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The Trickle-In Effect: Modeling Passenger Behavior in Delay Management

: Schöbel, Anita; Pätzold, Julius; Müller, Jörg P.

Volltext ()

Cacchiani, V.:
19th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems, ATMOS 2019 : September 12-13, 2019, Munich, Germany
Wadern: Schloss Dagstuhl, Leibniz-Zentrum für Informatik, 2019 (OASIcs - OpenAccess Series in Informatics 75)
ISBN: 978-3-95977-128-3
Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS) <19, 2019, Munich>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer ITWM ()

Delay management is concerned with making decisions if a train should wait for passengers from delayed trains or if it should depart on time. Models for delay management exist and can be adapted to capacities of stations, capacities of tracks, or respect vehicle and driver schedules, passengers' routes and further constraints. Nevertheless, what has been neglected so far, is that a train cannot depart as planned if passengers from another train trickle in one after another such that the doors of the departing train cannot close. This effect is often observed in real-world, but has not yet been taken into account in delay management. We show the impact of this "trickle-in" effect to departure delays of trains under different conditions. We then modify existing delay management models to take the trickle-in effect into account. This can be done by forbidding certain intervals for departure. We present an integer programming formulation with these additional constraints resulting in a generalization of classic delay management models. We analyze the resulting model and identify parameters with which it can be best approximated by the classical delay management problem. Experimentally, we show that the trickle-in effect has a high impact on the overall delay of public transport systems. We discuss the impact of the trickle-in effect on the objective function value and on the computation time of the delay management problem. We also analyze the trickle-in effect for timetables which have been derived without taking this particular behavioral pattern of passengers into account.