LAUSR

Abstract With the increasing demand and range of urban mobility, public transport systems are playing an increasingly important role in providing people with efficient and affordable access to education, employment, markets, and other key services. Public transport planners can predict passenger loads and levels of service by using transit assignment models. Therefore, having a consistent transit assignment model has become an important issue. In addition, estimating the path choice factors, considering that network cost attributes might be non-deterministic, is complicated. In view of these, this paper adapted a transit assignment model, based on the Bayesian Model proposed in the authors’ recent study, to validate the results of the model for multimodal transit networks under uncertainty and random variations of path choice parameters. In that model, path choices are represented by a multinomial logit model, and its coefficients are estimated via a Markov Chain Monte Carlo (MCMC) method. In this paper, the path choice parameters of the model are first calibrated by the individual travel history data in the AM peak period, and then are used to predict the passenger flow on different paths in the PM peak period. Second, based on the percentage error between each posterior estimate and the actual observation from AFC data on the segment level, the path segments are categorized into four groups. Then, by defining the segment attributes, the strengths and weaknesses of the model are analysed for the four groups.