A PASSENGER STATION CHOICE MODEL FOR THE BRITISH RAIL NETWORK. Simon J Adcock TCI Operational Research

Transcription

1 A PASSENGER STATION CHOICE MODEL FOR THE BRITISH RAIL NETWORK Simon J Adcock TCI Operational Research 1. INTRODUCTION Many demand models for the rail network in Britain tend to consider passenger demand in terms of station-to-station flows, rather than passengers' entire journeys. With increased levels of car ownership, passengers are prepared to travel considerable distances to stations, giving them a range of possible stations to choose from. Rail privatisation has meant that this may involve a choice of competing rail operators - with a consequently greater interest in understanding the way in which rail demand is shared between competing stations. This paper describes research to investigate the factors which determine a rail passenger's choice of station, and to provide estimates of the relative importance placed on these factors in choosing a station. Application of these results enables us to forecast abstraction of passengers between stations in response to service changes, as well as providing an approach to predict the effect on demand of demographic changes. The station choice model is based on survey data recording where passengers start and complete their overall journeys and their chosen rail leg. The data covers the whole of the mainline and London Underground networks and comprises several hundred thousand passenger records. Knowing the start and end locations enables us to identify alternative rail legs which the passenger could reasonably have chosen. The possible journeys are then assessed in terms of the journey time, service frequency, number of interchanges and the access and egress distances. Finaily, a logit model is constructed to reveal the relative importance placed on each journey characteristic. The inclusion of tube alternatives allows an assessment of how passengers respond to the qualitative differences between rail and London Underground travel. 2. CURRENT RAIL DEMAND FORECASTING METHODS The established methodology for comparing the trains a passenger may choose for a given rail trip uses the concept of a 'generalised journey time' (GJT). This is derived as the sum of the actual journey time, a penalty for the number of interchanges involved and a frequency component to account for the difference between the departure time and the passenger's preferred departure time. Weighting the generalised journey times with the profile of passenger demand throughout the day gives an average generalised journey time for a raft trip. Changes in the timetable will affect the generalised journey time. The expected change in demand for the raft trip is calculated using the following formula: 141

2 Forecast demand New _GJT 1 elasticity of demand Current demand = Current GJTJ This method has been calibrated over several years using observed changes in demand arising from major service changes and has proved a reliable approach. It forms the basis of the computer system MOIRA which forecasts the demand and revenue implications of postulated changes to passenger timetables. MOIRA has been developed and is maintained by TCI Operational Research (formerly the British Rail Operational Research Unit) and is used as a planning tool by the majority of the train operating companies. An approximation in this model is that demand is treated as if it-were fixed at the station, so MOIRA will never predict that demand at one station will be affected by service changes at another - whereas some passengers will switch to another station if they can take advantage of an improved service. One aim of our project is to produce a model of station choice which can be readily implemented in MOIRA. If we extend our concept of the (rail) generalised journey time to include penalties for the access and egress legs, we can use the existing demand elasticities in the formula: Forecast Current demanddemand = I 1+ Change_cu_rr~ntin overall_g jtutil ity jlelasticity of demand In the case of passengers who do not switch stations the change in overall utility is dependent simply upon the change in generalised journey time, so the demand forecast for this group of passengers is the same as with the previous method. For those passengers who do switch station the change in overall utility comprises the change in generalised journey time plus any changes in the access and egress penalties. 3. PRELIMINARY STUDIES 3.1 Analysis of Data Sources The first stage of this research was an extensive analysis of the survey data which we hoped to use to build the station choice model. We compiled a data set comprising over 230,000 records, each including the start and end postcodes of the passenger's journey and the origin and destination stations of the rail leg which was made. The data was collected from a variety of surveys providing coverage of trips made on the rail and London Underground networks. Our aim was to check that the data was of sufficient quality for the modelling process to be feasible, and also to identify characteristics of passengers which we might wish to include in the model. During this stage we compiled tables of stations popular with railheaders (those passengers choosing to access a station other than their nearest), the distances

3 passengers were prepared to travel to railhead and the stations from which demand had been abstracted. We also identified major instances of a pair of stations competing over a common catchment area. In the majority of these cases the area of strong competition falls within 15kin of both stations. 3.2 Literature Search We carried out a literature search to identify: Case studies where changes in available services have given rise to railheading Characteristics of passengers which may influence the propensity to railhead The distance passengers are prepared to access stations and the distribution of these distances The most rewarding sources proved to be stated preference surveys carried out to investigate specific proposals to develop stations. These are generally tailored to the local circumstances in each case, but nevertheless reveal the key factors which should form the basis of a station access model. The following factors seem to be of particular importance in passenger choice: The components of generalised journey time (actual journey time, -interchange and frequency penalties) Fare Access and egress distances Ease of car parking Ease ofroadaccess Levels of car ownership (and multiple car ownership) The purpose of the passenger'sjoumey Of these factors the first three could be readily included in the station choice model (straight line distances were used for access and egress). Ease of car parking and road access present greater problems - simple measures such as the size of the station car park are clearly not adequate since local circumstances such as a good alternative car park will be important. Our preferred approach would be a valuation of the car park by the group of passengers to whom it is important (an analysis of a limited number of stations showed a good correlation between highly valued car parks and stations with high proportions of railheaders) though such a survey covering all the stations in the country is probably impractical. As these factors are features of individual stations we propose that any implementation of our station choice model should include the calibration of a 'station attractiveness' constant to be added to the passenger's utility to take into account these factors (and other station facilities) which are not included in the model. The more cars a passenger's household has available the more likely he is to railhead. The biggest increase occurs between households with one car and those with two. This probably reflects the greater affluence of multiple car households, but also a second car parked at the station still leaves a car available for use. This factor cannot be incorporated into MOIRA in its present form (by excluding it from the station 143

4 model we implicitly assume that patterns of car ownership are roughly similar across the country). Passengers with different purposes for travel have significantly different propensities to railhead. Journey purposes are known to be highly correlated with different ticket types, so we allow the model to treat these separately. We summarise the features of different ticket types below: Full price tickets - For long distance trips, these include a high proportion of business travellers who are known to have a high propensity to railhead. For shorter distances, reduced tickets are often unavailable or heavily restricted, so we expect more of a mixture of journey types and a correspondingly lower level of railheading. In addition, the benefits of railheading (in terms of savings in generalised journey time) are generally less for shorter trips, so we would not expect high levels of railheading Reduced price tickets - a high proportion of these are likely to be leisure trips, and as such will be a mix of passengers with a high propensity to rallhead (for long distance visits and holidays) and those with a lower propensity to railhead (for shorter journeys) Season Tickets - these are generally held by commuters who have very low levels of railheading, especially for short distances. This is probably because choice of an appropriate station is likely to be a contributory factor when the commuter chooses where to live. In addition the commuter might be unable to have exclusive use of the household car each day, whereas an infrequent traveller could view it as an option. Looking at case studies, we found that stations (even those with a high proportion of railheaders) had a 'core' catchment area with a radius of around 15kin accounting for 90% or more of passengers. The remaining passengers travelling great distances were generally taking advantage of local factors (such as good access to a fast motorway or a cheap coach link) which are not dealt with in the station model. We thus decided to concentrate our modelling on passengers' choices within the core catchment areas. 4. THE STATION CHOICE MODEL In the light of the preliminary results described above, we formulated a model based on the following factors: Generalisedjourney time Fare Access and egress distances Mileage travelled on London Underground Whether the nearest station to the passengers home is used (for season ticket holders) For each rail trip recorded in the survey data, we generated up to t0 alternative rail legs which the passenger might have considered, through identifying stations in the neighbourhood of the passengers ultimate start and end locations. Large stations (those attracting most ticket revenue) were allowed a larger catchment area (up to

5 35kin) than smaller ones. We postulated that the passengers choice may be modelled using a simple linear utility function of the form: utility(tripl) = a I * Generalised journey time + a 2 * Fare + a 3 * Access and egress distances + a 4 * Mileage travelled on London Underground + a 5 * flag indicating whether the nearest station to the passengers home is used For the fare and access and egress distance factors, separate values for the linear parameters (ai) are allowed for flail price tickets, reduced price tickets and season tickets, and for different journey time bands (0 to 20 minutes, 20 to 50 minutes, and over 50 minutes). The Alogit program (Hague Consulting) was used to fit a logit model to the data. The logit probability that a given trip alternative 1 is chosen is given by: eutility for alternative 1 eutility for alternative i i The huge amount of data contributing to the model resulted in a reasonably high degree of statistical significance for the parameter estimates. The ratios of the parameter estimates show us how passengers are prepared to trade off the various factors when choosing a rail trip alternative. In general, the results are consistent with previous railway industry research: * Full price ticket holders demonstrate the highest values of time, followed by season ticket holders. The values of time increase as the joumey time increases, implying that greater values of time are associated with greater distance trips Passengers are prepared to travel greater access distances to save the same amount of time for joumeys involving longer rail legs e Passengers are prepared to travel greater distances when travelling from their home to a station rather than from their destination station to the end of their overall journey. This may be because a car is available or the passenger may have better knowledge of public transport at the 'home' end Passengers are much more averse to spending time on the London Underground than on a train. They even prefer to accept extra access distance rather than the equivalent extra tube mileage. Season ticket holders place a very high value on using the station closest to their home. In choosing where to live, a commuter will consider many factors affecting the quality of his life, of which a nearby station with a convenient service to his work is just one.

6 5. FUTURE DEVELOPMENT We are now working to implement the station choice model in the MOIRA system - calibration against revenue data will enable us to estimate 'attractiveness' parameters for each station, to take into account factors such as availability of car parking or good road access. As access to stations is by road, an appropriate description of the road network leading to the station is likely to provide a more accurate model than the simple straight line distances we have used to date. Use of public transport to access the station is a more realistic option for some passengers than for others. TCI Operational Research is currently doing research to investigate the impact of current bus links to stations, to develop methods for evaluating the potential for new links and to identify promising locations. This work may lead to the development of a station choice model incorporating multi-modal access links.