The GCOST™ Model for Demand Estimation Dr Nigel G Harris The

The GCOST™ model for demand estimation Dr Nigel G Harris

The Railway Consultancy Ltd, 43A Palace Square, London. 217.

Abstract

The privatised railway in Britain has brought increased funds for investment, but less time for implementation. This brings the problem of how accurate demand forecasting can occur in the limited time available. The GCOST™ TM model is a recently-developed spreadsheet-based application which is both behaviourally-sound and reasonably accurate. It is based on conventional generalised cost (gc) theory, with the elements of gc being entered for a relatively limited number of traffic zones. Total traffic levels can be estimated from a gravity-based formulation if Census-type statistics (e.g. on population, and trip distribution by all modes) are not available. A logit statistical function is applied to the total gc of each mode of transport for each Origin:Destination pair to allocate traffic between the modes. A pair of models has even been used in a hierarchic application of logit modelling.

1 Introduction

In the 1960s and 1970s, transport planners developed ever-larger models of urban areas, using the new-found capability of the computer. Four-stage models, comprising trip generation, trip distribution, mode split and assignment elements were the norm in many urbanised areas in the Western world during a period of considerable investment in transport infrastructure. Although originally intended for highways, strategic and public transport studies were also carried out. Examples of these include an integrated look at the problems of Birmingham (Jones et al') and the Central London

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Rail Study, based on the LTS model (Department of Transport et al~). Unfortunately, these models also took large quantities of human time to assemble and maintain, and the 1980s saw this approach become less fashionable. Combined with this, the amount of infrastructure development necessitating this type of modelling fell.

The late 1980s saw the development of more strategic models e.g.

START, Edinburgh (Bates), and so on. This partly reflected the change of emphasis onto policy measures, such as car-parking restraint, environmental issues, and so on. Key techniques developed, including the application of logit models to allocate trips to modes on a probabilistic, rather than a deterministic, basis. Stated Preference methods developed to provide information from respondents on qualitative aspects of transport, and on new modes, such as light rail.

The late 1990s began to require transport models which could combine both the strategic elements of planning policies, and individual schemes. However, on the rail side, such models were generally not available. Only a few conurbations have retained the ability to consider the impacts of major projects across the main modes of transport (e.g. Glasgow (Drewette & Dewar*), London (Bach

& Harris^). Elsewhere, scheme modelling faded away, as the level of investment fell with recession (and, in Britain, the privatisation process).

Against this background, a need was felt for a model to appraise small-scale railway schemes. Over two hundred new stations have been opened in Britain in the last 20 years, but some of them have been supported by unnecessarily deep modelling. In addition, many more schemes are appraised than actually get built, so there is a considerable amount of planning activity in estimating the demand for new stations and services. We therefore aimed to design a model which is relatively simple to use, but reasonably sophisticated in its internal construction. Over a series of projects, the GCOST™ model has been developed.

2 Theoretical Principles

There are a number of key principles which underlie modern transport planning. Although these are explained in greater detail elsewhere (Harris'), attention should be drawn to: • the consideration of travel time spent in different ways;

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• the summation of the time and fare elements of Journeys into 'generalised cost'; • passengers' responses to changes in generalised cost through

elasticities; • the different responses of individuals to the same changes in travel

quality; • the definition of a study area, within which a trip matrix of trips between zoned Origins and Destinations can be used to proxy for

trip-making behaviour (although a number of 'external zones' may be used to represent the rest of the world)

Early transport planners assumed that all passengers take the easiest travel option i.e. that with the lowest generalised cost. However, for a variety of reasons, (including poor information, personal preferences, and inaccurate modelling), the modeller's suggested best option does not carry all the traffic. The logit model was developed to overcome this, allocating most (but not all)' passengers to the best option. Simplified, the logit equation may be represented by functions such as: P(a)= e—

where p (a) the probability of a passenger choosing mode a

Ga = the generalised cost of using mode a

3 Operation of the Model

In any modelling activity, data collection is reasonably onerous. Indeed, this is what justifies the continuation of the multi-modal model in urban areas where transport development activity is considerable. In those ongoing situations, ongoing data collection on a whole range of transport and socio-economic variables is required, often through a programme of surveys. For one-off schemes, however, data only needs to be collected on the key elements of journeys likely to be affected by the proposals. This needs to include the key modes (usually car, bus and rail). It also needs to make some attempt at differentiating passengers into groups. The classic peak: offpeak split not only reflects changes in road congestion and public transport service quality, but also reflects variations in the predominant journey purpose. As elasticity (the level of responsiveness to changes in

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generalised cost) is itself a function of journey purpose, this provides a supporting behavioural rationale for such a split.

The modelling approach therefore includes data collection on the key elements of travel, which include:

• access time; • waiting time; • in-vehicle time;

• interchanges (where appropriate); • egress time;

• public transport fares/parking charges

However, a key question relates to which passenger groups are likely to be affected. Some experience is required to double-guess those Origin:Destination flows where the scheme may have an impact. The key flows are clearly those from immediately around the proposed facility - for instance, housing estates near a new station. In Britain, research has shown that 62% of demand for a new suburban railway station emanates from households within 800m of the site (Preston^), so trips by any such population must be included, to a range of destinations. Note that all destinations do not have to be included, as this would be unnecessarily onerous; in addition, long-distance trip rates are remarkably low. As long as a reasonable selection of destinations is made (with many near the facility, and a few further away), its potential impacts can be investigated with some confidence.

If the catchment area is easily comprehended, estimating the total number of trips (i.e. the trip rate) is considerably more difficult. We have used two potential approaches. First, one can usually obtain data on resident population, and apply trip rates. However, these vary significantly by socioeconomic group. Even if one is more sophisticated by using a gravity model to distribute these trips amongst destinations on the basis of distance, control data is needed from counts or surveys, in order to calibrate the model.

In the London area, the GCOST™ model has been applied more directly, since information is available on the interaction of different boroughs within London through the Journey-to-Work data collected as part of the ten-yearly censuses.

In summary, the main elements of the modelling approach are:

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• generalised costs built up by element • trip end data taken from population figures • a logit model used to allocate the trips between the alternatives

Its structure is as summarised in Figure 1.

4 Case Study A.. The Gospel Oak - Barking

Line Development Study

London's rail network is heavily radial, and generally well-used, especially in the peaks. However, there are exceptions. The GCOST™ model was used for a study of the Gospel Oak - Barking local line for

Silverlink Trains, as part of a line development study. This line is a pocket of diesel operation within Greater London, and runs a half-hourly service in the outer Northern suburbs of London (see Figure 2). Unfortunately, its connections with other lines are rather poor (comprising only interchange at Barking, Blackhorse Road (to

London Underground services) and Gospel Oak (to its more successful sister orbital line the North London Line)). As the trains are elderly (1950s stock, being due for replacement later in 1998) and therefore unreliable, public perception of the service is poor (or non-existent) and demand levels rather below what might be expected.

15 on-line, and 15 off-line zones were used to analyse the impacts of service frequency changes, and potential off-line service extensions.

Unsurprisingly, given the high cost of leasing rolling stock in the UK, few options proved commercially worthwhile, but the analysis did indicate the relative value of different courses of action, which are now under discussion by the operator. It also highlighted the benefits of the existing service which, although acknowledged to be poor quality, competes well against very slow road times through London's congested streets, and a relative absence of parallel bus routes.

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Revenue

Trips

Generalised cost

Fares & charges

Interchanges

In-vehicle time

Waiting time

Access & egress

Figure 1. Structure of the GCOST Model

Sheffield Newcastle

Cambridge Birmingha m

Cardif f

Barking Southend

CENTRAL LONDON I

IC/3 3* Core Route Sister Route - North London Line p

Other Lines

Figure 2. Location map of the Gospel Oak - Barking Line

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5 Case Study B.. The Allerton Interchange Study

Liverpool, in North West England, has long been eclipsed by Manchester, its neighbouring city 60 km away. As Manchester Airport nears the capacity of its current phase of development, it therefore came as no surprise when authorities in Liverpool examined the case for development of their airport instead of a continuation of growth at

Manchester. Unfortunately, Liverpool airport has relatively poor surface access. It is 3 km from the nearest railway, at a point where a junction has led to the siting of two different stations on different lines

400m apart (see Figure 3). This is hardly conducive to providing access from a wider hinterland by public transport, especially when it is remembered that Liverpool's poor economic performance, and the. presence of the Ford factory at Halewood, leads to low levels of traffic congestion, and relatively high levels of car ownership

Merseytravel, the local authority transport body, therefore commissioned a study to investigate the potential of a major transport interchange at Allerton, where the two railway lines cross. This was also associated with development of land between the station and airport for industrial and commercial purposes. Because of the railway geography of Liverpool, rail usage rates are particularly low in parts of the city, and the proposed interchange was also conceived as a major potential park and ride site for interurban services.

In this case, the GCOST™ model framework was more ambitious, with a total of 74 zones being used. In fact, a network model using TRIPS software was coded up to provide the required information on total generalised cost, with this being dumped into spreadsheet format.

However, the significant nature of capital expenditure envisaged (£3m plus) meant that greater technical development was also necessary. In particular, there is a technical problem associated with the logit model, known as the Independence of Irrelevant Alternatives (the infamous 'red bus/blue bus' problem (Mayberry^)). Put simply, if a new alternative is introduced, the logit model will tend to abstract traffic to it from all the other modes equally. This is the case even if one alternative is merely painted differently, whereas another is totally different in character.

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Southport

Birkenhead Manchester

Garston Hunt's Cross

Airport London

Figure 3. Location Map of the Liverpool and Allerton Area

The technical 'fix' for this is the well-known introduction of a nest of logit models to produce a hierarchical approach (see Figure 4). In the Allerton study, traffic was split into that with a car available, and that without. Car-available traffic was allocated between car and the best public transport alternative, whereas non-car-available traffic was merely allocated between bus and rail. The output travel demand was summed across the two types of passenger to provide an overall indication of demand.

Public Private Transport Car

Rail Bus

Figure 4. Simple Hierarchical Logit Model Structure as Applied in the Allerton

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This study was further complicated by the numerous different operators using the rail network at this point - Virgin West Coast, Virgin Cross Country, Merseyrail, North West Regional Railways, Central Trains, Wales & West, and Regional Railways North East. The investment problems anticipated by the author (Harris^) did indeed materialise. The project only progressed after complicated arrangements were developed to prevent any one operator being financially disadvantaged by the development (a problem which did not occur in a nationalised railway, where headquarters made an overall decision as to what was best for the overall railway).

6 Conclusions

After many years of comprehensive but expensive multi-modal models, railway operators have a need for models which are quicker and cheaper to implement, whilst still being behaviourally and computationally sound. The GCOST™ model has therefore been developed using logit analysis attached to conventional generalised cost calculations, and has shown its adaptability to a range of railway service development problems.

References [1] Jones, D., May, T. & Wenban-Smith, A. (1990) "Integrated Transport Studies - Lessons from the Birmingham Study', Traff. Engng. & Ctrl.

33 pp. 572576. [2] Department of Transport, British Rail Network SouthEast, London Transport & London Underground, "Central London Rail Study' (1990). [3] Drewette, A. & Dewar, G., "Strathclyde Integrated Transport Model",

TRIPS Users' Group meeting, London (1996). [4] Bach, M. & Harris, N.G., "Transit Network Modelling - A New Approach", 4* international conference on Microcomputers in Transportation, American Society of Civil Engineers, Baltimore (1992).

[5] Harris, N. G., "Introduction", chapter 1 pp. 9-17 in Harris, N.G. & Godward, E.W. (eds.) "Planning Passenger Railways", TPC, Glossop (1992). [6] Preston, J.M., "New Stations and Services", chapter 8 pp. 128-147 in

Fowkes, A.S., & Nash, C.A., (eds) "Analysing Demand for Rail Travel", Avebury, Aldershot (1991). [7] Mayberry, J.P., "Structural Requirements for Abstract-Mode Models of Passenger Transportation" (1970).

[8] Harris, N.G., "Railway Investment and Privatisation", Trans. Econ. 21 (l)pp. 34-38.(1994).