Assessing the Potential Performance of New Local Railway Stations
S P Blainey, J M Preston, University of Southampton, UK
An integrated methodology which combines a station site search procedure with trip generation and distribution models to forecast usage. Financial and social appraisal methods are used to identify the stations with the best case for construction.
This paper reports on work undertaken in the UK as part of a PhD project funded by the EPSRC through Rail Research UK. The aim of this project was to develop a range of methods for forecasting the demand for new local railway stations with particular emphasis on the use of GIS. Existing methods for assessing the financial case for constructing new local railway stations have often been found wanting, with the forecasts produced proving to be inaccurate. This means that promising proposals for new stations may never be implemented because it is not possible to make a financial case for construction. Determining the social case for new stations is even more problematic as it requires data on modal transfers and their impacts, on accident and congestion reduction, and on environmental betterment, as well as generalised cost data to determine user benefits.
In an attempt to overcome these problems an integrated methodology for investigating the potential for new local railway stations within a given area has been developed and is described in this paper. The first stage in this methodology is to use a GIS-based procedure to identify potential station sites within the area being studied, taking account of the location of existing stations and of population and employment centres. Trip end demand models calibrated using geographically weighted regression are then used to forecast the total number of passenger trips generated at each of these sites based on a range of demand- and supply-side variables. These trips are distributed between destination stations using a probability-based model based on generalised journey times, intermodal competition and the presence of intervening opportunities. The results from these demand models are then geocoded, allowing the predicted travel patterns to be mapped. The extent of demand abstraction from neighbouring stations is then assessed, along with the expected time taken for demand levels at the new station to stabilise. Construction costs for the new stations are estimated based on data from recently-opened stations. These are used together with estimates of the net revenue generated at the new station to determine the sites where demand is most likely to justify the cost of construction. The sites can then be ranked to indicate which should be given priority for further development. The results of this financial appraisal can then be compared with those from a simplified social cost-benefit analysis. This procedure should be able to improve the case for constructing new local stations by identifying sites where there is both a financial and social benefit, and by quantifying the trade-offs involved at sites where there is a social case but not a financial case (and vice versa).
Association for European Transport