Skip to main content
SpringerLink
Log in

Inertia effects of past behavior in commuting modal shift behavior: interactions, variations and implications for demand estimation

  • Published:
Transportation Aims and scope Submit manuscript

Abstract

This paper focuses on empirically investigating the inertia effects of past behavior in commuting modal shift behavior and contributes to the current state of the art by three aspects. Firstly, this study introduces and tests the potential influences of the inertia effects of past behavior on the traveler’s preferences regarding level-of-service (LOS) variables, besides the impacts of inertia effects on the preference for the frequently used transport mode in the past. Secondly, the mode-specific inertia effects are investigated to distinguish the differences in the inertia effects for different transport modes based on posterior individual-specific parameter estimations. Thirdly, the factors contributing to the heterogeneity of inertia effects including demographics and travel contexts, are quantitatively examined. A joint random parameter logit model using a revealed and stated preference survey regarding commuting behavior is employed to unravel the three aspects. The results reveal significant interactions of inertia terms with LOS variables indicating the influences of past behavior on travelers’ evaluations on attributes of their previous choices. The mean values and variances of inertia effects for different transport modes are significantly and substantially distinct. For instance, the inertia effects of frequently using car are substantially positive representing strong stickiness to the car, while the inertia effects of frequently using the metro have large variances among travelers and mostly appear as dispositions to change. Besides, the effects of personal characteristics and travel contexts on the magnitude of the inertia effects of different transport modes are identified as well. A demand estimation analysis is utilized to investigate the influences of three aspects on predicting travel demands in various contexts. Incorporating the interactions and mode-specific inertia effects can remarkably improve the model performance. The demand estimation will be biased if they are neglected.

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

Access this article

Log in via an institution

Price includes VAT (Finland)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. Before the pilot survey for the public, we did other preliminary tests about our questionnaires in our university, whose respondents were students or staff. In the pilot survey, the questionnaires performed quite well in terms of question interpretation and understandability. Previous experience about the SP scenario design for Chinese respondents (Li et al. 2017; Gao et al. 2018) also contributed to the success of the survey design in this study.

  2. Additionally, we tested the potential influences of socio-economic attributes on the coefficient of cost and found a significant (at the confidence level of 99%) and substantial influence of income on the coefficient of cost. Hence, we considered the influences of income on the coefficient of travel cost.

  3. We would like to clarify that considering the income as a continuous variable is empirically feasible in the empirical analysis. Treating the income with a set of dummy variables could be more accurate but lead to a lot of coefficients to be estimated. We choose to use a continuous variable for income on account of our main research purposes and reducing model parameters. But it is theoretically better to use dummy variables for modeling.

References

  • Adamowicz, W.L.: Habit formation and variety seeking in a discrete choice model of recreation demand. J. Agric. Resour. Econ. 19(1), 19–31 (1994)

    Google Scholar 

  • Amador, F.J., González, R.M., de Ortúzar, J.D.: Preference heterogeneity and willingness to pay for travel time savings. Transportation 32(6), 627–647 (2005)

    Article  Google Scholar 

  • Amador, F.J., González, R.M., de Ortúzar, J.D.: On confounding preference heterogeneity and income effect in discrete choice models. Netw. Spat. Econ. 8(2), 97–108 (2008)

    Article  Google Scholar 

  • Axhausen, K.W., Hess, S., König, A., Abay, G., Bates, J.J., Bierlaire, M.: Income and distance elasticities of values of travel time savings: New Swiss results. Transp. Policy 15(3), 173–185 (2008)

    Article  Google Scholar 

  • Ben-Akiva, M.E., Lerman, S.R.: Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press (1985)

    Google Scholar 

  • Bhat, C.R.: Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model. Transp Res Part B Methodol 35(7), 677–693 (2001)

    Article  Google Scholar 

  • Bhat, C.R., Castelar, S.: A unified mixed logit framework for modeling revealed and stated preferences: formulation and application to congestion pricing analysis in the San Francisco Bay area. Transp Res Part B Methodol 36(7), 593–616 (2002)

    Article  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)

  • Börjesson, M., Eliasson, J.: Experiences from the Swedish value of time study. Transp Res Part A Policy Pract 59, 144–158 (2014)

    Article  Google Scholar 

  • Börjesson, M., Cherchi, E., Bierlaire, M.: Within-individual variation in preferences: equity effects of congestion charges. Transp. Res. Rec. 2382(1), 92–101 (2013)

    Article  Google Scholar 

  • Bradley, M.A., Daly, A.J.: Estimation of logit choice models using mixed stated preference and revealed preference information. In: Understanding travel behaviour in an era of change, pp. 209–232, Pergamon Press, Oxford (1997)

  • Cantillo, V., de Ortúzar, J.D., Williams, H.C.: Modeling discrete choices in the presence of inertia and serial correlation. Transp. Sci. 41(2), 195–205 (2007)

    Article  Google Scholar 

  • Chatterjee, K.: Modelling the dynamics of bus use in a changing travel environment using panel data. Transportation 38(3), 487–509 (2011)

    Article  Google Scholar 

  • Chatterjee, K., Ma, K.: (2006). Behavioural responses to a new transport option—a dynamic analysis using a six-month panel survey. In: 11th International Conference on Travel Behaviour Research, Kyoto, Japan (2006)

  • Cherchi, E., Cirillo, C.: Day-to-day variability and habit in modal choices: a mixed logit model on panel data. In: Proceedings of 12th World Conference on Transport Research, Lisbon, Portugal (2010)

  • Cherchi, E., Cirillo, C.: Understanding variability, habit and the effect of long period activity plan in modal choices: a day to day, week to week analysis on panel data. Transportation 41(6), 1245–1262 (2014)

    Article  Google Scholar 

  • Cherchi, E., Ortúzar, J. de D. : Mixed RP/SP models incorporating interaction effects. Transportation 29(4), 371–395 (2002)

    Article  Google Scholar 

  • Cherchi, E., Ortúzar, J. de D.: On the use of mixed RP/SP models in prediction: accounting for systematic and random taste heterogeneity. Transp. Sci. 45(1), 98–108 (2011)

    Article  Google Scholar 

  • Cherchi, E., Manca, F.: Accounting for inertia in modal choices: some new evidence using a RP/SP dataset. Transportation 38(4), 679–695 (2011)

    Article  Google Scholar 

  • Cherchi, E., Meloni, I., Ortúzar, J.de D.: The latent effect of inertia in the choice of mode. In 13th International Conference on Travel Behavior Research, Toronto, Canada (2013)

  • Cherchi, E., Rashidi, T., Börjesson, M., Bierlaire, M.: Habitual latent behavior and dynamic effect of inertia: lagged-based versus hazard-based hybrid mode choice model (No. 17-04958) Transportation Research Board 96th Annual Meeting, Washington DC, United States (2017)

  • Gao, K., Sun, L., Tu, H., Li, H.: Heterogeneity in valuation of travel time reliability and in-vehicle crowding for mode choices in multimodal networks. J. Transp. Eng. Part A Syst. 144(10), 04018061 (2018)

    Article  Google Scholar 

  • Gao, K., Yang, Y., Sun, L., Qu, X.: Revealing psychological inertia in mode shift behavior and its quantitative influences on commuting trips. Transport. Res. F: Traffic Psychol. Behav. 71, 272–287 (2020)

    Article  Google Scholar 

  • Gao, K., Yang, Y., Zhang, T., Li, A., Qu, X.: Extrapolation-enhanced model for travel decision making: an ensemble machine learning approach considering behavioral theory. Knowl.-Based Syst. 218, 106882 (2021a)

    Article  Google Scholar 

  • Gao, K., Zhang, T., Li, Z.: Modeling measurements towards effect of past behavior on travel behavior. In: Smart Transportation Systems 2021, pp. 141–157. Springer, Singapore (2021)

  • Gärling, T., Axhausen, K.W.: Introduction: habitual travel choice. Transportation 30(1), 1–11 (2003)

    Article  Google Scholar 

  • Garvill, J., Marell, A., Nordlund, A.: Effects of increased awareness on choice of travel mode. Transportation 30(1), 63–79 (2003)

    Article  Google Scholar 

  • González, R.M., Martínez-Budría, E., Díaz-Hernández, J.J., Esquivel, A.: Explanatory factors of distorted perceptions of travel time in tram. Transport. Res. Part F Traffic Psychol. Behav. 30, 107–114 (2015)

    Article  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)

    Article  Google Scholar 

  • Hensher, D.A., Greene, W.H.: The mixed logit model: the state of practice. Transportation 30(2), 133–176 (2003)

    Article  Google Scholar 

  • Hess, S.: Conditional parameter estimates from Mixed Logit models: distributional assumptions and a free software tool. J Choice Modell 3(2), 134–152 (2010)

    Article  Google Scholar 

  • Hess, S., Rose, J.M., Polak, J.: Non-trading, lexicographic and inconsistent behaviour in stated choice data. Transp. Res. Part D Transp. Environ. 15(7), 405–417 (2010)

    Article  Google Scholar 

  • Idris, A., Shalaby, A., Habib, K.: Towards a learning-based mode shift model: a conceptual framework. Transp Lett 4(1), 15–27 (2012)

    Article  Google Scholar 

  • Idris, A.O., Habib, K.M.N., Shalaby, A.: An investigation on the performances of mode shift models in transit ridership forecasting. Transp. Res. Part A Policy Pract. 78, 551–565 (2015)

    Article  Google Scholar 

  • Kouwenhoven, M., de Jong, G.C., Koster, P., van den Berg, V.A., Verhoef, E.T., Bates, J., Warffemius, P.M.: New values of time and reliability in passenger transport in The Netherlands. Res. Transp. Econ. 47, 37–49 (2014)

    Article  Google Scholar 

  • Kowalick, J.: Psychological inertia. TRIZ J. (8) (1998)

  • Li, Z., Hensher, D.A.: Crowding and public transport: a review of willingness to pay evidence and its relevance in project appraisal. Transp. Policy 18(6), 880–887 (2011)

    Article  Google Scholar 

  • Li, Z., Hensher, D.: Crowding in public transport: a review of objective and subjective measures. J. Public Transp. 16(2), 107–134 (2013)

    Article  Google Scholar 

  • Li, H., Gao, K., Tu, H.: Variations in mode-specific valuations of travel time reliability and in-vehicle crowding: implications for demand estimation. Transp. Res. Part A Policy Pract. 103, 250–263 (2017)

    Article  Google Scholar 

  • Liu, W., Li, X., Zhang, F., Yang, H.: Interactive travel choices and traffic forecast in a doubly dynamical system with user inertia and information provision. Transp. Res. Part C Emerg. Technol. 85, 711–731 (2017)

    Article  Google Scholar 

  • Márquez, L., Pico, R., Cantillo, V.: Understanding captive user behavior in the competition between BRT and motorcycle taxis. Transp. Policy 61, 1–9 (2018)

    Article  Google Scholar 

  • Morikawa, T.: Correcting state dependence and serial correlation in the RP/SP combined estimation method. Transportation 21(2), 153–165 (1994)

    Article  Google Scholar 

  • Pas, E.I., Sundar, S.: Intrapersonal variability in daily urban travel behavior: some additional evidence. Transportation 22(2), 135–150 (1995)

    Article  Google Scholar 

  • Pendyala, R. (1999) Measuring day-to-day variability in travel behavior using GPS data. Final Report, FHWA, Washington, DC. http://www.fhwa.dot.gov/ohim/gps

  • Rashedi, Z., Mahmoud, M., Hasnine, S., Habib, K.N.: On the factors affecting the choice of regional transit for commuting in Greater Toronto and Hamilton Area: application of an advanced RP-SP choice model. Transp. Res. Part A Policy Pract. 105, 1–13 (2017)

    Article  Google Scholar 

  • Rose, J.M., Bliemer, M.C., Hensher, D.A., Collins, A.T.: Designing efficient stated choice experiments in the presence of reference alternatives. Transp. Res. Part B Methodol 42(4), 395–406 (2008)

    Article  Google Scholar 

  • Sadhukhan, S., Banerjee, U.K., Maitra, B.: Commuters willingness-to-pay for improvement of transfer facilities in and around metro stations—a case study in Kolkata. Transp. Res. Part A Policy Pract. 92, 43–58 (2016)

    Article  Google Scholar 

  • Schlich, R., Axhausen, K.W.: Habitual travel behaviour: evidence from a six-week travel diary. Transportation 30(1), 13–36 (2003)

    Article  Google Scholar 

  • Simon, H.A.: Rationality as process and product of thought. Am. Econ. Rev. 68(2), 1–16 (1978)

    Google Scholar 

  • Srinivasan, K.K., Bhargavi, P.: Longer-term changes in mode choice decisions in Chennai: a comparison between cross-sectional and dynamic models. Transportation 34(3), 355–374 (2007)

    Article  Google Scholar 

  • Srinivasan, K.K., Mahmassani, H.S.: Modeling inertia and compliance mechanisms in route choice behavior under real-time information. Transp. Res. Rec. 1725(1), 45–53 (2000)

    Article  Google Scholar 

  • SURCTDRI: Comprehensive Transportation Annual Report of Shanghai (Year of 2015). Shanghai Uban and Rural Construction and Transportation Development Research Institute (SURCTDRI), Shanghai (2015)

  • SURCTDRI: Transportation Operation Annual Report of Shanghai (Year of 2017). Shanghai Uban and Rural Construction and Transportation Development Research Institute (SURCTDRI), Shanghai (2018)

  • Susilo, Y.O., Axhausen, K.W.: How firm are you? A study of the stability of individual activity-travel-location patterns using the Herfindahl-Hirschman Index. In: 11th World Conference on Transport Research World Conference on Transport Research Society, Berkeley CA, United States (2007)

  • Swait, J., Adamowicz, W., Bueren, M.V.: Choice and temporal welfare impacts: incorporating history into discrete choice models. J. Environ. Econ. Manag. 47(1), 94–116 (2004)

    Article  Google Scholar 

  • Thorhauge, M., Swait, J., Cherchi, E.: The habit-driven life: accounting for inertia in departure time choices for commuting trips. Transp. Res. Part A Policy Pract. 133, 272–289 (2020)

    Article  Google Scholar 

  • Tirachini, A., Sun, L., Erath, A., Chakirov, A., Hayashi, Y.: Valuation of sitting and standing in metro trains using revealed preferences. Transp. Policy 47, 94–104 (2016)

    Article  Google Scholar 

  • Train, K.: Discrete Choice Methods with Simulation. Cambridge University Press, New York, NY (2009)

  • 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 

  • Van Exel, J., Rietveld, P.: Inertia of travel behaviour: A stated preference analysis of commuting. In: Transport Developments and Innovations in an Evolving World, pp. 87–122. Springer (2004)

  • Verplanken, B., Aarts, H., Knippenberg, A.V.: Habit, information acquisition, and the process of making travel mode choices. Eur. J. Soc. Psychol. 27(5), 539–560 (1997)

    Article  Google Scholar 

  • Xie, C., Liu, Z.: On the stochastic network equilibrium with heterogeneous choice inertia. Transp. Res. Part B Methodol. 66, 90–109 (2014)

    Article  Google Scholar 

  • Yáñez, M.F., Cherchi, E., Ortuzar, J.D.D., Heydecker, B.: Inertia and shock effects on mode choice panel data: implications of the Transantiago implementation. In: 12th international conference on travel behaviour research, Jaipur, India (2009)

  • Zhang, J., Yang, H.: Modeling route choice inertia in network equilibrium with heterogeneous prevailing choice sets. Transp. Res. Part C Emerg. Technol. 57, 42–54 (2015)

    Article  Google Scholar 

  • Zhou, B., Xu, M., Meng, Q.: A discrete day-to-day link flow dynamic model considering travelers’ heterogeneous inertia patterns. Transportmetrica A Transp. Sci. 16(3), 1400–1428 (2020)

    Article  Google Scholar 

Download references

Acknowledgements

This study is supported by the National Key R&D Program of China (2019YFE0108300 and 2019YFE0112100). The study is also partly funded by Chinese Scholarship Council (CSC No. 201706260079).

Author information

Authors and Affiliations

  1. Department of Architecture and Civil Engineering, Chalmers University of Technology, 412 96, Gothenburg, Sweden

    Kun Gao

  2. College of Transportation Engineering, Tongji University, Key Laboratory of Road and Traffic Engineering of the Ministry of Education, 4800 Cao’an Road, Jiading District, 201804, Shanghai, People’s Republic of China

    Minhua Shao, Lijun Sun & Huizhao Tu

  3. Institute for Transport Planning and Systems (IVT), ETH Zurich, Stefano-​Franscini-Platz 5, 8093, Zurich, Switzerland

    Kay W. Axhausen

  4. Department of Transport and Planning, Delft University of Technology, P.O. Box 5048, 2600 GA, Delft, The Netherlands

    Yihong Wang

Authors
  1. Kun Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Minhua Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kay W. Axhausen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lijun Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huizhao Tu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yihong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

KG: Research design, survey design, data analysis and manuscript writing. MHS: Research design, model formulation, discussion and manuscript writing. KWA: Modeling suggestion, discussion and manuscript improvement. LJS: Research design, discussion and project oversight. HZT: Survey design, survey conduction and manuscript revision. YHW: Suggestions for improvements and manuscript revision.

Corresponding author

Correspondence to Minhua Shao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, K., Shao, M., Axhausen, K.W. et al. Inertia effects of past behavior in commuting modal shift behavior: interactions, variations and implications for demand estimation. Transportation 49, 1063–1097 (2022). https://doi.org/10.1007/s11116-021-10203-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11116-021-10203-6

Keywords

Search

Navigation