Cited By
View all- Chen XCheng ZJin JTrépanier MSun L(2023)Probabilistic Forecasting of Bus Travel Time with a Bayesian Gaussian Mixture ModelTransportation Science10.1287/trsc.2022.021457:6(1516-1535)Online publication date: 10-Oct-2023
What Do We Know When? Modeling Predictability of Transit Operations
Pages 15684 - 15695
Abstract
Predictions of transit delays are crucial to passengers and operators. Passengers utilize the predictions to decide on departure time, route choice, and mode choice, whereas operators decide on schedules, timetables, rolling stock allocation, and control actions. We introduce the concept of predictability of transit travel times as the study of the reduction of the predicted variability with the temporal approaching of a predicted event. We evaluate predictability on a real-life test case in Zurich, Switzerland, spanning multiple transit lines over one year of operations. The concept is shown based on predictions obtained by a state-of-the-art Bayesian network approach, where we show how predictability (in general) can be modeled as an exponential decay phenomenon. The study of predictability of transit operations leads to additional insights for control actions and system analysis compared to other complementary concepts such as punctuality or regularity, for instance, concerning bunching, identification of bottlenecks, and passenger routing.
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View all- Chen XCheng ZJin JTrépanier MSun L(2023)Probabilistic Forecasting of Bus Travel Time with a Bayesian Gaussian Mixture ModelTransportation Science10.1287/trsc.2022.021457:6(1516-1535)Online publication date: 10-Oct-2023