The capture of personal travel data through smart card use

Transcription

1 USE OF PUBLIC TRANSPORT SMART CARD DATA FOR UNDERSTANDING TRAVEL BEHAVIOUR Mousumi Bagchi Steer Davies Gleave Peter White Professor of Public Transport Systems, University of Westminster 1.1. INTRODUCTION What are smart cards? Smart card based fare payment systems are being introduced all around the world. Hong Kong has the Octopus card, Singapore has the EZ-Link card and London has the Oyster card. In the UK, since the 1990s, a series of busbased smart card schemes have been introduced in places such as Bradford, Chester, Edinburgh, Nottingham and Southampton. Smart cards are similar in look and size to credit cards and contain a computer chip that is capable of storing and processing information. They can be used in place of traditional fare media such as magnetic stripe cards and paper tickets. On smart cards can be placed electronically a range of travel products such as travel passes and other travel tickets, such as single bus tickets. They can also have what is known as stored value, where a monetary amount is credited to the card and then debited as and when trips are made using the card. They can also be multi-functional, acting as a loyalty card and being used as an identification card to signify eligibility to access particular services such as borrowing library books The capture of personal travel data through smart card use As a by-product of smart card use, large volumes of personal travel data are automatically captured, because each time a smart card is used, either to make a trip or to purchase/renew a travel product to be placed on the card, details of the trip and/or purchase made with that card are recorded, and can be linked to that card and/or the individual if the card is registered to that individual. This new travel data source will supply transport service providers with continuous trip data covering longer time periods than it is possible to obtain using traditional sample surveys. Crucially, these and other generic attributes of smart card data allow the deficiencies of existing travel data sources to be overcome in relation to the analysis of certain travel behaviours. Two such examples are being able to examine the level of usage of transport services by particular groups of users (inferring trip rates) and examining levels of busto-bus interchange among bus users. A brief explanation of each is now given followed by a description of the structure of the remainder of the paper. Transport service providers have traditionally used travel diary surveys to examine trip rates, where individuals record details of the trips they have made over a given period of time. However, it is difficult to examine trip rates

2 over periods of time longer than 7 days because of the high cost of such surveys and because of the problems of survey participants recalling details of their trips. Where bus-to-bus interchange is concerned, using existing bus ticket machine data, service providers have not been able examine the extent to which bus users interchange from bus-to-bus because they have not thus far been able to link ticket machine boardings data to an individual ticket or the individual. In both cases, being able to obtain this information would help in bus service and product planning. Given these advantages, it is also widely perceived that smart card data can replace sample survey data. This paper will show however that sample surveys will play a complementary role rather than being replaced by smart card data, and that this will also depend on the particular analysis being undertaken with the smart card data Structure of paper This paper will firstly look at the nature of smart card data. The focus in this paper is on smart card data from bus systems, but the information presented can also be seen to have relevance for public transport smart card data in general. Analyses of trip rates and bus-to-bus interchange will then be presented based on sample smart card data from a pilot smart card scheme undertaken by the Merseyside Passenger Transport Executive Merseytravel in the late 1990s on buses operated by Southport and District Bus Company in Southport, Merseyside, and from a scheme introduced commercially by operator First in Bradford in Bradford, West Yorkshire, covering a wider range of users. Following this, discussion will focus on what these analyses tell us about smart card data, its processing and analysis, and the role of smart card data as a new data source for travel behaviour analysis THE NATURE OF SMART CARD DATA CAPTURE OF PERSONAL TRAVEL DATA The diagram in Figure 1 summarises the types of information that can be collected from smart card use, followed by an explanation of each information grouping. It should be noted that the actual collection of the variables of information indicated in Figure 1 would depend on how the smart card system and its databases are set up by the scheme implementers.

3 Attributes of data Figure 1. Public transport smart card data attributes Overall collection of data Collected from smart card use? Spatial information Temporal information Structural information Product information Card number Cardholder name and address Cardholder age Cardholder gender Trip origin (stage boarded) Trip destination (stage alighted) Time of trip (origin) Date of trip Trip purpose X Product purchase time and date Product purchase location Type and price of product Service number boarded Cardholder information Linking of data: As mentioned at the beginning of this paper, each time a smart card is used, details of the trip that is being made with that card will be recorded. Of central significance is that this trip data can be attributed to a given card, as each card possesses a unique serial number, and additionally, to the individual, if the cardholder s name and address details are known or the card is personalised with their photo. The automatic capture of trip information through card use will allow transport service providers to establish which of those cards have been used, which is something that is not possible with existing non-electronic travel tickets. Also, to date, transport service providers have remained unaware of who their customers are and when their travel tickets have been used. With smart cards they have the ability to do this to varying degrees. If the card is transferable, then trips made using the card can be linked to that card but not to a given individual, as different users trip information can be recorded by the use of that card. The Bradford card is transferable, whereas the Southport smart card was not transferable. As such, when discussing the results of the Bradford analysis later, the term card is primarily used rather than the term user or individual as with the Southport analysis. Volume and scope of the data: Transport service providers will have access to much larger volumes of data on their passengers than it is possible to obtain through existing data sources. However, this will depend on the on the take up of smart cards by those passengers and the way in which service providers develop the scheme and offer the smart card to the public. Service providers will not be able to obtain a 100% sample of trips made on their services because there will be a proportion of passengers who do not take up and use the smart cards and those who will continue to pay cash fares only.

4 In addition, the smart card system will not be able to capture information on the purpose for which a given trip is being made, as this is information that cannot be automatically detected, although time of day might be used as a proxy. Neither can travel data be obtained on non-users of particular transport services, something that sample surveys will continue to be undertaken to find out. Continuous information: Theoretically, all trips made using a particular card within the allowed area of use and on participating transport services, will be recorded. Continuity will be broken if an individual does not use the card to make a bus trip (e.g. forgets) or if they consciously alternate between paying cash fares and using the smart card. Consequently, these situations and others (e.g. card and/or system failure) will arise where trips made by an individual are not recorded. However, because the capture of data will be continuous, it will cover longer periods of time than data collected from traditional sample surveys, which are often undertaken to capture trip data by given individuals for a period of a day or a week at the most. Spatial information Each smart card based trip should have boarding and alighting information recorded for it (at varying levels). However, in the UK, it will be difficult to collect alighting information for smart card based bus trips until exit reading of smart cards takes place as passengers disembark from buses, or unless there is driver interaction to physically record the alighting stage for a persons smart card based trip by asking where they are travelling to at boarding. The technology to enable exit reading to happen exists, but has to be proven in field tests as being reliable in providing this information to the level required by service providers. In Britain, many buses have only one door, mixing entry and exit flows. This may make it difficult to obtain good quality exit reading as well as entry reading. Conversely, elsewhere in Europe, most buses have multiple doors, making it easier to position exit readers. Consequently, sample surveys to establish stage based origin and destination for samples of trips will still need to be undertaken to establish journey length, where this information is needed by local transport authorities for their concessionary fares reimbursement processes. However, for exact origins and destinations to be recorded (e.g., by postcode), direct passenger surveys will continue to be needed. As mentioned earlier, the analysis presented below relates to examples from bus networks. While the same broad principles apply to rail networks, it is worth pointing out there is usually much better scope for obtaining comprehensive data on entry and exit at stations, especially where all stations are gated as on the London Underground system. Temporal information The date and time of each trip will be recorded.

5 Structural information Details of the mode and service (e.g. bus number) will be recorded for each trip. Information will be available on all participating services in the scheme. Cardholder information Information can be captured at the card issue stage on name, address, age and gender information. This means that service providers will know who their smart card customers are. At this stage, the service provider is also afforded the opportunity to capture information on variables such as household car ownership. Difficulties will exist in obtaining updates of dynamic information such as cardholder address unless there is an incentive for the cardholder to inform the service provider of any change of address. An important consideration with regard to the capture of cardholder information and any data linked to the cardholder is privacy of information. Whilst it is not possible to cover this topic in detail in this paper, we can outline some of the legislative requirements of transport service providers in this respect. In the UK, each organisation having access to what are termed generically as personal data must seek registration with the Information Commissioner. The terms of the registration will depend on what types of data are collected and the stated purposes for which the data will be used. In addition, scheme owners must seek the consent of the cardholder at the card registration stage regarding how the personal data will be used. They must also seek to do everything they can to hold the data securely and not pass the data on to third parties (those not stated as part of the registration with the Information Commissioner). To put things in perspective however, transport smart card data are just one data source in a world where there are many personal data sources (e.g. credit card data). As such, privacy of information concerns have to be seen in the context of a variety of personal data sources that are now available. Purchase information Data will be collected on the type of ticket purchased with the smart card, the price of the ticket, and the place of purchase. Given the attributes of smart card data, the following sections present an analysis of smart card data undertaken to examine whether these attributes provide the benefits as stated at the beginning of the paper.

6 1.3. BRADFORD AND SOUTHPORT SMART CARD SCHEMES AND DATA PROCESSING METHODOLOGY Bradford scheme First in Bradford is one of the major bus operators in the West Yorkshire area of England, running services in and around Bradford, and into neighbouring districts such as Leeds. The company operates approximately 250 vehicles. First in Bradford (FiB) operate a smart card based fare payment scheme on their buses. The smart card is marketed as FirstCard. The FirstCard has functionality as stored value, travel pass and loyalty card. The uses of the different functions differ according to the type of ticket product being purchased for use with the smart card. For example, one day bus passes and period bus passes can be purchased and placed on the smart card using cash. As part of this transaction, the stored value element of the card would not need to be used. However, for other ticket types, such as single tickets, cash tendered for payment is credited first to the stored value component of the card and then debited again to record the ticket purchase (subsequent transactions for day pass use record only the use of the card for that journey) The Southport smart card trial In 1995, Merseytravel, the PTE for the Merseyside area in North West England, decided to trial smart cards for use by concessionary (elderly only) travellers on selected bus services operated by Southport and District Bus Company (an operating name of Merseyside Transport Limited (MTL) in the Southport area). MTL is now part of Arriva plc. Merseytravel offers free travel for the elderly on all bus, rail and ferry services in the designated PTE travel area (except for journeys starting between 0631 and 0929 Monday to Friday). As Merseytravel operate one of the larger concessionary travel schemes in the country, the main reasons for undertaking the smart card trial were to: 1) Assess if the data obtained from use of the smart cards could in some way replace existing surveys of bus passengers, as a large part of the PTE's workload involves undertaking surveys, for use in reimbursement and revenue allocation processes. 2) Increase the amount of 'total' data on journeys undertaken (above and beyond that collected via existing sample surveys). The card used for the trial was a MIFARE contactless memory card that was marketed as 'SWIFT CARD'. It had functionality as both a travel pass and photo ID card for concessionary travel entitlement. The trial took place over a period of 4 years, starting at the beginning of 1996 and finishing at the end of A qualitative study (focus group based) was undertaken by TTR (1996) early into the trial to analyse the ergonomic and operational aspects of the trial.

7 Data processing methodology Data for processing were extracted from the offices of Merseytravel and First in Bradford. Only smart card trip data were extracted. Names and address details of cardholders were not extracted. In order to make the data analysis manageable, following data validations, a randomly drawn sample of roughly 10% of cards issued was made from both samples and all trip records for those corresponding cards were extracted. For Southport this came to 480 cards and 90,062 journey records. For Bradford, the randomly drawn sample consisted of 3028 cards, with 396,331 journey and monetary transaction records. Checks were also made to see if the samples were of sufficient size for the analyses being undertaken. It may be assumed that these samples are representative of the groups holding smart cards, although not necessarily of the population (for example, in Southport in the sense of all pensioners in the area eligible for free off-peak travel). All analyses were undertaken on these randomly drawn samples DATA ANALYSIS RESULTS Trip definition When analysing smart card data, we can start with the assumption that all trip records in a smart card dataset represent individual trips by default (i.e. rides on buses or entry/exit in a rail system). However, if we adopt the UK National Travel Survey (NTS) definition of a trip, meaning a one way journey from one activity to another, which may consist of journey stages defined as a change of mode during that one way journey, or same mode transfers, then it can be seen that the bus operators trips are actually boardings which need to be grouped in order to derive actual trips. The smart card system will not be able to identify trip stages or identify same mode transfers, unless in this last example, a transfer ticket has been purchased on the smart card where change of vehicle within a certain time period is defined as a transfer trip and is recorded as such. Processing of smart card data requires a decision to be made on whether to apply a set of processing rules to group records to derive the actual number of trips according to the NTS definition or to leave each record to represent a boarding. Grouping rules will also differ according to the type of trip being defined. If we are considering separate boardings, then what is being looked at are unlinked trips. Where boardings are grouped, for example to look at bus-to-bus interchange, then what are being looked at are linked trips. In this section, to infer trip rates, unlinked trips are being analysed. The cards in the Southport and Bradford samples had each been used for different lengths of time by their users. For example, some may have been used on buses for a period 1 year, some for 18 months, some for a few days and so forth. Consequently, it was decided to sample trips over comparable short time periods, (similar time periods to those covered by travel diary surveys). Initially a 7 day period was first sampled. Thereafter, 14, 21, 28 and

8 35 consecutive day periods (start date the same for all sampled time periods) were sampled. No bank holidays were found to occur during the selected periods. Trip rates were derived for each of the time periods indicated above. In the following sub-section, these are presented and discussed. For reasons of comparison, all analyses of the Bradford data are restricted to trips made by concessionary elderly travellers Analysis 1 - Concessionary elderly smart card use trip rates Taking the Bradford sample first, it is probable that concessionary travellers are underrepresented in the sample because the majority of the elderly opt to pay cash fares as and when they travel, as a 20 pence off peak flat fare operates in the West Yorkshire area. There is also little incentive to use any of the other products available on the smart card, for example, an elderly person would need to make at least 9 journeys using an off peak day bus pass ticket to gain a net saving from having purchased it. In addition, presently, an elderly person has to show their West Yorkshire concession eligibility ID card to the bus driver in order to pay the discounted fare. Ergonomically, it may be difficult to for a less dextrous elderly person to show their ID card to the driver in addition to placing their smart card over the smart card reader, and handing over to the driver some cash (if they wished to recharge their stored value). This may further discourage the elderly from taking up and using the smart card. It is expected that the more frequent elderly travellers, who travel within a small area in the Bradford area, use the smart card. Approximately 8% of smart card ticket purchases recorded in Bradford sample B were OAP single bus ticket purchases. This accounts for 3% of trips in the sample. Trip rates were inferred for this group and are shown in table 1. Time period Cards Trips Trip rate per card over period St. Dev 95% Confidence interval Trip rate per card per day Min / Max no. of trips Std. error 7 days to / days to / days to / days to / days to / Table 1.Trip rates for cards used for concessionary elderly travel - Bradford The table shows that over a period of 7 days, a small number of cards (24) were used to undertake 122 trips, giving a 7 day trip rate of There is a drop in the trip rate per card per day figure from the 7 day to the 14 day period. This is because from the 8-14 day period onwards, people with a wider variety of frequencies of use will have used the cards and are thus picked up. Trip rates inferred from the Southport are shown in Table 2. A statistically significant difference was found for trip rates per person per day between the

9 7 day period and the 14 day period for the Southport elderly travellers (p<0.01). Again, what can be observed are high frequency users being picked up in the 7 day period, and those with different frequencies of use being picked up in days 8-14 onwards. In addition, several of the 179 individuals may not have travelled in days Stability in the trip rate per person per day can be observed for the 21 day period onwards. This is something that would be difficult to pick up using travel diaries that rarely cover periods of longer than 7 days. Time period Indivs Trips Trip rate per person over period St. Dev 95% Confidence interval Trip rate per person per day Min / Max no. of trips Std. Error 7 days to / days to / days to / days to days to / / Table 2. Trip rates for concessionary elderly travel pass users - Southport The trip rates for the elderly concessionary travellers in Southport appear lower than for those in Bradford, although fairly typical of elderly free concession travel nationally (Bradford being atypical due to only highfrequency users finding the smartcard worthwhile). It is also known that the Southport smart card trial participants could use their existing conventional free travel pass in addition to their trial smart card, and this may have had the effect of reducing the number of trips undertaken using the smart card. Given the uncertainty over whether the results observed from smart card data are real, and perhaps in the absence of historical information for comparative purposes, it will be necessary for transport service providers to undertake sample surveys. The surveys would be necessary for two purposes: 1) To check results, identifying trips which have not been recorded in the smart card system, either through the users not using their card, or due to card or system error 2) To gain information on other variables that could help explain the results observed (e.g. car ownership and trip purpose). Factors could then be developed and applied to the data to correct it where differences were found between the smart card data and sample survey data. These surveys should ideally be undertaken in parallel with results being obtained from smart card data and on a cohort of smart card users.

10 Analysis 2 - Bus-to-bus interchange (Linked trips) Linked trips (also sometimes known as transfer trips) were identified and estimated from the two samples. A linked trip for the purposes of this paper is defined as two or more boardings on different bus services by an individual on a given day within a certain time limit, as part of a one way journey from one activity to another. In traditional rider surveys, linked trips are often identified either by asking people to state which trip is a linked trip if they are recording their journeys as part of a diary survey, or, in on- board bus surveys, often the question Have you interchanged with another bus and/or other mode of transport as part of this journey is asked. People are then additionally asked to give details of the time that transfer or interchange took place. Identifying such trips is important for network planning to ensure that bus services are meeting the needs of the user base - for example, if a high percentage of people are making a particular linked trip, then a through bus service could be introduced. Again, time and budget constraints mean that service providers find it difficult to collect information on a regular basis. Consequently, this section looks at how smart card data can be used to infer linked trips Methodology applied - rules based processing For both samples, linked trips were identified for each individual card for each day that card had been used. The time lapsed between successive boardings was examined in each case. This was applied over the entire set of trips undertaken by each group, rather than sampling specific time periods. Four variables were used for linked trip identification. These were card number, date of travel, time of travel, and bus service number used. The default transfer time limit was set at 30 minutes. The setting of this time limit can be seen as arbitrary. However, it was felt that the elderly concessionary group might undertake activities over shorter time periods than other user groups. This highlights some problems of logic in rules based processing. For example, routine trips to the post office to collect pension payments might be the only reason a journey has been made and could typically be undertaken within a few minutes after disembarking from a bus. Consequently, longer transfer time limits for this group may pick up return trips as well as transfer trips, whereas a short transfer time limit would pick up transfer trips and a small number of genuine return trips that happened to satisfy the transfer trip criteria. This is an issue illustrated in the description of the 'Tripperpas' scheme by Cheung (2001) where it was noted that the smart card scheme owners acknowledged that a one hour transfer time limit on card use would allow return trips to be made. One way of addressing this analysis issue is to examine the direction of travel as indicated by the fare stage information cross-checked with operator fare stage tables. An attempt was made to look at this and incorporate it in analyses. However, this requires information on both the boarding and

11 alighting stages of a trip, which is only recorded for single ticket journeys. Additionally, fare stage tables and routes change frequently, which means it is difficult to obtain accurate information over the entire length of time covered by a smart card dataset. Given these drawbacks, it was decided not to proceed with this aspect of the analysis. What this means is that when identifying transfer trips, transfer time limits need to be set with reference to the user group being examined. What would help to make the setting of the time limit less arbitrary is information on the purpose for which a journey was undertaken, and the length of time the journey took (if exit reading took place). We could then derive activity length, which would help to make the setting of the time limit more accurate. Unfortunately, with smart card data we have neither piece of information Results Group Dataset Boardings (n) Inferred trips (Linked or separate boardings (n)) Linked trips (n) Linked trips (%) Boardings to inferred trips ratio Elderly concessionary Bradford % 1.08 Elderly concessionary Southport % 1.04 Table 3. Summary of linked trip results Taking Bradford first, after applying the linked trip rules indicated above, 7504 actual trips were derived (i.e. linked trips and remaining unlinked trips). From this, 568 linked trips were identified consisting of two or more consecutive boardings in 30 minutes or under of each other on different services. Therefore, linked trips constitute 7.6% of actual trips undertaken by elderly concessionary travellers over the course of the Bradford scheme being in operation to date. Of these linked trips, 98% were two boardings linked trips, with the remainder being three and four boardings linked trips. We can also compare the linked trip figures inferred for the elderly concessionary group from the Southport data to the Bradford results. The figures suggest that elderly concessionary travellers in Southport make a smaller proportion of linked trips than elderly concessionary travellers in Bradford. This may be due to a number of reasons: The Southport concessionary travellers were allowed to use their existing free travel pass that allowed travel on buses and trains, at the same time as being allowed to use their trial smart card. Consequently, some travellers may have opted to use their existing free pass, deflating the number of linked trips recorded by the smart card system.

12 Since only users with a high frequency of bus boardings were likely to find the smartcard attractive in Bradford, it is likely that a greater element of bus-to-bus interchange was observed.. Local transport factor - The Southport area may have a high degree of bus/rail interchange - should this be the case then the bus-only smart card system would not pick up the rail part of the interchange journey and therefore fewer interchange trips would be estimated. Also, in general, pensioners are less likely to make trips involving bus-to-bus interchange, since most journeys are for local shopping, etc. For bus users as a whole, an estimate may be made from NTS data (the ratio of boardings to stages) suggesting an average ratio of This is likely to be affected by more complex journeys made by other groups, notably for work and education DISCUSSION AND CONCLUSIONS - WHAT DO THE FINDINGS SUGGEST ABOUT SMART CARD DATA? The review of smart card attributes and the results of the sample smart card data analysis suggest a number of things about the efficacy of the data for travel behaviour analysis. Whilst the Southport and Bradford data may not be indicative of smart card data in general, there are a number of important issues the practical experience of having analysed the data allows to be highlighted These are now presented below The nature of smart card data Smart card systems will generate larger volumes of personal travel data for transport service providers, and this information can be linked to the individual smart card and/or individual card user. However, this must be separated from the issue of smart card systems being able to provide complete trip information. As mentioned, in the UK, bus trip alighting stage will only be recorded for all trips if smart card users are required to have their cards read on exit from the vehicle. In the absence of exit reading, destination information may be recorded at the outset of the trip for some trips if there is bus driver interaction with the smart card user, but this will not allow the alighting time of the trip to be recorded. For example, if distance-based fares operate in a particular area, and a person boards a bus with a smart card containing stored value and wishes to pay for their bus trip using that stored value, then the driver will have to ask the passenger where they wish to travel. Based on this information, they will charge the passenger accordingly and the destination stage will be recorded. In this way, the transport service provider can mitigate the loss of destination information but this will extend boarding times offsetting one of the main benefits of smart cards - and may not be possible at all if a flat fare operates.

13 The analyses showed that the lack of trip purpose/activity and destination information was a constraint in the identification and estimation of bus-to-bus interchange. It was also not possible to examine the amount of time taken to complete a particular trip, as there was no recording of the trip alighting time. Consequently, the setting of the transfer or interchange time limit became arbitrary in the absence of both activity and journey time information. In general therefore, the gains in information from smart card data may be offset by the lack of information in other respects Processing of smart card data and interpretation of results Rules based processing: The processing of smart card data required a decision to be made on the sampling of the data, and was found to be dominated by rules-based processing of the data. Definitions and concepts: The process of analysis showed that with smart card data, the operator defines what they mean by a trip, which can be linked or unlinked. With smart card data, what we initially have are disaggregate boardings data that have to be grouped to derive linked trips. The latter definition will result in the actual number of trips being less than the number of boardings recorded and may have implications for the way in which decisions are made about trip level reimbursement and revenue apportionment mechanisms. Effect of scheme and fare regime factors: The results observed from analysing smart card data must be seen in the context of the parameters of the smart card scheme from which the data are taken, and the transport environment in which the scheme operates (e.g. fare regime of a PTE). Both analyses concerned trips made by elderly concessionary travellers, but very different results were observed. In the Southport example, smart card users were allowed to simultaneously use their existing free travel pass. As a result, this could have deflated the trip rates observed. In the Bradford case, higher trip rates were observed, possibly because the Bradford card is taken up by the more frequent elderly concessionary travellers who prefer using stored value to paying cash fares as and when they travel. Observing trends that would have been difficult to pick up with traditional data sources: The results showed that with smart card data it is possible to examine travel patterns over longer periods of time than possible using traditional sample survey methods such as travel diaries, as the analysis of the trip rates indicated patterns of stability in trip rates that have traditionally been difficult to pick up using travel diary surveys The role of sample surveys The role of sample surveys in the smart card data era will differ according to the analysis being undertaken with the smart card data. In this paper, it is being suggested that sample surveys will perform a complementary role, in

14 checking results observed from smart card data analysis and helping to provide information that cannot be obtained from the smart card system, such as journey purpose, as described above ACKNOWLEDGEMENTS This paper is based on work undertaken by Mousumi Bagchi for her doctoral thesis at the University of Westminster (UoW), under the supervision of Prof. Peter White. Funding for the research was provided in earlier stages by the Rees Jeffreys Road Fund and UoW, and subsequently by the UK Department for Transport. The authors also wish to express thanks to Merseytravel and First in Bradford for allowing access to their data for this research study. All interpretations based on such data are those of the authors alone BIBLIOGRAPHY CHEUNG, Y.H.F (2001) Experience of the Tripperpas in the Dutch municipality of Groningen, Proceedings of the European Transport Conference [CD ROM], Cambridge September. TRANSPORT AND TRAVEL RESEARCH (TTR) (1996) Evaluation of the Southport Swift Travelcard Trial, Unpublished report for Merseytravel.