Meaningful modeling of section bus running times by time varying mixture distributions of fixed components

Abstract

Understanding the variability of bus travel time is a key issue in the optimization of schedules, transit reliability, route choices analysis, and transit simulation. The statistical modeling of bus travel time data is of increasing importance given the increasing availability of data. In this paper, we introduce a novel approach to model day-to-day variability of running times of urban buses on a section level. First, the explanatory power of conventionally used distributions is examined based on likelihood and effect size. We show that mixture models are a powerful tool to increase fitting performance, but the applied components need to be justified. We propose a time varying mixture distributions of fixed components, by which we can ensure meaningful component distributions. Hence, with our modeling approach, we reduce the complexity of mixture models and increase the explanatory power and fit compared to conventional models.