Public preferences for mobility as a service: Insights from stated preference surveys

Transportation Research Part A xxx (xxxx) xxx–xxx

Contents lists available at ScienceDirect

Transportation Research Part A journal homepage: www.elsevier.com/locate/tra

Public preferences for mobility as a service: Insights from stated preference surveys Chinh Q. Ho , Corinne Mulley, David A. Hensher ⁎

Institute of Transport and Logistics Studies, The University of Sydney Business School, NSW 2006, Australia

ARTICLE INFO

ABSTRACT

Keywords: Mobility as a service (MaaS) Mobility preferences UK and Australia Willingness to pay MaaS barriers MaaS impact MaaS DSS

As mobility as a service (MaaS) continues to evolve with increasing interest throughout many countries, a key driver of its success will be the take up by the community of users seeking an alternative way of accessing individual modal options. Whether a packaging of modal services into a mobility bundle will appeal to the travelling population will depend on what appeal such packages can offer compared to purchasing travel via mode-specific outlets. This paper is one of a growing number exploring the role that everyday travel needs and socio-economic setting might play in defining mobility plans that gather significant appeal from the community. Building on our research in Sydney, Australia, we undertake a stated choice analysis in Tyneside, UK to see the extent to which differences in preferences and possible similarities exist in the demand for different subscription models and willingness to pay for mobility services in the two settings. Barriers to a widespread adoption of MaaS are also analysed, as are the potential impacts of MaaS adoption on public transport use and the way people access public transport services. A decision supporting system was developed to translate the modelling results into a practical and userfriendly tool for MaaS developers/innovators to assess market potential based on customer willingness to pay.

1. Introduction Mobility as a Service (MaaS) is a fast-emerging concept that aims to redefine transport markets with flexible, efficient, userfocused, and personalised services. It unites existing transport options, deploying the most appropriate mode for each journey, recognising customer preferences and real-time conditions of the transport network when the trip is requested. MaaS relies on a digital platform to integrate various forms of transport into a single, on demand service. In the MaaS eco-system, mobility is considered as a service to be delivered as opposed to being acquired via asset ownership such as private cars. Best described as a one-stop shop for mobility services, MaaS offers an opportunity for individuals to improve the efficiency of the urban transport network through collaborative consumption. In doing so, MaaS mitigates potential risks associated with the rise of point-to-point and ondemand services powered by disruptive transport technologies including ride-sourcing (e.g., Uber) and in due course, self-driving vehicles. The MaaS concept has been trialled in various forms in Europe with Finland being the pioneer in commercialising mobility plans under the MaaS framework. Since 2016, Helsinki residents have been able to use the Whim app to plan and pay for their everyday travel, either with a pre-pay option as part of a monthly mobility subscription or with a pay-as-they-go option (Hartikainen et al., Corresponding author at: Institute of Transport and Logistics Studies, The University of Sydney Business School, NSW 2006, Australia. E-mail addresses: [email protected] (C.Q. Ho), [email protected] (C. Mulley), [email protected] (D.A. Hensher). ⁎

https://doi.org/10.1016/j.tra.2019.09.031

0965-8564/ © 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Chinh Q. Ho, Corinne Mulley and David A. Hensher, Transportation Research Part A, https://doi.org/10.1016/j.tra.2019.09.031

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

2019). Other countries such as Sweden (e.g., Sochor et al., 2015a,b, 2016; Smith et al., 2018; Strömberg et al., 2018), Austria (König et al., 2016), the UK (Kamargianni and Matyas, 2017; Ramazzotti et al.), the Netherlands (Jittrapirom et al., 2017a, 2017b; Ebrahimi et al., 2018), Germany, France, Singapore, Canada, and the US (see Utriainen and Pöllänen, 2018 and Kamargianni et al., 2016 for a review) have tested more limited mobility bundles on real people and real networks with real mobility plans. Notwithstanding these active engagements of many jurisdictions and their enthusiasm for this emerging mobility paradigm, MaaS is still in its infancy with much development, innovation, adaptation, and experimentation still to be undertaken. Given MaaS is such a recent advance, there exists only limited evidence to inform the basis of MaaS development, including the potential uptake of MaaS and its impacts on travel behaviour. To date, there are only a handful of studies in the literature specific to MaaS and these focus on operational business models (Kamargianni and Matyas, 2017), institutional and infrastructure requirements for the delivery of MaaS (Sochor et al., 2015a,b, Mukhtar-Landgren et al., 2016; Goodall et al., 2017), the potential impacts on public transport contracts and operations (Hensher, 2017; Smith et al., 2018), and a few critical reviews of the MaaS literature (Jittrapirom et al., 2017a, 2017b; Utriainen and Pöllänen, 2018; Lyons et al., 2019). With rare exception (Caiati et al., 2018; Ho et al., 2018; Matyas and Kamargianni, 2018; ITS Australia, 2018; Hartikainen et al., 2019), little substantive insight into user preferences and willingness to pay that determine potential uptake has been undertaken. Furthermore, the literature sheds very little light on how MaaS adoption might translate into changes to travel behaviour and whether the impacts vary across environmental and institutional settings. This paper investigates the potential uptake of MaaS by identifying public preferences for mobility packages within the stated choice experiment environment. Economic and social factors that make MaaS offers more attractive than conventional unimodal transport options (e.g., private car and/or public transport service as it currently exists) are identified to inform MaaS providers how best to package, cost and market mobility plans to different segments of the population based on their travel needs and willingness to pay (WTP) for new mobility services. To do so, advanced discrete choice models are developed using behavioural data recently collected in Sydney, Australia and Tyneside, England using computer-assisted personal interview (CAPI) surveys conducted using the face-to-face method. These two datasets, collected with a similar design and survey method, provide a unique opportunity to inform the potential impact of MaaS on private car and public transport use, including door to door travel and first/last mile of public transport journeys, as well as providing a comparative assessment of public preferences in different institutional settings (regulated with integrated ticketing system in Sydney vs. deregulated in Tyneside) showing how MaaS offerings might be more (or less) attractive to users. The remainder of this paper is organised into five sections. The next section presents a detailed outline of the survey instrument and especially the stated choice experiment, before describing the empirical context and sampling plan. This is followed by a descriptive analysis of the data collected which provides informative evidence of potential interest in MaaS for different socioeconomic contexts. The remaining sections set out the choice model characteristics used to obtain parameter estimates for the stated choice model, and after discussing the findings, report the willingness to pay estimates that inform the value of specific service levels and what a package of such mobility services might be priced at as part of a starting position for buy in. The final section concludes with the main findings. 2. The survey 2.1. Survey instrument To address the research questions relating to the potential uptake of MaaS in a UK context, travellers’ WTP for mobility services, and the potential impacts of MaaS adoption on public transport (PT) use, including accessing and exiting PT, this study uses a state-ofthe-art choice experiment with Tyneside identified as the laboratory city. The stated choice survey method is undertaken as the most suitable method for addressing these research questions because MaaS plans as a package of modal options is not yet available in the study area. Tyneside, the conurbation comprising the district authorities of North Tyneside, South Tyneside, Newcastle upon Tyne, Gateshead and Sunderland, is selected as a case study in the UK context because the area has both multi-modal conventional public transport services and new mobility options such as car club and peer-to-peer car sharing, Uber or similar, and bike hire. The presence of all these modes is important as potential participants are more likely to be familiar with all modes of transport planned to be included in the MaaS offerings, including public transport, car-share Uber/taxi, and bike-share. Thus, the offerings presented in the experimental setting can be seen as a possible integration of the existing transport options to provide improved mobility services. In participating in such an experiment, each respondent is asked about their current circumstances and travel patterns for a typical week from which monthly mobility plans are designed for them to consider and potentially subscribe to. These mobility plans are customised to each participant by pivoting around their use of different transport modes in a typical week. The survey instrument, designed as a Computer-Assisted Personal Interview (CAPI), has been tested and implemented in Sydney, Australia (Ho et al., 2018). For implementation in Tyneside, UK, the survey instrument was modified to suit the local context and to reflect on the lessons learnt from a survey in Sydney. Modifications to the survey instrument include various re-wordings and a new efficient design where monthly mobility plans are offered to Tyneside residents (as compared to fortnightly plans for Sydney residents). Lessons learnt from the survey in Sydney regarding the recruitment and sampling strategies, question wording, choice task presentation and visualisation, survey method, and the usefulness of attitudinal questions were considered for revision or reuse in the Tyneside survey. Based on feedback from both the interviewers and the interviewees, as well as data analysis, we decided to use the same geographical stratification sampling method with a face-to-face administration while changing the recruitment strategy from fixed locations (three selected shopping centres in Sydney) to various recruitment locations (shopping centre, downtown, residential 2

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Fig. 1. Survey flow and overview of information collected.

areas) that the contracted third party market research company can cover in Tyneside. We also considered recruiting households instead of individuals, but this requires significant changes to the survey structure and hence was reserved for another study. Another reflection on the Sydney participant’s feedback relates to a breakdown of current travel costs into the fixed cost of owning a car (if the respondent has one) and the variable costs of travel (see text in Fig. 2). This aims to make sure the Tyneside interviewees understand the cost of owning and using their car while indicating their preferences for MaaS by comparing the current cost of travel including the sunk cost of car ownership with that of future MaaS. In essence, the survey aims to bring this sunk cost to the surface since many car users would not have a full picture of costs when purchasing and/or keeping a vehicle (Turrentine and Kurani, 2007). The questionnaire and survey flow are summarised in Fig. 1, with details provided below. The survey instrument has five parts, complemented by a 2-min video1 explaining how MaaS may work at the beginning to set the scene for the experimental survey. In the instrument itself, the first part seeks socio-demographics and the respondent’s circumstances including the ability to drive, disabilities, daily access to car and its market value and age (from which sunk costs are estimated), smartphone ownership, internet use, car-share membership (and car-share pod distance from home). This is followed by questions relating to weekly travel patterns of the respondent in terms of the number of one-way trips undertaken by different modes (public transport, Taxi/Uber and Car) for every day of the week, daily public transport (PT) fare, daily taxi/Uber cost, daily distance and time travelled by car, daily parking cost, as well as typical access mode and access time if public transport is used. This is used to generate heterogenous designs by pivoting. A respondent is then introduced to the concept of MaaS and MaaS plans including how to interpret each transport component of the MaaS plan. The survey instrument then summarises the respondent’s current travel record and presents this side by side with three mobility plans in a form of a choice task in the fourth part. The respondent is asked to compare and indicate which option they would prefer. Each mobility plan includes three to four mobility services (taxi, bike-share, car-share and/or PT) depending on whether the respondent would be able to drive or ride PT. Existing public transport modes in Tyneside include bus, metro, train, and ferry, with integrated fares available through a Network One Travel Ticket, as well as all operators – Bus, Metro, Ferry and Rail also operating their own fare system. In the stated choice experiment, given the way MaaS is seen as an integrating opportunity, all public transport was incorporated under one umbrella mode and branded with the logo of Nexus – an entity that organises public transport services in 1

URL to the MaaS introduction video is available at https://vimeo.com/96486671. 3

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Tyneside. As for car-sharing, two schemes – namely back-to-base car-sharing (i.e., round trip) or one-way car-sharing – are included to refine the product offering and to look to future service possibilities. The one-way car-share option served as a proxy for investigating preferences for self-driving vehicles operating under a MaaS framework, without the need to introduce self-driving vehicles per se. The back-to-based car-share is branded as Co-wheels whilst the one-way service was called Car2Go to maintain branding consistency. Bike-share was introduced as an unbranded offer with the characteristics of the newly introduced to Tyneside floating bike-share scheme operated by Mobike. MaaS plans including bike-share were not restricted as to where the bike could be left, unlike the current scheme in Tyneside which limits bike use to the immediate area around the city centre. Each respondent was asked to answer four choice tasks, each involving four options including two monthly plans customised to each respondent, a pay-as-you-go plan (PayG) and a status quo (SQ) option, which when selected triggered a question seeking the respondent’s reason for not taking up MaaS offers. It is emphasized that the choice task includes the respondent’s current travel record, referred to as the ‘status quo’, as a selectable option and this is very important for estimating the potential uptake of MaaS offerings. Fig. 2 provides an example choice task (out of four tasks) for a respondent who reported their use of public transport as 12 days in a typical month with about 15 h driving a car a total distance of 600 miles in this period.2 The interviewer explained to each respondent that the monthly cost of their current travel arrangement was estimated based on their reported use of different transport modes and their car value (if they had one). The monthly transport cost, when it included the ownership of a car, included the fixed costs of owning a car (comprising depreciation, maintenance and replacement, insurance, forgone interest, and excise licence fee) as well as variable costs of fuel, parking, public transport fares and taxi costs (see Fig. 2). Vehicle running costs were estimated based on information provided by RAC motoring services (www.emmerson-hill.co.uk). The MaaS offerings are generated in real time by the survey instrument embedded with the state-of-the-art D-efficient designs (see Hensher et al., 2015) with priors obtained from the Sydney study (Ho et al., 2018). Three pivoting designs were used with Table 1 showing the pivot levels and assignment rules. The experiment was designed using NGene (Choice Metrics 2012) with 12 choice tasks, blocked into three sets of four. The respondent was then offered an opportunity to create their own mobility plan (CIY) in the sense that they can bundle different services into a monthly plan. The survey instrument then prices the CIY plan and asks if the respondent would buy. The final part of the survey randomly selects one of the MaaS offerings which the respondent stated they would buy and asked how this might change their travel patterns in terms of their transport usage. 2.2. Survey administration and sampling methods The survey was administered via face-to-face interview using the CAPI instrument described in Section 2.1. Transport Systems Catapult procured Facts market research company (http://www.facts.uk.com/) to recruit and interview participants in the study area. The target respondents are people aged 18+. The stratification sampling method was used to obtain spatial coverage and a mixed population while avoiding particularly deprived areas, identified via the Index of Multiple Deprivation (IMD), to ensure locations where people choose to live are selected. A sample of 300 interviews was contracted. Interviews were conducted on laptops between the 15th and 26th of February 2018 by well qualified interviewers. A total of 310 interviews were conducted, but data from 20 interviews were lost due to unstable internet connection during the interview, leaving a sample of 290 interviews for analysis. On average, each interview took 16.7 min with a standard deviation of 8.6 min (a minimum of 5.42 mins and a maximum of 55 min). The average interview time is very similar to that of the Sydney survey, which took a well-trained interviewer an average of 17 min to complete one interview. 3. Profile of the sample and the potential uptakes of MaaS 3.1. Sample profile Table 2 provides a profile of the participants to the Tyneside and Sydney surveys. The average age of the sample was 49 years old, with a standard deviation of 17 years. Fulltime workers account for more than one-third (38%), followed by part-time workers (18%), retirees (16%) and the unemployed (8%). The sample is well balanced in terms of gender and access to a car on a daily basis; however, there is a good proportion of sampled respondents who rely on public transport and modes other than the private car, as reflected in the 36% percentage of respondents not having a valid driving licence (100% − 64% = 36%). Couples only and persons living alone account for nearly half of the sample (45%), but households with the presence of at least one child also account for a large share. This suggests that the sample has a good mix of households with and without chauffeuring responsibility. The former households are more dependent on the private car than the latter (Maat and Timmermans, 2007; Ho, 2013; Ho and Mulley, 2015), and thus having a good mix of households is good for providing a more accurate level of potential uptake when MaaS is introduced in the study area. Socio-demographic data of the study area are not available to assess how representative the sample is to the population; however, the same sampling method was used in Sydney which leads to a sample that represents the population quite well (see Ho et al., 2018). Fig. 3 shows the spatial distribution of the participants by their home postal codes. As can be seen, the respondents are spread across the study area with an almost equal numbers of interviews conducted in Newcastle city, Gateshead, North Tyneside, South 2

Monthly travel record is estimated based on the reported use of different transport modes in a typical week. 4

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Fig. 2. An illustrative choice screen.

Tyneside, and Sutherland – the five district authorities that make up the study area.3 This means that the participants are located in different environmental settings with different levels of public transport services and private car use. This aspect of the sample again provides credibility for the stratification sampling method used in this project for establishing potential uptakes of MaaS. Table 3 provides summary statistics of the participants in terms of their travel needs in a typical week, both for Tyneside and Sydney. On a typical Monday, a Tyneside respondent on average undertook 1.02 PT trips, 0.01 trips by taxi and 1.47 trip by car as a driver or as a

3 There are two respondents living outside, live on the border with, the study area. Keeping or removing these two respondents does not change the results.

5

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Table 1 Pivot levels of the SP experiment designs and assignment rules. Mode

Attribute

Attribute level [Reference: Pivot level]

Design

Apply for respondents w/ current travel record of …

PT Shared car PT Shared car

Days with unlimited use per month Monthly hours use of car-share Days with unlimited use of PT Hours use of car-share in bundle

[0: +2,+4,+8] [20: ± 10, ± 6, ± 2] [8: +0,+4,+8] [12: ± 6, ± 4, ± 2]

D1.1 D1.1 D1.2 D1.2

Low level of PT use (0 or 1 day/week)

PT Shared car PT PT PT

Days with Hours use Days with Days with Days with

[16: +0,+4,+8] [8: ± 6, ± 4, ± 2] [0: +2,+4,+8] [8: +0,+4,+8] [16: +0,+4,+10]

D1.3 D1.3 D2.1 D2.2 D2.3

High level of PT use (5–7 days/week)

Shared car Shared car Shared car

Hours use of car-share in bundle Hours use of car-share in bundle Hours use of car-share in bundle

[4: +1,+2,+3,+4,+5,+6] [12: +2,+4,+6,+8,+10,+12] [20: +2,+4,+6,+8,+10,+12]

D3.1 D3.2 D3.3

Low level of car use (< 3 h/week) Medium level of car use (3–5 h/week) High level of car use (> 5 h/week)

Shared car Shared car Shared car Taxi Bike-share PT

Car-sharing scheme Advance booking time Hourly rate if PayG % Discount off every taxi bill Rate of 30 min rent if PayG Daily fare

Round-trip, One-way 15, 30, 60 min £4, 5, 6 10%, 20% £1, 1.5, 2 £3, 3.5, 4

D1, D3 D1, D3 D1, D3 All All All

All All All All All All

Credit Price tag

Roll-over, Lost Formulae

All All

All respondents All respondents

unlimited use per month of car-share in bundle unlimited use per month unlimited use per month unlimited use per month

Medium level of PT use (2–4 days/week)

Low level of PT use (0 or 1 day/week) Medium level of PT use (2–4 days/week) High level of PT use (5–7 days/week)

respondents w/ licence respondents w/ licence respondents w/ licence respondents respondents respondents

Table 2 Profile of the participants in Tyneside, UK and Sydney, Australia.

Respondents age Male (1/0) Full-time worker (1/0) Part-time worker (1/0) Unemployed (1/0) Retiree (1/0) Fulltime student (1/0) Part-time student (1/0) Pensioner (1/0) Look after home (1/0) Use internet every day (1/0) Use internet few days/week (1/0) Holding valid driving licence (1/0) Having access to car daily (1/0) Having a smartphone (1/0) Number of household cars Number of household driver licences Single person household (1/0) Couple household with children < 15 and 15+ (1/0) Couple only household (1/0) Single parent household with child(ren) < 15 (1/0) Couple household with child(ren) < 15 years (1/0) Single parent household with child(ren) 15+ (1/0) Couple household with child(ren) 15+ (1/0) Single parent household with children < 15 and 15+ (1/0) Sample size (number of people age 18 + )

Tyneside Mean (sd)

Sydney Mean (sd)

46 (17) 0.47 0.38 0.18 0.10 0.16 0.06 0.01 0.04 0.06 0.75 0.17 0.64 0.57 0.87 0.92 (0.81) 1.20 (0.95) 0.16 0.09 0.29 0.03 0.18 0.04 0.15 0.00 290

39 (14) 0.45 0.48 0.17 0.08 0.05 0.13 0.03 0.02 0.02 0.97 0.03 0.85 0.74 1.00 1.57 (0.98) 2.10 (1.01) 0.09 0.03 0.26 0.00 0.12 0.06 0.13 0.00 252

passenger, with an average car distance of 11.88 miles and car travel time of 20 min. These travel statistics appear to be stable throughout weekdays but change significantly on the weekend, when the average number of taxi trips per person peaks at 0.42 on Saturday and PT trips bottom at 0.31 on Sunday, respectively. The travel patterns are quite similar between Tyneside and Sydney sample, with the latter being verified as a representative sample of the population in terms of travel demand (see Ho et al., 2018). It is also noted that the average number of motorised trips per person per weekday is around 2.5, consistent with the motorised trip rate observed in many large-scale household travel surveys (Department of Main Roads, 2012; Statistics, 2014; Geurs et al., 2015; Evans et al., 2018).4 This suggests that on 4 Where statistics were not readily available, average motorised trip rate was computed from the average trip rate and average share of motorised trips.

6

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Fig. 3. Distribution of the participants by postcode.

average, the travel demand of the sample is likely to be representative of the population travel demand. Fig. 4 shows the respondent profile in terms of car usage in a typical week. Fig. 4a shows that, for a typical week, a large percentage of the participants either did not use a car at all (38%), or used a car very often at 5–7 days/week (42%). In terms of car hours, a vast majority of the respondents (95%) used a car less than 7 h/week (or on average less than 60 min per day), with less than 3% of the sample using a car 10 h or more in their typical week. 3.2. Descriptive analysis of MaaS potential uptake This section provides a descriptive analysis of MaaS potential uptake and identifies key factors that may influence the participant’s preference for MaaS subscription. The impact of current travel patterns on MaaS uptake are explored first, followed by the impact of household and individual characteristics. 3.2.1. Impact of current travel pattern on MaaS uptake Fig. 5 shows the stated shares of MaaS plans by the frequency of car use. Across the entire sample, the stated shares of the MaaS are 45%, of which 32% would subscribe to monthly plans tailored to their needs and a further 13% would use as PayG. As expected, MaaS shares decrease as the frequency of car use increases. Interestingly, car non-users express the highest tendency to subscribe to MaaS plans, while infrequent car users are most likely to pay as they go. This may indicate the value-adding of a MaaS platform that links multiple public transport services (bus, train, metro, light rail, bike-share, taxi service) and offers integrated mobility solutions that are valuable to the users, even though these transport modes are already available as separate modes in the UK context. Further investigation is required before a firm conclusion can be made. One in four respondents who reported to use a car 5+ days per week stated that they would subscribe to monthly plans tailored to their needs. Providing an opportunity for these people to bundle their own monthly plan does not increase uptake. Indeed, this reduces the subscription rate to 16%. The empirical data are also segmented by the frequency of public transport and taxi/Uber use. Fig. 6 shows the potential uptake of MaaS segmented by the number of public transport trips the respondent reported to undertake in their typical week of travel. The more frequently the respondent uses public transport, the more likely they are to buy into MaaS offerings. It is interesting to note that the opposite result was observed for Sydney where frequent PT users are less likely than average to choose a MaaS bundle (see Section 7

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Table 3 Travel profile of sampled respondents by date of the typical week. Date

Travel demand

Tyneside

Sydney

Mean

Std. Dev.

Mean

Std. Dev.

Monday

PT trips per person Taxi trips per person Car trips per person Car miles per person Car minutes per person

1.02 0.01 1.47 11.88 20.13

1.39 0.08 3.43 31.68 40.62

1.12 0.03 1.25 12.56 28.60

1.24 0.21 2.01 22.55 51.65

Tuesday

PT trips per person Taxi trips per person Car trips per person Car miles per person Car minutes per person

0.99 0.01 1.49 11.67 19.77

1.43 0.14 3.45 30.50 40.03

1.11 0.04 1.29 12.84 28.52

1.22 0.24 2.10 22.17 48.20

Wednesday

PT trips per person Taxi trips per person Car trips per person Car miles per person Car minutes per person

0.98 0.05 1.52 12.71 21.31

1.43 0.28 3.45 31.28 41.96

1.09 0.04 1.36 13.24 30.43

1.33 0.24 2.16 22.42 52.94

Thursday

PT trips per person Taxi trips per person Car trips per person Car miles per person Car minutes per person

0.97 0.04 1.59 14.57 22.72

1.44 0.25 3.45 42.71 43.59

1.11 0.03 1.42 13.93 30.91

1.30 0.22 2.09 23.04 49.64

Friday

PT trips per person Taxi trips per person Car trips per person Car miles per person Car minutes per person

1.05 0.17 1.55 14.03 22.86

1.60 0.50 3.50 37.68 43.81

1.11 0.15 1.33 12.93 29.93

1.21 0.47 2.10 23.23 53.79

Saturday

PT trips per person Taxi trips per person Car trips per person Car miles per person Car minutes per person

0.95 0.42 1.75 13.29 23.26

1.59 0.92 3.60 25.36 54.81

0.72 0.20 1.62 15.44 32.04

1.13 0.53 1.77 24.68 40.89

Sunday

PT trips per person Taxi trips per person Car trips per person Car miles per person Car minutes per person

0.31 0.03 1.04 11.19 17.46 290

0.93 0.24 1.86 32.03 36.50

0.66 0.05 1.39 13.41 25.13 252

1.03 0.28 1.64 37.51 35.24

Sample (#people aged 18+ )

5 for further comparison and explanations). It is encouraging that up to 20% of the public transport non-users in Tyneside stated that they would buy the pre-defined mobility plans tailored to their needs. Note that these pre-defined plans always include some amount of public transport use, provided the respondent said they do not meet any difficulty in using public transport. Thus, this finding suggests that MaaS plans have the potential to increase public transport usage amongst the non-users (as well as users). Table 3 presents the analysis of data by taxi/Uber usage. This suggests that a MaaS plan is very attractive to those who use taxi/ Uber regularly, and at a minimum of once in their typical week. This is expected as the offer of a 10% or 20% discount on every taxi/ Uber bill used in the experiment would be very attractive to regular taxi/Uber users, accounting for one-third of the sample (98/ 290 = 33.8%). 3.2.2. Impact of socio-demographics on MaaS uptake Given the substantial difference in potential uptake of the MaaS subscription model amongst the participants with different travel patterns reported above, this section further segments the market by the socio-demographics of the participant to add further insight, controlling for the influence of the participant’s current travel pattern. Fig. 7 shows the impact of household car ownership on the likelihood of MaaS take-up and which of the subscription models is likely to be preferred by the participant. Households are classified into three groups of no car, car negotiating and car sufficient households, where car-negotiating households are defined as households with fewer cars than drivers, and car sufficient households are those with at least as many cars as household drivers (Ho and Mulley, 2013). A number of observations can be drawn from Fig. 7 which combines frequent car user and very frequent car user into one group to increase the sample size for the comparison to be statistically meaningful. First, controlling for the frequency of car use, the likelihood of subscribing to MaaS does not appear to change with household car ownership levels, with the percentage of “not subscribe” being the same, statistically, between participants from car negotiating households and those from car sufficient households. However, once subscribed, their preferred models are different with participants from car negotiating households being more 8

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Fig. 4. Weekly car usage of the sampled respondents: Newcastle upon Tyne, UK Feb 2018.

Fig. 5. Stated shares of MaaS by frequency of car use: Newcastle upon Tyne, UK Feb 2018.

Fig. 6. Stated shares of MaaS plans by frequency of public transport use.

9

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Fig. 7. Impact of household car ownership on MaaS uptake, segmented by frequency of car use.

likely to subscribe as a regular user of MaaS than as a pay-as-you-go user. The opposite is true for participants from car-sufficient households that prefer PayG to monthly subscription (see the middle panel of Fig. 7). It should be noted that the frequency of car use and household car ownership are highly correlated with each other, with none of the participants from non-car households reporting to use the car three or more times in a typical week, and very few of the participants (3) from non-car households (100) using a car 1–2 times a week. Fig. 8 reports the outcome of analysis using age instead of household car ownership to classify the respondents. Holding age constant, the likelihood of MaaS take-up varies by frequency of car use with higher levels of car use associated with lower likelihood of subscription to monthly MaaS plans. PayG option appears to be attractive to infrequent car users of all ages, except for those who are 65+. Within each category of car use, the preferred MaaS model is clear so that non-car users and frequent car users prefer monthly MaaS plans while infrequent car users prefer PayG. The likelihood of subscription take-up also appears to vary by age group, especially for those who use a car 3–7 days per week where the impact of age is very clear (the likelihood of MaaS subscription decreases as age increases). Fig. 9 provides a segmentation analysis which groups the participants by their frequency of PT use instead of car use. Similar observations can be drawn. That is, holding car ownership constant, the likelihood of MaaS uptake generally increases with the frequency of PT use. This pattern is consistent with the one observed in Fig. 7 and can be explained by a strong and negative correlation between the frequency of PT use and the frequency of car use. On the other hand, given the level of PT use, monthly plans, and to a lesser extent, PayG options both appear to be more attractive to frequent and very frequent PT users from households of all car ownership levels. This is encouraging in the sense that MaaS offers are attractive to current PT users in Tyneside, and maybe more

Fig. 8. Impact of age on MaaS uptake, segmented by frequency of car use. 10

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Fig. 9. Impact of household car ownership on MaaS uptake, segmented by frequency of PT use.

broadly in the UK context (see Matyas and Kamargianni, 2018 for the case of London). It is noted that an opposite preference was found for Sydney regular PT users but this does not mean that the results in Sydney were discouraging (further discussion is provided in Section 5). The descriptive analysis results have a significant implication for modelling. That is, the current travel needs should be considered in estimating MaaS market share as both frequency of car use and PT use, including taxi and Uber, were found to have a strong correlation with the option selected. However, due to a strong correlation between public transport use and car use (r = −0.543), the modelling specification needs also to explore a preferred way to segment the population. The segmentation could be by public transport use or by car use so as to obtain a statistically better model, measured by the goodness of fit. Model specification and estimation are both discussed in the next section. 4. Choice model and willingness to pay estimates 4.1. Model specification Informed by the modelling experience gained from the similar study conducted in Sydney, Australia, this project uses a non-linear model to describe the behavioural responses to the MaaS offerings. The derivation of this model is provided in Ho et al. (2018) and summarised herein for convenience. The utility that the traveller q derives from an alternative j is specified as a heteroscedastic conditioning function that recognises individual-specific circumstances (see Swait and Adamowicz, 2001; Hensher and Ho, 2016):

U jq = Iq Ujq = Iq (Vjq +

jq ),

(1)

j = 1, ...,J

where U jq the total utility that describes how attractive the mobility plan j is to the respondent q, which is a product of current travel patterns Iq and the standard utility function Uiq. To recognise that the same MaaS plan may be more attractive to some people than to others, Iq is specified as a parametric function of socio-demographics and individual travel needs, as in Eq. (2): L

Iq = 1 +

K l zl

l =1

+

k yk

(2)

k=1

where z l is a set of L variables describing the current travel patterns (such as type of car users or whether the respondents is a member of Co-Wheels car-share) and yk is a set of socio-demographic characteristics (e.g., age, gender or employment status), and γl, φk are parameters to be estimated, together with the vector of parameters β representing respondent’s preferences for various mobility services, X, included in the MaaS plan. When all γl, φk are not statistically different from zero, Iq take the value of 1 such that the heteroscedastic conditioning model collapses to a homoscedastic random utility maximisation (RUM). Note also that the conditioning function Iq varies across individuals (subscript q) but not across alternatives within a choice task. It is therefore necessary to normalise the conditioning function of one alternative (e.g., the status quo) to 1 and allow others (e.g., MaaS plans) to be freely estimated. Assuming that the random variables Iq jq follow an iid Gumbel distribution with unit scale factors, Eqs. (1) and (2) can be rewritten as the standard multinominal logit model (MNL) as in Eq. (3) on the condition that the conditioning function Iq is non-negative (see Ho et al., 2018). The probability that individual q choosing an alternative j in a choice task containing J alternatives are: 11

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Table 4 Potential uptakes of MaaS offerings by frequency of Taxi/Uber usage. Selected Option

Taxi/Uber usage in a typical week

Not subscribe/Status quo Monthly plan PayG Sample size (#choice scenarios) Number of respondents

Prjq =

exp(Iq × J exp(Iq i=1

Vqj ) × Vqi )

=

exp[ (1 + J exp[ (1 i=1

L z + l=1 l l L + l=1 l z l

None

At least once

64% 25% 11% 768 192

37% 46% 17% 392 98

K y × k=1 k k ) K + k = 1 k yk )

( Xqj )] × ( Xqi )]

(3)

Preference heterogeneity may be layered on top of model (3) in the form of random parameters (4). qm

= ¯m +

m vqm

(4)

where ¯m is the population mean of the individual parameter qm associated with attribute m, vqm is the individual specific heterogeneity with mean zero and standard deviation one, m is the standard deviation of the distribution of qm around ¯m . 4.2. Estimation results Table 4 presents the empirical model results, estimated using Nlogit v6. The following exercise was undertaken to select the “best” final model. First, we randomly selected a holdout sample of 20% and estimated the model on the remaining 80% of the participants. Statistically insignificant variables were gradually removed or brought back, and the model refined. Implementing a probability weighted sample enumeration (PWSE) strategy on our hold out sample, we compared the predictive performance of different model specifications and selected a final model for the 80% sample. Appendix A shows the model validation results in terms of their predictive performance for the hold-out sample, comparing them with those obtained in the estimation. It is noted that forecasting performance of different model specifications was very stable, with all models tested in the validation process having close to a 95% accuracy across all choices (including no subscription to MaaS). Using the best model identified from the 80% sample, a final model based on the full sample was then estimated and results discussed below. Estimation results shown in Table 5 suggest two sources of heterogeneity in preferences. The first one links to the heteroscedastic conditioning function Iq where preferences for MaaS plans were found to vary systematically across different types of car user and socio-demographic groups. The second component relates to the utility function itself in which preferences for MaaS components were found to vary randomly across the respondents, according to a constrained triangular distribution with its mean equal its spread. Other distributions such as normal and log-normal were also tested with the model specification but the constrained triangular distributions were found to fit the data best, probably because it has shorter tails than the alternative distributions do. The final model has a log-likelihood value of −1,153.6 at convergence, rejecting the non-random parameter model (LL = −1,313.9) at 99% level of confidence based on a likelihood-ratio test. Most parameter estimates have the expected sign with the kernel density plots shown in Fig. 10 assisting the interpretation of model parameters. Fig. 10 shows that the standard utilities (Viq) of MaaS options are all negative (−0.12 to 0). Thus, a positive parameter for individual-specific variables included in the conditioning function Iq suggests a lower likelihood of subscription because the negative standard utilities are scaled by a factor larger than 1 (see Eq. (2)), lowering the utility, and hence choices. By contrast, a negative parameter in the conditioning function suggests the opposite. That is, people in these groups are more likely to take up MaaS. Note also that the conditioning function is in the positive domain (see value on the horizontal axis), satisfying the non-negative condition required. Table 5 shows that the effects coded variable ‘aged 55+’ has a significantly positive parameter, suggest that older people are less likely to take up MaaS offerings. The parameter associated with the reference group (i.e., aged 18–24) can be computed as the negative sum of other parameters associated with age (i.e., −[0.190 + 0] = −0.190). That is, MaaS offerings appear to be more attractive to young people, and this is consistent with trends observed around the world with younger people turning away from acquiring driving licences and private cars (Raimond and Milthorpe, 2010; Chatterjee et al., 2018). This is further evidenced in the negative parameter associated with individuals who own a smartphone and use internet every day (−0.365) (see Deloitte, 2017 for smartphone ownership and use by age group). The positive parameter associated with car-negotiating households (0.066), defined as households having more drivers than cars, is of particular interest. In contrast, car-sufficient households, defined as those with at least as many cars as drivers, will have a negative parameter (−0.066). Together this suggests that car-sufficient households are more likely than average to buy into MaaS while car-negotiating households are less likely than average to take up the offers. This appears counter-intuitive at first but the evidence indeed lends support to the common belief that MaaS could be a good substitute for the second household car, but not the only car in the household (Karlsson et al., 2017; Smith et al., 2018). A quote from the respondent may be of use here: “I would rather use my own car but would consider it for my partner and get rid of her car because I pay for both”. Turning to the utility function, most of the parameter estimates are statistically significant and have the expected sign. The 12

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Table 5 Estimation results for the stated choice of MaaS plans in presence of the status quo, Tyneside, UK 2018. Description

Alta

Para

Sig

t-value

Heteroscedastic conditioning function (Iq) Car non-users (1/0/−1, base = infrequent users) Car frequent users (3–4 days/week, 1/0/−1, base = infrequent users) Car very frequent users (5+ days/week, 1/0/−1, base = infrequent users) Own smartphone and use internet every day (1/0) Respondent aged 55+ (1/0/−1, base = age under 25 years old) Car-negotiating households (1/0/−1, base = car-sufficient)

−SQ −SQ −SQ −SQ −SQ −SQ

0.288 −0.068 −0.190 −0.365 0.190 0.066

***

6.55 −1.64 −5.82 −8.65 5.89 3.24

Standard utility function (U) Monthly fee of MaaS plans ($) Monthly fee of Create-It-Yourself plan ($) Unlimited PT days per month, mean = std devb One-way car-share hours per month, mean = std devb Round-trip car-share hours per month, mean = std devb Taxi discount (% off every taxi bill) Hours entitled to free floating bike-share Unused credit lost (1/0/−1), reference = roll-over Constant of monthly Plan A Constant of monthly Plan B Constant of PayG option Number of days using PT in a typical month (day) Number of hours using car in a typical month (h)

A, B CIY Plans Plans Plans Plans Plans Plans A B PayG SQ SQ

−0.087 −0.101 0.341 0.464 0.379 0.068 0.001 −0.250 −1.624 −2.149 −2.588 −0.067 0.032

***

Model summary statistics Number of choice tasks (#interviewees) Number of model parameters Log likelihood at convergence AIC/N

*

*** *** *** ***

*** *** *** *** *** ** *** *** *** *** *** ***

−9.67 −10.01 9.67 9.72 10.36 8.99 2.49 −3.90 −5.39 −7.09 −17.46 −10.23 6.56

1,450 (290) 19 −1,153.6 1.617

Note: a SQ = Status quo, −SQ = all alternatives except SQ, Plans = all monthly plans (plans A, B, CIY). b Random parameters follow a constrained triangular distribution with mean = spread. *** Significance at 99% level of confidence. ** Significance at 95% level of confidence. * Significance at 90% level of confidence.

Fig. 10. Kernel density of the standard utility (Vqj) and conditioning (Iq) function.

positive parameters associated with number of days (hours) entitled to unlimited PT (car-share) use indicates that, all else being equal, people prefer more days with unlimited PT use and more hours of car-share use: this is as expected. However, the preference for carshare and PT entitlement varies significantly across the respondents, evidenced by the significance of the random parameters associated with these mobility elements. As expected, a one-way car-share format is preferable to the round trip, return to base carshare, reflected in the larger parameter for the former than the latter. In addition, being able to roll unused credits (i.e., entitlements to car hours and PT days) over to the next month increases the likelihood of MaaS uptake. The number of bike-share hours included in the MaaS plan has a positive parameter estimate, as expected. The magnitude of this parameter is very small, reflecting the rather less important role of bike-share in motivating MaaS plan uptake. Again, the result is expected since currently Tyneside residents can use floating bike-share for free for the first 15 min, and the rate after that is very 13

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Table 6 Estimated willingness to pay (WTP) for different mobility entitlements of MaaS plan. MaaS component

Unit

Average WTP

95% CI of WTP

Average market price

An hour use of one-way car-share A full day use of one-way car-share An hour use of round-trip car-share A full day use round-trip car-share Every 15 min increase in advance booking time A day of unlimited public transport use An hour use of free-floating bike-share 10% discount to every taxi bill Rolling un-used credits to next month

£/h £/day £/h £/day £ £/day £/h £/month £/month

5.28 36.96 4.32 30.24 0* 3.72 0.01 7.81 2.86

[4.81, 5.74] [33.65, 40.20] [3.98, 4.63] [27.88, 32.41] n/a [3.50, 4.26] [0, 0.02] [6.03, 9.60] [1.34, 4.38]

n/a n/a £5.50a £38.50a n/a 3.40/4.05/4.95b £1.50 n/a n/a

Note. * Estimate is not significantly different from zero. a Hour rate is based on Co-Wheels car club rate for a medium car (Yaris Hybrid). b Daily fare caps (one zone/two zones/all zones) are based on Network One 4-week period ticket, assuming 5 days travel per week.

affordable, at £0.75 per 30-min. The open-ended comments provided by respondents reveal that some people do not value bike-share as part of the MaaS offerings, either because they already have a bike or because they would never use it. Typical reasons are “I already have a bike, so that would be a waste” and “I would be paying for services [the use of a bike] that I am not interested in”. Regarding the pay-as-you-go as option, it appears that offering a digital platform that can unify multiple modes of transport into a smart app without offering discounts is not attractive enough for respondents to subscribe. This is reflected in the significantly negative constant associated with the PayG option. For the public to take up this PayG option, the MaaS technology (i.e., the app) will need to be accompanied by some discounts for using taxi/Uber and car-share. Discounts to bike-share and car-share rate do not appear to influence the uptake level of PayG option (parameters were not statistically significant and were removed), consistent with the finding reported in the previous paragraph. 4.3. Willingness to pay estimates Willingness to pay (WTP) estimates can be estimated from the parameters associated with different mobility entitlement and the cost parameter. Confidence intervals of these WTP’s can also be estimated using the Delta method (see Armstrong et al., 2001; Hensher et al., 2015 Chapter 7). WTP measures how much a respondent would be willing to pay to have each of the mobility services increase/decrease by a unit of an attribute of interest in a package. It is therefore a very useful metric to present to understand public preferences for MaaS. Table 6 shows that, on average, Tyneside residents are willing to pay £5.28 (95% CI = [£4.81, £5.74]) for an hour use of one-way car-share and £4.32 (95% CI = [£3.98, £4.63]) for an hour use of station-based car-share. As a full day use of car-share is priced at 7 h, both in the experiment and in Tyneside, a daily rate of one-way and station-based car share can be computed at £35.96 and £30.24, respectively. As for public transport, an average respondent would be willing to pay £3.72 for one more day entitlement to unlimited use of PT. Table 6 also provides a market price in Tyneside for reference. Note that WTPs for carshare and public transport use vary significantly across the sampled respondents, with the full distribution of these values provided in Fig. 11. 5. MaaS up-take: Barriers and potential impact of MaaS on PT use 5.1. Barriers to MaaS uptake Understanding the main barriers to MaaS widespread uptake is as useful as understanding the characteristics of early adopters. The latter suggests segments of the population for marketing purposes while the former allows MaaS providers to design MaaS products in a way that improves the attractiveness of the MaaS offerings. Reasons for not taking up MaaS provided as verbatim by non-adopters, defined as the respondents who chose the Status Quo option, are analysed and presented as word-clouds in Fig. 12, comparing Sydney with Tyneside. The bigger the word, the more respondents mentioning that word as part of the reason for not taking up MaaS, despite that PayG has always been an option and the monthly mobility packages have been customised to each respondent based on their reported travel record. The main reason for Sydney residents to not take up MaaS relates to the private car which about three quarters of the Sydney respondents are having access to on a daily basis, compared to about one-half of Tyneside residents (see Table 2). A quote from the respondent may make this clearer: “I prefer the freedom and privacy and flexibility of my own car”. By contrast, Tyneside residents are more economic thinking, with cost of transport, be it the private car, the free bus pass offered to senior travellers, or the mobility package on offer, being centred to the reasons for not taking up MaaS offerings as illustrated by a respondent’s reason as: “I can see benefits and costs look good but it is about time and convenience and transporting children”.

14

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Fig. 11. Kernel density of WTP for use of car-share (£/h) and unlimited PT travel (£/day). Note: WTPCAHR1 is WTP for one-way and WTPCAHR2 is WTP for back-to-base car-share.

Fig. 12. Main reasons for not taking up MaaS offerings in (a) Sydney and (b) Tyneside.

5.2. Potential impact on public transport use Once a MaaS offering is selected, the respondent was asked to indicate the three most likely impacts on public transport use and the way they access public transport services. Fig. 13 compares the potential impacts of MaaS on public transport use in Sydney and Tyneside across the three products MaaS providers may offer: customised plans, PayG, CIY. Under the subscription model, be it pre-defined or created by the customer, MaaS shows a great potential to increase public transport use with about 50% of the subscribers in Sydney and nearly 60% of subscribers in Tyneside stating that they would use more public transport. In contrast, only 10% of PayG users in Sydney stated that they would use more public transport while more than half indicated that their public transport use would remain the same (i.e., no impact). The equivalent evidence for Tyneside is that 40% of PayG users would use more public transport and 30% would see no impact. Although some differences between Sydney and Tyneside are observed in terms of how MaaS uptake may impact public transport use, it is clear that the adopted model, be it periodical subscription or pay-as-they-go, has a significant implication on how the adopter may change their use of public transport. Periodic subscribers show a greater tendency to use public transport more while PayG users are more likely to maintain their current level of public transport use and in some cases, replacing some public transport trips with Taxi/Uber or car-share. However, there is a clear contrast in stated behaviour between Sydney and Tyneside PayG adopters in terms of how MaaS may change their PT use. This finding could be explained by the difference in institutional setting between the two study areas with regulated and integrated ticketing system in Sydney vs. deregulated in Tyneside. More specifically, public transport services in Sydney are regulated and heavily subsidised by the government where the users pay an Opal fare (a pre-pay, integrated, multi-modal and distance-band based ticketing system) that accounts for about 25% of the cost of providing the service 15

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Fig. 13. Stated impact on public transport use by subscription option: MaaS customer surveys 2017/18.

Fig. 14. Potential impact of MaaS uptake on first/last mile issues associated with PT use: MaaS customer surveys 2017/18.

and is capped for full-fare travellers at $15.80 per day or $63.20 per week (IPART, 2019). Public transport services in Tyneside, however, are highly deregulated with many operators and a complicated fare structure. While there is a network wide travel ticket (i.e., Network One), which is a multi-modal and zone-based ticket, this integrated ticket targets regular travellers and those using buses run by different operators. Other than Network One, each bus operator has their own fare system and separate travelcards, only available on their own services, as does metro, ferry and rail. It is a zonal system for metro, but buses are a law unto themselves. Thus, having a MaaS app with integrated ticketing and fare system appears to be more appealing in a deregulated setting of Tyneside where a significant of respondents stated that they might use more public transport, even under the pay-as-they-go arrangement. Conversely, public transport users in the regulated environment of Sydney seem to be satisfied with the Opal system and see less value in the MaaS app under the PayG arrangement (more details are provided in Ho et al., 2018). Fig. 14 show MaaS potential in changing the way in which Sydney and Tyneside residents access PT services by a subscription model. There is a great deal of speculation as to how MaaS technologies will facilitate multimodal journeys by addressing the first and last mile issue associated with PT use. Hence, it is important to provide quantitative evidence as to how this may happen. Despite these speculations, an overwhelming percentage of prospective adopters stated that subscribe to MaaS would not change the way they access PT services, especially in Sydney, with only 20% of MaaS adopters stating that they would use taxi or car-share to access PT services and this planned behaviour does not vary by the subscription model. Interestingly, upon subscription, 40% of Tyneside adopters stated that they would walk or cycle more to PT while only 10% of Sydney adopters stated the same. This large difference is likely linked to the MaaS packages offered to Sydney and Tyneside respondents, with MaaS offerings to the former having no freefloating bike-share while the latter included bike-share.5 5

Bike-share was not available in Sydney at the time the survey in Sydney was conducted (March – April 2017). 16

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Fig. 15. A schematic view of MaaS Decision Support System (DSS).

The collective evidence on bike-share and WTP for its use suggests that although the Tyneside respondents are not willing to pay for bike-share (see Table 6), they would use bike-share to access PT if this is included as part of the MaaS offerings. Matyas and Kamargianni (2018) reports a similar finding in London while ITS Australia (2018, p.8) reports that “Australians rejecting any MaaS product with bike sharing included”. This is controversial since people may not be willing to pay for bike-share but there should be no reason for rejecting it, particularly when it is offered as a complement/free service; however, neither of these studies estimate the user WTP for bike-share to verify this and our previous study in Sydney did not include bikeshare in MaaS offerings since bike share was not available in Sydney at the time. Thus, whilst further study is required, it may be safe to conclude that if we aim to promote more sustainable choices, providing options/services that the population value, but are not willing to pay for, is a good way of altering travel behaviour and manage travel demand. 6. Model application: Decision support system A MaaS Decision Support System (DSS) is designed to assist the interpretation of the population willingness to pay for MaaS services so that Small Medium Entrepreneur (SME) MaaS innovators can use the modelling results to estimate the market demand and provide MaaS packages that are appealing to potential users. This is a practical and simple way to communicate the research findings to a broader community, including MaaS providers and/or aggregators. In other words, the DSS translates the modelling results into practical assessment of WTP and market demand, which are useful metrics for MaaS developers and innovators. Fig. 15 provides a screenshot of this DSS that is running on Excel with macros. The DSS is effectively a user-interface of the MaaS stated choice model in the sense that users can specify the mobility bundle they are interested in providing and the DSS will estimate how much of the population would buy the bundle if it was priced at a certain level. To the extent allowed though the user-interface, technical features were embedded in the DSS to limit the range of the slide-bars and the drop-down buttons (input) to avoid the DSS users interpolating the model beyond the bounds of the experiment. The DSS is based on the WTP estimates presented in Table 6. Specifically, we first simulate the distribution of random parameters using the 5,000 random draws and the parameter estimates of public transport day, one-way car-share hour and round-trip car share hour shown in Table 6 (see Hensher and Greene, 2003 on how to replicate the distribution of various random parameters, including the constrained triangular distribution used in this paper). Based on these simulated parameters and the cost parameters, also shown in Table 5, we compute user WTP for each unit of mobility entitlement, be it one day of public transport or one hour of car-share. Note that these WTP vary across the simulated sample due to the random distribution of the corresponding parameters while the WTP for other characteristics of a MaaS plan (i.e., the ability to roll-over unused credit, percentage of taxi discount, and bike-share hours) 17

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

are the average WTP across the sample. The WTP for any MaaS plan is then the sum product of the user WTP for each individual entitlement and the amount of entitlement included in that mobility bundle, the latter defined by SMEs by sliding the bars or selecting an option from a drop-down button embedded in the DDS tool above. Given the distribution of user WTP for each monthly mobility bundle and the price that SME wants to charge for providing mobility services, we can estimate the potential uptake, which is simply the percentage of area under the WTP curve (purple line) and on the right hand side of the price tag (red vertical line). The demand curve for any mobility bundle (bottom plot in Fig. 15) can be established by changing the price tag and computing the proportion of the simulated sample which has a higher WTP for that bundle than its price tag. It should be noted that in order to develop the DDS tool described above, we assume that those who have a WTP higher than the price-tag will take up MaaS offers. We also assume that the model parameters derived from a sample of randomly selected respondents could be generalised to the study areas. These are the standard assumptions required for any statistical model application. However, transferring or applying the model parameters to another environmental setting, should only be undertaken with caution since travellers may have different transport modes available to them, and even when they have the same set of transport options to the participants in this study, their experience with these modes and their usage may well be different. Another caveat relates to the uptake level estimated by the DSS which is based on the experimental study that first askes the traveller to report their travel need and then offers them a tailored mobility bundle. To obtain the level of uptake estimated by the DSS, MaaS providers must understand customer’s current travel patterns and design a package that customises to their travel needs. Understanding the traveller’s need is critical to obtain any decent uptake, apart from marketing and technical features of MaaS apps. If mobility bundles are offered “blindly” without understanding how much each customer needs for each month, the level of uptake will be much lower than is estimated by the DSS. 7. Discussion and conclusions This paper is an initial contribution to understanding the potential demand for MaaS operated under different business models such as monthly subscription, pay-as-they-go, or create it themselves. The study used stated choice experiments administered face-toface in Sydney, Australia and Tyneside, UK to obtain customer responses to MaaS offerings and to estimate user WTP for mobility services if/when such the services are available in the local markets. Understanding market appetite is very important for MaaS providers in designing mobility packages and business models that are appealing to the travelling population. In this regard, the DSS developed herein is particularly useful for MaaS providers in designing a commercially viable MaaS product to obtain a certain market share or return on investment, given the demand curve and the distribution of WTP in the estimated population. The previous demand study in Sydney identified the influence of current travel patterns on the demand for MaaS and follow-on study in the UK context reinforces the importance of understanding customers, in terms of both socio-economic circumstances and travel needs, to create customised mobility bundles that would maximise the likelihood of uptake. By first seeking to understand how much the customer consumes different mobility services through their weekly travel patterns before offering them a mobility bundle tailored to their needs, this experimental study has consistently found that over 40% of the sample would take up MaaS, more as subscription users than as pay-as-you-go users. In both the environmental settings of Sydney and Tyneside, offering the customer the ability to bundle further mobility services into their fortnightly/monthly plan has not increased the uptake level, probably because the pre-defined plans have already been customised to each traveller and they are offered, in the experiment, the same level of discount as in the CIY plans.6 This study based on Tyneside data employs market segmentation to divide the population into three groups for sampling and marketing. People who use both car and PT, comprising most of the sample, were offered MaaS bundles which includes both carshare hours and number of days with unlimited PT use, plus other mobility services such as taxi and bike-share. People in this group show the highest level of interest in MaaS through their stated choices while those who are captive to PT services (i.e., not able to drive) show the least interest, with non-PT users lying in between. The findings are reasonable since MaaS offers an opportunity to dispose of the car, and hence cost of car ownership and use. This is exactly what we asked the respondents to compare: the current cost of travel including the sunk cost of car ownership vs. the future MaaS cost. Potential adopters see the economics of MaaS (i.e., buying mobility services) in comparison to the traditional car ownership model (i.e., buying mobility assets) but for a large proportion of the population, nearly 50%, the car will still be king. Car-lovers value the convenience of their own cars, and while seeing the benefit of MaaS, they are not willing to sacrifice their cars. Therefore, MaaS is better marketed as a substitute for a second household car. Both descriptive and modelling results suggest that car-sufficient households are more likely than car-negotiating households to take up MaaS, with members of the former stating to use MaaS as on-going subscribers while the latter through pay-asthey-go option. This study highlights an important question for policy makers and MaaS innovators around the business model of MaaS, given that the impact of MaaS on PT use has been found to vary substantially by MaaS user type. Offering a Pay-as-you-go option increases MaaS uptake but this model promotes less sustainable choices because pay-as-they-go adopters plan to maintain their travel patterns while monthly subscribers stated that they might use more PT and active modes. On the one hand, the Pay-as-you-go model represents the status quo and is easier to understand than the subscription model. On the other hand, economies of scale make MaaS bundles more attractive to some segments of the population, and this is confirmed by both surveys in Sydney and Tyneside. 6 In real-life, it may be more reasonable to assume that pre-defined bundles are slightly cheaper than CIY bundles which require additional administration resource. In the experiment, however, we use the same discount levels for the two bundles.

18

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Subscribers will benefit more than Pay-as-you-go users from larger discounts on public transport fares and car share rates, as well as Uber and taxi discounts. Mobility providers, such as a car-sharing company, will be guaranteed a certain level of consumption, and therefore can offer more discounts to subscribers as opposed to Pay-as-you-go users. To avoid introducing economic distortions via a subscription model, the buyers of MaaS plans should be provided with an option to role-over their unused credit to the next periods if they do not use up their entitlements (i.e., car kms/hours or public transport days, taxi/Uber service) such that unnecessary trips by car or taxi/Uber will not be conducted just for the sake of using up the credit they have purchased. Allowing for credit transfers, of course, increases the costs of administration (Sochor et al., 2015b, 2016) and therefore should come with a premium, and it appears that users are willing to pay for this. Another important finding of this research is that the travelling public appear to value the convenience of MaaS apps but they are not prepared to pay for it, reflected in the statistical insignificance of Pay-as-you-go model parameters. This suggests that technologies will need to be accompanied by some discounts to guarantee a widespread adoption of MaaS. The fact that users are not willing to pay for MaaS apps alone does not necessarily mean that the Pay-as-you-go option has little role to play, since PayG would provide an extra 10% of the travelling public a chance to experience MaaS services, and overt experience has been shown in other studies to be an important factor in deciding uptake (Hensher and Ho, 2016). Similarly, while people are not willing to pay for bike-share under the MaaS plan, they show they would cycle more to access PT services if bike-share is included in their mobility plan. Matyas and Kamargianni (2018) reports a similar finding in London while ITS Australia (2018, p.8) reports that “Australians rejecting any MaaS product with bike sharing included”. This is controversial since people may not be willing to pay for bike-share but there should be no reason for rejecting it, particularly when it is offered as a complement/free service; however, neither of these studies estimate the user WTP for bike-share to verify this and our previous study in Sydney did not include bikeshare in MaaS offerings since bike share was not available in Sydney at the time. Thus, whilst further study is required, it may be safe to conclude that if we aim to promote more sustainable choices, providing options/services that the population value, but are not willing to pay for, is a good way of altering travel behaviour and manage travel demand. The insight gained from this MaaS demand study would be improved if separate models for different segments of the population could be estimated. This would require a bigger sample size but would allow us to estimate potential uptake for each group of the population, as opposed to the average level amongst the entire population as presented in this paper. Another direction for on-going research is to examine the demand for MaaS by tourists, businesses, and households instead of individuals. The latter may represent the largest market but the former groups, especially tourists, may be the first adopters. Another avenue for further research relates to the variation in MaaS uptake level by geospatial location, which this paper has investigated but failed to establish a significant relationship. Acknowledgments This paper contributes to the research program of the Volvo Research and Education Foundation Bus Rapid Transit (BRT+) Centre of Excellence. This research was partially funded by the Catapult Transport Systems, and the University of Sydney Business School Pilot Research Scheme. The authors are very thankful to the four anonymous reviewers and the Co-editor in-chief, Prof. Juan de Dios Ortúzar, for their constructive comments which have significantly improved the paper. Appendix A. Model validation with 20% hold out sample See Tables A1 and A2. Table A1 Model specifications with 80% sample: identifying the best model for application. Model 1

Model 2

Model 3

Model 4

Description

Para

t-value

Para

t-value

Para

t-value

Para

t-value

Heteroscedastic conditioning function (Iq) Car non-users Car frequent users Car very frequent users Own smartphone & use internet every day Household with 2+ children* Respondent's aged 55+ Respondent's aged 35–44 * Car-negotiating household

0.321 −0.113 −0.192 −0.439 0.001 0.089 −0.010 0.052

5.540 −2.700 −5.410 −8.600 0.070 2.800 −0.390 2.930

0.317 −0.113 −0.188 −0.444

5.56 −2.71 −5.43 −8.72

0.315 −0.112 −0.187 −0.441

5.510 −2.670 −5.290 −8.610

0.289 −0.030 −0.204 −0.430

5.880 −0.630 −5.690 −8.480

0.089

2.98

3.930

3.05

2.970 −0.430 3.070

0.134

0.053

0.093 −0.010 0.053

0.062

3.080

Standard utility function (U) Fortnightly fee of MaaS plans ($) Fortnightly fee of CIY MaaS plan ($) Fortnightly fee of PayG MaaS plan ($) unlimited PT days, mean = std dev

−0.101 −0.111 −0.028 0.357

−8.150 −7.940 −0.690 7.740

−0.101 −0.110 −0.027 0.358

−8.30 −8.04 −0.68 7.89

−0.101 −0.110 −0.027 0.357

−8.270 −8.030 −0.670 7.870

−0.100 −0.113

−8.310 −8.330

0.351

7.970

(continued on next page) 19

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Table A1 (continued) Model 1

Model 2

Model 3

Model 4

Description

Para

t-value

Para

t-value

Para

t-value

Para

t-value

one-way car-share hours, mean = std dev round-trip car-share hours, mean = std dev car-share hour rate if PayG ($/h)* Advance booking time (min)* Entitled to taxi discount (% off bill) free floating bike-share hours Unused credit lost Constant of MaaS Plan A Constant of MaaS Plan B Constant of PayG MaaS Plan Car ownership and use monthly cost ($)* PT days in a typical month (day) Car hour used in a typical month (h)

0.488 0.411 −0.306 −0.008 0.078 0.001 −0.191 −1.308 −1.863 −2.203 −0.0004 −0.064 0.015

7.360 7.810 −1.260 −0.770 7.590 2.480 −2.550 −3.090 −4.380 −7.770 −1.080 −8.350 2.010

0.482 0.406 −0.301

7.64 8.02 −1.24

0.481 0.405 −0.299

7.620 7.980 −1.240

0.505 0.426

7.850 8.390

0.078 0.001 −0.195 −1.433 −2.006 −2.214 −0.0004 −0.063 0.015

7.70 2.53 −2.61 −3.65 −5.10 −7.86 −0.95 −8.25 1.99

0.078 0.001 −0.194 −1.428 −2.000 −2.213 −0.0004 −0.063 0.015

7.680 2.540 −2.600 −3.640 −5.080 −7.870 −0.980 −8.250 1.990

0.086 0.001 −0.191 −1.429 −2.011 −3.097

8.540 2.360 −2.550 −3.760 −5.270 −13.820

−0.070 0.029

−9.460 5.590

Note: * denotes statistically insignificant variables that were switched on and off during the search for the best model.

Table A2 Forecasting performance of model specifications across MaaS plans. No subscription

Monthly subscription

Pay-as-you-go

Your own plan

PWSE

Model

Sample

Actual

Predicted

Actual

Predicted

Actual

Predicted

Actual

Predicted

Accuracy

1

80% 20% 80% 20% 80% 20% 80% 20%

58.16% 52.26% 58.16% 52.26% 58.16% 52.26% 58.16% 52.26%

57.54% 53.45% 57.54% 53.45% 57.54% 53.41% 57.19% 53.41%

24.74% 28.71% 24.74% 28.71% 24.74% 28.71% 24.74% 28.71%

25.26% 29.36% 25.26% 29.36% 25.26% 29.39% 25.09% 28.68%

10.00% 11.29% 10.00% 11.29% 10.00% 11.29% 10.00% 11.29%

10.00% 8.72% 10.00% 8.72% 10.00% 8.74% 10.53% 9.74%

7.11% 7.74% 7.11% 7.74% 7.11% 7.74% 7.11% 7.74%

7.28% 8.46% 7.28% 8.46% 7.28% 8.46% 7.19% 8.17%

98.68% 94.86% 98.68% 94.86% 98.68% 94.90% 98.05% 96.90%

2 3 4

sample hold-out sample hold-out sample hold-out sample hold-out

Note: Model 4 is best in terms of forecasting performance with an accuracy of 96.6% based on 20% hold out sample.

Appendix B. Supplementary material Supplementary data to this article can be found online at https://doi.org/10.1016/j.tra.2019.09.031.

References Armstrong, P., Garrido, R., Ortúzar, J.D., 2001. Confidence intervals to bound the value of time. Transp. Res. Part E: Logistics Transp. Rev. 37 (2), 143–161. Caiati, V., Rasouli, S., Timmermans, H., 2018. Subscription and bundling preferences for mobility as a service: a sequential portfolio choice experiment. In: Transportation Systems in the Connected Era: Proceedings of the 23rd International Conference of Hong Kong Society for Transportation Studies (HKSTS), pp. 683–1391. Chatterjee, K., Goodwin, P., Schwanen, T., Clark, B., Jain, J., Melia, S., Middleton, J., Plyushteva, A., Ricci, M., Santos, G., Stokes, G., 2018. Young people’s travel – What’s changed and why? Review and analysis. Deloitte, 2017. Smart everything, everywhere: Mobile Consumer Survey 2017. Ebrahimi, S., Sharmeen, F., Meurs, H., 2018. Innovative Business Architectures (BAs) for Mobility as a Service (MaaS) - Exploration, Assessment, and Categorization Using Operational MaaS Cases. Transportation Research Board 97th Annual Meeting, 7-11/01/2018. Evans, A., Cummings, J., Slocombe, M., Corvaglia, F., 2018. National Travel Survey: England 2017. Available at https://assets.publishing.service.gov.uk/government/ uploads/system/uploads/attachment_data/file/729521/national-travel-survey-2017.pdf. Geurs, K.T., Thomas, T., Bijlsma, M., Douhou, S., 2015. Automatic trip and mode detection with move smarter: first results from the Dutch mobile mobility panel. Transp. Res. Proc. 11, 247–262. Goodall, W., Fishman, T.D., Bornstein, J., Bonthron, B., 2017. The rise of mobility as a service. Deloitte Rev. [Online]. Hartikainen, A., Pitkänen, J.-P., Riihelä, A., Räsänen, J., Sacs, I., Sirkiä, A., Uteng, A., 2019. WHIMPACT: Insights from the world’s first Mobility-as-a-Service (MaaS) system. Available: https://maas-alliance.eu/whimpact-insights-from-the-worlds-first-mobility-as-a-service-maas-system/. Hensher, D.A., 2017. Future bus transport contracts under a mobility as a service (MaaS) regime in the digital age: are they likely to change? Transp. Res. Part A: Policy Practice 98, 86–96. Hensher, D.A., Greene, W.H., 2003. The Mixed Logit model: The state of practice. Transportation 30, 133–176. Hensher, D.A., Rose, J.M., Greene, W.H., 2015. Applied Choice Analysis, second ed. Cambridge University Press, Cambridge. Hensher, D.A., Ho, C.Q., 2016. Experience conditioning in commuter modal choice modelling – does it make a difference? Transp. Res. Part E: Logistics Transp. Rev. 95, 164–176. Ho, C., 2013. An investigation of intra-household interactions in travel mode choice. PhD. The University of Sydney. Ho, C., Mulley, C., 2013. Incorporating intrahousehold interactions into a tour-based model of public transport use in car-negotiating households. Transp. Res. Rec.: J.

20

Transportation Research Part A xxx (xxxx) xxx–xxx

C.Q. Ho, et al.

Transp. Res. Board 2343, 1–9. Ho, C., Mulley, C., 2015. Intra-household Interactions in tour-based mode choice: the role of social, temporal, spatial and resource constraints. Transp. Policy 38, 52–63. Ho, C.Q., Hensher, D.A., Mulley, C., Wong, Y.Z., 2018. Potential uptake and willingness-to-pay for mobility as a service (MaaS): a stated choice study. Transp. Res. Part A: Policy Practice 117, 302–318. IPART 2019. Maximum Opal Fares 2020 - 24. Available at https://www.ipart.nsw.gov.au/files/sharedassets/website/shared-files/pricing-reviews-transport-servicespublications-opal-public-transport-services-to-june-2024/issues-paper-opal-public-transport-services-to-june-2024-april-2019.pdf. ITS Australia, 2018. Mobility as a Service in Australia: Customer insights and opportunities. Jittrapirom, P., Caiati, V., Feneri, A.-M., Ebrahimigharehbaghi, S., Alonso-González, M., Narayan, J., 2017a. Mobility as a service: a critical review of definitions, assessments of schemes and key challenges. Urban Plan. 2, 13–25. Jittrapirom, P., Marchau, V.A.W.J., Meurs, H., 2017b. Dynamic adaptive policymaking for implementing mobility as a service (maas). In: European Transport Conference, 4-6/10/2017. Barcelona, Spain. Kamargianni, M., Matyas, M., 2017. The business ecosystem of mobility-as-a-service. In: 96th Transportation Research Board (TRB) Annual Meeting. Washington DC, United States. Karlsson, I.C.M., Sochor, J., Aapaoja, A., Eckhardt, J., König, D., 2017. Deliverable 4: Impact Assessement. König, D., Eckhardt, J., Aapaoja, A., Sochor, J., Karlsson, M., 2016. Deliverable 3: Business and operator models for MaaS. MAASiFiE project funded by CEDR. Lyons, G., Hammond, P., Mackay, K., 2019. The importance of user perspective in the evolution of MaaS. Transp. Res. Part A: Policy Practice 121, 22–36. Maat, K., Timmermans, H., 2007. Household car ownership in relation to residential and work location. In: Transportation Research Board. 86th Annual Meeting, CDROM, 21-25 January 2007 Washington D.C. Matyas, M., Kamargianni, M., 2018. The potential of mobility as a service bundles as a mobility management tool. Transportation 1–18. Mukhtar-Landgren, D., Karlsson, M., Koglin, T., Kronsell, A., Lund, E., Sarasini, S., Sochor, J.L., Wendle, B., 2016. Institutional conditions for integrated mobility services (IMS): Towards a framework for analysis. Raimond, T., Milthorpe, F., 2010. Why are young people driving less? Trends in licence-holding and travel behaviour. Australasian Transport Research Forum 2010, 29 September – 1 October 2010 Canberra, Australia. Ramazzotti, D., Ambrosino, G., Nelson, J.D., Boero, M. From the concept of flexible mobility services to the ‘shared mobility services agency. Paratransit: Shaping the Flexible Transport Future. Roads, T.a.M., 2012. Travel in south-east Queensland: An analysis of travel data from 1992 to 2009. Smith, G., Sochor, J., Karlsson, I.C.M., 2018. Mobility as a Service: development scenarios and implications for public transport. Res. Transp. Econ. 69, 592–599. Sochor, J., Karlsson, I.C.M., Strömberg, H., 2016. Trying out mobility as a service. Transp. Res. Rec.: J. Transp. Res. Board 2542, 57–64. Sochor, J., Strömberg, H., Karlsson, I.C.M., 2015a. Implementing mobility as a service. Transp. Res. Rec.: J. Transp. Res. Board 2536, 1–9. Sochor, J., Strömberg, H., Karlsson, I.C.M., 2015b. Implementing mobility as a service: challenges in integrating user, commercial, and societal perspectives. Transp. Res. Rec. 2536, 1–9. Statistics, B.o.T., 2014. Household Travel Survey Report: Sydney 2012/13. Available at https://www.transport.nsw.gov.au/sites/default/files/media/documents/ 2017/HTS%20Report%20Sydney%202012-13.pdf. Strömberg, H., Karlsson, I.C.M., Sochor, J., 2018. Inviting travelers to the smorgasbord of sustainable urban transport: evidence from a MaaS field trial. Transportation 45, 1655–1670. Swait, J., Adamowicz, W., 2001. Choice environment, market complexity, and consumer behavior: a theoretical and empirical approach for incorporating decision complexity into models of consumer choice. Organ. Behav. Hum. Decis. Process. 86, 141–167. Turrentine, T.S., Kurani, K.S., 2007. Car buyers and fuel economy? Energy Policy 35, 1213–1223. Utriainen, R., Pöllänen, M., 2018. Review on mobility as a service in scientific publications. Res. Transp. Bus. Manage. 27, 15–23.

21