Characteristics of Premium Transit Services that Affect Choice of Mode

Abstract

Traditionally, travel models use travel time and cost to assess the usefulness of each mode of transportation to make a particular trip. Other factors that affect the selection of mode are accounted for using a single constant term that represents other attributes. In many cases, these attributes represent conditions that may not be the same for all trips. Travel forecasting models would benefit by incorporating an expanded list of non-traditional attributes so that the probability of using transit to make a trip is more specifically related to the characteristics of a potential transit journey. Potential non-traditional transit characteristics include on-board and station amenities, reliability, span of service, and service visibility/ branding. These characteristics are not typically directly considered in travel forecasting models. This research sought to improve the understanding of the full range of determinants for transit travel behavior and to offer practical solutions to practitioners seeking to represent and distinguish transit characteristics in travel forecasting models. The key findings of this research include the value of non-traditional transit service attributes on travelers’ choice of mode, in particular the influence of awareness and consideration of transit service on modal alternatives, and the importance of traveler attitudes toward both awareness and consideration of transit and on the choice of transit or auto in mode choice. The appendices present detailed research results including a state-of-the-practice literature review, survey instruments, models estimated by the research team, model testing, and model implementation and calibration results. The models demonstrate an approach for including non-traditional transit service attributes in the representation of both transit supply (networks) and demand (mode choice models), reducing the magnitude of the modal specific constant term while maintaining the ability of the model to forecast ridership on specific transit services. The testing conducted in this project included replacing transit access and service modes, such as drive to light rail or walk to local bus, as alternatives in the mode choice model with transit alternatives defined by the elements of the path, such as a short walk to transit path, a no-transfer path, or a premium service path.

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