Sustainable Global Service Intention as objective for Controlling Railway Network Operations in Real Time. R. Wüst, F.Laube, S.Roos, G.

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1 Sustainable Global Service Intention as objective for ontrolling Railway Network Operations in Real Time R. Wüst,.Laube, S.Roos, G.aimi* Swiss ederal Railways, Infrastructure, H 3000 ern 65, (Switzerland) *TH Zurich, Institute for Operations Research, H 8092 Zürich, (Switzerland) Abstract Public transport in Switzerland is based on a regular cyclic timetable connecting long distance and regional train services with local bus services and tourist ship lines. Thanks to this integrated cyclic timetable it is possible to offer connections on a point to point network at reasonably high frequencies. The commercial idea behind this offer can be called global service intention (GSI). In case of operational disturbances it is a significant challenge for the Swiss ederal Railways (S) to find the best substitution which is available for a delayed or disrupted train service in the given case. The criteria for a good or even the best substitution must attempt to minimise inconvenience caused to patrons and hence can be defined in terms of the total delay time of all the passengers concerned by the disturbance. Some years ago, S (together with the TH Zurich) started to investigate how the GSI can be decomposed into smaller manageable subunits, the local service intentions (LSI) which are defined for areas around stations with saturated traffic density. In addition, methods have been developed enabling LSI s to be automatically converted into local timetables and production plans. On the other hand a new planning concept is currently being introduced by S. This concept is based on discrete time slots within which trains can enter, exit or pass through saturated station areas. These time slots are called Pulses and have a temporal extent of approximately the minimum headway in the entry or exit corridor of the station area. Different trains are able to move either at the same time on non conflicting routes or within different time slots on conflicting routes. Due to this discretisation of the scheduling problem into rather coarse but intelligent temporal and spatial units, the solution space is considerably reduced compared to a continuous scheduling problem. As a consequence, semi-automatic or even manual rescheduling of arrival, departure and travel times and simultaneously associating conflict free tracks in real time are possible in practice. In 2006 S has started to implement a feasibility test of the core component of the new concept in Lucerne. The core component consists of the real time rescheduling based on the monitoring of train travel and departure times, associating new time slots in case of exceeded tolerances and communicating the new time slots and new target departure and travel times to the train drivers, train guards and operators. Thanks to the strict time control the dispersion in departure and travel times can be significantly reduced (up to 80%). Operational reserves become much more transparent and manageable. This is an important requirement for increasing capacity of saturated station areas and optimising the local service intention in case of operational irregularities. In a next step, several saturated station areas are controlled together. This will allow S to optimise dispatching criteria in terms of the global service intention. The impact for S will be to change the key performance indicator from the average punctuality of trains to minimum inconvenience caused to patrons measured, for example, as the average total trip delay. 1 ackground 1.1 The oncept of ahn 2000 In Switzerland as in most developed countries, rail transportation s main competitor is the car as private transport plays a key role in mobility. To be competitive, public transport has to provide a maximum of benefits similar to private traffic. Main arguments for mode choice are travel time from door to door including convenience and transport availability. Public transportation as a mass transit mode has distinct characteristics to be taken into account. A maximum use of trains and buses has to be generated by attributing demand flows to main transport corridors. This is achieved either by a high demand density along the lines or a hinterland well-linked with the trunk route system.

2 Travel times using public transport play a decisive role and are comprised of different components: accessing a station, waiting, riding, eventual interchanging and transferring from the final station to the destination. Therefore, successful public transport has to provide a dedicated feeder system to and from stations, punctual and reliable services together with convenient and quick connections and an effective customer information system. In order to approach these aims all together, the passenger transport chain in particular required a complete redesign. In a first step, Switzerland s successful Rail2000 -concept had been introduced stepwise over 25 years before it was completed in 2005 (or further details see [4]). The design followed a clear strategy throughout the entire supply chain. The resulting process chain is summarised in igure 1. Demand Service Intention Production Plan igure 1: Design process Rail The Supply hain of Rail 2000 A service pattern that gives an adequate choice of travel opportunities serving a maximum of existing and potential customers must be offered in order to satisfy demand. High quality public transport combines different service types (e.g. stopping trains, expresses, etc) in a clever manner. Keep it simple for the customer is the key of any successful service concept: Regular stopping patterns and headways as well as connecting with relevant routes in appropriate hubs of the network provide convenient, quick and a maximum number of direct links for customers. This service pattern is condensed into the Service Intention (SI, see igure 1), which is the functional requirement for the timetable development. With the help of origin-destination matrices, an offer of train services with lines and frequencies is developed to meet the customer needs. Since at this point it is not known whether this offer is feasible it is called Service Intention rather than Service Offer. The Rail 2000 service concept consists of Train-lines and frequencies specifying the customer-relevant information, such as stop stations, interconnection possibilities for each service type, Arrival and departure platforms at stations and Rolling stock. The basic principle with respect to journey times is as fast as necessary to enable good connections in the next hub. All-day regular departing and arrival times for each service type facilitate the use of public transport, in particular for occasional users. In the second step, the feasibility of this service intention is checked by generating a feasible schedule. If this is possible, a schedule is provided as proof of feasibility, otherwise both steps have to be repeated until a feasible service intention is found. The design of this timetable takes into account mixed traffic. It attributes on the same network systematic slots for international, national and local passenger trains as well as national and international (transit) freight trains. The desired regular interval timetable is characterised by parallel time-distance graphs symmetry of arrival and departure times around the defined symmetry time of the system ffects of such a systematic timetable are the creation of systematic transfer connection groups in hubs the crossing of two given trains occurs always at the same place and the same minute of a full hour 2

3 an increase in market demand due to the regularity of the service higher productivity through a systematic production process Optimisation of rolling stock usage through systematically minimised terminus times Once the timetable is finalised, it is converted into the production plan which consists of the timetable and in addition all the detailed resource assignments needed for the daily production. If due to some incident a certain required resource is missing, the production plan has to be adapted in a way that the intended timetable still can be realised. If this is not possible, the service intention provides the objective for designing an alternative timetable in real time which comes closest to the original promise made to the customer. 2 The Service Intention onsider the example of connecting trains and minimizing delays to customers as a measurement of service level: does providing a high level of service entail ensuring that connecting trains always wait for late trains? Or does it entail ensuring that connecting trains never wait for late trains? Or perhaps it involves a combination of both strategies depending on the individual circumstances? 2.1 Problem formulation: an xample onsider the following scenario: Train A from Zurich to Lucerne carrying 500 passengers is running 10 minutes late. All the passengers on this train require a connection to Arth-Goldau provided by train at Zug. Train from Lucerne to Arth-Goldau currently has 200 passengers on it and they are waiting to depart in 2 minutes from Zug. Train provides the same connection to Arth-Goldau from Zug, but it departs 15 minutes later than train. A possible strategy for the aforementioned scenario might be as follows: ensure that the lowest possible number of passengers have to endure a delay. In this case, train will wait for train A, since only 200 customers will be delayed instead of 500. However, consider the situation where the commercial value of train leaving on time is far greater than the commercial value lost by further delaying the 500 passengers from train A, e.g. if train provides further critical connections at Arth-Goldau that have no substitute. It is still possible for train A passengers to use train to reach their final destination within a 5 minute delay threshold, but customers waiting for train at Arth-Goldau could be subject to a greater delay. These are, of course, just 2 possible scenarios out of a multitude of possibilities especially when you consider the complexity of even a small segment of the entire railway network. The given example shows that taking operational and planning decisions aimed at providing customers with a high level of service based on varying criteria is not a straightforward process there are several variables involved in making an optimal decision such as passenger satisfaction, commercial considerations, and overall stability of the schedule. irstly, the importance of particular train services must be identified; secondly the impact on the overall customer service (and inherently the loss of associated value) when particular train services are cancelled must be considered; thirdly, it should be possible to evaluate whether proposed services (and subsequent changes to these services) are realisable with the given resource constraints (rolling stock and topology). The notion of the Global Service Intention (GSI) is built upon several other concepts that need to be defined first. These are presented in the following section. 2.2 asic Definitions for functional demonstrator 1. Train Service Intention: describes a train service from a start station to an end station and consists of the following: a unique identifier; an importance value (realistically it should be a combination of various factors such as passenger loads and commercial importance for example); a list of stations served by the service intention; 3

4 a minimum transition (duration) time for the service intention from start station to end station; a list of connections that the service intention provides and at what stations the connections take place i.e. a list of other service intentions that this service intention provides a connection for and the stations at which these connections take place. the timeframe (period within the hour) in which the service intention originates at the start station and the timeframe in which the service intention has stops at stations along the way. 2. Local service intention (LSI): the set of service intentions corresponding to a particular station region. or example, the set of service intentions available from the station of Lucerne. 3. Global service intention (GSI): the collection of all service intentions on the railway network. 4. Stability: a timetable is considered stable when it is possible to absorb a specified set of degraded resources while continuing to fulfill the GSI. 5. Iterative ontrol Algorithm (IA): a scheduler that can derive a concrete timetable based on the GSI. See igure 3 and [1] for further information. 2.3 Graphical Representation The data structure (model) used for the GSI is shown in the following example (see igure 2) Luzern bikon uchrain Längenbold Gisikon-Root Rotkreuz hämleten Zythus ham Alpenblick hollermüli Schutzengel Zug Lindenpark Neufeld aar : :7.5 0:15 A A A A A A 0:22.5 0:30 0:37.5 0:45 0:52.5 1:0 1:7.5 e 1 c1 IR_D IR_D 1:15 1:22.5 IR_D 1:30 1:37.5 1:45 1:52.5 2:0 igure 2: xample of GSI graphical representation The data structure is a space-time graph with the following properties: The time dimension is represented as a sequence of temporal layers in igure 2. or the purposes of a functional study, an hour is broken up into layers of 7.5 minutes each (this is a parameter). xperience has shown that 7.5 minutes is the typical threshold at which a customer will tolerate waiting for a train, so it makes a good choice when designing a GSI as deficiencies in service intentions can quickly be exposed. ach station is represented as a set of nodes. One node per station exists in each of the temporal layers. Within each node is defined a list of connections of service intentions and a train change time 4

5 for customers. A connection example is shown as a blue line labeled c1 in igure 2 connecting service intentions and IR_D at station Rotkreuz. Stations are classified as either Main stations or Small stations: o Main station: a station that lies exactly on the centre of the temporal layer (e.g. at 0 mins, 7.5 mins, 15 mins, etc.). These stations are considered to be the main system nodes of the railway network. This means that if a service intention originates or stops at one of these stations, it has a restriction where the scheduler (IA) must ensure that in the final timetable the train must be at the station within a time equal to or within a certain threshold Δ (which is 3.5 minutes in the given example of igure 2) of the relevant centre of the temporal layer. or example, in igure 2 there is a service intention named IR_D going from aar to Lucerne. The service intention is at centre 0 mins of the 9th temporal layer at node Zug and ends at node Lucerne at centre 22.5 mins of the 12th temporal layer. This means that the scheduler is restricted to producing a timetable entry for this service intention that is 0.0 mins +- Δ and 22.5 mins +- Δ at those particular stations. Of course, the transition time for the service intention must also be valid and enforced for this process to work. An example of a final timetable entry derived from this service intention could therefore be: Zug (01:02) Lucerne (01:25) with a transition time of 23 minutes. o Small station: a station that lies on the boundaries of the temporal layers. Unlike the main stations, during timetable generation there is no restriction on the times that the scheduler (IA) must assign to trains starting or stopping at these stations in the timetable. In case of operative rescheduling, only delays are allowed as departure times have been committed in the published timetable. Service intentions are represented as directed edges connecting nodes within or across temporal layers in the graph (depending on the transition time length). The edges store all the pertinent information about the service intention (a unique identifier, importance value, transition time) and in conjunction with the implied information obtained from the node structure of the GSI, other information that is defined is: the timeframe of the service intention (i.e. the temporal layers through which the service intention edge passes through) and the list of stations served. The GSI is therefore a high level representation of a final timetable. This abstract representation can be used to derive a final timetable as described in section 1.2. It can also be used to provide useful information on the effects of disruptions and cancellations of train services on the customer. In [7], V. Mahadevan proposes to use the transitive hull as a superior method of relaxing the GSI versus using simple value calculations, recalling that service intentions can possibly provide connections to other service intentions. So cutting a service intention from the GSI can potentially affect other service intentions as well. Therefore simply subtracting the value of a standalone service intention is not the most accurate measure of determining the value lost when the GSI is relaxed. The collection of service intentions and their associated values that are affected by a service intention cut should therefore be considered when relaxing the GSI. The transitive hull of the GSI captures these relationships; when a service intention is removed from the GSI, the transitive hull can be recomputed and compared with the previous transitive hull to ascertain the value lost across the collection of service intentions. However, in this case an importance value must be assigned to each connection as well so that it can be determined what value is lost explicitly due to the connection loss in addition to the value loss caused by removing the standalone service intention. It is also possible to simply specify a static set of rules for GSI compromises based on predefined criteria. or example, one could specify that a certain set of service intentions must never be removed from the GSI in a static rule set or have a linear ordering of service intentions to be relaxed. 3 Generation Process Once the GSI is defined in a way similar to the example of 2.1, it is straightforward to decompose it into a set of Local Service Intentions (LSI as defined in 2.2). These LSI s together with the available resources and the geographical constraints serve as input to either the planning process where the planner endeavours a conflict-free solution for the timetable (TT) or the dispatcher tries to resolve a conflict in real time and to reschedule the original TT. 5

6 In both cases the resulting TT has to consider the available resources and to satisfy all the given constraints for the local TT. In the -Planning process where the planner generates the TT for a whole timetable period, it is assumed that at production time the required resources will be available out of a certain stock containing predefined quantities of each production resource such as rolling stock, personnel stock, track capacity, etc. In the last step Production Plan of the design process chain of igure 1 the TT is transformed into a Production Plan where concrete resources out of the stock are assigned to the individual trains on a daily basis. In case of the real-time rescheduling of the TT it is usually a particular resource-assignment which is no longer valid due to a certain incident like for instance an occasional track speed restriction or a locomotive with a machine malfunction. In this case the actual TT has to be adapted in such a way that the rescheduled TT is still conflict-free with the new resource assignments and either the given GSI is still valid or only minimally compromised (back-loop in igure 3 if SI is not satisfied). Global SI (incl. Quality riteria) Available Resources (rolling stock, topology) Time/Distance onstraints Local SI Local SI Local SI Iterative ontrol Algorithm (IA) ind global tentative TT Detect conflict, Add constraint NO heck feasibility locally on detailed topology TT easible? YS TT constrained? additional onstraint Report onflict-free TT ompromise the GSI (evaluate punishments for failing to satisfy offers) NO SI satisfied? YS igure 3: Process overview of timetable generation using the IA 3.1 The PULS 90 network decomposition approach In order to divide the planning problem into smaller, more manageable and resolvable constituent units, the infrastructure network is segregated during the development of the GSI into major junction areas (big stations with their inlet areas which are called condensation zones ). In these locations there is almost no spare capacity (due to functional reasons such as the high number of line connections, and other resource allocations etc.). The surrounding parts of the network, with train travel time reserves, are called the compensation zones (see also [9]). Within the condensation zones the speed of the planned track slots is almost identical to the maximum permitted speed. Hence, within these areas, minor differences between the travel speeds of different trains can then be ignored when determining track slot utilisation. This effect is captured in the concept of the PULS-Pattern which is specifically designed for each condensation zone (see igure 4). ecause of the comparatively small amount of possible operationally viable combinations in the PULS- Pattern (see igure 4 below, left side) compared to a continuous search space as in a normal timedistance diagram (see igure 4, right side), it becomes possible to make a quick manual assignment of trains to track slots and to suitable infrastructure elements. Additionally, this also makes it possible to rapidly calculate new schedule scenarios. onflicts and predetermined dependencies between the trains can easily be detected and managed. The PULS-concept is described in detail in [8]). 6

7 igure 4: PULS-Pattern Diagram with track slots (left side) and a normal Time Distance Diagram showing the same track slots (right side) On the long stretches of track in-between these major junction areas the planed track slots are supposed to have enough reserves so that the train can still reach the intended arrival time of its required slot in the neighbouring junction area, even if small perturbations occur. While the major junction areas have no temporal reserves, and therefore can only be controlled in terms of route (re-)assignments, trains in the compensation zones can be accelerated or slowed down according to their actual slot assignment in the adjoining saturation zone that they are heading to or from. 3.2 Automatic Generation of train schedules A computational test environment for the automatic generation of periodic timetables for a given GSI utilising PULS-patterns in condensation zones has been developed and tested at the TH Zurich. igure 3 shows the process diagram for the algorithmic implementation (IA). The goal is to create detailed train schedules, which specify an exact itinerary through the railway topology with passing times for each train. This way the provided timetable is guaranteed to be conflictfree, i.e., assuming no delays, all trains can run exactly as planned without creating safety conflicts. As it appears intractable to consider the detailed topology all at once, a two-level approach for generating conflict-free train schedules is used. In the macroscopic (or macro) level, given a GSI, an abstraction from the detailed track topology for creating a draft timetable is used. In the microscopic (or micro) level, starting with the draft timetable from the macro level, detailed train schedules are constructed by considering locally precise topologies, the corresponding safety system as well as accurate train dynamics. The macroscopic level can be modelled as a Periodic vent Scheduling Problem (PSP, see [5]) whose output (departure and arrival times) serves as the input for the micro level to check feasibility by finding a feasible routing. onstraints of the PSP model are the travel times of trains between portals, dwell times in a station, connection constrains, headway constraints as well as other minor constraints. The classical PSP model with fixed passing times, however, is quite restrictive and could lead to infeasibility at the microscopic level, especially in condensation zones where the speed profile is fixed. To allow for more flexibility, an augmented PSP (lexible PSP, or PSP) was developed that assigns time slots (lower and upper bounds) instead of exact passing times. This additional flexibility can be then exploited when scheduling in the micro level to increase chance to find a feasible solution. Results show that it is possible to generate macro schedules for scenarios like the whole central Switzerland in few minutes, where each train has an average flexibility of 8 minutes [3]. 7

8 In the microscopic level the new scheduling approach that decomposes the problem geographically into condensation and compensation zones [1] was applied. Different policies for generating micro train schedules can then be applied to the two zones according to their distinct properties. As condensation zones are expected to have high traffic density, they comprise bottleneck resources that should be occupied as shortly as possible. Therefore, trains are required to travel through the condensation zones with maximum speed, i.e. no time reserves are included. Thus, the speed profile is completely fixed and it is sufficient to assign one passing time per train (e.g. at the portal or at the platform) from which all other passing times within the condensation zone are derived, once the itinerary has been fixed. This passing time is chosen from the provided time slots of the PSP that lead to the optimal local schedule fulfilling the given local service intention. On the other hand, in compensation zones there is a large variety of speed profiles for the train to connect the two main stations. These zones will be scheduled after the condensation zones. Their portal passing times, which are now fixed by the scheduling in condensation zones, can be matched into a conflict-free schedule by optimising the quality of the speed profiles as the main degree of freedom in the conflict-free solution. The crucial aspect of microscopic scheduling is the guarantee that the train schedule is conflict-free. Two trains are in conflict if they allocate the same topology resource element within a certain safety time span. This is typically modelled by a conflict graph [10]. A conflict-free schedule therefore corresponds to an independent set of maximum cardinality in the conflict graph. Hence, an alternative model, called Resource Tree onflict Graph (RTG, [2]) was required, which is based on an integer multi commodity flow formulation that incorporates the railway topology as well as passing times and speed profiles of the trains. Train itineraries are represented in a tree structure to identify the location of conflicts. or each track element the train allocation times are gathered in sets that contain all train itineraries competing for the resource at the same time. Whereas the structure of the conflict graph formulation is usually weak, substantially stronger constraints can be generated by considering all trains simultaneously (instead of just two) for each resource. As a consequence, the resulting integer program is then solved with a commercial solver much faster, as illustrated in Table 1. Scenario # trains in 30 # conflicts conflict graph Solution time conflict graph [s] # conflicts RTG Solution time RTG [s] Lucerne, service intention erne, condensed hypothetical LSI Table 1: Run Time Performance in Local calculation for Lucerne and erne, which has a much more complex topology and hence a bigger solution space More detailed information concerning the IA can be found in [1], [2]and [3]. 4 Test environment for Local Service Intention in Lucerne In order to proof the practical application of the SI-concept outlined above, a pilot project has been set up in the area of Lucerne. Whereas the GSI and the global timetable describes the service offered on the global S-network and is today under responsibility of 5 different operations control centres (etriebsleitzentrale), each controlling a certain region (see igure 5), the LSI of the area of Lucerne (see dotted line in lower left of igure 5) is controlled by the traffic control centre Lucerne (ernsteuerzentrum, SZ) from where all relevant interlockings (e.g. the red circle of the station of Lucerne) in the area are remote-controlled. 8

9 Disposition/GSI /GSI etriebsleitzentral etriebsleitzentrale e etriebsleitzentrale Region Mitte Region etriebsleitzentrale etriebsleitzentrale Region Mitte Mitte Region Mitte Region Mitte Operations/LSI SZ/Lucerne SZ/Luzern SZ/Lucerne Safety Interlocking/ahnhof Interlocking/ahnhof Interlocking/Station igure 5: Hierarchical organisation of network train operations in S Thus, in a first pilot phase the intention is to control the objectives of the operations in a local network area which in the organisational structure corresponds to the level of the SZ Lucerne. igure 6 illustrates the conventional information and control flow with the control level of the SZ Lucerne highlighted. Development Yearly Disposition Operation TO Yearly Operation ontrol Telephone Train state & position Safety System Online Train U S T O M R Yearly, daily adjusted High update frequency Medium update frequency Low update frequency Traffic ontrol If correct information is available in time ustomer Information System igure 6: onventional Information and control flow One of the major disadvantages of the existing situation is that there is only little communication between the Disposition and Operation level on the one hand and the Operation and the Train Operating ompany 9

10 (TO) on the other hand. urthermore, there is no clear dispatching objective that the disposition and the operation level can agree on when operational disturbances occur. As a consequence, dispatching decisions are often ad hoc and in case of larger problems, there is no clear guideline for the problem resolution. In such situations it is hard to find a temporary solution that can be clearly communicated to the relevant actors and the customers involved. ompensation zone ondensation zone Traffic ontrol Room Lucerne Gütsch ST-LT90 Lz Station Lz Station (backup) Information Assistant TIS Seetal TIS ntlebuch Operation Assistant Shift supervisor igure 7: Test perimeter and associated operations controllers in the traffic control centre (TIS: Train control and Information System) igure 7 illustrates the geographical test area together with the line responsibilities in the traffic control room. During the pilot phase, the new role of the Rescheduling Officer (RO) will be assigned to the shift supervisor who takes the responsibility for the LSI. In order to be able to make rescheduling decisions based on the LSI he will be equipped with the Rescheduling ramework (RW) which is designed to provide him with all required information. ach time a schedule deviation occurs, he receives a notification together with the information about the type and the magnitude of the deviation from the actual planned system state. ased on this information he tries to find a new conflict-free plan basically using a PULS-user interface (presenting a display, similar to the left part of igure 4) that allows him to modify the track slot properties according to the actual resource availability and to change the spatio-temporal assignments of the different track slots until all existing conflicts including track occupation and required connections etc. are resolved. igure 8 shows the information flow of this rescheduling process. 10

11 Initial extract Reverse ngineering LSI (incl. Quality Local SI riteria) (onstrained) available Resources (rolling stock, topology) Time/Distance onstraints Rescheduling Officer SZ Lucerne Manual attempt to find conflict-free local TT using new Rescheduling ramework NO ompromise the LSI LSI satisfied? YS Rescheduled igure 8: Process overview of the adapted iterative timetable generation initialised with a reverse engineered LSI and using the Rescheduling ramework to reschedule the timetable and/or relax the LSI constraints respectively As soon as the new schedule is committed by the RO, the corresponding changes to the production plan are generated automatically. The production plan contains the new instructions and information for all other actors involved, including train drivers, conductors and the different operators in the traffic control room. y programming the interlocking (using the TIS) the operators activate the required adaptations to the signalling. The information assistant informs the customers by appropriate announcements either in the station or in the trains through messages to the conductors. The train drivers are instructed to alter their speed if necessary in order to avoid stops in front of red signals. The enhanced system environment for the pilot phase in Lucerne is indicated in the following diagram: Development Yearly Disposition Operation ontrol Yearly Telephone Train state & position Operation Safety System Online Traffic ontrol TO Train U S T O M R Yearly, daily adjusted High update frequency Medium update frequency Low update frequency LSI Tolerance thresholds Real time scheduler Online Rescheduling Rescheduling rame work Train Driver & ustomer information Online ustomer Information Systems igure 9: Information and control flow in test setup of the field test in Lucerne (see also [6] for further details) Several improvements can be noticed. irstly, the LSI serves as an objective for dispatching decisions which is commonly agreed between the disposition and the operation level. On the other hand with the help of a threshold detector, the RO is much better informed about intolerable deviations from the current production plan. Trigger conditions for executing a rescheduling can be made explicit. Due to the better decision support provided by the RW, the RO can react faster and more efficient than before. 11

12 Already during the first low level implementation of the pilot environment it shows that, in comparison the conventional situation, the TO and the customer are better integrated in the information and control flow. This is achieved mainly through the online communication to the train driver, the conductor and the customer. What may be the most important improvement of the RW is that it supports operation to ensure that most of the time there is a valid schedule available which serves as a basis for providing all actors with the required information, and in time. When technical problems arise, being able to provide the customer with the right information about an alternative connection to reach his final destination in time is an important benefit. Another decisive factor for the required high precision in controlling the traffic flow is the efficient communication to the train driver. Therefore the system environment of the pilot contains an online connection to a dedicated cabin display. Information about new track speeds is communicated to the train driver by transmitting the deviation of the actual time at the current train position from the time which was planned according to the actual schedule. Technically this is achieved by measuring the current train position on board and comparing it with the planned position at the actual time. The latter is first transmitted to the on board unit before the train trip starts and is updated each time that there is a change in the time or route information of the appropriate track slot due to a rescheduled production plan. In addition to the time deviation in seconds, the on-board unit contains information about the recommended driving mode (e.g. a accelerate, decelerate, keep speed etc.) and a recommendation about where within the 8 minutes trip time ahead the existing time deviation should or respectively can be compensated. The possible time compensation (considering technical and safety restrictions) is indicated as white bars whereas the recommended time compensation is indicated as grey bars. Positive values indicate the amount of seconds which have to be made up for (i.e. when the train is late) and negative values indicate that train has to lose time (i.e. when he is ahead of schedule). igure 10 shows an example for a situation of a slightly delayed trip. igure 10: Driver display. xample for a slight delay Legend [t] Next 8 minutes trip time ahead [ t] running time deviation in seconds Total time deviation with regard to schedule Maximal recoverable delay in corresponding time window Recommended correction in corresponding time window Indication field for train number, origin, destination and intermediate station Driving mode: example for intermediate fast running 5 Outlook: rom Local to Global Service Intention Within the first three chapters of this article we tried to outline the concept of the GSI as the functional objective for controlling the network traffic in S. In chapter 4 we described the first pilot implementation of an LSI-based operating environment. With the installation and practical test of the RW in the traffic control centre in Lucerne, we intend to provide a proof of concept of the LSI-based dispatching approach. Our test cases are small timetable constellations in which comparatively small operational problems are encountered and that can be solved manually with the help of the RW. However, these cases help us to learn more about the single elements of the concept so that we can increase the problem scope stepwise in the future. As soon as our experience with the SI and the RW has been increased, we intend to extend the test scenarios in such a way that also the disposition level and correspondingly larger portions of the network can be integrated into the test problems. Therefore we need to trial several condensation zones (e.g. Olten and Arth-Goldau in addition to Lucerne) in order to gain experiences with the GSI. The 12

13 theoretical work described in chapters 2 and 3 will be the basis for future developments and extensions of the RW so that larger problem scenarios, which in the pilot phase lead to a fall-back to conventional methods, can also be handled by the RO. The clear distinction between the Disposition and Operation level must be resolved in the future. Whilst the global part of the concept will be subject to deeper investigations, the local part can be put into production in the near future. 6 onclusions Never before, the objective of real time rescheduling during operation has been focused on reducing the overall customer annoyance, taking total trip duration into account. To achieve this, it is necessary to keep the production plan in sync with the actual operation even if irregularities and disturbances occur. This is achieved by monitoring travel and departure times of trains and resource states in general and rescheduling trains in case of exceeded tolerances. On the other hand train drivers are empowered to keep to the required tolerances by receiving all the necessary information such as travel time and the admitted tolerances constantly on a special display. This allows them to compensate for deviations. The ongoing implementation of the ahn 2000 process chain results in a consequent integration of planning and production methods which allow keeping operation of railway traffic under control of service intentions. Acknowledgements We would like to thank our colleague V.Mahadevan for providing us the example data and a prototype implementation of the GSI demonstrator, as well as A.Smith for his support with the nglish language. References [1] aimi G., urkolter D., Herrmann Th., hudak., Laumanns M. Design of a railway scheduling model for dense services, Journal of Networks and Spatial conomy, 2008 [2] aimi, G., hudak,., uchsberger, M., Laumanns, M., Solving the train scheduling problem in a main station area via a resource constrained space-time integer multi-commodity flow, Technical report, Institute for Operations Research, TH Zurich, [3] aimi, G., uchsberger, M., Laumanns, M. and Schüpbach, K., Periodic Railway Timetabling with vent lexibility, In. Liebchen et al, editors, ATMOS th Workshop on Algorithmic Methods and Models for Optimisation of Railways, Schloss Dagstuhl, 2007 [4] Laube., Mahadevan V., ringing costumer focus into every nut and bolt of the railway: Swiss federal Railway's Path into the future, WRR Seoul, 2008 [5] Liebchen,., Möhring, R., The Modeling Power of the Periodic vent Scheduling Problem: Railway s - and eyond, In. Geraets et al, editors, ATMOS th Workshop on Algorithmic Methods and Models for Optimisation of Railways, Springer, [6] Luethi M., Nash A., Weidmann U., Wüst R. Increasing Railway apacity and Reliability through Integrated Real-Time Rescheduling, WTRS erkeley, 2007 [7] Mahadevan V. (2007), Describing and valuating Train Services on the Swiss Railway Network from a New Perspective, onference paper STR, Monte Verità / Ascona, 2007 [8] Roos S, ewertung von Knotenmanagement-Methoden für isenbahnen, Masters thesis at the Institute for Transport Planning and Systems, TH Zurich, 2006 [9] Wüst R., Dynamic rescheduling based on predefined track slots, WRR Montreal, [10] Zwaneveld, P.J., Kroon, L.G., Romeijn, H.., Salomon, M., Dauzère-Pérès, S., Van Hoesel, S.P.M., Ambergen, H.W., Routing Trains through Railway Stations: Model ormulation and Algorithms, Transportation Science 30(3),