A GIS-based appraisal framework for new local railway stations and services

Transport Policy 25 (2013) 41–51

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A GIS-based appraisal framework for new local railway stations and services Simon P. Blainey n, John M. Preston Transportation Research Group, Faculty of Engineering & the Environment, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom

a r t i c l e i n f o

abstract

Available online 20 December 2012

This paper describes the development of an integrated appraisal procedure for new local railway stations. The procedure is not intended to act as a ‘solid state’ set of guidelines, but instead to provide a framework containing current best practice, within which individual elements can be updated and enhanced as new evidence becomes available without affecting the rest of the methodology. The framework includes a number of different elements, which are brought together in an appraisal spreadsheet but which can be altered without affecting other sections of the procedure. First, the best locations for new stations within the study area are chosen, with potential sites selected, catchments defined, infrastructure capacity assessed and potential service patterns planned. Usage at the potential stations is predicted using trip end and flow level demand models, and the extent of abstraction from neighbouring stations is considered. The benefits and costs of constructing the stations are then estimated, including direct financial gains and losses as well as wider economic impacts and social costs and benefits. Finally, these are brought together to calculate financial and social net present values and benefit–cost ratios, with break-even demand levels for new stations also provided. The framework provides a consistent means of estimating the costs and benefits of the large number of schemes for new local railway stations in the UK, and use of a standardised methodology allows those schemes with the best case for construction to be prioritised. Since rail privatisation in the mid-1990s the costs of new stations have escalated dramatically, meaning that stations have to demonstrate much greater revenue-earning potential than was previously the case to justify construction. By providing more accurate forecasts and thus reducing uncertainty over scheme costs and benefits, this appraisal procedure can help to ensure that the best return is obtained on the limited investment funds which are available. & 2012 Elsevier Ltd. All rights reserved.

Keywords: Railway station Appraisal Demand modelling Catchment Abstraction

1. Introduction 1.1. Background UK passenger rail use is currently at record levels, which together with changing travel patterns means that a large number of new railway stations have opened in the UK in recent years (see Table 1), with many further stations and lines proposed (see for example Association of Train Operating Companies (ATOC), 2009a). In order to adequately serve a changing society the UK rail network needs to be a dynamic system, and similar conditions also prevail in several other western European countries such as Germany and the Netherlands. However, no comprehensive or up-to-date procedure for appraising new stations currently exists, making it difficult to prioritise between schemes and decide which have the best case for construction. While some guidance on demand forecasting and appraisal for new stations does exist in the Passenger Demand Forecasting Handbook (PDFH) produced by the Association of Train Operating Companies (ATOC)

n

Corresponding author. Tel.: þ44 23 8059 2834; fax: þ 44 23 8059 3152. E-mail address: [email protected] (S. P. Blainey).

0967-070X/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tranpol.2012.11.008

(2009b) and on the UK Department for Transport’s Transport Analysis Guidance website (WebTAG) (Department for Transport, 2011), the models recommended in the former are somewhat outdated (based mainly on work from the 1980s) and the procedures outlined in the latter can be difficult to apply in practice. This means that there has been a lack of consistency between the methods used by the various consultants who have produced business cases for new stations in recent years. This has in turn contributed to a perception among many stakeholders that station usage at newly opened stations has been much greater than the levels that had been forecast. The Department for Transport and Transport Scotland commissioned a report in 2009 to investigate whether or not this was the case, which found that while in general demand was slightly under-forecast, this was not by a consistent factor (Steer Davies Gleave, 2011), perhaps unsurprisingly given the range of methods used. This study went on to consider the development of a generic demand forecasting methodology, but claimed that trip rate demand models cannot be generalised, despite some evidence to the contrary (Blainey, 2010). While official advice in this field therefore currently leaves a lot to be desired, a large volume of work on local rail demand forecasting and appraisal has been undertaken in recent years at the University of Southampton’s Transportation Research Group

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(TRG), building on the existing body of research in this area to produce a generic and transferable appraisal methodology for new local railway stations. This is not intended to act as a ‘solid state’ set of guidelines, but instead to provide a framework containing current best practice and outlining the choices available, within which individual elements can be updated and enhanced as new evidence becomes available without affecting the rest of the methodology. It complements and is consistent with both PDFH and WebTAG (except where new evidence is available), while giving much more detailed consideration of the specific conditions and issues relating to new local railway stations. All elements within the framework should be considered during appraisal, but it may not prove necessary to make use of all elements in all cases. This paper outlines and describes these various elements, which are shown in Fig. 1 along with the interrelationships between them. A key element linking these Table 1 Openings and closures of railway stations in Britain 1970–2011. Source: Based on Preston (2001) – updated.

1970–1974 1975–1979 1980–1984 1985–1989 1990–1994 1995–1999 2000–2004 2005–2009 2010–2011 Total

Open

Closed

Net balance

18 38 44 96 72 31 11 24 3 337

130 13 10 14 1 3 1 8 0 180

 112 þ25 þ34 þ82 þ71 þ28 þ 10 þ16 þ3 þ157

elements is the use of Geographic Information Systems (GIS) for integrating and processing the wide range of datasets required by the different elements of the framework. All datasets with a spatial element (which in practice means virtually all the data used in the methodology) were imported into ArcGIS, allowing them to be easily managed and linked for appraisal purposes. While the framework as described here was developed specifically for use in the UK, with local recalibration it should in principle be equally suitable for use in other European countries, and potentially also further afield. It is illustrated here using the South-East Wales area as a case study, and in particular a proposed station at Energlyn (shown in Fig. 2), as this was the area focused on in the project which initiated this strand of research (Blainey, 2009) and development of Energlyn station is listed as a priority in the Welsh Government’s National Transport Plan (Welsh Government, 2011). The remainder of this section gives a brief overview of previous work in this field. Section 2 then describes the issues involved in selecting sites for new station, before Section 3 discusses the various modelling methods available for predicting passenger numbers. Section 4 describes how the benefits generated by new stations can be quantified, with Section 5 outlining methods for calculating the costs incurred. These costs and benefits are then compared in Section 6, before Section 7 discusses the implications of this work and possible future developments. 1.2. Previous work The main source of rail demand forecasting advice in the UK, the PDFH (Association of Train Operating Companies (ATOC) (2009b)), is mainly concerned with modelling the demand impacts of changes to

Fig. 1. Conceptual diagram of appraisal framework for new local railway stations.

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Fig. 2. Location of proposed station at Energlyn, South Wales.

existing services using an elasticity-based framework which is unsuitable for use with new stations and services where the base level of demand is zero. A range of absolute rail demand models have been developed in the past, with notable UK examples including work by Preston (1991) and Fowkes and Preston (1991) for local stations, by Lythgoe (2004) and Wardman et al. (2007) for interurban stations and by Jones and White (1994) for cross country services. Similar work has been carried out in other countries, such as the new station demand models calibrated by Lane et al. (2006) in the US, and work on station access and station choice carried out by Brons et al. (2009) and Debrezion et al. (2009) in the Netherlands. There has also been a great deal of work in recent years on demand forecasting for high speed rail, for example by Ben-Akiva et al. (2010) and HS2 Ltd (2012). However, there had been little recent work on demand models for new local stations in Britain before work commenced on this project, despite the major changes experienced by the UK rail industry since the early 1990s. The work described here forms arguably the first coherent and comprehensive GIS-based methodology for the appraisal of new local railway stations, although GIS have in the past been used to aid many of the elements which form part of the rail appraisal process. For example, Lythgoe (2004) used GIS to implement a flexible approach to station catchment definition, and Whelan and Wardman (1999a), Lane et al. (2006) and Wardman et al. (2007) used GIS to collate and process data for demand modelling. Commercial and governmental GIS applications such as Experian’s MOSAIC geodemographic profiles and the Department for Transport’s Tempro have also been used to provide inputs for such modelling in the past (see for example Meaney et al., 2010).

2. Choosing a location 2.1. Site selection The first stage in assessing the performance of potential new local railway stations is to identify possible station sites. Horner and Grubesic (2001) developed a GIS-based methodology for evaluating potential sites for park and ride stations in Columbus,

Ohio, but this was designed for an area with no existing local railway stations, while the basic population-based methodology used by Association of Train Operating Companies (ATOC) (2009a) was focused on new lines rather than on new stations and did not identify precise station locations. A semi-automated GIS-based site search procedure has therefore been developed based on criteria set out by Preston (1987). Once the geographical area of interest is defined, a GIS is used to allocate all census output areas (usually containing 200–600 residents) within this area to their nearest station in terms of access time, using GIS transport network data from the Ordnance Survey. Units within an acceptable distance of an existing station were removed from the dataset. This maximum ‘acceptable’ distance will be influenced by the method chosen for station catchment definition (see Section 2.2), but was set at 4 min uncongested drive time ( 1.5–2 km road distance) for the trial application of the methodology. A buffer zone was then created around all existing railway lines, with a 2 km boundary used to ensure that all communities close enough to be effectively served by existing lines were included (again this will be affected by evidence on catchment size), and all population units outside this zone were removed from the dataset,. Population and employment density were calculated for the remaining units using census data, and then displayed as chloropleth maps (which show different densities using gradated colour bands) in a GIS to allow identification of clusters of population units with high population and/or employment density. These form potential zones for station construction, and 421 such zones were identified in England and Wales. Precise point locations for new stations were then identified using the GIS based on ease of access and physical construction constraints. The same criteria as used previously were used to define catchments for the new stations, with some minor locational adjustments made to include the maximum number of target population units while minimising overlap with existing station catchments. 2.2. Catchment definition In order to forecast both demand from and the wider impacts of a new station it is necessary to produce a definition of its

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catchment, the area from which it draws passengers. Most new station studies in the UK have made use of relatively simplistic catchment definition methods, based on the all-or-nothing allocation of population units to stations and constant distance decay functions. For example, the PDFH recommends the use of 800 m and 2 km catchment zone boundaries (Association of Train Operating Companies (ATOC), 2009b), while recent work by Blainey (2009) found that a 4 min uncongested drive time boundary gave the best results. As car travel will normally form the fastest access mode in uncongested conditions, such boundaries should also include those accessing the stations by other modes. Such catchment boundaries are usually chosen either by approximating observed results from small-scale surveys or by maximising demand model fit across a calibration case study, but while they can give good results when used in trip end and direct demand models (see Section 3) these catchments do not necessarily reflect actual travel behaviour. Data from the UK National Rail Travel Survey (NRTS) on the actual locations of ultimate trip origins and destinations has therefore been geocoded and used to assess the accuracy of these theoretical catchments and to calibrate station choice models. This research found that these simple catchments failed to capture 20–45% of trip ends, and that there was a great deal of variability between stations in the relative performance of particular definition methods. It was clear that station choice decisions are heavily influenced by factors other than access distance, and several types of station choice model were therefore calibrated. Multinomial logit (MNL) choice models were found to give the most reliable results, with Model (1) having a r2adj value of 0.79 (the MNL equivalent of R2adj) in North-East England and 0.63 in Wales (Blainey and Evens, 2011), which compares favourably to the choice models developed by Whelan and Wardman (1999b) and Debrezion et al. (2009). While this model produced destination-specific station choice probabilities, more general choice models suitable for use with trip-end demand models were also calibrated. The size of the choice set in all models was restricted to ten stations, as the NRTS data indicated that this would reflect the vast majority of observed behaviour while limiting model complexity:   exp ak þ bAdk þ ZF k þ jPsk þ zTdkl P ik ¼ Pk ¼ 10    exp ak þ bAdk þ ZF k þ jPsk þ zTdkl k¼1

ð1Þ

where Pik Adk Fk Psk Tdkl

a, b, Z, f

is the probability that a passenger from location i uses the kth station is the distance from location i to station k is the train frequency at station k is the number of car parking spaces provided at station k is the total distance from origin i to destination station l via origin station k are parameters determined during calibration

2.3. Capacity analysis If a new station is to be opened at a particular location, it is important to ensure that sufficient network capacity is available to allow services to call at the new station. Even if the station is to be served by existing services, the additional stops these make will increase the amount of capacity they take up, by increasing their journey (and therefore track occupation) time. If new services are required then it will be necessary to find sufficient paths for these to operate, which can be an increasingly difficult task on Britain’s congested rail network. While two wellestablished capacity utilisation measures exist, the UIC (International Union of Railways) 406 methodology and the Capacity Utilisation Index (CUI) (used by Network Rail in the UK), in their current form these should only be applied to sections of plain line, not to junctions or stations, which will often be the key constraints on capacity. Work is therefore ongoing to extend the CUI methodology from links to nodes, based on the use of minimum junction margins and platform reoccupation times (Armstrong et al., 2011), and this will permit easy and rapid identification of ‘spare’ capacity in the areas around new stations which would permit them to be adequately served by train services. It is necessary to apply such a methodology at an early stage in scheme appraisal, as if it was found that extensive infrastructure investment was required to allow a small-scale station scheme to fulfil its potential, there would be little point in taking the scheme further. 2.4. Service planning As stated in Section 2.3, checking that capacity exists to serve a new station forms a crucial element of the appraisal process. Once spare capacity has been identified, it is then necessary to undertake initial planning of the level of service to be provided at the new station. While stakeholders may wish to investigate the demand impact of offering different levels of service at the station, it is important to have some idea of the level of service which is likely to be provided at an early stage in the appraisal process, as research has shown that service levels have a fundamental impact on demand and should therefore be included in even the simplest demand models. Service levels are likely to be heavily influenced by the location of the new station, as in many cases new stations will be served by existing services making an additional stop, and therefore the level of service provided will depend on the number of trains already operating on the route through the station which can easily be checked in electronic timetables. If new services are to provided, then as described above capacity limitations may affect the level of service that can be offered, with for example the provision of additional services from the Ebbw Vale line (reopened in 2008) to Newport currently impossible due to a lack of capacity.

3. Predicting passenger numbers

and z 3.1. Trip end models

While the use of such choice models to define catchments for both demand forecasting and site selection is likely to give more accurate results than the use of more conventional catchments, they have not yet been calibrated across the whole country and add a further layer of complexity to the appraisal process. It may therefore still be preferable in many circumstances to use zonal catchments, and for simplicity these are retained for the remainder of the procedures described in this paper.

The total number of trips from a new station can be forecast using trip end models, which are regression models which forecast annual trips at a station based on a number of explanatory variables. Over 100 model forms were tested on a calibration dataset of 1499 existing local railway stations in England and Wales with a range of datasets brought together in a GIS (see Blainey, 2010 for full details) including freely available data on total station usage and Ordnance Survey mapping data provided under the OpenData initiative. Calibration of the

S.P Blainey, J.M Preston / Transport Policy 25 (2013) 41–51

V^ i ¼ a

n X a

!b P a wa

r Z

n

F di T li J ti4 Pki Bi Teki Eli

Existing stations 450 400 350 300 250 200 150 100 50 0

Number of stations

models using Geographically Weighted Regression (GWR) (Fotheringham et al., 2002) allowed spatial variations in the effect of the explanatory variables on model demand to be explicitly accounted for by the model, and the best model form (2) explained 82.4% of the variation in the calibration dataset (Blainey, 2009).

ð2Þ

estimated number of passenger entries and exits per year at station i Pa resident population in output area a (a,y,n) output areas whose closest station by car travel time is station i wa weight attached to population unit a, given by (t þ1)  3.25 (a large number of weighting functions were tested, with this one giving the best model fit) t road travel time from population unit a to its closest station Fi train frequency at station i over a normal weekday T distance in km from station i to the nearest non-local station Ji4 number of jobs located within 4 min drive of station i Pki number of parking spaces at station i Bi dummy variable taking the value e1 if Station i is a Travelcard boundary station, and e0 otherwise Tei dummy variable taking the value e1 if Station i is a terminus, and e0 otherwise Eli dummy variable taking the value e0 if Station i is served by electric trains, and e0 otherwise a, b, d, t, r, Z, k are parameters determined during calibration and n

45

V^ i

This model was developed into a demand forecasting spreadsheet, which was used to predict demand at the 421 sites identified using the site selection procedure. It suggested that 144 of these sites would be used by over 100,000 passengers per year (including the site at Energlyn, forecast to be used by 159,454 passengers per year), putting them above around 37% of existing stations (see Fig. 3 for station usage frequency distributions). Such stations are likely to be those with the greatest case for construction, although further analysis would be necessary to confirm this, as the service patterns from the new station and resultant level of accessibility to key destinations will play a significant role in determining demand levels. Flow level models (described below) can account for such factors, and should therefore give a more accurate indication of station viability. However, such models are more computationally complex and have much greater data requirements, including flow level ticket sales data which is not in the public domain. The trip end forecasts are in contrast simple to produce using data which is all readily available online, and therefore provide a quick means of checking the likely viability of a large number of potential sites for new stations, and to the authors’ knowledge this is the first time that such a large scale analysis of possible sites for new stations on existing lines has been undertaken.

Trips per year

Proposed stations Number of stations

160 140 120 100 80 60 40 20 0

Trips per year Fig. 3. Frequency charts of station usage for existing and proposed UK railway stations.

3.2. Flow level models To forecast the revenue generated by new stations and to understand the impact additional passengers will have on rail services, it is necessary to model the distribution of trips to destinations. A case study dataset made up of local stations in South-East Wales was used to calibrate a range of flow level models, of two main types, non-linear intervening opportunity trip distribution models (see Kanafani, 1983), and direct demand regression models (Blainey and Preston, 2010a). These were based on LENNON ticket sales data, which gives the number of tickets of different types sold between particular station pairs. While the trip distribution models seemed intuitively to be more realistic, as they were able to account for the effects of intervening opportunities and as probability-based models automatically constrain the total number of trips to be equal to that predicted by the trip end models, the direct demand models had a better fit with the observed data. This fit was further improved by recalibrating the models using GWR, with the coordinates of the origin stations used to define the spatial location of the flows (Blainey and Preston, 2010b), allowing Model (3) to explain 66.8% of the variation across 1289 flows from 68 stations: !b X r g Z V^ ij ¼ a Pa W a J t Pk Ex Do Rsd Csk H Rf kml ð3Þ i4

a

i

j

ij

ij

ij

ij

ij

where V^ ij Exj Dij

is the predicted number of trips per year made from station i to station j is the total number of trips ending at station j in the year being modelled is the straight line distance (in km) from station i to station j

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S.P Blainey, J.M Preston / Transport Policy 25 (2013) 41–51

Rsij

is the rail journey time from station i to station j divided by Dij Csij is the car journey time from station i to station j divided by Dij Hij is the service headway in minutes between station i and station j Rfkmij is the fare per rail km for travel from station i to station j a, b, t, r, g, o, d, k, are parameters determined during calibration Z and l

The use of GWR in direct demand models appears to have great potential for enhancing the flow level forecasting of rail demand, by enabling local variations in the effect of parameters on rail demand to be taken into account. Although Model (3) is still in need of some refinement, no previous flow level model has been able to account for such spatial variations in the factors influencing demand. While the Exj variable might seem to introduce circularity into the model, as a new station would increase total trips to the destination, as the variable is not a constraint it in fact forms an effective proxy for destination attractiveness. A potential limitation is that the sum of predictions across all flows from a station will not necessarily sum to be equal to the prediction for that station from the trip end model (for Energlyn the sum of flow level predictions is 210,924, over 30% higher than the trip end predictions). It would be simple to apply a scaling factor to the flow level results if such a constraint was felt to be desirable, but it is likely that the difference in predictions occurs because the direct demand model takes into account the influence of service patterns and accessibility to particular destinations on demand, meaning that it gives a more accurate prediction of total station usage. The models described here would require recalibration before being used in areas other than South-East Wales, but there is no fundamental reason why the general model forms should not be equally valid in other geographic contexts. The main constraints on the use of direct demand models are likely to be the availability of (commercially

confidential) LENNON data for calibration, and the time required for assembly of the large and complex calibration datasets. 3.3. Demand abstraction Unless a new station is opened in an area which is entirely remote from existing stations (an unlikely scenario for most of the UK), some of the passengers using the new station will almost certainly be abstracted from neighbouring pre-existing stations. This will have an impact on the overall business case from the scheme, as if the majority of trips at a new station are abstracted from other stations then the net revenue benefit from the new station will be small. Two methods have been used to assess the extent of this abstraction. The first involved collating usage data for the existing stations adjacent to ten new stations opened between 2003 and 2006, and for each of them calculating the difference between usage in the year before opening of the new station and in the two following years. To account for underlying demand growth, area mean demand growth levels were calculated based on non-adjacent stations in the same area, and the difference between these area means and the growth recorded at the adjacent stations was then calculated to establish whether growth at the latter stations was lower than expected. This showed that abstraction was far from being a universal phenomenon (Blainey, 2009), as while in some cases growth at adjacent stations was suppressed by up to 15%, at other stations demand increased by up to 30% more than the area mean growth level. The second method for assessing abstraction was based on predicted behaviour, and involved using the station choice models described in Section 2.2 to forecast the probability of choosing existing stations before and after the opening of a new station. Application of this methodology to case study stations in South Wales predicted a clear change in the shape of station catchments (Fig. 4 shows an example following the opening of a station at Energlyn), but because NRTS data is only available for a single date, it was not possible to verify whether the predicted change in station choice probabilities reflected actual travel behaviour (Blainey and Evens, 2011). This lack of conclusive evidence means

Fig. 4. Probability of using Aber station before and after opening of Energlyn station.

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47

that abstraction has not currently been included in the appraisal methodology, but this is an area which would warrant further research as the current methodology is likely to overestimate net demand for rail.

4. Adding up the benefits 4.1. Change in revenue The primary source of additional revenue at new stations comes from the fares paid by passengers travelling to and from the stations, and this can be estimated in several ways, depending on the amount of data available. Most previous new station studies have simply multiplied an estimated average single fare (based on comparable existing stations) by the total number of trips forecast, which gives a quick and approximate indication of revenue, but fails to account for differences in destination choice between stations. If flow level demand forecasts are available then revenue can be estimated on a flow-by-flow basis and then summed to give a total annual figure, which for the Energlyn example was £404,625 (this and subsequent financial figures in 2008 prices). Errors may though still arise where a number of different ticket types are available for particular flows, and if possible data on ticket-type split from adjacent stations should be used to allocate passengers to ticket types allowing a more accurate calculation of expected revenue from the new station. The revenue figure for Energlyn using the trip end models and South Wales mean fares was £398,635, indicating that in this case the choice of demand model made little difference to the estimated revenue. As stated above, a proportion of revenue from the new station is likely to be transferred from adjacent stations, and if abstraction can be accurately predicted then this should be used to adjust the total revenue estimate. In addition to fare revenue, new stations may generate other smaller revenue streams. If a station car park is provided and parking spaces are charged for then this will provide additional income. There may also be potential to provide and charge for commercial advertising space at new stations, and revenue can sometimes be generated by renting out station buildings and retail units to private businesses. While the benefits from these sources are likely to be minimal at the small local stations considered here, it is straightforward to add them in to the business case if they are likely to be significant at a particular location. 4.2. User benefits New rail users attracted to travel from a new station will obviously derive some level of benefit from this travel as otherwise they would not choose to travel by rail, and such benefits should be included in the business case for a new station. User benefit can be calculated using an aggregate approach based on the functional relationship between rail demand and the generalised cost of rail (expressed in terms of the fare charged), as illustrated in Fig. 5. User benefit is calculated by integrating the demand curve with respect to the fare between the limits F0 and F1, but as this does not give a finite result with the double logarithmic form used in Models (2) and (3) an alternative negative exponential form was assumed. The fare elasticity used in the function can be varied in line with official advice or with locally varying estimates from GWR models, but for our case study the median elasticity of 1.027 taken from Model (3) was used. This elasticity is very close to those given in the PDFH (Association of Train Operating Companies (ATOC) (2009b)) for non-season ticket travel outside

Fig. 5. Theoretical relationship between rail demand and rail fare.

Table 2 Diversion rates (%) for additional rail demand. Source: Balcombe (2003, p. 105). Trip type

Bus

Car

Air

Cycle/walk

Generated

Urban Interurban

41 20

33 60

n/a 6

1 n/a

24 14

London. Such estimation can be expected to capture benefits arising from travel-time savings, as such savings will form an element of the utility derived from travel and traded off against the generalised cost of a journey when making travel choice decisions. The value of user benefits arising from a new station at Energlyn was estimated at £292,836 per year. 4.3. Non-user benefits Many trips from new stations will be expected to be abstracted from other modes, leading to non-user benefits as a result of reduced congestion, noise, and environmental pollution. The only data available on the proportion of new rail demand which is abstracted from other modes came from Balcombe (2003) and are summarised in Table 2. The urban set of diversion rates appears most suitable for new local stations, but again these diversion rates could be easily adjusted if better evidence became available. While WebTAG includes estimation procedures for the value of non-user benefits, these proved too complex to implement in practice. A more practical solution was provided by using estimated marginal costs and revenues from the road sector given by Sansom et al. (2001), with net costs per km multiplied by the vehicle kilometres removed from the highway network (calculated using GIS based on the diversion rates in Table 2) to give an estimated valuation of the non-user benefits generated by the proposed stations. Trips captured from road travel will generate the vast majority of non-user benefits at new local stations, as bus, cycle and walk trips generate much lower levels of externalities (and there will be minimal diversion from air at such stations). This methodology should therefore produce a reasonable estimate of non-user benefits, which were predicted to amount to £143,389 per year at Energlyn. 4.4. Wider impacts Research has shown that rail schemes can generate wider economic benefits (often known as agglomeration benefits) by permitting access to an increased labour pool, easier access to suppliers and ‘knowledge spillovers’ (Marshall and Webber, 2007). However, estimation of such benefits is complex, and while they may be significant for major rail improvement schemes, the wider economic benefits generated by opening a single new local station are likely to be extremely small.

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However, new local stations may have other impacts on the areas they serve, and the impact of station opening on local population, employment and house price levels has been investigated, based on analysis of 41 case study stations and a similar number of control sites (Blainey and Preston, 2010c). This found that in the long-run population may grow by up to 8% more in the area around a new station than would otherwise have been the case, with growth concentrated around the edge of the urban area served. No significant effect on employment was detected, although rail’s share of the travel to work market grew slightly faster in the areas around new stations than elsewhere. More significant was the finding that the opening of a new station led to a 7–10% uplift in house prices in the station’s five-digit postcode sector. While it would be difficult to capture these benefits to offset construction costs, and while they would not be realised until properties were sold, they should arguably still be included in the business case. This evidence is supported by research commissioned by Network Rail which found that investment in major stations can increase property values by up to 30% (Steer Davies Gleave, 2011). As such benefits are not yet included in government appraisal guidance, they were not included in the example calculations given in this paper, but were estimated to amount to £40–57 million for Energlyn station if applied to all properties within the postcode sector. However, these benefits could be downstream manifestations of the primary benefits accruing at a new station, and further research is required to establish the extent to which this is the case.

5. Counting the costs 5.1. Construction costs Extensive analysis was undertaken on station construction costs, with data collected for 121 stations opened in the UK between 1986 and 2008. Costs were found to have increased massively over this period, possibly because of stringent accessibility legislation and higher expectations of station quality. For the purposes of appraisal attention was focused on stations opened since 2000. Over this period the average construction cost for two platform stations was £4,926,657 in 2008 prices, whereas the average cost for single platform stations was £1,353,520. However, there was significant variation in costs around these means, partly as a result of variations in platform length between stations. The mean cost per carriage length of platform was therefore calculated, and the resultant figure of £470,610 (2008 prices) used to estimate construction costs for new stations, giving a cost of £4,706,100 for Energlyn. While it would be expected that more precise estimates of construction costs would be produced during scheme planning, this approximated methodology allows the likely viability of schemes to be assessed without the need for detailed (and costly) engineering plans. It is possible that Network Rail’s modular station programme may reduce new station costs (Network Rail, 2011), but insufficient evidence is currently available to assess this. If the new station is to be located on an entirely new route, then scheme construction costs will be much higher, but while they falls outside the scope of the analysis described here, it would be a straightforward matter to incorporate such costs in the appraisal framework. Even if the new station is located on an existing route, the capacity analysis (see Section 2.3) may indicate that additional infrastructure would be required to allow trains to serve the station, and again the costs of such infrastructure would need to be incorporated in the appraisal. If this was the case, then the phenomenon of appraisal optimism would need to be considered, where the estimated costs of major infrastructure schemes can often be much lower than actual costs (Flyvbjerg et al., 2005).

5.2. Operating and maintenance costs Few data are available on the costs of operating and maintaining new stations themselves, with only three UK estimates found during this work, all from new station feasibility studies. All were very similar, with a mean cost of £38,329 per year (2008 prices). This figure does not include staff costs, and if staff were provided at a new station they would form a major component of operating costs. This issue is complicated by evidence suggesting that the presence of staff at stations can lead to an increase in rail demand (Preston et al., 2008), and staff can also improve revenue capture by reducing the level of ticketless travel. Trains making additional stops at new stations will inevitably consume more fuel because of the extra power used up in acceleration. Work is ongoing at TRG to estimate the effects of additional stops on energy consumption, taking account of specific line conditions and disaggregated by rolling stock type. A general estimate was made of the costs of an additional stop for the types of train used on local services in South-East Wales, and assuming that the train accelerated back to full line speed following the stop gave a figure of £0.21–1.26 (depending on train type and length) in 2008 prices, giving a total of £5366– 32,193 per year for Energlyn. While the true figure is likely to lie between these figures, the maximum figure was used for appraisal calculations to avoid any potential for optimism bias. If additional services are to be provided to serve the new station, then lease, operation and staffing costs for these trains will also need to be considered, and these may be estimated based either on average or marginal costs. The average cost per vehicle kilometre (a measure which accounts for train length, as opposed to train kilometres) of operating regional railway services in the UK has been estimated at £10.24 (made up of £4.63 Network Rail costs and £5.61 TOC costs), based on figures from Booz & Co (2010), and this figure can simply be multiplied by the vehicle km required for the new service to give a total cost estimate. Alternatively, a cost elasticity with respect to train density may be used to estimate the marginal costs of operating additional services. Research in the British TOC sector by Smith and Wheat (2009), Wheat and Smith (2010), and Smith and Wheat (in press) suggests that an elasticity of around 0.8 is appropriate, assuming that the services were operated by the incumbent TOC, so this elasticity can then be used to adjust total operating costs based on the change in total train kilometres. 5.3. Impact on existing passengers In addition to those existing passengers who switch to using the new station (see Section 3.3), the construction of new railway stations will result in a small disbenefit for existing passengers on trains serving the new station as their journey times will be extended. Analysis of recently opened stations showed that on average 1.64 min was added to existing journey times (Blainey, 2009). The aggregate disbenefit for each new station can then be calculated by multiplying this figure by the value of time of the existing passengers and the number of passengers affected. This gave a total disbenefit of £168,079 for Energlyn. It could also be argued that this disbenefit will cause some passengers at the margins to stop using rail, and an elasticity of rail demand with respect to journey time (for example from the PDFH, Association of Train Operating Companies (ATOC), 2009b) could be applied to estimate this demand impact. However, it is a matter of debate whether such small changes in journey time are in fact perceived by passengers in the same way as larger changes (Association of Train Operating Companies (ATOC), 2009b).

S.P Blainey, J.M Preston / Transport Policy 25 (2013) 41–51

6. Making a case 6.1. Benefits vs costs Once the various benefits and costs described above have been estimated, they should be brought together to carry out a costbenefit analysis (CBA). The factors considered in this analysis will depend on the financing arrangements for the new station. If a station is being promoted purely by a private sector developer, then a financial appraisal (4) which considers only the monetary costs and benefits of the scheme may be most appropriate, but otherwise a full social CBA (5) is recommended for rail projects in the UK: NPV f ¼

N X Ri VC i OC i K i i¼0

NPV s ¼

ð4Þ

ð1þ r Þi

N X Ri þ UBi þ NUBi VC i OC i K i UC i

ð5Þ

ð1 þr Þi

i¼0

where financial net present value of the scheme the social net present value of the scheme fare revenue in year i vehicle related costs in year i station maintenance and operating costs in year i capital cost in year i user transport benefits in year i non-user benefits in year i cost to existing users in year i interest rate project life

NPVf NPVs Ri VCi OCi Ki UBia NUBia UCi r N

49

assuming a 60 year project life and an interest rate of 3.5%. The DfT uses BCR values to assess the value for money of a scheme, with a BCR 44 indicating very high value for money, a BCR between 2 and 4 indicating high value for money, a BCR between 1.5 and 2 medium value for money, a BCR between 1 and 1.5 low value for money, and a BCR o1 poor value for money (DfT, 2011). Four of the 14 sites were found to offer high value for money in purely financial terms based on these criteria, while if the social BCR is used two sites offer very high value for money, four high value for money and three medium value for money. The example site at Energlyn had a financial BCR of 1.51 and a social BCR of 1.93, with the costs and benefits broken down in Table 3. While this procedure allows a detailed appraisal of new local station schemes to be carried out, it inevitably requires a significant quantity of data to be collected before it can be used. In some cases it may not initially be possible to justify such extensive data collection, and therefore a simplified procedure to estimate the financial breakeven demand level for new local stations was developed (6). This formula was used to produce breakeven demand levels for a range of station sizes and mean fare levels (assuming a 60 year project life), and these are summarised in Table 4 (Blainey, 2009). These can be used in conjunction with Model (2) to give a quick estimate of station viability while minimising data collection requirements, and could be adjusted to allow for abstraction if reliable evidence became available: T Bi ¼

K i þVC i þOC i F mi

ð6Þ

where TBi Fmi

is the financial breakeven number of trips at station i is the mean single fare at station i

The methodology was used to calculate financial and social NPVs and benefit–cost ratios (BCRs) for 14 potential new stations in South-East Wales identified using the site selection procedure, 6.2. The appraisal spreadsheet Table 3 Breakdown of discounted costs and benefits at Energlyn. Discounted benefits Revenue User benefits Non-user benefits Financial PVB Social PVB Discounted costs Vehicle and infrastructure costs Construction costs Maintenance and operating costs Existing passenger costs Financial PVC Social PVC Financial NPV Social NPV Financial BCR Social BCR

The various components of the appraisal procedure described in this paper were brought together in a single Excel-based spreadsheet, which is capable of producing both financial and social BCRs for planned new stations, once all relevant data have been input. It also provides flow level demand and revenue forecasts, and the financial and social net present value of the scheme. In its current form, this spreadsheet tool is only suitable for use in South-East Wales, because the spatial transferability beyond this area of the flow level models as currently calibrated has not been established. There are though no major barriers to extending the applicability of the tool to other areas, as long as data for model recalibration were made available. An advantage of the spreadsheet is that it is a simple matter to update particular elements of the appraisal procedure if improved evidence or techniques become available without affecting the remainder of the methodology.

£10,709,276 £7,750,533 £3,795,109 £10,709,276 £22,254,919 £852,057 £5,181,985 £1,052,789 £4,448,573 £7,086,831 £11,535,404 £3,622,445 £10,719,515 1.51 1.93

Table 4 Estimated financial breakeven demand levels for new local stations (trips per year). Mean fare Platform units

2 3 4 6 8 12 16

£1

£1.50

£2.00

£2.50 (Observed SE Wales mean)

£3.00

£3.50

130,313 149,179 168,045 205,778 243,510 318,974 394,439

86,875 99,453 112,030 137,185 162,340 212,650 262,959

65,157 74,590 84,023 102,889 121,755 159,487 197,219

52,125 59,672 67,218 82,311 97,404 127,590 157,775

43,438 49,726 56,015 68,593 81,170 106,325 131,480

37,232 42,623 48,013 58,794 69,574 91,136 112,697

50

S.P Blainey, J.M Preston / Transport Policy 25 (2013) 41–51

7. Discussion and future developments 7.1. Outcomes and advantages This paper has described the development of an integrated and comprehensive appraisal procedure for new local railway stations, which while not specific to any particular location is still capable of accounting for local factors affecting rail use. This appraisal procedure brings a number of potential benefits to both the rail industry and wider society. By removing the need to develop bespoke appraisal methodologies for individual schemes, it can make the production of business cases for new stations simpler and quicker, saving money for stakeholders and reducing the level of risk involved in promoting a new station. The use of a standardised methodology also increases comparability between schemes, making it easier to prioritise those new stations which have the best case for construction. Since rail privatisation in the mid-1990s the costs of new stations have escalated dramatically, meaning that stations have to demonstrate much greater revenue-earning potential than was previously the case to justify construction. By improving the quality of estimates of scheme costs and benefits, this appraisal procedure can help to ensure that the best return is obtained on the limited investment funds which are available 7.2. Future developments There is still potential for a number of refinements to the appraisal procedure to increase its reliability and transferability. Probably the most crucial is the calibration of the station choice and flow level demand models over a wider area which, while not posing any major theoretical problems, will require an extremely large quantity of data. The addition of a facility to account for the impact of atypical demand drivers, such as the proximity of a major sports facility or retail park, would also be advantageous. While the issues of abstraction and station choice have been considered in this paper, they are not yet fully incorporated in the modelling process, and this would increase the robustness of the forecasts produced. Some elements of the procedure also still need to be validated by applying them to actual schemes. The appraisal spreadsheet means that for most elements of the appraisal procedure user input is largely confined to data entry, but there is still potential for increasing the automation of the appraisal process by directly linking the spreadsheet to the GIS, which would reduce the potential for user error. There is also potential to develop the methodology for other public transport applications, such as the appraisal of bus routes and stations/ stops, light rapid transit, metros, and intermodal interchanges, allowing the merits of schemes to improve different modes to be directly compared. The format of the framework means that it is relatively straightforward to update particular sections as new evidence becomes available, and therefore as long as it is regularly updated to account for background changes in rail demand and demand drivers, there is no fundamental reason why it should not continue to provide a useful tool for rail planners and policy makers.

Acknowledgements Thanks are due to the NRTS team at the UK Department for Transport for providing access to the NRTS dataset, to Arriva Trains Wales for the provision of LENNON data, to Dr John Armstrong for developing Perl scripts for use in data processing and for advice on capacity analysis, and to James Pritchard for estimating the fuel costs of additional stops. Map data

&Crown Copyright/database right 2011; An Ordnance Survey/ EDINA supplied service. This paper contains Ordnance Survey data &Crown copyright and database right 2011. This work is based on data provided through EDINA UKBORDERS with the support of the ESRC and JISC and uses boundary material which is copyright of the Crown. Elements of the work reported here were funded variously by an EPSRC DTA studentship as part of Rail Research UK, by an EPSRC PhD-plus research fellowship, and as part of the EPSRC ‘Factor 20’ cross-disciplinary feasibility account.

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