Volume List  / Volume 6 (4)



DOI: 10.7708/ijtte.2016.6(4).02

6 / 4 / 378-389 Pages


Anu Plavara Alex - College of Engineering Trivandrum, India -

Manju Vasudevan Saraswathy - College of Engineering Trivandrum, India -

Kuncheria Palampoikayil Isaac - APJ Abdul Kalam Technological University Kerala, India -


Transportation network and modal availability play a significant role in trip generation. Mode choice models mainly use modal attributes and socioeconomic characteristics as explanatory variables. The choice of mode of transport also depends on unobservable/ latent variables in addition to the conventional variables. Latent variables are influenced by the attitude and behaviour of the individual. It can be noted from the literature that the latent variables affecting mode choice are location specific and hence they vary from place to place. Studies are not reported in cities where multi modal condition exists. Hence it is essential to study the influence of latent variables in developing countries, especially where a multi modal condition exists. The present study is aimed at incorporating the unobservable factors in activity based mode choice models in multi modal condition in a developing country. Latent variables incorporate the commuters behavioural, attitudinal, mode specific and life style attributes which influence in choosing a mode. The study concentrated on mode of commute before work activity. The modes considered in the study are walk/cycle, car, two wheeler, bus and train. In order to collect the travel behaviour, a self-descriptive questionnaire was prepared which consisted of questions on respondent’s attitude and behaviour and socio – demographic variables. The collected data were analyzed by conducting an Exploratory Factor Analysis (EFA) using principal component method. Confirmatory Factor Analysis (CFA) was carried out to confirm the factor structures identified by EFA. Structural Equation Models (SEM) were developed to correlate latent variables and socio demographic variables. Finally, two mode choice models were developed using Multi Nominal Logit (MNL) models. Case I considered the activity and travel characteristics and socio demographic variables, while case II considered activity and travel characteristics and latent variables. The socio demographic variables which affect the latent variables were replaced with latent variables in case II. It was found that the level of prediction of latent variable enriched mode choice model increased by 7.48% than that without latent variable.

Download Article

Number of downloads: 513


The authors are grateful to Kerala State Council for Science, Technology and Environment (KSCSTE) for funding the project.


Anwar, A.M.; Tieu, K.; Gibson, P.; Berryman, M.J.; Win, K.T.; McCusker, A.; Perez, P. 2014. Temporal and Parametric Study of Traveller Preference Heterogeneity Using Random Parameter Logit Model, International Journal for Traffic and Transport Engineering, 4(4): 437 - 455.


Ashok, K.; Dillon, W.R.; Yuan, S. 2002. Extending Discrete Choice Models to Incorporate Attitudinal and other Latent Variables, Journal of Marketing Research, 39(1): 31-46.


Atasoy, B.; Glerum, A.; Bierlaire, M. 2013. Attitudes Towards Mode Choice in Switzerland, disP-The Planning Review, 49(2): 101-117.


Ben-Akiva, M.E; Boccara, B. 1995. Discrete Choice Models with Latent Choice Sets, International Journal of Research in Marketing, 12(1): 9–24.


Brown, T.A. 2015. Confirmatory Factor Analysis for Applied Research, The Guilford Press. New York. 461 p.


Choo, S.; Mokhtarian, P.L. 2004. What Type of Vehicle do People Drive? The Role of Attitude and Lifestyle in Influencing Vehicle Type Choice, Transportation Research Part A, 38(3): 201–222.


Gopinath, D. A. 1995. Modeling Heterogeneity in Discrete Choice Processes: Application to Travel Demand. PhD thesis, Massachusetts Institute of Technology, USA. 360 p.


Johansson, M.V; Heldt, T; Johansson, P. 2006. The Effects of Attitude and Personality Traits on Mode Choice, Transportation Research Part A, 40 (6): 507 - 525.


Morikawa, T.; Ben-Akiva, M.; McFadden, D. 2002. Discrete Choice Models Incorporating Revealed Preferences and Psychometric Data. Econometric Models in Marketing Advances in Econometrics: A Research Annual, vol. 16. Elsevier Science Ltd. 29-56 p.


Prato C.G.; Bekhor, S., Pronello, C. 2012. Latent variables and route choice behaviour. Transportation, 39(2): 299-319.


Radam, I. F.; Mulyono, A.T., Setiadji, B.H. 2015. Influence of Service Factors in the Model of Public Transport Mode: A Banjarmasin – Banjarbaru Route Case Study, International Journal for Traffic and Transport Engineering, 5(2): 108 - 119.


Redmond, L. 2000. Identifying and Analyzing Travel-Related Attitudinal, Personality, and Lifestyle Clusters in the San Francisco Bay Area. Master Thesis, University of California, Davis, USA. 169 p.


Tangphaisankun, A; Osada, C; Okamura, T; Nakamura, F.; Wang , R. 2011. Influences of Commuters' Personality and Preferences on Travel Intention in Developing Countries: A Case of Bangkok, Journal of the Eastern Asia Society for Transportation Studies, 9(2011): 370-381.


Temme, D.; Paulssen, M.; Dannewald, T. 2008. Incorporating Latent Variables into Discrete Choice Models - A Simultaneous Estimation Approach Using SEM Software, BuR - Business Research, 1(2): 220-237.