Mobility Patterns in Shared, Autonomous, and Connected Urban Transport

  • Nicole Ronald
  • Zahra Navidi
  • Yaoli Wang
  • Michael Rigby
  • Shubham Jain
  • Ronny Kutadinata
  • Russell Thompson
  • Stephan Winter
Chapter
Part of the Lecture Notes in Mobility book series (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.

Keywords

Mobility on demand Demand-responsive transport Ride sharing Mobility as a service Simulated mobility 

Notes

Acknowledgements

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

References

  1. 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. 2.
    Shaheen, S.: Mobility and the sharing economy (editorial). Transport Policy (2016)Google Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 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. 6.
    Batty, M., et al.: Entropy, complexity, and spatial information. J. Geogr. Syst. 16(4), 363–385 (2014)CrossRefGoogle Scholar
  7. 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. 8.
    Benenson, I., Torrens, P.M.: Geosimulation: Automata-based Modeling of Urban Phenomena. Wiley, Chichester, UK (2004)CrossRefGoogle Scholar
  9. 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. 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. 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)CrossRefGoogle Scholar
  12. 12.
    Cottrill, C., et al.: Future Mobility Survey. Transport. Res. Record: J. Transport. Res. Board 2354, 59–67 (2013)CrossRefGoogle Scholar
  13. 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)CrossRefGoogle Scholar
  14. 14.
    Louviere, J.J., Hensher, D.A., Swait, J.D.: Stated Choice Methods: Analysis and Applications. Cambridge University Press, Cambridge, UK (2000)CrossRefMATHGoogle Scholar
  15. 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. 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)CrossRefGoogle Scholar
  17. 17.
    Contrino, H., McGuckin, N.: Demographics matter travel demand, options, and characteristics among minority populations. Public Works Manage. Policy 13(4), 361–368 (2009)CrossRefGoogle Scholar
  18. 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. 19.
    Litman, T.: Understanding Transport Demands and Elasticities (2013)Google Scholar
  20. 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)CrossRefGoogle Scholar
  21. 21.
    ActiveAge, An introduction to Demand Responsive Transport as a Mobility Solution in an Ageing Society, 2008Google Scholar
  22. 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. 23.
    Bearse, P., et al.: Paratransit demand of disabled people. Transp. Res. Part B: Methodol. 38(9), 809–831 (2004)CrossRefGoogle Scholar
  24. 24.
    Enoch, M.P., et al.: Evaluation study of demand responsive transport services in Wiltshire. Final report, Loughborough University, Loughborough (2006)Google Scholar
  25. 25.
    Häme, L.: Demand-responsive transport: models and algorithms. In: Department of Mathematics and Systems Analysis. Aalto University, Aalto (2013)Google Scholar
  26. 26.
    Koffman, D.: Operational experiences with flexible transit services, vol. 53. Transportation Research Board (2004)Google Scholar
  27. 27.
    Laws, R.: Evaluating Publicly-Funded DRT Schemes in England and Wales, Loughborough University (2009)Google Scholar
  28. 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)CrossRefGoogle Scholar
  29. 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. 30.
    Mageean, J., Nelson, J.D.: The evaluation of demand responsive transport services in Europe. J. Transp. Geogr. 11(4), 255–270 (2003)CrossRefGoogle Scholar
  31. 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)CrossRefGoogle Scholar
  32. 32.
    Rosenbloom, S., Fielding, G.J.: Transit Markets of the Future: The Challenge of Change, vol. 28. Transportation Research Board (1998)Google Scholar
  33. 33.
    Ryley, T.J., et al.: Developing Relevant Tools for Demand Responsive Transport (DRT). In: ATCO Conference, Liverpool (2013)Google Scholar
  34. 34.
    Scott, R.: Demand Responsive Passenger Transport in Low-Demand Situations December 2010 (2010)Google Scholar
  35. 35.
    Spielberg, F., Pratt, R.H.: Demand-Responsive/ADA-Traveler Response to Transportation System Changes (2004)Google Scholar
  36. 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)CrossRefGoogle Scholar
  37. 37.
    Victorian Integrated Survey of Travel & Activity 2009–10, Survey Procedures and Documentation, The Victorian Department of Transport (2011)Google Scholar
  38. 38.
    Public Transport Victoria, New PTV FlexiRide service for Yarrawonga and Mulwala: Melbourne, Australia, 2 (2013)Google Scholar
  39. 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)CrossRefGoogle Scholar
  40. 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. 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. 42.
    Daganzo, C.F.: Checkpoint dial-a-ride systems. Transp. Res. Part B: Methodol. 18(4–5), 315–327 (1984)MathSciNetCrossRefGoogle Scholar
  43. 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)CrossRefGoogle Scholar
  44. 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)CrossRefGoogle Scholar
  45. 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)CrossRefGoogle Scholar
  46. 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)CrossRefGoogle Scholar
  47. 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)CrossRefGoogle Scholar
  48. 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)CrossRefGoogle Scholar
  49. 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. 50.
    Santi, P., et al.: Quantifying the benefits of vehicle pooling with shareability networks. Proc. Natl. Acad. Sci. 111(37), 13290–13294 (2014)CrossRefGoogle Scholar
  51. 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. 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. 53.
    Wessels, R.: Combining Ridesharing and Social Networks, UTwente (2009)Google Scholar
  54. 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)MathSciNetCrossRefMATHGoogle Scholar
  55. 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)MATHGoogle Scholar
  56. 56.
    Psaraftis, H.N.: Scheduling large-scale advance-request dial-a-ride systems. Am. J. Math. Manage. Sci. 6, 327–367 (1986)MATHGoogle Scholar
  57. 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. 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)CrossRefGoogle Scholar
  59. 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. 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)CrossRefMATHGoogle Scholar
  61. 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)MathSciNetCrossRefMATHGoogle Scholar
  62. 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. 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. 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. 65.
    Koning, D.: Using column generation for the pickup and delivery problem with disturbances, Utrecht University (2011)Google Scholar
  66. 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)CrossRefMATHGoogle Scholar
  67. 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. 68.
    Pankratz, G.: A grouping genetic algorithm for the pickup and delivery problem with time windows. OR Spectr. 27, 21–41 (2005)MathSciNetCrossRefMATHGoogle Scholar
  69. 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)CrossRefGoogle Scholar
  70. 70.
    Xu, H., et al.: Solving a practical pickup and delivery problem. Transp. Sci. 37, 347–364 (2003)CrossRefGoogle Scholar
  71. 71.
    Pillac, V., et al.: A review of dynamics vehicle routing problems. Eur. J. Oper. Res. 225, 1–11 (2013)MathSciNetCrossRefMATHGoogle Scholar
  72. 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. 73.
    Bent, R., Van Hentenryck, P.: Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Oper. Res. 52, 977–987 (2004)CrossRefMATHGoogle Scholar
  74. 74.
    Gendreau, M., et al.: Parallel tabu search for real-time vehicle routing and dispatching. Transp. Sci. 33, 381–390 (1999)CrossRefMATHGoogle Scholar
  75. 75.
    Gendreau, M., Laporte, G., Séguin, R.: Stochastic vehicle routing. Eur. J. Oper. Res. 88, 3–12 (1996)CrossRefMATHGoogle Scholar
  76. 76.
    Yang, W., Mathur, K., Ballou, R.: Stochastic vehicle routing problem with restocking. Transp. Sci. 34, 99–112 (2000)CrossRefMATHGoogle Scholar
  77. 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)CrossRefGoogle Scholar
  78. 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

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nicole Ronald
    • 1
  • Zahra Navidi
    • 2
  • Yaoli Wang
    • 2
  • Michael Rigby
    • 2
  • Shubham Jain
    • 2
  • Ronny Kutadinata
    • 2
  • Russell Thompson
    • 2
  • Stephan Winter
    • 2
  1. 1.Department of Computer Science and Software EngineeringSwinburne University of TechnologyHawthornAustralia
  2. 2.Department of Infrastructure EngineeringThe University of MelbourneParkvilleAustralia

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