Refined choice set generation and the investigation of multi-criteria transit route choice behavior

To best make informed public transportation decisions, agencies must be aware of how individuals interact with the transit system. The key question to consider is how do passengers get from Point A to Point B and which attributes of the transit trip have the largest influence on that choice? To more accurately understand passengers’ interaction with transit systems, a new method for transit route choice estimation is proposed. This method modifies and builds upon a schedule-based forward label setting multi-criteria shortest path algorithm by introducing an iterative trip elimination methodology. This new methodology yields high quality transit path choice sets with exact regeneration of 70.5% of the observed paths. Moreover, detailed temporal information on all types of network links (in-vehicle, walking, and waiting) are produced with reasonable computational time of 0.5 s per path calculation and 6.9 s per individual's choice set generation. This increased specificity, in turn, heightens the validity and accuracy of the route choice model. Passenger information is sampled from a transit on-board survey containing origin–destination locations, demographic details, and trip-specific attributes. A multinomial logit model with stop-level path size correction term is estimated. This estimation yields a 67% recovery rate between the path with the highest estimated likelihood and the surveyed (taken) transit path. Furthermore, a transfer penalty of 28.8 min was estimated while the marginal rates of substitution for the waiting and walking time coefficients are in close alignment with similar values in the literature. Express bus routes were found to have a statistically significant negative impact on path utility for the lowest income thresholds while transitways (light rail, bus rapid transit, or commuter rail) had a positive associated perception for the highest household income class. Thus, support is found for the claim that transitways can potentially attract higher-income “choice” riders to the transit network. The results of this research can be used to improve ridership projections and highlight areas for policy improvements that could have the largest impact on retaining and attracting new passengers to the transit system.