5th International Seminar on Railway Operations Modelling and Analysis - RailCopenhagen Monday 13 May 2013 - Wednesday 15 May 2013 Technical University of Denmark Book of abstracts 5th International Seminar on Railway Operations Modelling and Analysis - RailCopenhagen / Tuesday 24 June 2014 Book of abstracts 5th International Seminar on Railway Operations Modelling and Analysis - RailCopenhagen / Book of abstracts Page 2 5th International Seminar on Railway Operations Modelling and Analysis - RailCopenhagen / Book of abstracts Book of abstracts 5th International Seminar on Railway Operations Modelling and Analysis RailCopenhagen2013 13-15 May, 2013 Page 3 5th International Seminar on Railway Operations Modelling and Analysis - RailCopenhagen / Book of abstracts 2 - Stability of railway dispatching solutions under a stochastic and dynamic environment Presenter: Dr. QUAGLIETTA, Egidio (Delft, University of Technology); Dr. GOVERDE, Rob M.P. (Delft, University of Technology) Railway traffic is strongly influenced by random disturbances during operations which cause deviations from the original schedule and thereby reducing performances. In order to cope with small perturbations, the design of a robust timetable can be an effective solution; but if larger disturbances or service disruptions are observed, it is necessary to adopt real-time dispatching measures to reschedule (reorder, retime or reroute) train services into new updated conflict-free train path plans. The rescheduling process involves a first phase in which the traffic state is predicted over a pre-set time horizon (called prediction horizon) on the basis of current traffic information (e.g. train positions, speeds) that is communicated at irregular time intervals to the Traffic Management System. If conflicting train paths are detected, a second phase is activated to compute suitable solutions addressed to restore planned operations as quick as possible or to minimize impacts of conflicts on service availability. The effectiveness of a rescheduling decision not only depends on the operational strategy identified to solve detected conflicts, but also on the actual traffic state that holds when such solution is implemented. During the time lag between the last field measurement and the practical application of the rescheduling plan (the so called control delay), the traffic state could evolve differently from the prediction due to its stochastic and dynamic nature. The control delay depends on different factors such as the type of monitoring system installed (e.g. train describers, GSM-R) and the frequency of train data updates to dispatching centres, the time to predict conflicts and elaborate a new suitable scheduling strategy, as well as the time needed by dispatchers to evaluate and communicate such solution to the interlocking system and possibly to the train. Intuitively, the larger such delay the higher is the probability that a certain rescheduling measure will be ineffective and even counterproductive for mitigating effects of detected conflicts. In this case, the solution should be recomputed and adjusted accordingly to current conditions of the network. During real operations, control measures cannot be updated and implemented continuously, since from one side there are constraints on technical times to obtain and transfer new plans to the system, while from the other side there is a practical unfeasibility for dispatchers to manage different alternatives in short time periods. In this context, particular attention must be paid to the stability of rescheduling plans. A plan can be defined as stable when its (initial) structure is invariant to random perturbations occurring on the network within a given time period Δt. In other words, the first part of a stable control strategy is the same for successive computations after each Δt with respect to updated traffic information and can thus be confidently implemented. In literature only little work [1] deal with stability of dispatching measures under a stochastic and dynamic environment. Instead, numerous approaches have been proposed so far [2, 3, 4] for efficiently generating optimal schedules to minimize train delays, through an open-loop optimization process which primarily involves assumptions of certain and deterministic conditions. In practice, since no advanced decision support tool is generally available, traffic controllers take real-time decisions according to their own experience or rules-of-thumb, without taking into account potential new conflicts due to the stochastic evolution of traffic states [5]. This paper addresses the stability of real-time schedules within a stochastic and dynamic system. To this aim an innovative framework is developed which integrates the Alternative-Graph based tool ROMA [4] for computing optimal rescheduling plans with a stochastic microscopic model for simulating railway traffic. Optimal plans returned by ROMA for a given perturbed scenario are transferred to and implemented in the simulator as if this latter is the real field. An application to a real case study is performed to evaluate open-loop optimization solutions while considering random and dynamic changes in traffic conditions during the control delay. In particular an investigation is conducted to understand how sensitive schedule stability is with respect to the control delay and the prediction horizon. This also gives insight in the maximal time before occurrence of the expected conflicts that a stable solution can be pursued. Then optimal rescheduling plans are recomputed at regular time intervals with respect to current traffic states, assuming continuous updates of train information and the minimum technically possible control delay to put such plans into operation. These successive solutions are compared in order to understand how the optimal control strategy changes over time due to stochastic disturbances and dynamic evolution of traffic behaviour. Understanding the stability of successive dispatching solutions is a first step into closed-loop railway traffic control and is preparatory to an effective management of real-time perturbations. In a rolling horizon approach the stable part of a dispatching plan can be implemented at some point with confidence that this will be the optimal choice for any control decisions at a later stage. References [1] Meng L., Zhou X., Robust Single-Track Train Dispatching Model Under A Dynamic And Stochastic Environment: A Scenario-Based Rolling Horizon Solution Approach, Transportation Research Part B, Vol. 45, pp.1080-1102, 2011. [2] Dorfman M.J., Medanic J., Scheduling Trains On A Railway Network Using A Discrete Event Model Of A Railway Traffic, Transportation Research Part B, Vol. 38, pp. 81-98, 2004. [3] Tornquist J., Persson J., N-Tracked Railway Traffic Re-Scheduling During Disturbances, Transportation Research Part B, Vol. 41, pp.342-362, 2007. [4] D’Ariano A., Improving Real-Time Train Dispatching: Models, Algorithms And Applications, PhD Thesis, Delft University of Technology, the Netherlands, 2008. [5] Sahin I., Railway Traffic And Train Scheduling Based On Inter-Train Conflict Management, Transportation Research Part B, Vol.33, pp. 511-534, 1999. Page 4 5th International Seminar on Railway Operations Modelling and Analysis - RailCopenhagen / Book of abstracts Page 5 5th International Seminar on Railway Operations Modelling and Analysis - RailCopenhagen / Book of abstracts 3 - Railway line capacity consumption of different railway signalling systems under scheduled and disturbed conditions Presenter: Dr. GOVERDE, Rob (Delft University of Technology) The characteristics of a signalling and automatic train protection (ATP) system have a significant impact on the capacity and stability of a railway line. The capacity consumption of a railway line can be calculated using the UIC timetable compression method for given infrastructure characteristics, rolling stock characteristics, and timetable pattern (UIC, 2004). This compression method is based on a deterministic microscopic calculation of conflict-free train paths with minimum headway times using blocking time theory (Hansen and Pachl, 2008). In addition, the UIC capacity method gives empirically derived guidelines on the total required buffer time in a timetable pattern to be stable for delays. However, this method does not consider the actual capacity consumption under disturbed conditions. In the presence of delays, train path conflicts occur depending on the scheduled buffer time between train paths. In practice this means that a train has to brake in response to the signalling system and possibly wait in rear of a stop signal. This leads to changed train trajectories with increased blocking times so that following trains may also be affected. The actual response to the signals depends on the specific signalling system and constraints of the ATP system, and can be quite different. In a standard three-aspect signalling system, trains have to brake from two signals in advance of the occupied block to a restricted speed and continue with this restricted speed until a final brake before a red signal. In contrast, a cab signalling system as ETCS allows a train to proceed until an approach indication point that is determined by a dynamically computed braking curve onboard. This braking curve depends on the actual train speed and braking characteristics as well as on the infrastructure description until the end of the movement authority (such as slopes). Also the time of re-acceleration after an improved