An Integrated Choice and Latent Variable Model to Explore the Influence of Attitudinal and Perceptual Factors on Shared Mobility Choices and Their Value of Time Estimation

Published Online:https://doi.org/10.1287/trsc.2019.0933

References

  • Abou-Zeid M, Ben-Akiva M (2014) Hybrid choice models. Hess S, Daly A, eds. Handbook of Choice Modelling (Edward Elgar Publishing, Cheltenham, UK), 383–412.Google Scholar
  • Abou-Zeid M, Ben-Akiva M, Bierlaire M, Choudhury C, Hess S (2010) Attitudes and value of time heterogeneity. Applied Transport Economics—A Management and Policy Perspective (De Boeck Publishing, Bruxelles, Belgium), 523–545.Google Scholar
  • Algers S, Bergström P, Dahlberg M, Lindqvist Dillén J (1998) Mixed logit estimation of the value of travel time. Working paper, Uppsala University, Lausanne, Sweden.Google Scholar
  • Alpizar F, Carlsson F (2003) Policy implications and analysis of the determinants of travel mode choice: An application of choice experiments to metropolitan Costa Rica. Environ. Development Econom. 8(4):603–619.CrossrefGoogle Scholar
  • Amador FJ, González RM, Ortúzar JD (2005) Preference heterogeneity and willingness to pay for travel time savings. Transportation 32(6):627–647.CrossrefGoogle Scholar
  • Axhausen KW, Hess S, König A, Abay G, Bates JJ, Bierlaire M (2008) Income and distance elasticities of values of travel time savings: New Swiss results. Transportation Policy 15(3):173–185.CrossrefGoogle Scholar
  • Bahamonde-Birke FJ, Hanappi T (2016) The potential of electromobility in Austria: Evidence from hybrid choice models under the presence of unreported information. Transportation Res. Part A: Policy Practice 83:30–41.CrossrefGoogle Scholar
  • Bahamonde-Birke FJ, Kunert U, Link H, Ortúzar JD (2017) About attitudes and perceptions: Finding the proper way to consider latent variables in discrete choice models. Transportation 44(3):475–493.CrossrefGoogle Scholar
  • Bastin F, Cirillo C, Toint PL (2010) Estimating nonparametric random utility models with an application to the value of time in heterogeneous populations. Transportation Sci. 44(4):537–549.LinkGoogle Scholar
  • Beck MJ, Rose JM, Greaves SP (2017) I can’t believe your attitude: A joint estimation of best worst attitudes and electric vehicle choice. Transportation 44(4):753–772.CrossrefGoogle Scholar
  • Belgiawan PF, Schmöcker JD, Abou-Zeid M, Walker J, Fujii S (2017) Modelling social norms: Case study of students’ car purchase intentions. Travel Behav. Soc. 7:12–25.CrossrefGoogle Scholar
  • Ben-Akiva M, Walker J, Bernardino AT, Gopinath DA, Morikawa T, Polydoropoulou A (2002) Integration of choice and latent variable models. Mahmassani HS, ed. Perpetual Motion: Travel Behaviour Research Opportunities and Application Challenges (Pergamon, Amsterdam), 431–470.Google Scholar
  • Bhat CR (2011) The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models. Transportation Res. Part B: Methodological 45(7):923–939.CrossrefGoogle Scholar
  • Bhat CR, Dubey SK (2014) A new estimation approach to integrate latent psychological constructs in choice modeling. Transportation Res. Part B: Methodological 67:68–85.CrossrefGoogle Scholar
  • Bierlaire M (2015) Monte-Carlo integration with pythonbiogeme, Technical report TRANSP-OR 150806, Transport and Mobility Laboratory, ENAC, EPFL, Lausanne, Switzerland.Google Scholar
  • Bierlaire M (2016a) Estimating choice models with latent variables with PythonBiogeme. Technical report TRANSP-OR 160628, Transport and Mobility Laboratory, ENAC, EPFL, Lausanne, Switzerland.Google Scholar
  • Bierlaire M (2016b) 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, Lausanne, Switzerland.Google Scholar
  • Bolduc D, Alvarez-Daziano R (2010) On estimation of hybrid choice models. Hess S, Daly A, eds. Choice Modelling: The State-of-the-Art and the State-of-Practice (Emerald, Bingley, UK), 259–287.CrossrefGoogle Scholar
  • Bolduc D, Boucher N, Alvarez-Daziano R (2008) Hybrid choice modeling of new technologies for car choice in Canada. Transportation Res. Record J. Transportation Res. Board 2082:63–71.CrossrefGoogle Scholar
  • Bolduc D, Ben-Akiva M, Walker J, Michaud A (2005) Hybrid choice models with logit kernel: Applicability to large scale models1. Lee-Gosselin MEH, Doherty ST, eds. Integrated Land-Use and Transportation Models (Elsevier, Amsterdam), 275–302.CrossrefGoogle Scholar
  • Burkholder M (2015) The world’s 6 best bike share programs. Accessed May 4, 2019, http://www.outwardon.com/article/the-worlds-6-best-bike-share-programs/6/.Google Scholar
  • Chen J, Li S (2017) Mode choice model for public transport with categorized latent variables. Math. Problems Engrg. 1:1–11.Google Scholar
  • Cherchi E, Ortúzar JD (2011) On the use of mixed RP/SP models in prediction: Accounting for systematic and random taste heterogeneity. Transportation Sci. 45(1):98–108.LinkGoogle Scholar
  • Chorus CG, Kroesen M (2014) On the (im-) possibility of deriving transport policy implications from hybrid choice models. Transportation Policy 36:217–222.CrossrefGoogle Scholar
  • Correia G, Abreu e Silva J, Viegas J (2010) Using latent attitudinal variables for measuring carpooling propensity. Proc. 12th World Conf. Transport Res., Lisbon, Portugal.Google Scholar
  • Daziano RA, Bolduc D (2013) Incorporating pro-environmental preferences toward green automobile technologies through a Bayesian hybrid choice model. Transportmetrica A: Transportation Sci. 9(1):74–106.CrossrefGoogle Scholar
  • Efthymiou D, Antoniou C (2016) Modeling the propensity to join carsharing using hybrid choice models and mixed survey data. Transportation Policy 51:143–149.CrossrefGoogle Scholar
  • Fernández-Heredia Á, Jara-Díaz S, Monzón A (2016) Modelling bicycle use intention: The role of perceptions. Transportation 43(1):1–23.CrossrefGoogle Scholar
  • Fishman E, Washington S, Haworth N (2012) Barriers and facilitators to public bicycle scheme use: A qualitative approach. Transportation Res. Part F: Traffic Psych. Behav. 15(6):686–698.CrossrefGoogle Scholar
  • Fleischer A, Tchetchik A, Toledo T (2012) The impact of fear of flying on travelers’ flight choice: Choice model with latent variables. J. Travel Res. 51(5):653–663.CrossrefGoogle Scholar
  • Global Opportunity Explorer (2016) Taiyuan: World’s fastest electric taxi fleet overhaul. Accessed May 4, 2019, http://explorer.sustainia.me/cities/taiyuan-worlds-fastest-electric-taxi-fleet-overhaul.Google Scholar
  • Hao Y (2017) Car-sharing market floundering. Accessed May 4, 2019, http://www.chinadaily.com.cn/bizchina/motoring/2017-03/20/content_28609872.htm.Google Scholar
  • Hensher DA (2001a) Measurement of the valuation of travel time savings. J. Transportation Econom. Policy 35(1):71–98.Google Scholar
  • Hensher DA (2001b) The valuation of commuter travel time savings for car drivers: Evaluating alternative model specifications. Transportation 28(2):101–118.CrossrefGoogle Scholar
  • Hensher DA, Rose JM, Greene WH (2005) Applied Choice Analysis: A Primer (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Hess S, Bierlaire M, Polak J (2004) Development and application of a mixed cross-nested logit model. XXIth Eur. Transport Conf. (No. TRANSP-OR-CONF-2006-044).Google Scholar
  • Hess S, Bierlaire M, Polak J (2005) Estimation of value of travel-time savings using mixed logit models. Transportation Res. Part A: Policy Practice 39(2):221–236.CrossrefGoogle Scholar
  • Hess S, Train KE, Polak JW (2006) On the use of a modified Latin hypercube sampling (MLHS) method in the estimation of a mixed logit model for vehicle choice. Transportation Res. Part B: Methodological 40(2):147–163.CrossrefGoogle Scholar
  • Hiles D (2015) The world’s surprising top 8 bike share programs. Accessed May 4, 2019, http://www.icebike.org/bike-share-programs/.Google Scholar
  • Jara-Diaz SR (2003) On the goods-activities technical relations in the time allocation theory. Transportation 30(3):245–260.CrossrefGoogle Scholar
  • Jensen AF, Cherchi E, Mabit SL (2013) On the stability of preferences and attitudes before and after experiencing an electric vehicle. Transportation Res. Part D: Transportation Environ. 25:24–32.CrossrefGoogle Scholar
  • Jiang T, Song M, Jiang Y, Li M, Zou H (2013) Toward transit metropolis: Status quo analysis for Chinese major cities. Procedia Soc. Behav. Sci. 96:2621–2634.CrossrefGoogle Scholar
  • Johansson MV, Heldt T, Johansson P (2006) The effects of attitudes and personality traits on mode choice. Transportation Res. Part A: Policy Practice 40(6):507–525.CrossrefGoogle Scholar
  • Jolliffe I (2002) Principal Component Analysis (John Wiley & Sons, Ltd., Hoboken, NJ).Google Scholar
  • Jorge D, Correia G (2013) Carsharing systems demand estimation and defined operations: A literature review. Euro J. Transportation Infrastructure Res. 13(3):201–220.Google Scholar
  • Kaiser H (1958) The varimax criterion for analytic rotation in factor analysis. Psychometrica 23(3):187–200.CrossrefGoogle Scholar
  • Kamargianni M, Polydoropoulou A (2013) Hybrid choice model to investigate effects of teenagers’ attitudes toward walking and cycling on mode choice behavior. Transportation Res. Record J. Transportation Res. Board 2382(1):151–161.CrossrefGoogle Scholar
  • Kamargianni M, Ben-Akiva M, Polydoropoulou A (2014) Integrating social interaction into hybrid choice models. Transportation 41(6):1263–1285.CrossrefGoogle Scholar
  • Kamargianni M, Dubey S, Polydoropoulou A, Bhat C (2015) Investigating the subjective and objective factors influencing teenagers’ school travel mode choice—An integrated choice and latent variable model. Transportation Res. Part A: Policy Practice 78:473–488.CrossrefGoogle Scholar
  • Kim J, Rasouli S, Timmermans H (2014) Expanding scope of hybrid choice models allowing for mixture of social influences and latent attitudes: Application to intended purchase of electric cars. Transportation Res. Part A: Policy Practice 69:71–85.CrossrefGoogle Scholar
  • Kim J, Rasouli S, Timmermans H (2017a) The effects of activity-travel context and individual attitudes on car-sharing decisions under travel time uncertainty: A hybrid choice modeling approach. Transportation Res. Part D: Transportation Environ. 56:189–202.CrossrefGoogle Scholar
  • Kim J, Rasouli S, Timmermans H (2017b) Satisfaction and uncertainty in car-sharing decisions: An integration of hybrid choice and random regret-based models. Transportation Res. Part A: Policy Practice 95:13–33.CrossrefGoogle Scholar
  • La Paix Puello LC, Geurs KT (2015) Modelling observed and unobserved factors in cycling to railway stations: Application to transit-oriented-developments in the Netherlands. Eur. J. Transportation Infrastructure Res. 15(1):27–50.Google Scholar
  • Li W (2019) A mode choice study on shared mobility services: Policy opportunities for a developing country. Doctoral dissertation, University College London, London.Google Scholar
  • Li W, Kamargianni M (2018) Providing quantified evidence to policy makers for promoting bike-sharing in heavily air-polluted cities: A mode choice model and policy simulation for Taiyuan-China. Transportation Res. Part A: Policy Practice 111:277–291.CrossrefGoogle Scholar
  • Li W, Kamargianni M (2019) Steering short-term demand for car-sharing: A mode choice and policy impact analysis by trip distance. Transportation, ePub ahead of print May 28, https://doi.org/10.1007/s11116-019-10010-0.Google Scholar
  • Likert R (1932) A technique for the measurement of attitudes. Arch. Psych. 140:1–55.Google Scholar
  • Louviere J, Hensher D, Swait J (2003) Stated Choice Methods: Analysis and Applications (Cambridge University Press, Cambridge, UK).Google Scholar
  • Mackie PJ, Wadman M, Fowkes AS, Whelan G, Nellthorp J, Bates J (2003) Values of travel time savings UK. Working Paper 567, Institute of Transport Studies, University of Leeds, Leeds, UK.Google Scholar
  • Maldonado-Hinarejos R, Sivakumar A, Polak JW (2014) Exploring the role of individual attitudes and perceptions in predicting the demand for cycling: A hybrid choice modelling approach. Transportation 41(6):1287–1304.CrossrefGoogle Scholar
  • McCarthy OT, Caulfield B, O’Mahony M (2016) How transport users perceive personal safety apps. Transportation Res. Part F: Traffic Psych. Behav. 43:166–182.CrossrefGoogle Scholar
  • McFadden D (1989) A method of simulated moments for estimation of discrete response models without numerical integration. Econometrica 57(5):995–1026.CrossrefGoogle Scholar
  • Motoaki Y, Daziano RA (2015) A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand. Transportation Res. Part A: Policy Practice 75:217–230.CrossrefGoogle Scholar
  • Munoz B, Monzon A, Lopez E (2016) Transition to a cyclable city: Latent variables affecting bicycle commuting. Transportation Res. Part A: Policy Practice 84:4–17.CrossrefGoogle Scholar
  • Ortúzar JD, Willumsen LG (2011) Modelling Transport, 4th ed. (Wiley, Hoboken, NJ).CrossrefGoogle Scholar
  • Paschalidis E, Basbas S, Politis I, Prodromou M (2016) “Put the blame on … others!”: The battle of cyclists against pedestrians and car drivers at the urban environment. A cyclists’ perception study. Transportation Res. Part F: Traffic Psych. Behav. 41:243–260.CrossrefGoogle Scholar
  • Paulssen M, Temme D, Vij A, Walker JL (2014) Values, attitudes and travel behavior: A hierarchical latent variable mixed logit model of travel mode choice. Transportation 41(4):873–888.CrossrefGoogle Scholar
  • Piatkowski DP, Marshall W, Johnson A (2017) Identifying behavioral norms among bicyclists in mixed-traffic conditions. Transportation Res. Part F: Traffic Psych. Behav. 46:137–148.CrossrefGoogle Scholar
  • Raveau S, Alvarez-Daziano R, Yanez M, Bolduc D, Ortúzar JD (2010) Sequential and simultaneous estimation of hybrid discrete choice models: Some new findings. Transportation Res. Record J. Transportation Res. Board 2156(1):131–139.CrossrefGoogle Scholar
  • Romero A, Tasciotti L, Acosta F (2017) Means of transportation choice for the residents of Villavicencio, Colombia: A quantitative analysis. Transportation Res. Part F: Traffic Psych. Behav. 44:134–144.CrossrefGoogle Scholar
  • Salomon I, Mokhtarian PL (1998) What happens when mobility-inclined market segments face accessibility-enhancing policies? Transportation Res. Part D: Transportation Environ. 3(3):129–140.CrossrefGoogle Scholar
  • Sarkar PP, Mallikarjuna C (2018) Effect of perception and attitudinal variables on mode choice behavior: A case study of Indian city, Agartala. Travel Behav. Soc. 12:108–114.CrossrefGoogle Scholar
  • Shires JD, De Jong GC (2009) An international meta-analysis of values of travel time savings. Evaluation Program Planning 32(4):315–325.CrossrefGoogle Scholar
  • Smith B, Olaru D, Jabeen F, Greaves S (2017) Electric vehicles adoption: Environmental enthusiast bias in discrete choice models. Transportation Res. Part D: Transportation Environ. 51:290–303.CrossrefGoogle Scholar
  • Song F, Hess S, Dekker T (2018) Accounting for the impact of variety-seeking: Theory and application to HSR-air intermodality in China. J. Air Transportation Management 69:99–111.CrossrefGoogle Scholar
  • Toutiao (2017) Things about bike-sharing in Taiyuan. Accessed May 4, 2019, http://www.toutiao.com/i6434389096147206657/.Google Scholar
  • Trottenberg P, Belenky P (2011) Revised Departmental Guidance on Valuation of Travel Time in Economic Analysis (U.S. Department of Transportation, Washington, DC).Google Scholar
  • Vij A, Walker JL (2016) How, when and why integrated choice and latent variable models are latently useful. Transportation Res. Part B: Methodological 90:192–217.CrossrefGoogle Scholar
  • Vinayak P, Dias FF, Astroza S, Bhat CR, Pendyala RM, Garikapati VM (2018) Accounting For multi-dimensional dependencies among decision-makers within a generalized model framework: An application to understanding shared mobility service usage levels. Transport Policy 72:129–137.Google Scholar
  • Walker J, Ben-Akiva M (2002) Generalized random utility model. Math. Social Sci. 43(3):303–343.CrossrefGoogle Scholar
  • Wardman W (1998) The value of travel time: A review of British evidence. J. Transportation Econ. Policy 32(3):285–316.Google Scholar
  • Xinhua (2017) Car-sharing services taking the fast lane in China. Accessed May 4, 2019, http://www.chinadaily.com.cn/business/motoring/2017-02/15/content_28204616.htm.Google Scholar
  • Yanez MF, Raveau S, Ortúzar JD (2010) Inclusion of latent variables in mixed logit models: Modelling and forecasting. Transportation Res. Part A: Policy Practice 44(9):744–753.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.