EP2130741B1 - Method and device for localising trains in a rail network - Google Patents

Abstract

The method involves locating a part of the mobile telephones in a train (Z). A railway system lies in a service area of a cellular mobile radio system. The trains move along a railroad (W) through the spatial areas (LA1,LA2) of the mobile radio system. A collective data is detected for the spatial areas, where the data indicates the changing points of time that are determined by evaluating the collective data. Independent claims are included for the following: (1) a system for localizing trains in a railway system; and (2) a control center for a system for localizing trains in a railway system.

Description

The invention relates to a method for locating trains in a rail network according to the preamble of claim 1 and a system performing the method and a center therefor according to the preamble of one of the independent claims.

The localization of trains is known per se and is carried out by the respective operator of the railway network using their own network systems. For this example, as in the DE4235105 A1 described method used in which via a wired network, a so-called. Line train control. The train control and localization can also be carried out over radio networks of the operator, which is then spoken of the so-called. Radio train control. Also the GB 2 273 424 A discloses such a system.

The operators of rail networks, such as Deutsche Bahn AG, use these methods and systems to detect the whereabouts of the trains, especially for security reasons. The information about the whereabouts or the localization of the trains is also used to inform the customer about the planned trains, eg by loudspeaker announcements on the platform or through updated arrival / departure plans on the internet. However, there is a conflict of interest of the operator: Firstly, he would like to inform his customers comprehensively, on the other hand, he would like to present as positive as possible and therefore gives only limited information about occurring train delays. The customer is therefore dependent on the information services of the rail network or railway operator. It would be desirable to have an operator-independent localization and monitoring of rail traffic, from which statements could be made about the planned trains.

In the field of road transport, there are already technical proposals that allow a localization of vehicles. It also uses existing mobile networks.

In the DE 102 25 033 A1 For example, a method is described in which in a mobile radio system for a respective location area, the so-called location area, an evaluation of collectively recorded data takes place in the form of location updates (see there, inter alia, claim 1). Geo-referencing assigns specific sections of the route to the areas of residence, and then the data collected during the location update is provided with timestamps. The problem of locating and tracking trains is not dealt with there.

In the DE 103 33 793 A1 a method is described in which at the cell level individual data of individual mobile devices are evaluated to determine the number and / or speed of moving in the individual radio cells mobile devices. Besides, too Number of non-moving mobile devices is determined and compared with the previous data to generate traffic information (see, inter alia, claim 1). Again, the problem of locating trains is not dealt with.

In the EP 17 42 190 A2 A method is described in which the data is collected and evaluated for an area comprising several radio cells (see, inter alia, summary). For this purpose, when a mobile device enters the area in each case, the identification of the first radio cell of this area is ascertained and provided with a time stamp. If, when changing over to a next area, the corresponding data is collected there as well, a travel time can be determined from the comparison of the data. However, the problem of locating trains is not dealt with here either.

The present invention, however, with the question of how mobile data trains operator-independent localized and tracked on the rail network and from this information about the scheduling of the trains can be obtained.

It is therefore an object of the present invention to provide a method for locating trains in a rail network, as well as a system and a control center therefor which are advantageously suitable for use independent of the rail network or rail operator.

In particular, a method and devices are proposed, which are used for locating trains in a rail network in the supply area of a cellular mobile radio system in which mobile devices are registered, are suitable, wherein at least part of the mobile devices are located in the trains, which move through spatial areas of the mobile radio system along at least one rail track.

In order to achieve the above object, the present invention provides a method in which collective data is detected for adjacent spatial areas, to which at least one rail is assignable, indicating switching times for switching between the adjacent spatial areas for registered mobile devices; and in which by means of evaluation of the collective data for a plurality of registered mobile radios common change times are determined and recognized as attributable to trains that operate on the at least one rail track, wherein by comparing the common change times with scheduled change times, e.g. can be obtained by timetable data and / or statistical evaluation of the collective data, for trains running the respective common change time is assigned to a train to identify the train in the rail network and locate.

Accordingly, the processes already implemented in the mobile radio system for tracking mobile devices, such as the so-called Location Update, can be used to localize trains independent of the operator. If areas are considered that cover stations, then the determination of the scheduled change times can already be done by accessing the usual schedule data, which can be easily realized, for example, via queries from the Internet and / or CD-ROM stored schedules. Alternatively or additionally, in particular for any route sections the scheduled changeover times are determined by statistical evaluation of the collective data collected there.

The localization and tracking of trains according to the invention is made possible by a system operating independently of the operator of the rail network and can thus be used at any time and without restriction and made available to the customers. The customer can use the localization and tracking of trains, for example, via a corresponding mobile and preferably Internet-based data service and can thus be informed about the current whereabouts and about delays of trains.

The inventive method is thus preferably realized within a mobile radio system by evaluations of mobile radio signals or data, which are detected by mobile devices, which are in trains and which are thus moved through the spatial area. The method is ideally suited for detecting the frequent movement and / or the stay of many mobile radio devices in the supply area of rail networks on the basis of mobile data recorded on the network side, and thus to carry out the localization of trains. The collectively detected signals are therefore mobile radio signals from mobile devices located in the trains, the signals being recorded and evaluated centrally. Through the evaluation of these already existing and numerous detectable signals a sufficiently large database is created, which allows a well-founded localization and tracking of a single train.

Preferably, the method is designed so that each common change time for several mobile devices the simultaneous entry into and / or the simultaneous exit from the respective spatial area indicates.

At least for spatial areas that cover stations, the scheduled change times can be easily derived from timetable data of a railway operator.

At least for spatial areas that do not cover stations, but any sections, the scheduled change times are preferably determined by a multiple, especially daily, repeated evaluation of the collective data, it being recognized whether common change points also represent scheduled change times.

At least for sections on which several trains run in real time, the collective data, which are recognized as being assignable to a train, are evaluated for a temporal and spatial tracking of the trains via several adjacent spatial areas.

Preferably, a currently determined common change time is compared with at least one scheduled change time to determine a deviation indicating a delay of a train to which the scheduled change time can be assigned.

Preferably, collective travel times are determined from the collective data or from the changeover times determined therefrom, which in each case indicate the transit time of a train through the spatial area. It is advantageous if by means of the collective travel times different types of trains are recognized to from the change times several registered mobile devices to determine the common change times and assign the trains.

These and other advantageous embodiments of the invention will become apparent from the dependent claims.

• Zuordnung der kollektiven Daten zu Zügen anhand von Kennungen einzelner Mobilfunkgeräte;
• Verfolgung von Zügen anhand von Kennungen einzelner Mobilfunkgeräte;
• zur Identifizierung von Zügen Vergleich der Wechselzeitpunkte mit planmäßigen Wechselzeitpunkten, die für den jeweiligen Bereich gelten, wobei der jeweilige Bereich einen beliebigen Streckenabschnitt oder einen Bahnhof abdeckt;
• über mehrere Tage wiederholte Analyse (Offline) der Wechselzeitpunkte zur Bestimmung von planmäßigen Wechselzeitpunkten, die auch für Bereiche gelten, die keinen Bahnhof abdecken;
• Identifizierung von Fahrstrecken von Zügen mittels Vergleich der für mehrere Bereiche aktuell ermittelten Wechselzeitpunkten mit planmäßigen Wechselzeitpunkten.

Accordingly, it is advantageous if the method according to the invention also comprises at least one of the following optional additional steps:

• Assignment of collective data to trains based on identifiers of individual mobile devices;
• Tracking trains based on identifiers of individual mobile devices;
• for the identification of trains Comparison of the changeover times with scheduled changeover times which apply to the respective area, the respective area covering any section of the route or a station;
• repeated analysis over several days (offline) of changeover times to determine scheduled changeover times, which also apply to areas that do not cover a station;
• Identification of routes of trains by means of comparing the change times currently determined for several areas with scheduled change times.

The collective data can be determined by means of a, in particular statistical, evaluation of entry and exit times that indicate the entry into the area or the exit from the range of active and non-active mobile devices. This preferably takes place in connection with the location update method which is actually carried out in the mobile radio system.

In order to be able to carry out the localization more precisely, a localization of the train in a single sub-area can be carried out by means of an evaluation of individual data indicating the presence of individual active mobile radio devices in subareas of the spatial area. The spatial area corresponds to a radio coverage area of the cellular mobile radio system and the sub areas are single or multiple radio cells of this radio coverage area. In this connection, the individual data, e.g. be determined by means of an evaluation of data for a radio exchange between the radio cells with respect to the entrance and / or exit of the active mobile devices.

In addition, the evaluation of the individual data can be supplemented by additional data or information on the transmission and / or reception field strength of mobile radio signals in order to achieve even more accurate localization of the train within a subarea, e.g. within a radio cell.

Fig. 1

zeigt in schematischer Darstellung zwei angrenzende Funkversorgungsbereiche (Location Areas), durch die ein Schienenweg führt und in denen das Verfahren durchgeführt wird;

Fig. 2

zeigt in vereinfachter Darstellung die Ergebnisse aus einer zeitlichen Beobachtung bzw. Überwachung der kollektiven Reisezeiten innerhalb eines der beiden des Funkversorgungsbereiche;

Fig. 3

zeigt entsprechende Ergebnisse aus einer tageweise wiederholten Beobachtung bzw. Überwachung der kollektiven Reisezeiten;

Fig. 4

zeigt in ihre Funkzellen unterteilte Funkversorgungsbereiche, durch die zwei Schienenwege führen;

Fig. 5

zeigt ein Ablaufdiagramm für ein erfindungsgemäßes Verfahren.

In the following, the invention and the resulting advantages will now be described in detail by means of embodiments, reference being made to the accompanying drawings:

Fig. 1

shows a schematic representation of two adjacent radio coverage areas (Location Areas), through which a rail path leads and in which the method is performed;

Fig. 2

shows a simplified representation of the results of a temporal observation or monitoring of the collective travel times within one of the two of the radio coverage areas;

Fig. 3

shows corresponding results from a daily repeated observation or monitoring of the collective travel times;

Fig. 4

shows subdivided radio coverage areas into their radio cells, through which two tracks lead;

Fig. 5

shows a flowchart for a method according to the invention.

The FIG. 1 shows a schematic representation of a region detail with two spatial areas LA1 and LA2, each corresponding to a radio coverage area (Location Area) of a cellular mobile radio network, to which the method is applied by way of example. Each area LA1 or LA2 comprises a plurality of radio cells and is traversed by a railway W, on which various trains Z, such as suburban trains of urban transport as well as ICE long-distance trains run. The entry and exit times are recorded by all mobile devices. A change from one area to another is reported by all mobile devices (black circles) and recorded as collective data. The individual radio cell changes with further information or individual data are recorded only for active devices (open squares). According to the invention, a first can already be based on the collective data Localization of a train are performed by common data exchange times are determined by data analysis, which can be assigned to the individual trains by comparing with schedule data.

The Fig. 2 shows for the in Fig. 1 in a simplified representation, the results from a one-a-mile temporal observation or monitoring of the collective data or raw data RDT and the resulting accumulations of switching times as well as collective travel times at different times of the day. On the ordinate of the diagram, the travel time TR is plotted, which is defined as the difference between the exit and entry time and indicates the length of stay of a mobile device. This corresponds to the transit time of a train, if the respective mobile device is in this train. On the abscissa the exit time TA is plotted in the observation period between 5 o'clock in the morning and 12 o'clock in the afternoon. Each under a. Location updates recorded raw data RDT of each mobile device are shown as measuring points. The data RDT form accumulations that correspond to individual trains. These are shown as open squares at the location of mean travel time and exit time. As an additional indication, the number of individual data belonging to the accumulations could be specified. It can be seen that there are band areas in which the collected data accumulate around a typical travel time, such as the travel time TRu or TRv. Each band corresponds to a class of moves. Here, for example, a class of trains with a typical collective travel time of about 240 seconds, so 4 minutes, recognized. The collective travel time TRv belongs to this and indicates, for example, a fast-moving long-distance train. Another class has a typical collective travel time of about 420 seconds, or 7 minutes. The collective travel time TRu belongs to this and indicates, for example, a slower moving local train.

Individual measurement data that are far away from these statistical clusters or outside the bands can not be assigned to a corresponding collective travel time. This scatter data is very likely from mobile devices that are not in a train and is therefore ignored. The raw data evaluated here are described as collective data RDT to express that they can collectively collect, eg in the context of the so-called Location Update. As based on the Fig. 2 is illustrated, the collective data RDT can also indicate whether possibly several mobile devices collectively, ie simultaneously or very quickly, switch between radio coverage, which is the case especially for trains. Because not infrequently located in trains several registered mobile devices (in long-distance trains quite well 40 and more), for the quasi simultaneously a change between adjacent radio coverage areas.

Already by evaluating the collective data RDT can be detected, at what times trains in the observed range. Based on the collective travel times TR can also be recognized whether the train is likely to be a fast or slow train. For example, the accumulation of data at the exit time TAu indicates that there at about 9:40 o'clock a rather slower train Zu with a travel time of about TRu = 400 sec can be observed. At the exit point TAv, which is at 10:00 o'clock, a rather fast train Zv is observed with a travel time of about TRv = 230 sec.

In order to identify the trains concretely, the data can be compared with timetable data, so that in each case a specific train can be located. In this case, the radio coverage area is e.g. at a station for which timetable information is available. The detected change or exit times TA are then compared with the local departure and / or arrival schedules. Thus, e.g. the accumulation of data at TAv and TRv is assigned to the train Zv, which is shown in the timetable as "ICE 278" for this time, namely at 10:00 o'clock. Once the data has been concretely assigned to a train, it can be correlated and compared with data recorded in other radio coverage areas along the train route. Thus, a continuous localization and tracking of individual trains is possible.

Preferably, the data collection is repeated regularly, in particular repeated on a daily basis to determine scheduled change times and to recognize any deviations.

In the Fig. 3 corresponding results from a daily repeated observation or monitoring of the collected collective data RDT are shown. It can be seen that this is already in the Fig. 2 Significantly repeating patterns from day to day. The trains identified by the timetable are marked by ellipses and marked with the train number. The fast trains make the ICE trains running on the route, the slower trains the S-Bahn connections. The representation covers the observation period of three days, namely from 10 to 12 July.

It can be seen that only relatively few trains have greater fluctuations in the determined change times TA. For example, for the trains Zy and Zz on July 12, the respective change time is much later than on the two previous days. This deviation is understood as an indicator of a delay of the respective train. Since the travel times TR do not change significantly for the trains Zy and Zz, it can be assumed that the delays already existed when entering the radio coverage area. Also, most of the other trains, such as the trains Zu and Zv, show only minor fluctuations in the travel time TR. On the other hand, the trains Zw and Zx each on 11 July each have a very different travel time. For the train Zw the journey time TRw11 is on July 11th even at about 820 sec, which corresponds to a delay of about 600 sec compared to the data of the other days. The increase in the travel time TR is here recognized as an indicator of a disturbance prevailing in the radio coverage area (e.g., construction site), but does not have to have a particularly deleterious effect on a train's delay, e.g. is illustrated in the example shown of the trains Zw and Zx.

Thus, the deviations of the change times TA are essentially understood as delays of trains, the deviations from travel times, however, are considered as a local disturbance. However, if in each case for the same train in several radio coverage areas a much increased travel time to be observed, so has the Accumulation of travel times as a significant delay of the train.

The method described here makes it possible to use routines in the mobile radio system with which a large-area change can be detected for all mobile devices. This can be determined in the inactive mobile devices from data of the so-called. Location update messages and active mobile devices from data of the corresponding handover message. In addition to the changeover time, at least the large area from which the terminal originates as well as the large area and cell into which it has changed are known.

The traffic flows passing through a large area are made noticeable by the fact that certain entry-exit combinations occur more frequently. From the time of entry TE into the location area and the exit time TA from the location area, the travel time TR can be calculated as the difference between exit time and entry time (TR = TA-TE).

Since all mobile devices are detected at this data level, the current level of enforcement with mobile devices means there is a sufficient number of individual measurements per train. By statistical methods of cluster analysis, the individual clusters can now be identified and grouped into individual clusters. A cluster is thus defined by the amount of the associated mobile subscribers. For the individual clusters, suitable key figures (eg average travel time, average entry or exit time) can then be determined. Each of these clusters now represents a single move.

If you record and compare the data over several days, a daily repeating pattern appears (see Fig. 3 ). If one assumes that trains normally run as planned, plan deviations can already be detected here. As exit from a large area also represents entry into the next large area, the information is linked so that a train can be tracked across the entire data-covered area. Even in the case that individual trains in a section not only spatially but also overlap in time so that they can not be separated within a large area and appear as a train, so this circumstance on the basis of the known amount of the associated mobile subscribers of each Trains from previous or following large areas are recognized and assigned correctly (s. Fig. 4 ). This means that the complete route on the route network can be determined for a train, whereby the change times are known for all large area changes. By comparing with the timetable, the individual trains can be identified. In the example, only the data for a large area was available. Nevertheless, most trains could already be clearly identified by comparison with the arrival timetable of the corresponding destination station (s. Fig. 3 ).

This makes it possible to clearly identify trains at this stage and track their movement on the network in real time and monitor the punctuality of trains by comparing them with the target schedule.

In order to be able to localize the trains even more precisely, data of the active mobile radio devices determined individually at the radio cell level are still being evaluated.

Once the individual moves have been identified and the number of associated participants has been determined, the whereabouts of the turn can now be determined more precisely on the basis of the individual data. The achievable accuracy depends on the amount of information at this data level. If individual data of an associated participant are available, the position of the train can be ascertained based on the information about the individual cell changes at least to the cell. If there is more information (for example, the distance to the serving cell, reception levels), then the position can also be specified more precisely within the cell.

Since only a fraction of the mobile devices are active at a time, the individual data accumulates less frequently than the previously considered collective data. The data traffic at this level is essentially determined by the telephoning behavior of the train passengers. However, this should nowadays be such that a nearly complete localization can be achieved at this level.

To illustrate, the Fig. 4 a plurality of divided into their radio cells radio coverage areas LA1, LA2, LA3, etc., through which, for example, two rail W1 and W2 lead.

Within the areas LA 1 and LA 3, the two trains Zu and Zv are spatially separated, ie they have different Combinations of input cell and output cell and thus belong to different traffic flows.

Within area LA2, both trains Zu and Zv belong to the same rail or traffic flow (identical input and output cell). Since, however, trains within such a range are usually sufficiently separated in time, they can be identified individually (see also Fig. 2 and 3 ). However, if trains also overlap in time so that no separation is possible within the area LA2, this can be seen from the associated amount of mobile subscribers, since the amount associated with the individual trains is known from the other areas LA1 and LA2.

Based on FIG. 5 In the following, this method will be described with reference to the flowchart shown there by way of example.

First, in a step 110 for a radio coverage area, collective data RDT is acquired as raw data or input data, the exit times (see TA in FIG Fig. 2 ) of registered mobile devices in this radio coverage area. These times are described here as change times TA, in order to express that entry times could also be detected instead of or in addition to the exit times TA. The raw data are described here as collective data RDT to express that they can collectively, for example in the context of the so-called. Location updates, can be detected. In addition, these data RDT can indicate whether possibly multiple mobile devices collectively, ie simultaneously or very timely, between radio coverage areas (see LA1, LA2 ... in Fig. 4 ) change, which is especially the case with trains. Because not infrequently located in trains several registered mobile devices (in long-distance trains quite well 40 and more), for the quasi simultaneously a change between adjacent radio coverage areas (s. Fig. 4 ) he follows.

Thus, it can then be determined in a step sequence 120 based on the collective data RDT and the determined change times whether registered mobile devices in a larger number simultaneously or jointly from one radio coverage area to the next, so that it can be seen that the corresponding collective data RDT trains can be assigned. Other data obtained from individual, e.g. in cars, mobile devices come, can be discarded.

The assignment of the collective data or the corresponding mobile devices to trains, which is carried out in step 120, initially involves an abstract assignment, because the individual trains are still unknown and unidentified. However, this abstract mapping already allows tracking of the unknown trains (see step 130), e.g. to get typical movement profiles that can give hints to specific moves. It could also be determined based on a data analysis carried out for several radio coverage areas, the load of the rail network itself and the frequency of occurring delays.

The evaluation of the collective data RDT carried out in steps 110 and 120 also includes, for example, a statistical evaluation of the in Fig. 2 shown data collection, in which the exit times TA on the X-axis and the difference described here as travel time TR of exit time TA and entry time TE are plotted on the y-axis. The travel time TR indicates the respective length of stay of a mobile device in a radio coverage area and occurs in particular in the case of trains in cluster form, as it Fig. 2 represents. As a result, it can be recognized which of the acquired raw data RDT belong to collectively moving mobile radio devices. If the raw data RDT have accumulations of travel times TR at specific exit times TA, it is assumed in accordance with the invention that mobile devices of collectively traveling persons are involved. Therefore these travel times (eg TRu and TRv in Fig. 2 ) are described here as collective travel times, which can each be assigned to a train.

By a data analysis of collective travel times, for example, a differentiation between different types of trains is possible. Because the relatively long travel times, such as TRu, show rather slow-moving or often holding trains, such as the S-Bahn Zu. The short travel times indicate rather fast long-distance trains, such as the express train or InterCityExpress Zv. Even if no concrete data assignment to trains is possible, such as the assignment of the data TRv and TAv to the ICE278 (s. Fig. 3 ), this data analysis can already reveal whether individual trains may not be on schedule and particularly late.

The collective data that can be assigned abstract trains are tracked in a step 130 over several radio coverage areas to determine at least abstract profiles representing individual trains. In particular, the respective common changeover times can be recorded and evaluated.

To identify individual moves will be in one step. 140 compared the common change times with scheduled change times, which are provided in the sub-step 139.

If the data collected relates to a radio coverage area that covers a station, the timetable data or data derived therefrom can be used as scheduled changeover times. By comparing the measured common change times with the data from the arrival and / or departure timetable for this station trains can already be identified specifically and also determined if necessary, whether individual trains do not run as planned. For example, if the scheduled arrival time of the S-Bahn S41 is 09:35 o'clock (s. Fig. 2 ), the train detected on the basis of the data collected on July 10th to July 12th can easily be identified as S-Bahn S41 and localized.

Or it is for example for the train Zz (s. Fig. 3 ) on 12 July, a current change time TAz12 measured, which is well behind the other change times and thus deviates significantly from the scheduled time TAz. The scheduled time can be derived from the timetable and / or can also be determined by statistical evaluation of the raw data RDT by checking which time or at least narrow period prevails and must be considered as a scheduled change time TAz. For example, if the scheduled arrival time of the ICE76 and the ICE600 at 10:40, it can be seen that both trains on July 12 significant delays of about 20 or 30 minutes have (s. Fig. 3 ).

Preferably, in step 139, the scheduled change times are determined not only for stations, but for any sections and provided for the comparison (step 140). For this purpose, the data collected over several days are preferably evaluated in steps 145 and 146 as part of a preferably offline data analysis. If, at the border of the respective radio coverage area, common changeover times occur frequently at the same time over several days, these data represent scheduled changeover times, which are made available as reference data in step 139. Through the tracking already carried out in step 130, a precise identification and localization of specific moves can then be carried out in step 140.

For example, it can be determined that the ICE76 enters the radio coverage area LA1 on a daily basis at 10:30 am and then changes to the next radio coverage area LA2 at 10:40 am. At 11:15, the ICE 76 changes to the radio cell area LA3, etc.

These data provided in step 139 relate to any route sections and thus include the train identity, the location of the change (transition from one radio coverage area to the next) and the respective point in time. In order to have reliable data, the online train identification (step 140) was verified in offline step 145 by comparison with timetable data. On the basis of the data collected over several days, it can be determined, among other things, whether the current train identification (in step 140) is correct is. For example, in step 145, timetable changes are detected, or it is recognized that, for example, a special train operates on the considered section of the route that has been erroneously recognized as a rule in step 140. These and other errors can thus be corrected in step 145. In step 146, the location and time of the changes can then be determined by statistical evaluation of the data for the train correctly recognized on the route under consideration.

With these exact data then present in block 139, i.a. Determination of any delays occurring (step 150) are specified. In addition, with the data from block 139, the current identification of trains in step 140 can be performed precisely for any section of track. The trains are therefore also exactly localized in sections.

If necessary, in a step 141 a more accurate localization e.g. based on individual data indicating hand-over times, field strength or the like.

On the basis of the data comparison carried out in step 140, delays can then be detected independently of the rail operator in a step 150 and made available or transmitted to the user or customer as information. For this, e.g. the transmission of a corresponding mobile message, in particular an SMS message serve.

The in the Fig. 5 solid lines connecting, in particular, the blocks with the process steps 110, 120, 130, 140 and 150 indicate that these process steps continuously in real time (online). The dashed lines, on the other hand, relate to steps that can not be performed in real time (offline). These include the step 146, in which a statistical analysis of the change times (entry and exit times) is carried out over a longer period of time, with which the scheduled change times can also be determined for sections outside of stations.

In summary, therefore, a method and devices are proposed here, which can localize and identify trains on the basis of network-recorded mobile data in real time and track their movement in real time on the network. This information can also be used to check adherence to the timetable and to quantify deviations from it. The method relies essentially on information that is captured by all mobile devices. A more accurate localization of the trains can be realized by using the information that is detected only by active mobile devices.

The method is in principle beyond the passenger train traffic, e.g. Also applicable in freight train traffic, whenever larger quantities of mobile devices are transported in spatially clearly defined areas, e.g. also in passenger ship traffic or in container shipping, in particular on inland waters or rivers, provided that a sufficient number of containers are equipped for telematics purposes with mobile devices.

Claims (16)

1. Method (100) for localizing trains (Zu, Zv) in a railway network located in the service area of a cellular mobile radio system, in which system mobile radio devices are registered, wherein at least a portion of the mobile radio devices are located in the trains (Zu, Zv) that move through spatial areas (LA1, LA2) of the mobile radio system along at least one rail line (W1, W2), wherein the method has the following steps:

   - for adjacent spatial areas (LA1, LA2) to which at least one rail line (W1, W2) can be assigned, collective data (RDT) are acquired which indicate handoff times (TA) for handoff between the adjacent spatial areas (LA1, LA2) for registered mobile radio devices (110);

   - by means of analysis of the collective data (RDT), common handoff times (TAu, TAv) for multiple registered mobile radio devices are ascertained and are recognized as being assignable to trains (Zu, Zv) that travel on the at least one rail line (W1, W2) (120);

   - by means of comparison of the common handoff times (TAu, TAv) with scheduled handoff times for traveling trains (Zu, Zv), the applicable common handoff time (TAu) is assigned to a train (Zu) in order to identify and localize the train (Zu) within the railway network (140).
2. Method (100) according to claim 1, characterized in that each common handoff time (TAu) for multiple cellular mobile radio devices indicates the simultaneous entry into and/or the simultaneous exit from the applicable spatial area (LA1; LA2).
3. Method (100) according to claim 1 or 2, characterized in that the scheduled handoff times are derived from timetable data of a railway operator, at least for spatial areas that cover railway stations (139).
4. Method (100) according to any one of claims 1 through 3, characterized in that, at least for spatial areas that cover arbitrary track sections but no railway stations, the scheduled handoff times are ascertained by an analysis of the collective data (RDT) which is repeated multiple times, in particular daily, wherein it is detected whether common handoff times (TAu, ... TAz) also represent scheduled handoff times (146).
5. Method (100) according to any one of the preceding claims, characterized in that, at least for track sections on which multiple trains travel within a short period of time through multiple adjacent spatial areas, the collective data (RDT) that are recognized as assignable to a train are analyzed for temporal and spatial tracking of the trains (130).
6. Method (100) according to any one of the preceding claims, characterized in that one currently ascertained common handoff time (TAz12) at a time is compared with at least one scheduled handoff time (TAz) in order to identify a deviation indicating a delay of a train (Zz) to which the scheduled handoff time (TAz) is assignable (150).
7. Method (100) according to any one of the preceding claims, characterized in that collective travel times (TRu, TRv), each of which indicates the transit time of a train (Zu, Zv) through the spatial area (LA1), are determined from the collective data (RDT) or from the handoff times (TA) ascertained therefrom (110).
8. Method (100) according to claim 7, characterized in that different types of trains are recognized by means of the collective travel times (TRu, TRv) in order to ascertain the common handoff times (TAu, TAv) from the handoff times (TA) of multiple registered mobile radio devices and to assign said common handoff times to the trains (Zu, Zv) (120).
9. Method (100) according to any one of the preceding claims, characterized in that the method also includes at least one of the following additional steps:

   - assignment of the collective data (RDT) to trains (Zu, Zv) based on identifiers of individual mobile radio devices (120);

   - tracking of trains (Zu, Zv) based on identifiers of individual mobile radio devices (130);

   - for identifying trains (Zu, Zv), comparison of the handoff times (TA) with scheduled handoff times that apply for the applicable spatial area (LA1), wherein the applicable spatial area (LA1) covers a railway station (140);

   - analysis of the handoff times (TA) repeated over multiple days to determine scheduled handoff times that also apply to areas (LA) that do not cover a railway station (146);

   - identification of route segments of trains by means of comparing the handoff times currently ascertained for multiple areas (LA) with scheduled handoff times (140).
10. Method (100) according to any one of claims 2 through 9, characterized in that the collective data (RDT) are determined by means of an analysis, in particular statistical analysis, of entry and exit times that indicate the entry of the active and inactive mobile radio devices into the area (LA1, LA2) or their exit from the area (LA1, LA2) (110).
11. Method (100) according to claim 10, characterized in that the collective data are ascertained by means of an analysis of update data for the radio service area (LA2) with respect to the entry and exit of the active and inactive mobile radio devices (110).
12. Method (100) according to any one of the preceding claims, characterized in that, by means of an analysis of individual data that indicate the presence of individual active mobile radio devices in subareas (C*) of the spatial area (LA1), a localization of the train (Zu) in an individual subarea (C*) is carried out (141).
13. Method (100) according to claim 12, characterized in that the area is a radio service area (LA1) of the cellular mobile radio system and in that the subareas are single or multiple radio cells (C*) of the radio service area (LA1) (141).
14. Method according to any one of claims 12 or 13, characterized in that the analysis of the individual data is supplemented by additional data or information about the transmit and/or receive field strength of mobile radio signals in order to carry out a localization of the train (Zu) within a subarea (C*) (141).
15. System for localizing trains (Zu, Zv) in a railway network located in the service area of a cellular mobile radio system, in which radio system mobile radio devices are registered, wherein at least a portion of the mobile radio devices are located in the trains (Zu, Zv) that move through spatial areas (LA1, LA2) of the mobile radio system along at least one rail line (W1, W2), wherein the system has a control center with computing means that execute the following steps:

    - for adjacent spatial areas (LA1, LA2) to which at least one rail line (W1, W2) can be assigned, the computing means acquire collective data (RDT) which indicate handoff times (TA) for handoff between the adjacent spatial areas (LA1, LA2) for registered mobile radio devices;

    - by means of analysis of the collective data (RDT), the computing unit ascertains common handoff times (TAu, TAv) for multiple registered mobile radio devices and recognizes them as being assignable to trains (Zu, Zv) that travel on the at least one rail line (W1, W2);

    - by means of comparison of the common handoff times (TAu, TAv) with scheduled handoff times for traveling trains (Zu, Zv), the applicable common handoff time (TAu) is assigned to a train (Zu) in order to identify and localize the train (Zu) within the railway network (140).
16. Control center for a system for localizing trains (Zu, Zv) in a railway network located in the service area of a cellular mobile radio system, in which radio system mobile radio devices are registered, wherein at least a portion of the mobile radio devices are located in the trains (Zu, Zv) that move through spatial areas (LA1, LA2) of the mobile radio system along at least one rail line (W1, W2), wherein the control center has computing means that execute the following steps:

    - for adjacent spatial areas (LA1, LA2) to which at least one rail line (W1, W2) can be assigned, the computing means acquire collective data (RDT) which indicate handoff times (TA) for handoff between the adjacent spatial areas (LA1, LA2) for registered mobile radio devices;

    - by means of analysis of the collective data (RDT), the computing unit ascertains common handoff times (TAu, TAv) for multiple registered mobile radio devices and recognizes them as being assignable to trains (Zu, Zv) that travel on the at least one rail line (W1, W2);

    - by means of comparison of the common handoff times (TAu, TAv) with scheduled handoff times for traveling trains (Zu, Zv), the applicable common handoff time (TAu) is assigned to a train (Zu) in order to identify and localize the train (Zu) within the railway network (140).

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