Skip to main content
SpringerLink
Log in

An experiential learning-based transit route choice model using large-scale smart-card data

  • Published:
Transportation Aims and scope Submit manuscript

Abstract

Taking learning into account when modelling passengers’ route choice behaviour improves understanding and forecasting of their preferences, which helps stakeholders better design public transport systems to meet user needs. Most empirical studies have neglected the relationship between current choices and passengers’ past experiences that lead to a learning process about route attributes. This study addresses this gap by using real observed choices from smart-card data to implement a route choice model that takes into account the learning process of passengers during the inauguration of a new metro line in Santiago, Chile. An instance-based learning (IBL) model is used to represent individually perceived in-vehicle travel time in the route choice model. It accounts for recency and reinforcement of experience using the power law of forgetting. The empirical evaluation uses 8 weeks of smart-card data after the introduction of the metro line. Model parameters are evaluated, and the fit and behavioural coherence achieved by the IBL route choice model is measured against a baseline model. The baseline model neglects passenger learning from experience and assumes that all passengers use only trip descriptive information in their decision-making process. The IBL route choice model outperforms the baseline model from the fourth week after the introduction of the metro line. This empirical evidence supports the notion that after the introduction of a new metro line, passengers initially rely on descriptive travel information to estimate travel times for new alternatives. After a few weeks, they begin to incorporate their own experiences to update their perceptions.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

Not applicable

Code availability

Not applicable

Notes

  1. Some studies have used correlated travel times between route alternatives instead (Tan et al. 2015; Dixit et al. 2023). However, Dixit et al. (2023) found no significant difference in the predictive ability of the distance-based and in-vehicle travel-time-based models for the path-size correction term.

References

  • Anderson, M.K., Nielsen, O.A., Prato, C.G.: Multimodal route choice models of public transport passengers in the greater copenhagen area. EURO J. Transp. Logist. 6(3), 221–245 (2017)

    Article  Google Scholar 

  • Arriagada, J.: Formulation of new models of passenger behavior in public transport using fare collection data. PhD thesis, University of Chile (2022)

  • Arriagada, J., Gschwender, A., Munizaga, M.A., et al.: Modeling bus bunching using massive location and fare collection data. J. Intell. Transp. Syst. 23(4), 332–344 (2019)

    Article  Google Scholar 

  • Arriagada, J., Munizaga, M.A., Guevara, C.A., et al.: Unveiling route choice strategy heterogeneity from smart card data in a large-scale public transport network. Transp. Res. Part C: Emerging Technol. 134(103), 467 (2022)

    Google Scholar 

  • Ben-Akiva, M., Bierlaire, M.: Discrete choice methods and their applications to short-term travel decisions. In: Hall, R. (ed.) Handbook of Transportation Science, pp. 5–34. Kluwer, Alphen aan den Rijn (1999)

    Chapter  Google Scholar 

  • Ben-Akiva, M., Bierlaire, M.: Discrete choice methods and their applications to short term travel decisions. In: Handbook of Transportation Science, vol. 23, pp 5–33 (1999)

  • Ben-Elia, E., Shiftan, Y.: Which road do i take? a learning-based model of route-choice behavior with real-time information. Transp. Res. Part A: Policy Pract. 44(4), 249–264 (2010)

    Google Scholar 

  • Bierlaire, M.: PythonBiogeme: a short introduction. Report TRANSP-OR 160706, Series on Biogeme. Transport and Mobility Laboratory, School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne, Switzerland (2016)

  • Bogers, E.A., Bierlaire, M., Hoogendoorn, S.P.: Modeling learning in route choice. Transp. Res. Rec. 2014(1), 1–8 (2007)

    Article  Google Scholar 

  • Bovy, P.H., Bekhor, S., Prato, C.G.: The factor of revisited path size: alternative derivation. Transp. Res. Rec. 2076(1), 132–140 (2008)

    Article  Google Scholar 

  • Cascetta, E., Cantarella, G.E.: A day-to-day and within-day dynamic stochastic assignment model. Transp. Res. Part A: General 25(5), 277–291 (1991)

    Article  Google Scholar 

  • Chorus, C., Dellaert, B.: Inertia in travel choice: The role of risk aversion and learning. In: 12th International Conference on Travel Behaviour Research, Jaipur, Rajasthan, India, December 13-18, 2009, Citeseer (2009)

  • Dixit, M., Cats, O., Brands, T., et al.: Perception of overlap in multi-modal urban transit route choice. Transport. A Transp. Sci. 19(2), 2005180 (2023)

    Article  Google Scholar 

  • Erev, I., Barron, G.: On adaptation, maximization, and reinforcement learning among cognitive strategies. Psychol. Rev. 112(4), 912 (2005)

    Article  PubMed  Google Scholar 

  • Fusco, G., Bracci, A., Caligiuri, T., et al.: Experimental analyses and clustering of travel choice behaviours by floating car big data in a large urban area. IET Intel. Transport Syst. 12(4), 270–278 (2018)

    Article  Google Scholar 

  • Godachevich, J., Tirachini, A.: Does the measured performance of bus operators depend on the index chosen to assess reliability in contracts? an analysis of bus headway variability. Res. Transp. Econ. 90(101), 000 (2021)

    Google Scholar 

  • González, R.M., Marrero, Á.S., Cherchi, E.: Testing for inertia effect when a new tram is implemented. Transp. Res. Part A: Policy Pract. 98, 150–159 (2017)

    Google Scholar 

  • Gschwender, A., Munizaga, M., Simonetti, C.: Using smart card and GPS data for policy and planning: The case of transantiago. Res. Transp. Econ. 59, 242–249 (2016)

    Article  Google Scholar 

  • Guevara, C.A., Tang, Y., Gao, S.: The initial condition problem with complete history dependency in learning models for travel choices. Transp. Res. Part B: Methodol. 117(PB), 850–861 (2018)

    Article  Google Scholar 

  • Guo, Z.: Mind the map! the impact of transit maps on path choice in public transit. Transp. Res. Part A: Policy Pract. 45(7), 625–639 (2011)

    Google Scholar 

  • Henriquez-Jara, B., Guevara, C.A., Munizaga, M., Perez, O.D.: Introducing dual-system theory to travel behavior: The relationship between habits and sub-exploration of novel and better transportation options. (2023). Available at SSRN: https://ssrn.com/abstract=4578223 or https://doi.org/10.2139/ssrn.4578223

  • Hoogendoorn-Lanser, S., van Nes, R., Bovy, P.: Path size modeling in multimodal route choice analysis. Transp. Res. Rec. 1921(1), 27–34 (2005)

    Article  Google Scholar 

  • Horowitz, J.L.: The stability of stochastic equilibrium in a two-link transportation network. Transp. Res. Part B: Methodol. 18(1), 13–28 (1984)

    Article  Google Scholar 

  • Jánošíková, L., Slavík, J., Koháni, M.: Estimation of a route choice model for urban public transport using smart card data. Transp. Plan. Technol. 37(7), 638–648 (2014)

    Article  Google Scholar 

  • Lejarraga, T., Dutt, V., Gonzalez, C.: Instance-based learning: a general model of repeated binary choice. J. Behav. Decis. Mak. 25(2), 143–153 (2012)

    Article  Google Scholar 

  • Lu, X., Gao, S., Ben-Elia, E., et al.: Travelers’ day-to-day route choice behavior with real-time information in a congested risky network. Math. Popul. Stud. 21(4), 205–219 (2014)

    Article  MathSciNet  Google Scholar 

  • Mahmassani, H.S., Liu, Y.H.: Dynamics of commuting decision behaviour under advanced traveller information systems. Transp. Res. Part C: Emerging Technol. 7(2–3), 91–107 (1999)

    Article  Google Scholar 

  • Munizaga, M., Devillaine, F., Navarrete, C., et al.: Validating travel behavior estimated from smartcard data. Transp. Res. Part C: Emerging Technol. 44, 70–79 (2014)

    Article  Google Scholar 

  • Munizaga, M.A., Palma, C.: Estimation of a disaggregate multimodal public transport origin-destination matrix from passive smartcard data from santiago, chile. Transp. Res. Part C: Emerging Technol. 24, 9–18 (2012)

    Article  Google Scholar 

  • Nassir, N., Hickman, M., Ma, Z.L.: A strategy-based recursive path choice model for public transit smart card data. Transp. Res. Part B: Methodol. (2018)

  • Nguyen, S., Pallottino, S.: Equilibrium traffic assignment for large scale transit networks. Eur. J. Oper. Res. 37(2), 176–186 (1988)

    Article  MathSciNet  Google Scholar 

  • Nielsen, O.A., Eltved, M., Anderson, M.K., et al.: Relevance of detailed transfer attributes in large-scale multimodal route choice models for metropolitan public transport passengers. Transp. Res. Part A: Policy Pract. 147, 76–92 (2021)

    Google Scholar 

  • Raveau, S., Muñoz, J.C.: Analyzing route choice strategies on transit networks. Pontificia Universidad Católica, Tech. rep. (2014)

  • Raveau, S., Muñoz, J.C., De Grange, L.: A topological route choice model for metro. Transp. Res. Part A: Policy Pract. 45(2), 138–147 (2011)

    Google Scholar 

  • Raveau, S., Guo, Z., Muñoz, J.C., et al.: A behavioural comparison of route choice on metro networks: time, transfers, crowding, topology and socio-demographics. Transp. Res. Part A: Policy Pract. 66, 185–195 (2014)

    Google Scholar 

  • Schmöcker, J.D., Shimamoto, H., Kurauchi, F.: Generation and calibration of transit hyperpaths. Transp. Res. Part C: Emerging Technol. 36, 406–418 (2013)

    Article  Google Scholar 

  • Tan, R., Adnan, M., Lee, D.H., et al.: New path size formulation in path size logit for route choice modeling in public transport networks. Transp. Res. Rec. 2538(1), 11–18 (2015)

    Article  Google Scholar 

  • Tang, Y., Gao, S., Ben-Elia, E.: An exploratory study of instance-based learning for route choice with random travel times. J. Choice Modell. 24, 22–35 (2017)

    Article  Google Scholar 

  • Vacca, A., Prato, C.G., Meloni, I.: Should i stay or should i go? Investigating route switching behavior from revealed preferences data. Transportation 46(1), 75–93 (2019)

    Article  Google Scholar 

  • Yáñez, M.F., Cherchi, E., Ortúzar, J.D., et al.: Inertia and shock effects on mode choice panel data: implications of the transantiago implementation. In: 12th International Conference on Travel Behaviour Research (2009)

  • Yap, M., Cats, O., van Arem, B.: Crowding valuation in urban tram and bus transportation based on smart card data. Transport. A: Transp. Sci. 16(1), 23–42 (2020)

    Google Scholar 

Download references

Acknowledgements

This work was partially funded by ANID-PFCHA/Doctorado Nacional/2017- 21170750, ANID-FONDECYT 1191104, 1231584 and ANID PIA AFB230002.

Funding

This work was partially funded by ANID-PFCHA/Doctorado Nacional/2017- 21170750, ANID-FONDECYT 1191104, 1231584 and ANID PIA/PUENTE AFB220003ANID PIA AFB230002.

Author information

Authors and Affiliations

  1. Faculty of Physical and Mathematical Sciences, University of Chile, 8370439, Santiago, Chile

    Jacqueline Arriagada, C. Angelo Guevara & Marcela Munizaga

  2. Instituto Sistemas Complejos de Ingeniería (ISCI), 8370439, Santiago, Chile

    C. Angelo Guevara & Marcela Munizaga

  3. Department of Civil and Environmental Engineering, University of Massachusetts, Amherst, MA, 01003-0724, USA

    Song Gao

Authors
  1. Jacqueline Arriagada
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Angelo Guevara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marcela Munizaga
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Song Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JA: conceptualization, methodology, software, formal analysis, writing-original draft, funding acquisition, investigation AG: conceptualization, formal analysis, methodology, resources, writing-review and editing, supervision, funding acquisition, investigation MM: conceptualization, methodology, resources, data curation, writing-review and editing, supervision, funding acquisition, investigation SG: conceptualization, methodology, writing-review and editing, investigation.

Corresponding author

Correspondence to Jacqueline Arriagada.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

Not applicable

Consent to participate

Not applicable

Consent for publication

Not applicable

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Distribution of number of alternatives in origin–destination pairs

See Table 6.

Table 6 Distribution of number of alternatives in OD pairs
Full size table

Estimation of PSL and PSL-IBL using 4 weeks of data from November 2017

See Table 7.

Table 7 PSL model and PSL-IBL model estimates (t tests) using 4 weeks of data from November 2017
Full size table

Estimation of PSL and PSL-IBL models using 4 weeks of data from December 2017

See Table 8.

Table 8 PSL model and PSL-IBL model estimates (t tests) using 4 weeks of data from December 2017
Full size table

Estimation of PSL and PSL-IBL models using all weeks of data

See Table 9.

Table 9 Constrained models estimates (t tests)
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arriagada, J., Guevara, C.A., Munizaga, M. et al. An experiential learning-based transit route choice model using large-scale smart-card data. Transportation (2024). https://doi.org/10.1007/s11116-024-10465-w

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11116-024-10465-w

Keywords

Search

Navigation