Advertisement

Decision-Making Process Underlying Travel Behavior and Its Incorporation in Applied Travel Models

  • Peter VovshaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 805)

Abstract

The paper provides a broad overview of the state of the art and practice in travel modeling in its relation to individual travel behavior. It describes how different travel decision making paradigms led to different generations of applied travel models in practice – from aggregate models to disaggregate trip-base models, then to tour-based models, then to activity-based models, and finally to agent-based models. The paper shows how these different modeling approaches can be effectively generalized in one framework where different model structures correspond to different basic assumptions on the decision-making process. The focus of the paper is on three key underlying behavioral aspects: (1) how different dimensions of travel and associated individual choices are sequenced and integrated, (2) how the real-world constraints on different travel dimensions are represented, (3) what are the behavioral factors and associated mathematical and statistical models applied for modeling each decision-making step. The paper analyzes the main challenges associated with understanding and modeling travel behavior and outlines avenues for future research.

Keywords

Travel behavior Agent-based modeling Decision-making process 

References

  1. 1.
    Boyce, D., Williams, H.: Forecasting Urban Travel: Past, Present, and Future. Edward Elgar, Cheltenham (2015)CrossRefGoogle Scholar
  2. 2.
    NCHRP Synthesis 406: Advanced Practices in Travel Forecasting. Transportation Research Board (2010)Google Scholar
  3. 3.
    Ferdous, N., Bhat, C., Vana, L., Schmitt, D., Bowman, J., Bradley, M., Pendyala, R.: Comparison of Four-Step versus Tour-Based Models in Predicting Travel Behavior before and after Transportation System Changes – Results Interpretation and Recommendations. FHWA (2011)Google Scholar
  4. 4.
    Ye, X., Pendyala, R.M., Gottardi, G.: An exploration of the relationship between mode choice and complexity of trip chaining patterns. Transp. Res. Part B 41(1), 96–113 (2007)CrossRefGoogle Scholar
  5. 5.
    Vovsha, P.: Microsimulation travel demand models in practice in the US and prospects for agent based approach. In: Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems. Proceedings of the International Workshops of PAAMS 2017, Porto, Portugal, 21–23 June, pp. 52–68. Springer (2017)Google Scholar
  6. 6.
    Bhat, C.R., Goulias, K.G., Pendyala, R.M., Paleti, R., Sidharthan, R., Schmitt, L., Hu, H.-H.: A household-level activity pattern generation model with an application for Southern California. Transportation 40(5), 1063–1086 (2013)CrossRefGoogle Scholar
  7. 7.
    Vovsha, P., Hicks, J.E., Anderson, R., Giaimo, G., Rousseau, G.: Integrated model of travel demand and network simulation. In: Proceedings of the 6th Conference on Innovations in Travel Modeling (ITM), TRB, Denver, CO (2016)Google Scholar
  8. 8.
    Dugundji, E., Walker, J.: Discrete choice with social and spatial network interdependencies: an empirical example using mixed generalized extreme value models with field and panel effects. Transp. Res. Rec. 1921, 70–78 (2005)Google Scholar
  9. 9.
    Lemp, J.: Understanding joint daily activity pattern choices across household members using a latent class model framework. In: 93rd Annual TRB meeting (2014)Google Scholar
  10. 10.
    Vuk, G., Bowman, J., Daly, A.J., Hess, S.: Impact of family in-home quality time on person travel demand. Transportation 43(4), 705–724 (2016)CrossRefGoogle Scholar
  11. 11.
    Vovsha, P., Gliebe, J., Petersen, E., Koppelman, F.: Comparative analysis of sequential and simultaneous choice structures for modeling intra-household interactions. In: Timmermans, H. (ed.) Progress in Activity-Based Analysis, pp. 223–258. Elsevier Science Ltd, Oxford (2005)Google Scholar
  12. 12.
    Zhang, L., Chang, G.-L., Asce, M., Zhu, S., Xiong, C., Du, L., Mollanejad, M., Hopper, N., Mahapatra, S.: Integrating an agent-based travel behavior model with large-scale microscopic traffic simulation for corridor-level and subarea transportation operations and planning applications. Urban Plann. Dev. 139, 94–103 (2013)CrossRefGoogle Scholar
  13. 13.
    Paleti, R., Vovsha, P., Givon, D., Birotker, Y.: Impact of individual daily travel pattern on value of time. Transportation 42(6), 1003–1017 (2015)CrossRefGoogle Scholar
  14. 14.
    Hagenauer, J., Helbich, M.: A Comparative study of machine learning classifiers for modeling travel mode choice. Expert Syst. Appl. 78, 273–282 (2017)CrossRefGoogle Scholar
  15. 15.
    Golshani, N., Shabanopour, R., Mahmoudifard, S.M., Derrible, S., Mohammadian, A.: Comparison of artificial neural networks and statistical copula-based joint models. Presented at the 96th Annual Meeting of the Transportation Research Board, Washington, DC. (2017)Google Scholar
  16. 16.
    Maciejewsky, M., Bischoff, J., Horl, S., Nagel, K.: Towards a testbed for dynamic vehicle routing algorithms. In: Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems. Proceedings of the International Workshops of PAAMS 2017, Porto, Portugal, 21–23 June 2017, pp. 69–79. Springer (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.WSPNew YorkUSA

Personalised recommendations