Rail Demand Forecasting Estimation Final Report Final Draft, Redacted
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Rail Demand Forecasting Estimation Final Report Final Draft, Redacted Prepared for Department for Transport November 2016 Table of Contents 1 INTRODUCTION ..................................................................................................... 4 1.1 Purpose of Rail Demand Forecasting Study ............................................................ 4 1.2 Project Approach ................................................................................................... 11 1.3 Phase 2 Modelling Approach ................................................................................. 12 2 NTS MODELLING ................................................................................................. 14 2.1 NTS Models and Results ....................................................................................... 14 2.2 Socio-economic characteristics of rail users .......................................................... 20 2.3 The impact of network effects ................................................................................ 27 2.4 Time trend effects .................................................................................................. 28 2.5 Outputs to models derived from ticket sales (RUDD models) ................................ 29 3 TICKET SALES ANALYSIS ................................................................................... 31 3.1 Introduction ........................................................................................................... 31 3.2 Scope .................................................................................................................... 32 3.3 Enhancing Rail Ticket Sales Models with NTS Trip Rate Evidence ....................... 35 3.4 General Principles of Our Modelling ...................................................................... 44 4 ESTIMATED TICKET SALES MODELS ................................................................ 52 4.1 Variables Considered ............................................................................................ 52 4.2 Long Distance London Non-Seasons .................................................................... 61 4.3 Long Distance Non-London Non-Seasons............................................................. 64 4.4 Network Area to and from London Non-Seasons................................................... 67 4.5 Network Area to London Seasons ......................................................................... 71 4.6 Non London Short, Non-Seasons .......................................................................... 75 4.7 Non London Seasons ............................................................................................ 78 5 BACKCASTING..................................................................................................... 83 5.1 Approach to Backcasting ....................................................................................... 84 5.2 Rest of Country to London (Ordinary tickets) ......................................................... 86 5.3 Network Area to/from London (Ordinary tickets) .................................................... 88 5.4 Network Area to London (Season tickets).............................................................. 91 5.5 Non-London (Season tickets) ................................................................................ 93 5.6 Non-London short distance (Ordinary tickets) ........................................................ 95 5.7 Non-London long distance (Ordinary tickets) ......................................................... 98 5.8 Summary of results ............................................................................................. 100 2 Rail Demand Forecasting Estimation 6 CONCLUSIONS .................................................................................................. 103 6.1 Novel Analysis ..................................................................................................... 103 6.2 Application for forecasting purposes .................................................................... 106 6.3 Recommended elasticities and recommendations for forecasting ....................... 107 6.4 Recommendations for Data Collection ................................................................ 116 6.5 Recommendations for Implementing the Forecasting Framework ....................... 117 6.6 Recommendations for Further Research ............................................................. 118 ANNEX A DETAILED SPECIFICATION AND RESULTS OF THE NTS RAIL FREQUENCY MODELS ............................................................................................................. 121 ANNEX B RUDD DATA PROCESSING ........................................................................... 135 ANNEX C TICKET TYPE AND JOURNEY PURPOSE SPLITS ....................................... 148 ANNEX D QUALITY ASSURANCE SUMMARY ............................................................... 157 Final Report 3 1 Introduction and Executive Summary 1.1 Executive Summary 1.1.1 Background and Aims This study is concerned with quantifying how variables outside of the control of the rail industry, commonly termed external factors, impact upon the demand for rail travel. These variables tend to be key drivers of rail demand, with employment and income recognised as being particularly important drivers of demand in the recommendations of the railway industry’s Passenger Demand Forecasting Handbook (PDFH). The background to this project, and the reasons why further research on this crucial subject is clearly warranted, is that there is broad acceptance amongst key stakeholders and practitioners that: Rail growth figures derived from PDFH and WebTAG recommendations have not generally been performing well in explaining recent growth in rail demand (see charts below); Whilst the current forecasting framework covers the key demand drivers of income and employment there are other important influential variables which are currently not covered in PDFH; Recent econometric studies, which had aimed to provide updated values for existing PDFH parameters and insights into unaccounted influences on rail demand, have not provided entirely convincing findings; PDFH specifically under-forecasts non-London demand, particularly for commuting into core cities, a factor that has been recognised for some years. Given this background, the objective of the study was to improve the performance of elasticity based rail demand forecasting, to be achieved both by updated evidence on existing parameters and, within the constraints of budget and data availability, through enhancements to the existing PDFH forecasting framework. We should point out that this is not the first occurrence of PDFH performing poorly in explaining rail demand. PDFH v3, with its combination of positive GDP elasticities which were unable to offset outdated negative time trends, could not explain the strong and sustained demand growth in the years after privatisation. The result was that PDFH v4 in 4 Rail Demand Forecasting Estimation 2002, inspired by the investigations of the industry funded National Passenger Demand Forecasting Framework Study in 1999, provided both revised GDP elasticities and a significantly enhanced framework that replaced time trends with a range of variables dealing with inter-modal competition. 1.1.2 General Approach The same general approach has been followed here as with the PDFH v4 update: the provision of revised elasticities for existing parameters along with enhancements to the forecasting framework which here largely take the form of a broader range of socio- economic factors with an emphasis upon those for which there are forecasts. From the outset, our intention was to use National Travel Survey (NTS) data, which we believe to be a very much under-exploited resource as far as understanding rail demand is concerned, not so much as a free-standing forecasting tool, which it could be, but rather as a means of providing insights into the effects of a range of socio-economic factors on rail demand that are not addressed in current models but which, critically, could be used to enhance those models. Our argument is that whilst conventional rail demand models containing, say, GVA, employment, overall population and car ownership, could in principle be enhanced by adding a range of socio-economic variables, past experience, as evidenced in our literature review, shows that the free estimation of such effects is generally unsuccessful. Our approach was therefore to conduct NTS analysis to provide parameters relating to various socio-economic and demographic factors which can then be imported into conventional rail demand models, based on ticket sales data, to serve as constraints on key parameters for which free estimation would not provide credible results. The study was split into two phases. Phase 1, conducted over summer 2015, was largely exploratory and consisted of a number of work-streams: It conducted what can be regarded as the most extensive review of evidence relating to exogenous drivers of rail demand in Great Britain along with a discussion of the evolution of PDFH’s treatment of these key demand drivers. A data capability review covering the DfT’s Rail Usage and Demand Drivers Dataset (RUDD), which covers 20,000 flows over 20 years, to determine its fitness for purpose and to identify shortcomings and gaps that might be addressed. A review of NTS data, covering its content,