Accommodating variations in responsiveness to level-of-service measures in travel mode choice modeling

An individual's responsiveness to level-of-service variables affects her or his travel mode choice for a trip. This responsiveness will, in general, vary across individuals based on observed (to an analyst) and unobserved (to an analyst) individual characteristics. The current paper formulates a multinomial-logit based model of travel mode choice that accommodates variations in responsiveness to level-of-service measures due to both observed and unobserved individual characteristics in a comprehensive manner. The choice probabilities in the resulting model are evaluated using Monte Carlo simulation techniques and the model parameters are estimated using a maximum simulated likelihood approach. The model is applied to examine the impact of improved rail service on weekday, business travel in the Toronto--Montreal corridor. The empirical results show that not accounting adequately for variations in responsiveness across individuals leads to a statistically inferior data fit and also to inappropriate evaluations of policy actions aimed at improving inter-city transportation services.