LATENT VARIABLE ENRICHED MODE CHOICE MODEL FOR WORK ACTIVITY IN MULTI MODAL CONDITION PREVALENT IN INDIA
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Anu Plavara Alex - College of Engineering Trivandrum, 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.
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The authors are grateful to Kerala State Council for Science, Technology and Environment (KSCSTE) for funding the project.
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