Skip to main content
SpringerLink
Log in
Book cover

Disrupting Mobility pp 275–290Cite as

Mobility Patterns in Shared, Autonomous, and Connected Urban Transport

  • Chapter
  • First Online:

Part of the book series: Lecture Notes in Mobility ((LNMOB))

Abstract

A number of recent technological breakthroughs promise disrupting urban mobility as we know it. But anticipating such disruption requires valid predictions: disruption implies that predictions cannot simply be extrapolations from a current state. Predictions have to consider the social, economic, and spatial context of mobility. This paper studies mechanisms to support evidence-based transport planning in disrupting times. It presents various approaches, mostly based on simulation, to estimate the potential or real impact of the introduction of new paradigms on urban mobility, such as ad hoc shared forms of transportation, autonomously driving electrical vehicles, or IT platforms coordinating and integrating modes of transportation.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Nicas, J.: Google takes on uber with new ride-share service. 31 August 2016. www.wsj.com/articles/google-takes-on-uber-with-new-ride-share-service-1472584235. Accessed 1 Sept 2016

  2. Shaheen, S.: Mobility and the sharing economy (editorial). Transport Policy (2016)

    Google Scholar 

  3. Rayle, L., et al.: Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco. Transp. Policy 45, 168–178 (2016)

    Article  Google Scholar 

  4. Shaheen, S., Chan, N., Gaynor, T.: Casual carpooling in the San Francisco Bay area: understanding user characteristics, behaviors, and motivations. Transport Policy (2016)

    Google Scholar 

  5. Batty, M.: Cities as complex systems: scaling, interaction, networks, dynamics and urban morphologies. In: Meyers, R.A., (ed.) Encyclopedia of Complexity and Systems Science, vol. 1041–1071. Springer: New York, NY, (2009)

    Google Scholar 

  6. Batty, M., et al.: Entropy, complexity, and spatial information. J. Geogr. Syst. 16(4), 363–385 (2014)

    Article  Google Scholar 

  7. Simon, H.A.: Bounded rationality, in utility and probability. In: Eatwell, J., Milgate, M., Newman, P. (eds.) pp. 15–18. The Macmillan Press Ltd, New York, (1990)

    Google Scholar 

  8. Benenson, I., Torrens, P.M.: Geosimulation: Automata-based Modeling of Urban Phenomena. Wiley, Chichester, UK (2004)

    Book  Google Scholar 

  9. Torrens, P.M.: Geosimulation, automata, and traffic modelling. In: Hensher, D.A., et al. (eds.) Handbook of Transport Geography and Spatial Systems pp. 549–564. Elsevier: Amsterdam (2004)

    Google Scholar 

  10. Mokhtarian, P.L., Salomon, I.: How derived is the demand for travel? Some conceptual and measurement considerations. Transp. Res. Part A 35(8), 695–719 (2001)

    Google Scholar 

  11. Axhausen, K.W., Gärling, T.: Activity-based approaches to travel analysis: conceptual frameworks, models, and research problems. Transport Rev. 12(4), 323–341 (1992)

    Article  Google Scholar 

  12. Cottrill, C., et al.: Future Mobility Survey. Transport. Res. Record: J. Transport. Res. Board 2354, 59–67 (2013)

    Article  Google Scholar 

  13. Scholz, R.W., Lu, Y.: Detection of dynamic activity patterns at a collective level from large-volume trajectory data. Int. J. Geogr. Inf. Sci. 28(5), 946–963 (2014)

    Article  Google Scholar 

  14. Louviere, J.J., Hensher, D.A., Swait, J.D.: Stated Choice Methods: Analysis and Applications. Cambridge University Press, Cambridge, UK (2000)

    Book  MATH  Google Scholar 

  15. Tilahun, N.Y., Levinson, D.M., Krizek, K.J.: Trails, lanes, or traffic: valuing bicycle facilities with an adaptive stated preference survey. Transp. Res. Part A: Policy Pract. 41(4), 287–301 (2007)

    Google Scholar 

  16. Hess, S., Adler, T., Polak, J.W.: Modelling airport and airline choice behaviour with the use of stated preference survey data. Transp. Res. Part E: Logistics Transp. Rev. 43(3), 221–233 (2007)

    Article  Google Scholar 

  17. Contrino, H., McGuckin, N.: Demographics matter travel demand, options, and characteristics among minority populations. Public Works Manage. Policy 13(4), 361–368 (2009)

    Article  Google Scholar 

  18. Kattiyapornpong, U., Miller K.E.: Understanding travel behavior using demographic and socioeconomic variables as travel constraints. In: ANZMAC 2006: Advancing theory, maintaining relevance: Proceedings of the 2006 Australian & New Zealand Marketing Academy Conference: [Queensland University of Technology, School of Advertising, Marketing and Public Relations] (2006)

    Google Scholar 

  19. Litman, T.: Understanding Transport Demands and Elasticities (2013)

    Google Scholar 

  20. Rasouli, S., Timmermans, H.: Applications of theories and models of choice and decision-making under conditions of uncertainty in travel behavior research. Travel Behav. Soc. 1(3), 79–90 (2014)

    Article  Google Scholar 

  21. ActiveAge, An introduction to Demand Responsive Transport as a Mobility Solution in an Ageing Society, 2008

    Google Scholar 

  22. Anspacher, D., Khattak, A.J., Yim, Y.: Demand-responsive transit shuttles: who will use them? Calif. Partners Adv. Transit Highways (PATH) (2004)

    Google Scholar 

  23. Bearse, P., et al.: Paratransit demand of disabled people. Transp. Res. Part B: Methodol. 38(9), 809–831 (2004)

    Article  Google Scholar 

  24. Enoch, M.P., et al.: Evaluation study of demand responsive transport services in Wiltshire. Final report, Loughborough University, Loughborough (2006)

    Google Scholar 

  25. Häme, L.: Demand-responsive transport: models and algorithms. In: Department of Mathematics and Systems Analysis. Aalto University, Aalto (2013)

    Google Scholar 

  26. Koffman, D.: Operational experiences with flexible transit services, vol. 53. Transportation Research Board (2004)

    Google Scholar 

  27. Laws, R.: Evaluating Publicly-Funded DRT Schemes in England and Wales, Loughborough University (2009)

    Google Scholar 

  28. Lerman, S.R., et al.: A model system for forecasting patronage on demand responsive transportation systems. Transp. Res. Part A: General 14(1), 13–23 (1980)

    Article  Google Scholar 

  29. Maddern, C., Jenner, D.: Telebus mobility and accessibility benefits: final report. In 12th International Conference on Mobility and Transport for Elderly and Disabled transport (TRANSED): Hong Kong (2007)

    Google Scholar 

  30. Mageean, J., Nelson, J.D.: The evaluation of demand responsive transport services in Europe. J. Transp. Geogr. 11(4), 255–270 (2003)

    Article  Google Scholar 

  31. Nelson, J.D., Phonphitakchai, T.: An evaluation of the user characteristics of an open access DRT service. Res. Transp. Econ. 34(1), 54–65 (2012)

    Article  Google Scholar 

  32. Rosenbloom, S., Fielding, G.J.: Transit Markets of the Future: The Challenge of Change, vol. 28. Transportation Research Board (1998)

    Google Scholar 

  33. Ryley, T.J., et al.: Developing Relevant Tools for Demand Responsive Transport (DRT). In: ATCO Conference, Liverpool (2013)

    Google Scholar 

  34. Scott, R.: Demand Responsive Passenger Transport in Low-Demand Situations December 2010 (2010)

    Google Scholar 

  35. Spielberg, F., Pratt, R.H.: Demand-Responsive/ADA-Traveler Response to Transportation System Changes (2004)

    Google Scholar 

  36. Wang, C., et al.: Multilevel modelling of demand responsive transport (DRT) trips in greater Manchester based on area-wide socio-economic data. Transportation 41(3), 589–610 (2014)

    Article  Google Scholar 

  37. Victorian Integrated Survey of Travel & Activity 2009–10, Survey Procedures and Documentation, The Victorian Department of Transport (2011)

    Google Scholar 

  38. Public Transport Victoria, New PTV FlexiRide service for Yarrawonga and Mulwala: Melbourne, Australia, 2 (2013)

    Google Scholar 

  39. Ronald, N., Thompson, R.G., Winter, S.: A comparison of constrained and ad-hoc demand-responsive transportation systems. Transp. Res. Rec. 2536, 44–51 (2015)

    Article  Google Scholar 

  40. Ronald, N., Thompson, R.G., Winter, S.: Modelling ad-hoc DRT over many days: a preliminary study. In: 21st International Congress on Modelling and Simulation (MODSIM): Gold Coast, Qld, Australia, pp. 1175–1181 (2015)

    Google Scholar 

  41. NavidiKashani, Z., Ronald, N., Winter, S.: Comparing demand responsive and conventional public transport in a low demand context. In: First International Workshop on Context-Aware Smart Cities and Intelligent Transport Systems, Sydney (2016)

    Google Scholar 

  42. Daganzo, C.F.: Checkpoint dial-a-ride systems. Transp. Res. Part B: Methodol. 18(4–5), 315–327 (1984)

    Article  MathSciNet  Google Scholar 

  43. Diana, M., Quadrifoglio, L., Pronello, C.: Emissions of demand responsive services as an alternative to conventional transit systems. Transp. Res. Part D: Transport Environ. 12(3), 183–188 (2007)

    Article  Google Scholar 

  44. Diana, M., Quadrifoglio, L., Pronello, C.: A methodology for comparing distances traveled by performance-equivalent fixed-route and demand responsive transit services. Transp. Plann. Technol. 32(4), 377–399 (2009)

    Article  Google Scholar 

  45. Edwards, D., Watkins, K.: Comparing fixed-route and demand-responsive feeder transit systems in real-world settings. Transp. Res. Record: J. Transp. Res. Board 2352, 128–135 (2013)

    Article  Google Scholar 

  46. Chang, S., Yu, W.J.: Comparison of subsidized fixed-and flexible-route bus systems. Transp. Res. Record: J. Transp. Res. Board 1557, 15–20 (1996)

    Article  Google Scholar 

  47. Quadrifoglio, L., Li, X.: A methodology to derive the critical demand density for designing and operating feeder transit services. Transp. Res. Part B: Methodol. 43, 922–935 (2009)

    Article  Google Scholar 

  48. Beirão, G., Sarsfield, J.A.: Cabral, Understanding attitudes towards public transport and private car: A qualitative study. Transp. Policy 14(6), 478–489 (2007)

    Article  Google Scholar 

  49. Hensher, D.A., Stopher, P., Bullock, P.: Service quality—developing a service quality index in the provision of commercial bus contracts. Transp. Res. Part A: Policy Pract. 37(6), 499–517 (2003)

    Google Scholar 

  50. Santi, P., et al.: Quantifying the benefits of vehicle pooling with shareability networks. Proc. Natl. Acad. Sci. 111(37), 13290–13294 (2014)

    Article  Google Scholar 

  51. Amey, A.M.: Real-time ridesharing: exploring the opportunities and challenges of designing a technology-based rideshare trial for the MIT community, Massachusetts Institute of Technology (2010)

    Google Scholar 

  52. Chaube, V., Kavanaugh, A.L., Perez-Quinones, M.A.: Leveraging social networks to embed trust in rideshare programs. In: System Sciences (HICSS), 2010 43rd Hawaii International Conference on: IEEE (2010)

    Google Scholar 

  53. Wessels, R.: Combining Ridesharing and Social Networks, UTwente (2009)

    Google Scholar 

  54. Baldacci, R., Bartolini, F., Mingozzi, A.: An exact algorithm for the pickup and delivery problem with time windows. Oper. Res. 59, 414–426 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  55. Desrosiers, J., Dumas, Y., Soumis, F.: A dynamic programming solution of the large-scale single-vehicle dial-a-ride problem with time windows. Am. J. Math. Manage. Sci. 6, 301–325 (1986)

    MATH  Google Scholar 

  56. Psaraftis, H.N.: Scheduling large-scale advance-request dial-a-ride systems. Am. J. Math. Manage. Sci. 6, 327–367 (1986)

    MATH  Google Scholar 

  57. Zhou, J.: Routing by mixed set programming. In: Proceedings of the 8th International Symposium on Operations Research and Its Applications, pp. 155–166 (2009)

    Google Scholar 

  58. Ropke, S., Cordeau, J.F.: Branch and cut and price for the pickup and delivery problem with time windows. Transp. Sci. 43, 267–286 (2009)

    Article  Google Scholar 

  59. Badaloni, S., et al.: Addressing temporally constrained delivery problems with the swarm intelligence approach. Intell. Auton. Syst. 10, 264–271 (2008)

    Google Scholar 

  60. Bent, R., Hentenryck, P.V.: A two-stage hybrid algorithm for pickup and delivery vehicle routing problems with time windows. Comput. Oper. Res. 33, 875–893 (2006)

    Article  MATH  Google Scholar 

  61. Gronalt, M., Hartl, R.F., Reimann, M.: New savings based algorithms for time constrained pickup and delivery of full truckloads. Eur. J. Oper. Res. 151, 520–535 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  62. Hasle, G., Kloster, O.: Industrial vehicle routing. In: Hasle, G., Lie, K.A., Quak, E. (eds.) Geometric Modelling, Numerical Simulation, and Optimization, pp. 397–435. Springer, Berlin (2007)

    Google Scholar 

  63. Hosny, M., Mumford, C.: New solution construction heuristics for the multiple vehicle pickup and delivery problem with time windows. In: Proceedings of the Metaheuristic International Conference (2009)

    Google Scholar 

  64. Huang, Y.H., Ting, C.K.: Ant colony optimization for the single vehicle pickup and delivery problem with time window. In: Proceedings of the International Conference on Technologies and Applications of Artificial Intelligence (2010)

    Google Scholar 

  65. Koning, D.: Using column generation for the pickup and delivery problem with disturbances, Utrecht University (2011)

    Google Scholar 

  66. Lu, Q., Dessouky, M.M.: A new insertion-based construction heuristic for solving the pickup and delivery problem with time windows. Eur. J. Oper. Res. 175, 672–687 (2006)

    Article  MATH  Google Scholar 

  67. Nagata, Y., Kobayashi, S.: A memetic algorithm for the pickup and delivery problem with time windows using selective route exchange crossover. In: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature, pp. 536–545 (2010)

    Google Scholar 

  68. Pankratz, G.: A grouping genetic algorithm for the pickup and delivery problem with time windows. OR Spectr. 27, 21–41 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  69. Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transp. Sci. 40, 455–472 (2006)

    Article  Google Scholar 

  70. Xu, H., et al.: Solving a practical pickup and delivery problem. Transp. Sci. 37, 347–364 (2003)

    Article  Google Scholar 

  71. Pillac, V., et al.: A review of dynamics vehicle routing problems. Eur. J. Oper. Res. 225, 1–11 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  72. Kutadinata, R., Thompson, R., Winter, S.: Cost-efficient co-modal ride-sharing scheme through anticipatory dynamic optimisation. In: Submitted to the 23rd World Congress on Intelligent Transport Systems (2016)

    Google Scholar 

  73. Bent, R., Van Hentenryck, P.: Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Oper. Res. 52, 977–987 (2004)

    Article  MATH  Google Scholar 

  74. Gendreau, M., et al.: Parallel tabu search for real-time vehicle routing and dispatching. Transp. Sci. 33, 381–390 (1999)

    Article  MATH  Google Scholar 

  75. Gendreau, M., Laporte, G., Séguin, R.: Stochastic vehicle routing. Eur. J. Oper. Res. 88, 3–12 (1996)

    Article  MATH  Google Scholar 

  76. Yang, W., Mathur, K., Ballou, R.: Stochastic vehicle routing problem with restocking. Transp. Sci. 34, 99–112 (2000)

    Article  MATH  Google Scholar 

  77. Rigby, M., Winter, S.: Enhancing launch pads for decision-making in intelligent mobility on-demand. J. Location Based Serv. 9(2), 77–92 (2015)

    Article  Google Scholar 

  78. Broll, G., et al.: Tripzoom: an app to improve your mobility behavior. In Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia (MUM ’12), pp. 57:1–57:4. ACM, New York, NY, USA (2012)

    Google Scholar 

Download references

Acknowledgements

The authors acknowledge support through the Australian Research Council (LP120200130).

Author information

Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, VIC, 3122, Australia

    Nicole Ronald

  2. Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC, 3010, Australia

    Zahra Navidi, Yaoli Wang, Michael Rigby, Shubham Jain, Ronny Kutadinata, Russell Thompson & Stephan Winter

Authors
  1. Nicole Ronald
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zahra Navidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yaoli Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael Rigby
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shubham Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ronny Kutadinata
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Russell Thompson
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Stephan Winter
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ronny Kutadinata .

Editor information

Editors and Affiliations

  1. Dept Future Technologies and Europe, VDI/VDE Innovation + Technik GmbH Dept Future Technologies and Europe, Berlin, Germany

    Gereon Meyer

  2. Transport.Sustainability Research Center, University of California, Berkeley Transport.Sustainability Research Center, Richmond, California, USA

    Susan Shaheen

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Ronald, N. et al. (2017). Mobility Patterns in Shared, Autonomous, and Connected Urban Transport. In: Meyer, G., Shaheen, S. (eds) Disrupting Mobility. Lecture Notes in Mobility. Springer, Cham. https://doi.org/10.1007/978-3-319-51602-8_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-51602-8_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51601-1

  • Online ISBN: 978-3-319-51602-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation