Using Repeated-Measurement Stated Preference Data to Investigate Users’ Attitudes Towards Automated Buses Within Major Facilities

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 539)


The paper reports on the results of an investigation about users’ attitudes towards automated and conventional minibuses for routes within major facilities. A common stated preference questionnaire has been used in four European cities. The econometric analysis is based on the estimation of three binomial logit models: one model considers all independent observations, a copula logit and an error component logit take into account the correlation among error terms of the observations by the same individual. The observed attributes are waiting time, riding time and fare. Of particular interest, is the estimation of the alternative specific constant (ASC) of the automated minibus, because this represents the mean of all the unobserved attributes of the automated alternative that affect the choice. With a common specification of the systematic utilities of the automated and conventional alternatives, the results show a positive value of the ASC, which is indicative, the observed attributes being the same, of a relatively higher preference for automation. The differences in policy implications among the three models estimated are negligible.


Automated bus Stated preference Binomial logit Copula Error component 



The survey activities have been carried out by the partners of the CityMobil2 project involved in the feasibility studies in the four cities.


  1. Abdel-Aty, M.A., Kitamura, R., Jovanis, P.P.: Investigating effect of travel time variability on route choice using repeated-measurement stated preference data. Transp. Res. Rec. 1493, 39–45 (1995)Google Scholar
  2. Alessandrini, A., Alfonsi, R., Delle Site, P., Stam, D.: Users’ preferences towards automated road public transport: results from European surveys. Transp. Res. Procedia 3, 139–144 (2014a)CrossRefGoogle Scholar
  3. Alessandrini, A., Cattivera, A., Holguin, C., Stam, D.: CityMobil2: challenges and opportunities of fully automated mobility. In: Meyer, G., Beiker, S. (eds.) Road Vehicle Automation. Lecture Notes in Mobility, pp. 169–184. Springer, Heidelberg (2014b)CrossRefGoogle Scholar
  4. Berndt, E.K., Hall, B.H., Hall, R.E., Hausman, J.A.: Estimation and inference in nonlinear structural models. Ann. Econ. Soc. Meas. 3(4), 653–665 (1974)Google Scholar
  5. Bhat, C.R., Sener, I.N.: A copula-based closed-form binary logit choice model for accommodating spatial correlation across observational units. J. Geogr. Syst. 11(3), 243–272 (2009)CrossRefGoogle Scholar
  6. Cantillo, V., de Dios Ortúzar, J., Williams, H.C.W.L.: Modeling discrete choices in the presence of inertia and serial correlation. Transp. Sci. 41(2), 195–205 (2007)CrossRefGoogle Scholar
  7. Csepinszky, A., Giustiniani, G., Holguin, C., Parent, M., Flament, M., Alessandrini, A.: Safe integration of fully automated road transport systems in urban environments: the basis for the missing legal framework. In: Proceedings TRB Annual meeting, Washington D.C (2015)Google Scholar
  8. Delle Site, P., Filippi, F., Giustiniani, G.: Users’ preferences towards innovative and conventional public transport. Procedia Soc. Behav. Sci. 20, 906–915 (2011)CrossRefGoogle Scholar
  9. Greene, W.H.: Econometric Analysis, 7th edn. Pearson, Boston (2012)Google Scholar
  10. Guala, L., Alessandrini, A., Sechi, F., Delle Site, P., Holguin, C., Salucci, M.V.: Testing autonomous driving vehicles in a mixed environment with pedestrians and bicycles. In: Proceedings 22nd ITS World Congress, Bordeaux, France, October (2015)Google Scholar
  11. Jensen, A.F., Cherchi, E., Mabit, S.L.: On the stability of preferences and attitudes before and after experiencing an electric vehicle. Transp. Res. Part D Transp. Environ. 25, 24–32 (2013)CrossRefGoogle Scholar
  12. Karunaratne, P.M., Elston, R.C.: A multivariate logistic model (MLM) for analyzing binary family data. Am. J. Med. Genet. 76(5), 428–437 (1998)CrossRefGoogle Scholar
  13. McFadden, D.: The new science of pleasure: consumer choice behavior and the measurement of well-being. In: Hess, S., Daly, A. (eds.) Handbook of Choice Modelling. Edward Elgar (2014)Google Scholar
  14. Nelsen, R.B.: An Introduction to Copulas, 2nd edn. Springer, New York (2006)zbMATHGoogle Scholar
  15. NETMOBIL Consortium: EU Potential for Innovative Personal Urban Mobility. Deliverable D7 of the NETMOBIL (New transport system concepts for enhanced and sustainable personal urban mobility) project. Fifth Framework Programme, European Commission (2005)Google Scholar
  16. Price, W.L.: A controlled random search procedure for global optimization. In: Dixon, L.C.W., Szegö, G.P. (eds.) Towards Global Optimization 2. North Holland, Amsterdam (1978)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of FlorenceFlorenceItaly
  2. 2.University Niccolò Cusano – Telematica RomaRomeItaly
  3. 3.University of Rome La SapienzaRomeItaly
  4. 4.University Roma TreRomeItaly

Personalised recommendations