How Much of Which Mode? Using Revealed Preference Data to Design Mobility As a Service Plans

Abstract

Mobility as a service (MaaS) seeks to integrate emerging shared mobility modes with existing public transportation (PT). Decisive to its uptake will be attractive subscription plans that cater for heterogeneous mobility needs. Research on willingness to pay for such plans has commenced, yet remains divided on a central question: how much to include of which mode, and how? Complementing previous research building on stated preference data, in this study revealed preference data is used to analyze the viability of different subscription plan components (PT, car-sharing, bike-sharing, taxi), modes of inclusion (budgets in minutes and season tickets) and subscription cycles (weekly, monthly). PT season tickets are found to be viable for 83% of all respondents. Interestingly, the viability of minute budgets of car- and bike-sharing depends on subscription cycle length. Using a monthly subscription cycle, car-/bike-sharing appears viable to include in a bundle for 35%/31% of all respondents, respectively. Using a weekly subscription cycle, these figures drop to 1.4%/0.4%, respectively, as weekly variation in demand is much higher than monthly variation. In contrast to many current MaaS pilots, taxi use remains too infrequent to include as recurring credit in MaaS plans. Rather, pay-as-you-go is the economically more sensible option for consumers. This research therefore challenges the idea of all-inclusive mobility flat rates and suggests a more modular design.

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References

Kamargianni

, W. Li, M. Matyas, and A. Schäfer. A Critical Review of New Mobility Services for Urban Transport. Transportation Research Procedia , Vol. 14, 2016, pp. 3294–3303.

Jittrapirom

, V. Caiati, A. M. Feneri, S. Ebrahimigharehbaghi, M. J. Alonso González, and J. Narayan. Mobility As a Service: A Critical Review of Definitions, Assessments of Schemes, and Key Challenges. Urban Planning , Vol. 2, No. 2, 2017, pp. 13–25.

Mulley

Mobility As a Services (MaaS) – Does it Have Critical Mass? Transport Reviews , Vol. 37, No. 3, 2017, pp. 247–251.

Matyas

, and M. Kamargianni.

Survey Design for Exploring Demand for Mobility As a Service Plans.

Transportation , Vol. 46, 2019, pp. 1525–1558.

Esztergár-Kiss

, and T. Kerényi. Creation of Mobility Packages Based on the MaaS Concept. Travel Behaviour and Society , 2019. https://doi.org/10.1016/j.tbs.2019.05.007

C. Q.

, D. A. Hensher, C. Mulley, and Y. Z. Wong. Potential Uptake and Willingness-to-Pay for Mobility As a Service (MaaS): A Stated Choice Study. Transportation Research Part A: Policy and Practice , Vol. 117, 2018, pp. 302–318.

C. Q.

, C. Mulley, and D. A. Hensher. Public Preferences for Mobility As a Service: Insights from Stated Preference Surveys. Transportation Research Part A: Policy and Practice , Vol. 131, 2020, pp. 70–90.

Guidon

, M. Wicki, T. Bernauer, and K. W. Axhausen. Transportation Service Bundling – for Whose Benefit? Consumer Valuation of Pure Bundling in the Passenger Transportation Market. Transportation Research Part A: Policy and Practice , Vol. 131, 2020, pp. 91–106.

Matyas

, and M. Kamargianni. The Potential of Mobility As a Service Bundles As a Mobility Management Tool. Transportation , Vol. 46, 2019, pp. 1951–1968.

10.

Gärling

, and K. W. Axhausen.

Introduction: Habitual Travel Choice.

Transportation , Vol. 30, No. 1, 2003, pp. 1–11.

11.

Tirachini

Ride‐Hailing, Travel Behaviour and Sustainable Mobility: An International Review.

Transportation , 2019, pp. 1–37. https://doi.org/10.1007/s11116-019-10070-2.

12.

Erhardt

G. D.

, S. Roy, D. Cooper, B. Sana, M. Chen, and J. Castiglione. Do Transportation Network Companies Decrease or Increase Congestion? Science Advances , Vol. 5, No. 5, 2019, p. eaau2670.

13.

Lewis

E. O. C.

, and D. MacKenzie. UberHOP in Seattle: Who, Why, and How?. Transportation Research Record: Journal of the Transportation Research Board , 2017. 2650: 101–111.

14.

Rayle

, D. Dai, N. Chan, R. Cervero, and S. Shaheen. Just a Better Taxi? A Survey-Based Comparison of Taxis, Transit, and Ridesourcing Services in San Francisco. Transport Policy , Vol. 45, 2016, pp. 168–178.

15.

Tirachini

, and M. del Río. Ride-Hailing in Santiago De Chile: Users’ Characterisation and Effects on Travel Behaviour. Transport Policy , Vol. 82, 2019, pp. 46–57.

16.

Tirachini

, and A. Gomez-Lobo. Does Ride-Hailing Increase or Decrease Vehicle Kilometers Traveled (VKT)? A Simulation Approach for Santiago De Chile. International Journal of Sustainable Transportation , Vol. 14, No. 3, 2020, pp. 187–204.

17.

Alemi

, G. Circella, S. Handy, and P. Mokhtarian. What Influences Travelers to Use Uber? Exploring the Factors Affecting the Adoption of On-Demand Ride Services in California. Travel Behaviour and Society , Vol. 13, 2018, pp. 88–104.

18.

Graehler

, R. A. Mucci, and G. D. Erhardt. Understanding the Recent Transit Ridership Decline in Major US Cities: Service Cuts or Emerging Modes? Presented at 98th Annual Meeting of the Transportation Research Board, Washington, D.C., 2019.

19.

Tang

B. J.

, X. Y. Li, B. Yu, and Y. M. Wei. How App-Based Ride-Hailing Services Influence Travel Behavior: An Empirical Study from China. International Journal of Sustainable Transportation , 2019, pp.1–15. https://doi.org/10.1080/15568318.2019.1584932

20.

Fishman

, S. Washington, and N. Haworth. Bike Share: A Synthesis of the Literature. Transport Reviews , Vol. 33, No. 2: 148–165.

21.

Ricci

Bike Sharing: A Review of Evidence on Impacts and Processes of Implementation and Operation. Research in Transportation Business & Management , Vol. 15, 2015, pp. 28–38.

22.

Bachand-Marleau

, B. H. Y. Lee, and A. M. El-Geneidy. Better Understanding of Factors Influencing Likelihood of using Shared Bicycle Systems and Frequency of Use. Transportation Research Record: Journal of the Transportation Research Board , 2012. 2314: 66–71.

23.

Campbell

K. B.

, and C. Brakewood Sharing Riders: How Bikesharing Impacts Bus Ridership in New York City. Transportation Research Part A: Policy and Practice , Vol. 100, 2017, pp. 264–282.

24.

Fishman

, S. Washington, and N. Haworth. Bike Share’s Impact on Car Use: Evidence from the United States, Great Britain, and Australia. Transportation Research Part D: Transport and Environment , Vol. 31, 2014, pp. 13–20.

25.

Murphy

, and J. Usher. The Role of Bicycle-Sharing in the City: Analysis of the Irish Experience. International Journal of Sustainable Transportation , Vol. 9, No. 2, 2015, pp. 116–125.

26.

Shaheen

, H. Zhang, E. Martin, and S. Guzman. Hangzhou Public Bicycle: Understanding Early Adoption and Behavioral Response to Bikesharing. Transportation Research Record: Journal of the Transportation Research Board, 2011. 2247: 33–41.

27.

Caspi

, M. J. Smart, and R. B. Noland. Spatial Associations in Dockless Shared E-Scooter Usage. Presented at 99th Annual Meeting of the Transportation Research Board, Washington, D.C., 2020.

28.

Reck

D. J.

, S. Guidon, and K. W. Axhausen. Modelling Shared E-Scooters in Louisville, Kentucky: A Spatial Regression Approach. Presented at 99th Annual Meeting of the Transportation Research Board, Washington, D.C., 2020.

29.

Zuniga-Garcia

, and R. Machemehl. Dockless Electric Scooters and Transit Use in an Urban/University Environment. Presented at 99th Annual Meeting of the Transportation Research Board, Washington, D.C., 2020.

30.

Hensher

D. A.

, C. Mulley, C. Q. Ho, J. Nelson, G. Smith, and Y. Wong. Understanding MaaS: Past, Present and Future. Proc., 2nd International Conference on Mobility as a Service, Tampere, 2019.

31.

Lyons

, P. Hammond, and K. Mackay. The Importance of User Perspective in the Evolution of MaaS. Transportation Research Part A: Policy and Practice , Vol. 121, 2019, pp. 22–36.

32.

Sochor

, H. Arby, I. M. Karlsson, and S. Sarasini. A Topological Approach to Mobility As a Service: A Proposed Tool for Understanding Requirements and Effects, and for Aiding the Integration of Societal Goals. Research in Transportation Business & Management , Vol. 27, 2018, pp. 3–14.

33.

Reck

D. J.

, D. A. Hensher, and C. Q. Ho. MaaS Bundle Design. ITLS Working Paper, No. ITLS-WP-20-06, 2020. Submitted to Transportation Research Part A: Policy and Practice. Available at: https://ses.library.usyd.edu.au/bitstream/handle/2123/21987/ITLS-WP-20-06.pdf?sequence=1&isAllowed=y

34.

Matyas

, and M. Kamargianni. Using Mixed Methods to Examine User Preferences for Mobility As a Service Subscription Plans. Presented at 98th Annual Meeting of the Transportation Research Board, Washington, D.C., 2019.

35.

Stopczynski

, V. Sekara, P. Sapiezynski, A. Cuttone, M. M. Madsen, F. E. Larsen, and S. Lehmann.

Measuring Large-Scale Social Networks with High Resolution.

PLoS One , Vol. 9, No. 4, 2014, pp. e95978.

36.

Alessandretti

, S. Lehmann, and A. Baronchelli. Understanding the Interplay between Social and Spatial Behavior. EPJ Data Science , Vol. 7, No. 1, 2018, pp. 36.

37.

Bjerre-Nielsen

, K. Minor, P. Sapiezynski, S. Lehmann, and D. D. Lassen. Wi-Finding: Urban Transportation Sensing using Crowdsourced Wi-Fi. Pervasive and Mobile Computing , Forthcoming.

38.

Floater

, P. Rode, and D. Zenghelis. Copenhagen: Green Economy Leader Report . London School of Economics and Political Science, London, 2014.

39.

Rich

, and C. O. Hansen. The Danish National Passenger Model – Model Specification and Results. European Journal of Transport and Infrastructure Research , Vol. 16, No. 4, 2016, pp. 573–599.

40.

Rich

, and C. A. Vandet. Is the Value of Travel Time Savings Increasing? Analysis throughout a Financial Crisis. Transportation Research Part A: Policy and Practice , Vol. 124, 2019, pp. 145–168.

41.

Rich

The Weeday Demand Model in LTM – Model for Generation, Destination and Mode Choice . Doc. no: 35243-004. Technical University of Denmark, Lyngby, 2017.

42.

Papu Carrone

, V. M. Hoening, A. F. Jensen, S. E. Mabit, and J. Rich. Car Sharing Preferences and Mode Substitution: A Stated Choice Experiment. Proc., World Conference on Transport Research, Mumbai, 2019.

43.

Fosgerau

, K. Hjorth, and L. J. S. Vincent. The Danish Value of Time Study. Danish Transport Research Institute, Technical University of Denmark, Lyngby, 2007. http://www.transport.dtu.dk/-/media/Institutter/Transport/forskning/publikationer/publikationer-dtf/2007/the_danish_value_of_time_study_250208.ashx. Accessed July 29, 2019.

44.

Train

K. E.

, D. L. McFadden, and M. Ben-Akiva. The Demand for Local Telephone Service: A Fully Discrete Model of Residential Calling Patterns and Service Choices. Rand Journal of Economics , Vol. 18, No. 1, 1987, pp. 109–123.

45.

Lambrecht

, and B. Skiera. Paying Too Much and Being Happy about It: Existence, Causes, and Consequences of Tariff-Choice Biases. Journal of Marketing Research , Vol. 43, No. 2, 2006, pp. 212–223.

46.

Axhausen

K. W.

, H. Köll, and M. Bader. Public Transport Usage Intensity of Season Ticket Holders in the City of Innsbruck. Report to the Innsbrucker Verkehrsbetriebe GmbH, Innsbruck and Ampass, 1998.

47.

Wirtz

, P. Vortisch, and B. Chlond. Flat Rate Bias in Public Transportation – Magnitude and Reasoning. Presented at 94th Annual Meeting of the Transportation Research Board, Washington, D.C., 2015.

48.

Martin

, H. Becker, D. Bucher, D. Jonietz, M. Raubal, and K. W. Axhausen. Begleitstudie SBB Green Class – Abschlussbericht. Working Paper No. 1439. Institute for Transport Planning and Systems, ETH Zürich, Zürich, 2019.

49.

Gunn

H. F.

Travel Budgets—A Review of Evidence and Modelling Implications. Transportation Research Part A: General , Vol. 15, No. 1, 1981, pp. 7–23.

50.

Mokhtarian

P. L.

, and C. Chen. TTB or not TTB, That Is the Question: A Review and Analysis of the Empirical Literature on Travel Time (and Money) Budgets. Transportation Research Part A: Policy and Practice , Vol. 38, No. 9–10, 2004, pp. 643–675.

51.

Tanner

J. C.

Expenditure of Time and Money on Travel. Transportation Research Part A: General , Vol. 15, No. 1, 1981, pp. 25–38.

52.

Zahavi

, and A. Talvitie. Regularities in Travel Time and Money Expenditures. Transportation Research Record: Journal of the Transportation Research Board , 1980. 750: 13–19.

53.

Young

, and S. Farber. The Who, Why, and When of Uber and Other Ride-Hailing Trips: An Examination of a Large Sample Household Travel Survey. Transportation Research Part A: Policy and Practice , Vol. 119, 2019, pp. 383–392.

54.

Strömberg

, I. M. Karlsson, and J. Sochor. Inviting Travelers to the Smorgasbord of Sustainable Urban Transport: Evidence from a MaaS Field Trial. Transportation , Vol. 45, No. 6, 2018, pp. 1655–1670.