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Computation and Big Data for Transport pp 51–67Cite as

New Data and Methods for Modelling Future Urban Travel Demand: A State of the Art Review

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Part of the book series: Computational Methods in Applied Sciences ((COMPUTMETHODS,volume 54))

Abstract

This paper aims is to provide an overview of how new data collection methods and the various advances in urban travel demand modelling are improving the understanding of mobility. These new modelling applications and data allow for a study of both new disruptive transport services and changes in travel behaviours in the “Mobility as a Service” (MaaS) context that needs to be overcome in the future.

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References

  1. Aditjandra PT, Cao XJ, Mulley C (2012) Understanding neighbourhood design impact on travel behaviour: an application of structural equations model to a British metropolitan data. Transp Res Part A: Policy Pract 46(1):22–32

    Google Scholar 

  2. Agatz N, Erera AL, Savelsbergh MWP, Wang X (2011) Dynamic ride-sharing: a simulation study in metro Atlanta. Procedia-Soc Behav Sci 17:532–550

    Google Scholar 

  3. Anda C, Erath A, Fourie PJ (2017) Transport modelling in the age of big data. Int J Urban Sci 21(sup1):19–42

    Google Scholar 

  4. Arentze T, Hofman F, van Mourik H, Timmermans H (2000) ALBATROSS: multiagent, rule-based model of activity pattern decisions. Transp Res Rec 1706(1):136–144

    Google Scholar 

  5. Auld J, Mohammadian AK (2012) Activity planning processes in the agent-based dynamic activity planning and travel scheduling (ADAPTS) model. Transp Res Part A: Policy Pract 46(8):1386–1403

    Google Scholar 

  6. Bahamonde-Birke FJ, Kunert U, Link H, de Dios Ortúzar J (2017) About attitudes and perceptions: finding the proper way to consider latent variables in discrete choice models. Transportation 44(3):475–493

    Google Scholar 

  7. Bao Q, Kochan B, Bellemans T, Janssens D, Wets G (2015) Investigating micro-simulation error in activity-based travel demand forecasting: a case study of the FEATHERS framework. Transp Plan Technol 38(4):425–441

    Google Scholar 

  8. Bao Q, Kochan B, Shen Y, Bellemans T, Janssens D, Wets G (2016) Activity-based travel demand modeling framework FEATHERS: sensitivity analysis with decision trees. Transp Res Rec: J Transp Res Board 89–99

    Google Scholar 

  9. Bekhor S, Dobler C, Axhausen KW (2010) Integration of activity-based with agent-based models: an example from the tel aviv model and MATSim. Arb Verk Raumplan 628

    Google Scholar 

  10. Bekhor S, Kheifits L, Sorani, M (2014) Stability analysis of activity-based models: case study of the Tel Aviv transportation model. Eur J Transp Infrastruct Res 14(4)

    Google Scholar 

  11. Ben-Akiva M, Bierlaire M (1999) Discrete choice methods and their applications to short term travel decisions. Handbook of transportation science. Springer, Berlin, pp 5–33

    Google Scholar 

  12. Ben-Akiva M, Walker J, Bernardino AT, Gopinath DA, Morikawa T, Polydoropoulou A (2002) Integration of choice and latent variable models. Perpetual motion, travel behaviour research opportunities and application challenges, pp 431–470

    Google Scholar 

  13. Berrada J, Leurent F (2017) Modeling transportation systems involving autonomous vehicles: a state of the art. Transp Res Procedia 27:215–221

    Google Scholar 

  14. Bischoff J, Nagel K (2017) Integrating explicit parking search into a transport simulation. Procedia Comput Sci 109:881–886

    Google Scholar 

  15. Bohte W, Maat K (2009) Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: a large-scale application in the Netherlands. Transp Res Part C: Emerg Technol 17(3):285–297

    Google Scholar 

  16. Bowman JL, Ben-Akiva ME (2001) Activity-based disaggregate travel demand model system with activity schedules. Transp Res Part A: Policy Pract 35(1):1–28

    Google Scholar 

  17. Bricka SG, Sen S, Paleti R, Bhat CR (2012) An analysis of the factors influencing differences in survey-reported and GPS-recorded trips. Transp Res Part C: Emerg Technol 21(1):67–88

    Google Scholar 

  18. Calabrese F, Diao M, Di Lorenzo G, Ferreira J Jr, Ratti C (2013) Understanding individual mobility patterns from urban sensing data: a mobile phone trace example. Transp Res Part C: Emerg Technol 26:301–313

    Google Scholar 

  19. Calabrese F, Ferrari L, Blondel Vincent D (2015) Urban sensing using mobile phone network data: a survey of research. ACM Comput Surv (CSUR) 47(2):25

    Google Scholar 

  20. Carreira R, Patrício L, Jorge RN, Magee C (2014) Understanding the travel experience and its impact on attitudes, emotions and loyalty towards the transportation provider-a quantitative study with mid-distance bus trips. Transp Policy 31:35–46

    Google Scholar 

  21. Castiglione J, Freedman J, Bradley M (2003) Systematic investigation of variability due to random simulation error in an activity-based microsimulation forecasting model. Transp Res Rec 1831(1):76–88

    Google Scholar 

  22. Castiglione J, Bradley M, Gliebe J (2015) Activity-based travel demand models: a primer

    Google Scholar 

  23. Chen C, Ma J, Susilo Y, Liu Y, Wang M (2016) The promises of big data and small data for travel behavior (aka human mobility) analysis. Transp Res Part C: Emerg Technol 68:285–299

    Google Scholar 

  24. Chorus CG, Arentze TA, Timmermans HJP (2008) A random regret-minimization model of travel choice. Transp Res Part B: Methodol 42(1):1–18

    Google Scholar 

  25. Cich G, Knapen L, Maciejewski M, Bellemans T, Janssens D et al (2017) Modeling demand responsive transport using SARL and MATSim. Procedia Comput Sci 109:1074–1079

    Google Scholar 

  26. Davis B, Dutzik T, Baxandall P (2012) Transportation and the new generation. Why young people are driving less and what it means for transportation policy. Frontier Group

    Google Scholar 

  27. de Barcelona A (2013). Pla de Mobilitat Urbana de Barcelona 2013–2018

    Google Scholar 

  28. de Dios Ortuzar J, Willumsen, LG (2011) Modal split and direct demand models. Modelling transport, 4th edn. John Wiley & Sons, New York, pp 207–225

    Google Scholar 

  29. De Oña J, De Oña R, Eboli L, Mazzulla G (2013) Perceived service quality in bus transit service: a structural equation approach. Transp Policy 29:219–226

    Google Scholar 

  30. Delbosc A, Currie G (2015) Does information and communication technology complement or replace social travel among young adults? Transp Res Rec: J Transp Res Board 76–82

    Google Scholar 

  31. Diana M, Mokhtarian PL (2009) Grouping travelers on the basis of their different car and transit levels of use. Transportation 36(4):455–467

    Google Scholar 

  32. Djavadian S, Chow JYJ (2017) An agent-based day-to-day adjustment process for modeling mobility as a service with a two-sided flexible transport market. Transp Res Part B: Methodol 104:36–57

    Google Scholar 

  33. Du Y, Zhao C, Zhang X, Sun L (2015) Microscopic simulation evaluation method on access traffic operation. Simul Model Pract Theory 53:139–148

    Google Scholar 

  34. e Silva JDA, Morency C, Goulias KG (2012) Using structural equations modeling to unravel the influence of land use patterns on travel behavior of workers in Montreal. Transp Res Part A: Policy Pract 46(8):1252–1264

    Google Scholar 

  35. Eboli L, Mazzulla G (2007) Service quality attributes affecting customer satisfaction for bus transit. J Public Transp 10(3):2

    Google Scholar 

  36. Eboli L, Mazzulla G (2010) How to capture the passengers’ point of view on a transit service through rating and choice options. Transp Rev 30(4):435–450

    Google Scholar 

  37. Fagnant DJ, Kockelman KM (2014) The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios. Transp Res Part C: Emerg Technol 40:1–13

    Google Scholar 

  38. Feng T, Timmermans HJP (2014) Extracting activity-travel diaries from GPS data: towards integrated semi-automatic imputation. Procedia Environ Sci 22:178–185

    Google Scholar 

  39. Fifer S, Rose J, Greaves S (2014) Hypothetical bias in stated choice experiments: is it a problem? And if so, how do we deal with it? Transp Res Part A: Policy Pract 61:164–177

    Google Scholar 

  40. Furuhata M, Dessouky M, Ordóñez F, Brunet M-E, Wang X, Koenig S (2013) Ridesharing: the state-of-the-art and future directions. Transp Res Part B: Methodol 57:28–46

    Google Scholar 

  41. Garikapati VM, Pendyala RM, Morris EA, Mokhtarian PL, McDonald N (2016) Activity patterns, time use, and travel of millennials: a generation in transition? Transp Rev 36(5):558–584

    Google Scholar 

  42. Ghasri M, Rashidi TH, Waller ST (2017) Developing a disaggregate travel demand system of models using data mining techniques. Transp Res Part A: Policy Pract 105:138–153

    Google Scholar 

  43. Giesecke R, Surakka T, Hakonen M (2016) Conceptualising mobility as a service. In: 2016 11th international conference on ecological vehicles and renewable energies (EVER). IEEE, pp 1–11

    Google Scholar 

  44. Group, Resource Systems (2012) The ARC and SACOG experience with activity-based models: synthesis and lessons learned. Washington, DC

    Google Scholar 

  45. Guiliano G, Hayden SA (2005) Marketing public transport. Handbooks in transport, vol 6

    Google Scholar 

  46. Gundlegård D, Rydergren C, Breyer N, Rajna B (2016) Travel demand estimation and network assignment based on cellular network data. Comput Commun 95:29–42

    Google Scholar 

  47. Habib KMN (2011) A random utility maximization (RUM) based dynamic activity scheduling model: application in weekend activity scheduling. Transportation 38(1):123–151

    Google Scholar 

  48. Hasan S, Ukkusuri SV (2014) Urban activity pattern classification using topic models from online geo-location data. Transp Res Part C: Emerg Technol 44:363–381

    Google Scholar 

  49. He Q, Head KL, Ding J (2014) Multi-modal traffic signal control with priority, signal actuation and coordination. Transp Res Part C: Emerg Technol 46:65–82

    Google Scholar 

  50. Heilig M, Mallig N, Schröder O, Kagerbauer M, Vortisch P (2018) Implementation of free-floating and station-based carsharing in an agent-based travel demand model. Travel Behav Soc 12:151–158

    Google Scholar 

  51. Huang A, Gallegos L, Lerman K (2017) Travel analytics: understanding how destination choice and business clusters are connected based on social media data. Transp Res Part C: Emerg Technol 77:245–256

    Google Scholar 

  52. Ingvardson JB, Kaplan S, Nielsen OA, Di Ciommo F, de Abreu e Silva J, Shiftan Y (2017) The Commuting habit loop: the role of satisfying existence, relatedness, and growth needs in modal choice. Technical report

    Google Scholar 

  53. Injadat M, Salo F, Nassif AB (2016) Data mining techniques in social media: a survey. Neurocomputing 214:654–670

    Google Scholar 

  54. Johansson MV, Heldt T, Johansson P (2006) The effects of attitudes and personality traits on mode choice. Transp Res Part A: Policy Pract 40(6):507–525

    Google Scholar 

  55. Jurdak R, Zhao K, Liu J, AbouJaoude M, Cameron M, Newth D (2015) Understanding human mobility from Twitter. PloS One 10(7):e0131469

    Google Scholar 

  56. Kamargianni M, Polydoropoulou A (2014) Generation Y’s travel behavior and perceptions of walkability constraints. Transp Res Rec 2430(1):59–71

    Google Scholar 

  57. Kamargianni M, Li W, Matyas M, Schäfer A (2016) A critical review of new mobility services for urban transport. Transp Res Procedia 14:3294–3303

    Google Scholar 

  58. Kaplan S, e Silva JDA, Di Ciommo F (2014) The relationship between young people’ s transit use and their perceptions of equity concepts in transit service provision. Transp Policy 36:79–87

    Google Scholar 

  59. Kelen C, Vilarino P, Christou G (2017) Advanced demand data collection technologies for multi modal strategic modelling. Transp Res Procedia 27:1058–1065

    Google Scholar 

  60. Koppelman FS, Bhat C (2006) A self instructing course in mode choice modeling: multinomial and nested logit models

    Google Scholar 

  61. Kroesen M, Handy SL (2015) Is the rise of the e-society responsible for the decline in car use by young adults? Results from the Netherlands. Transp Res Rec 2496(1):28–35

    Google Scholar 

  62. Kuflik T, Minkov E, Nocera S, Grant-Muller S, Gal-Tzur A, Shoor I (2017) Automating a framework to extract and analyse transport related social media content: the potential and the challenges. Transp Res Part C: Emerg Technol 77:275–291

    Google Scholar 

  63. Kuhnimhof T, Buehler R, Dargay J (2011) A new generation: travel trends for young Germans and Britons. Transp Res Rec 2230(1):58–67

    Google Scholar 

  64. Li Q, Liao F, Timmermans HJP, Huang H, Zhou J (2018) Incorporating free-floating car-sharing into an activity-based dynamic user equilibrium model: a demand-side model. Transp Res Part B: Methodol 107:102–123

    Google Scholar 

  65. Linares MP, Barceló J, Carmona C, Montero L (2017) Analysis and operational challenges of dynamic ride sharing demand responsive transportation models. Transp Res Procedia 21:110–129

    Google Scholar 

  66. Mans J, Interrante E, Lem L, Mueller J, Lawrence M (2012) Next generation of travel behavior: potential impacts related to household use of information and communication technology. Transp Res Rec 2323(1):90–98

    Google Scholar 

  67. Matyas MB, Kamargianni M (2017) A holistic overview of the mobility-as-a-service. In: Hungarian transport research conference

    Google Scholar 

  68. McNally MG (2000) The activity-based approachPergamon. In: Hensher DA, Button K (eds) Hand Book of Transport Modelling

    Google Scholar 

  69. Miller EJ (2017) Modeling the demand for new transportation services and technologies. Transp Res Rec 2658(1):1–7

    Google Scholar 

  70. Nourinejad M, Roorda MJ (2016) Agent based model for dynamic ridesharing. Transp Res Part C: Emerg Technol 64:117–132

    Google Scholar 

  71. Pender B, Currie G, Delbosc A, Shiwakoti N (2014) Social media use during unplanned transit network disruptions: a review of literature. Transp Rev 34(4):501–521

    Google Scholar 

  72. Petrik O, Adnan M, Basak K, Ben-Akiva M (2018) Uncertainty analysis of an activity-based microsimulation model for Singapore. Future Gener Comput Syst

    Google Scholar 

  73. Pournarakis DE, Sotiropoulos DN, Giaglis GM (2017) A computational model for mining consumer perceptions in social media. Decis Support Syst 93:98–110

    Google Scholar 

  74. Rashidi TH, Abbasi A, Maghrebi M, Hasan S, Waller TS (2017) Exploring the capacity of social media data for modelling travel behaviour: opportunities and challenges. Transp Res Part C: Emerg Technol 75:197–211

    Google Scholar 

  75. Rasouli S (2016) Uncertainty in modeling activity-travel demand in complex urban systems. TRAIL Research School

    Google Scholar 

  76. Ribeiro MD, Larrañaga AM, Arellana J, Cybis HBB (2014) Influence of GPS and self-reported data in travel demand models. Procedia-Soc Behav Sci 162:467–476

    Google Scholar 

  77. Saleem M, Västberg OB, Karlström A (2018) An activity based demand model for large scale simulations. Procedia Comput Sci 130:920–925

    Google Scholar 

  78. Shiftan Y, Ben-Akiva M (2011) A practical policy-sensitive, activity-based, travel-demand model. Ann Reg Sci 47(3):517–541

    Google Scholar 

  79. Shiftan Y, Outwater ML, Zhou Y (2008) Transit market research using structural equation modeling and attitudinal market segmentation. Transp Policy 15(3):186–195

    Google Scholar 

  80. Shiftan Y, Barlach Y, Shefer D (2015) Measuring passenger loyalty to public transport modes. J Public Transp 18(1):7

    Google Scholar 

  81. Şimşekoğlu Ö, Nordfjærn T, Rundmo T (2015) The role of attitudes, transport priorities, and car use habit for travel mode use and intentions to use public transportation in an urban Norwegian public. Transp Policy 42:113–120

    Google Scholar 

  82. Sivakumar A (2007) Modelling transport: a synthesis of transport modelling methodologies. Imperial College of London

    Google Scholar 

  83. Stathopoulos A, Cirillo C, Cherchi E, Ben-Elia E, Li Y-T, Schmöcker J-D (2017) Innovation adoption modeling in transportation: new models and data. J Choice Model

    Google Scholar 

  84. Stock K (2018) Mining location from social media: a systematic review. Comput Environ Urban Syst

    Google Scholar 

  85. Tan W, Chai Y, Wang W, Liu Y (2012) General modeling and simulation for enterprise operational decision-making problem: a policy-combination perspective. Simul Model Pract Theory 21(1):1–20

    Google Scholar 

  86. Tyrinopoulos Y, Aifadopoulou G (2008) A complete methodology for the quality control of passenger services in the public transport business. Eur Transp 38(38):1–16

    Google Scholar 

  87. Van Acker V, Mokhtarian PL, Witlox F (2014) Car availability explained by the structural relationships between lifestyles, residential location, and underlying residential and travel attitudes. Transp Policy 35:88–99

    Google Scholar 

  88. Van Lierop D, El-Geneidy A (2016) Enjoying loyalty: the relationship between service quality, customer satisfaction, and behavioral intentions in public transit. Res Transp Econ 59:50–59

    Google Scholar 

  89. Van Wee B (2002) Land use and transport: research and policy challenges. J Transp Geogr 10(4):259–271

    Google Scholar 

  90. Veldhuisen J, Timmermans H, Kapoen L (2000) Microsimulation model of activity-travel patterns and traffic flows: specification, validation tests, and Monte Carlo error. Transp Res Rec: J Transp Res Board 126–135

    Google Scholar 

  91. Wang Y, Szeto WY, Han K, Friesz TL (2018) Dynamic traffic assignment: a review of the methodological advances for environmentally sustainable road transportation applications. Transp Res Part B: Methodol

    Google Scholar 

  92. Wegener M (2013) The future of mobility in cities: challenges for urban modelling. Transp Policy 29:275–282

    Google Scholar 

  93. Wismans L, De Romph E, Friso K, Zantema K (2014) Real time traffic models, decision support for traffic management. Procedia Environ Sci 22:220–235

    Google Scholar 

  94. Xiong Y, Zhang J (2016) Effects of land use and transport on young adults’ quality of life. Travel Behav Soc 5:37–47

    Google Scholar 

  95. Ye R, Titheridge H (2017) Satisfaction with the commute: the role of travel mode choice, built environment and attitudes. Transp Res Part D: Transp Environ 52:535–547

    Google Scholar 

  96. Zhang J, Chikaraishi M, Xiong Y, Jiang Y, Seya H (2016) Young people’s life choices and travel behavior: state-of-the-art and future perspectives. In: A discussion paper for the workshop ‘Young People’s Life Choices and Travel...’

    Google Scholar 

  97. Zheng H, Son Y-J, Chiu Y-C, Head L, Feng Y, Xi H, Kim S, Hickman M, et al (2013) A primer for agent-based simulation and modeling in transportation applications. Technical report, United States, Federal Highway Administration

    Google Scholar 

  98. Zhuge C, Shao C, Wang S, Hu Y (2019) Sensitivity analysis of integrated activity-based model: using MATSim as an example. Transp Lett 11(2):93–103

    Google Scholar 

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Authors and Affiliations

  1. Campus Nord UPC Jordi Girona, 1-3 Building C-3, 08034, Barcelona, Spain

    Sara A. Puignau Arrigain

  2. CIMNE Castelldefels Edifici C3 Parc Mediterrani de la Tecnología UPC C/ Esteve Terrades no 5, 08860, Castelldefels, Spain

    Jordi Pons-Prats

  3. CENIT, Barcelona, c/Jordi Girona s/n Campus Nord UPC, Edifici C1, 08034, Barcelona, Spain

    Sergi Saurí Marchán

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  1. Sara A. Puignau Arrigain
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Correspondence to Sara A. Puignau Arrigain .

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Editors and Affiliations

  1. International Center for Numerical Methods in Engineering, CIMNE, Universidad Politecnica Catalunya, Barcelona, Spain

    Pedro Diez

  2. Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland

    Pekka Neittaanmäki

  3. International Center for Numerical Methods in Engineering, CIMNE, Universitat Politècnica de Cataluny, Barcelona, Spain

    Jacques Periaux

  4. Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland

    Tero Tuovinen

  5. International Center for Numerical Methods in Engineering, CIMNE, Universidad Politecnica Catalunya, Barcelona, Spain

    Jordi Pons-Prats

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Puignau Arrigain, S.A., Pons-Prats, J., Saurí Marchán, S. (2020). New Data and Methods for Modelling Future Urban Travel Demand: A State of the Art Review. In: Diez, P., Neittaanmäki, P., Periaux, J., Tuovinen, T., Pons-Prats, J. (eds) Computation and Big Data for Transport. Computational Methods in Applied Sciences, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-030-37752-6_4

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