Reliability plays a key role in the experience of a rail traveler. The
reliability of journeys involving transfers is affected by the reliability of
the transfers and the consequences of missing a transfer, as well as the
possible delay of the train used to reach the destination. In questo documento, Noi
propose a flexible method to model the reliability of train journeys with any
number of transfers. The method combines a transfer reliability model based on
gradient boosting responsible for predicting the reliability of transfers
between trains and a delay model based on probabilistic Bayesian regression,
which is used to model train arrival delays. The models are trained on delay
data from four Swedish train stations and evaluated on delay data from another
two stations, in order to evaluate the generalization performance of the
modelli. We show that the probabilistic delay model, which models train delays
following a mixture distribution with two lognormal components, allows to much
more realistically model the distribution of actual train delays compared to a
standard lognormal model. Finalmente, we show how these models can be used
together to sample the arrival delay at the final destination of the entire
journey. The results indicate that the method accurately predicts the
reliability for nine out of ten tested journeys. The method could be used to
improve journey planners by providing reliability information to travelers.
Further applications include timetable planning and transport modeling.

Questo articolo esplora i giri e le loro implicazioni.

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