We consider a suburban railway network line in the greater Paris area, with sub-branches. Trains of the line are equipped both with automatic vehicle localization (AVL) and automatic passenger counting (APC) devices, leading to a rich data set with simultaneous measurements of variables related to railway operations (arrival delay, dwell time) and passenger flows (numbers of passengers alighting and boarding, total load at departure). We aim for one-station-ahead forecasting of each of these five variables independently from each other. To do so, we build a bi-auto-regressive approach consisting of using the past values of the variable of interest along a first dimension, given by past stations along the train ride, and along a second dimension, given by past trains at the station. A building block of this approach is a train-station representation that accommodates different types of train services. We identify repeated patterns in this representation and exploit this fact. Indeed, the proposed bi-auto-regressive models are based on linear regressions whose coefficients depend on the stations and possibly only on the location of the train ride within a repeated pattern. This results in models that have a smaller complexity than extremely local models tailored to the timetables, with no significant decrease in accuracy.