A clear understanding of the sources and amount of ridership on a new or improved travel mode is critical to evaluating the financial, travel flow, and external impacts of proposed improvements. The multinomial logit model traditionally used to model intercity mode choice may not adequately reflect traveler behavior because it restricts the relative probability of choosing between any pair of existing modes to be unchanged when other modes are introduced or changed. The nested logit model provides a computationally feasible generalization to the multinomial logit model, which allows for specified mode pairs to exhibit increased sensitivity to changes in service. Full information estimation of nested logit models allows efficient use of information and yields results directly comparable to multinomial logit models. Business travel in the Ontario-Quebec corridor of Canada is examined. A set of nested logit structures that allow for various combinations of differential sensitivity to changes in service quality of rail is estimated. Nested logit structures with bus-train or car-train nests prove superior to the multinomial logit model. Both of the nested logit models predict larger increases in rail shares than the multinomial logit model in response to rail service improvements, but the source of that increased ridership differs between the nested logit structures. This points to the need for models of individual choice that retain the advantages of nested logit while allowing pairwise similarity between alternatives.
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