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.