using the term 'background growth' we are referring to growth in demand which is due to factors external to the actions of the rail industry itself. It therefore includes socio-economic factors such as income growth, employment growth and demographic shifts, as well as competitive factors such as growth in car ownership, increases in congestion levels and the actions of the bus, coach and airline industries.

3.4 It therefore excludes the contribution that can be made by actions taken by the rail industry (service levels, pricing etc), and it excludes the impact of policy measures such as road pricing in the City of Edinburgh or other measures to restrain car traffic.

3.5 The forecasts presented here are therefore best thought of as a do-minimum scenario, ie "if we do nothing beyond what is already committed, this is what is expected to happen."

3.6 In order to forecast background growth we have used the Rail Industry Forecasting Framework (RIFF) Model to develop a model for the study area. The RIFF Model was developed as a result of an extensive Association of Train Operating Companies (ATOC)/Railtrack funded study into rail growth trends, focusing particularly on understanding trends for the five years up to 1999.

3.7 The RIFF model is based upon a 1999/00 CAPRI demand matrix of 614 x 614 micro zones - groupings of stations - incorporating journeys and revenue from all UK rail stations split by five ticket types (full fare, seasons, reduced - walkup, reduced - advance, miscellaneous). Demand 'drivers', things that will result in changes in the demand for rail travel, that are explicitly recognised in the model are;

• Population;
• Households;
• Employment;
• Car ownership;
• GDP;
• Road Congestion.

3.8 Working Paper (WP) 2 provides an overview of the model structure and WP 4 details the inputs and findings. The 'planning data' used in the Main Scenario model is consistent with the CSTCS analysis. A 'variant' scenario which used national planning data forecasts rather than the Glasgow & Clyde Valley Structure Plan data used in CSTCS was also prepared (WP1 for more details).

Unconstrained Growth

3.9 Table 3.2 shows the forecast growth (in percentage terms) between 2000 and 2020 throughout the study area.

Table 3.2 FORECAST GROWTH TO 2020 IN PASSENGER JOURNEYS AND MILES - MAIN SCENARIO

3.10 Overall, rail trips within and to/from the study area and the rest of Scotland are forecast to increase by 39% between 2000 and 2020 (41% if trips to/from England & Wales are included). Most of the growth is expected to come from the leisure market where growth in the order of 64% over 20 years is forecast. Commuting, on the other hand, is expected to grow at a much lower rate.

3.11 Within the study area, Glasgow is forecast to witness the highest passenger growth rates -fuelled largely by the economic planning assumptions within the Glasgow & Clyde Valley Structure Plan (GCVSP). In terms of passenger miles the growth in long distance travel to England & Wales gives Edinburgh the highest growth in passenger miles.

3.12 To test the degree to which the GCVSP economic planning assumptions are driving the forecasts a Variant Scenario was developed. This used the Department of Transport/Scottish Executive TEMPRO planning data assumptions throughout the study area. The background growth that RIFF forecasts when using this TEMPRO data is shown in Table 3.3. This shows how significant the GCVSP data is. With the TEMPRO data, overall growth in the study area would be much reduced at 19% with the growth in travel to Glasgow reduced from 46% to 26% and to Edinburgh from 34% to 19%. The latter figure might appear higher than expected given that the GCVSP planning data does not effect planning data in Edinburgh, but it reflects the fact that a significant proportion of travel into Edinburgh comes from areas in the west of the Central Belt (ie areas affected by the GCVSP forecasts).

Table 3.3 FORECAST GROWTH TO 2020 IN PASSENGER JOURNEYS AND MILES - VARIANT 1A (TEMPRO PLANNING DATA) SCENARIO

3.13 To ensure consistency with the other Scottish Executive studies, the CSTCS and Rail Links to Airports studies the analysis reported in the rest of this document is based upon the adoption of the higher, Main Scenario, background growth forecasts. The implications of this decision are discussed in Chapter 10.

Peak Hour Overcrowding - Constrained Demand

3.14 These forecasts are, however, 'unconstrained' in that they are not limited by the availability of capacity to carry the additional passengers.

3.15 A 'Peak Hour Capacity Impacts Model' has been developed to capture the impacts that peak hour passenger overcrowding has upon background growth in rail demand. This model is then used subsequently to assess the level of benefit which accrues if this overcrowding is relieved.

3.16 The model functions by calculating background growth in passenger loadings for each of the forecast years and the extent to which this increases overcrowding. Using Passenger Demand Forecasting Handbook (PDFH) recommendations the implied generalised journey time equivalence of this crowding increase can be calculated for each line. This is then used to calculate a reduction in demand for each of the forecast years due to the implied generalised time increase for each relevant OD pair in the future demand matrices.

3.17 The impact of peak hour overcrowding is an overall reduction in do minimum demand of 468,000 journeys in 2010 and 1.52 million journeys in 2020 compared with the unconstrained background growth forecasts. This is illustrated by area in Table 3.4 below.

Table 3.4 OVERCROWDING IMPACTS UPON DO MINIMUM PASSENGER JOURNEYS (000 's per year)

Summary

3.18 It is clear from the preceding analysis that the pressure on the rail network is expected to grow very considerably over the next twenty years, simply from factors outside of the control of the rail industry. Not having a strategy for the railways is not an option for this reason alone. However, the real opportunity for the railways and the planning authorities is how to capitalise on this expected demand growth and to develop a strategy that builds upon this such that the railways can play a greater role in the achievement of national and local objectives. Our approach to developing such a strategy is described in the next chapter.

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