JP2002249049A - Traffic control device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a traffic control device capable of predicting any deviation from the pre-scheduled train diagram in an early stage. SOLUTION: The present position and speed of the train are detected, and it is determined whether or not the train stops at a station, the train arrives at the station, or the train is traveling based on the detected position and speed (S1-S3). When the train stops or the train arrives at the station, the predicted delay time when the train arrives at the next station is calculated from the train diagram and the time interval data (S8, 10 and 11). When the train is traveling, and if the present speed of the train is the same as the speed in the standard traveling pattern, the future operation is regarded to follow the standard traveling pattern, the present delay time is set to be the predicted delay time. If the present speed is different, the learned delay time when the train arrives at the next station is set to be the predicted delay time (S4, 5, 7, 8 and 13). When the train is traveling within the unscheduled speed limit, the predicted delay time is calculated from the predetermined operational formula (S4, 5, 6 and 8).

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a train operation management device capable of predicting a deviation from a train schedule planned in advance at an early stage.

[0002]

2. Description of the Related Art It is desirable to operate a train according to a planned train schedule as much as possible. However, a train may be delayed as planned due to various factors such as an accident or failure of a starting train. Such a delay of the train also affects the operation of the subsequent trains and the like, and hinders the operation of the entire line section. Therefore, when a train delay occurs, it is necessary to detect this at an early stage and carry out appropriate traffic rescheduling, and it is useful to make a prediction diagram indicating future train operation for the reorganization judgment.

For this reason, in the conventional train operation management device,
By comparing the actual arrival / departure time at each station detected by a track circuit or the like at each station in the line section and the arrival / departure time at each station determined by a previously scheduled train schedule, a train delay is detected, When the delay time of the detected train exceeds a certain time, a prediction schedule was created. The predicted timetable is created by calculating the scheduled arrival and departure time of the train at each station ahead of the next station based on the actual departure and arrival time of the train.

[0004] Specifically, when a delay of a train is detected upon arrival at a station, first, at a station arrival time of the delayed train, a minimum time, which is the shortest time at which a predetermined train must be stopped for each station, is required. The scheduled time of departure of the train is calculated by adding the stop time. However, the scheduled departure time is determined so as not to be earlier than the departure time determined by the train schedule, and to ensure a departure time interval that is a predetermined time that must be secured with the preceding train. .

[0005] Then, the estimated time of arrival of the train at the next station is calculated by adding the minimum operation time, which is the shortest travel time of the train between stations, to the calculated estimated time of departure. However, the estimated arrival time is determined such that an arrival time interval, which is a predetermined time that must be secured with the preceding train, is secured. On the other hand, if a delay of the train is detected at the time of departure from the station, the scheduled departure time of the train at each station ahead is calculated in the same manner as described above, except for the addition of the minimum stop time, based on the actual departure time of the delayed train. calculate.

[0006] In this manner, the scheduled arrival and departure times of all the trains operating in the line section including the delayed train are calculated at each station, and a predicted timetable is created.

[0007]

However, in the conventional train operation management device as described above, the delay from the train schedule is detected only when the train arrives at the station. If the train stopped or slowed down, no delay could be found until the train arrived at the next station. For this reason, the creation of the prediction diagram is delayed, and the prediction diagram may not appropriately reflect the actual delay situation.

The present invention has been made in view of the above circumstances, and relates to a train operation management device capable of predicting a deviation from a train schedule at an early stage.

[0009]

For this reason, the invention according to claim 1 comprises a detecting means for detecting a current running condition of a train, and a predicted shift time with respect to a next station arrival time on a predetermined train schedule. Prediction means for making a prediction based on the current running condition of the train detected by the detection means, and prediction timetable making means for generating a prediction timetable based on the predicted deviation time predicted by the prediction means. .

[0010] In this configuration, the predicted deviation time from the arrival time at the next station in the train schedule is predicted on the basis of the current running condition of the train, so that a predicted schedule appropriately reflecting the current running condition of the train can be created. become. Further, the invention according to claim 2, wherein the predicting means compares a standard running pattern used for creating the train schedule with a current running state of the train detected by the detecting means, and determines a current train position. When the speed in the corresponding traveling pattern and the current train speed are the same, a current deviation time is set as the predicted deviation time.

In this configuration, when the speed in the standard running pattern corresponding to the current train position and the current train speed are the same, the subsequent train operation follows the standard running pattern, and the current deviation time is predicted. It is a shift time.
In addition, the invention according to claim 3 stores the actual deviation time that occurs when the train arrives at the next station for each running condition of the train detected by the detection means, and corresponds to the running condition of the train when the train arrives at the next station. A learning means for learning by updating the previously stored value to the latest value.

[0012] In this configuration, the actual deviation time that occurs when the train arrives at the next station is stored for each running condition of the train detected between the stations, and the stored value is updated to the latest value different from the previous value for learning. Further, the invention according to claim 4 is characterized in that the prediction means compares a standard traveling pattern used for creating the train schedule with a current traveling state of the train, and calculates the current traveling pattern corresponding to the current train position. When the speed is different from the current train speed, a learning value of the learning means corresponding to a current driving situation is set as the predicted deviation time.

In this configuration, when the speed in the standard traveling pattern corresponding to the current train position is different from the current train speed, the learning value of the deviation time corresponding to the current traveling situation is set as the predicted deviation time. Further, the invention according to claim 5 is configured such that, when the train travels in the temporary speed limit section, the prediction means predicts the prediction deviation time from a predetermined arithmetic expression predetermined based on the speed limit condition. .

In this configuration, the time required for the train to travel in the temporary speed limited section is calculated from a predetermined arithmetic expression to predict the predicted deviation time, so that the predicted deviation time when traveling in the temporary speed limited section can be easily calculated. become able to. Further, the invention according to claim 6 is a notifying unit for notifying the train side of information related to the shift state based on the predicted shift time of the train predicted by the predicting unit and the predicted schedule created by the predicted schedule creating unit. It was configured.

[0015] In this configuration, since information relating to the train deviation state based on the predicted deviation time and the predicted schedule is reported to the train side, the driving operation according to the actual deviation state of the train can be performed early. .

[0016]

Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a block diagram showing an embodiment of the train operation management device according to the present invention. In FIG. 1, a train operation management device 1 includes a train detection unit 2, a data storage unit 3, a running result storage unit 4, a delay detection unit 5,
Operation prediction unit 6, prediction timetable storage unit 7, delay notification unit 8
It is comprised including.

The train detecting section 2 detects the position of the train running on the train track almost continuously to detect the running state of the train. Specifically, for example, a plurality of ground radios communicating with a running train are provided along a track, and a triangulation method is used based on a propagation time of a radio wave generated when communication is performed between the ground radio and the train. A known method of substantially continuously obtaining the position of the train and a known method of continuously providing the short and short track circuits, detecting the train entering each short and short track circuit, and substantially continuously obtaining the position of the train are exemplified. . Thus, the train detecting unit 2 constitutes a detecting unit. The detecting means need not be the one described above as long as the running state of the train can be detected continuously or in a unit of a very short section.

The data storage unit 3 stores predetermined data necessary for predicting the future operation of the train. Specifically, for example, a train schedule indicating a train departure / departure time at each station planned in advance, a standard traveling pattern used to create the train schedule, a minimum operation time, a minimum stop time, a departure Time interval data set by driving restrictions such as time intervals and arrival intervals, and data representing temporary speed limit conditions are stored.

The running result storage unit 4 stores, for each running condition of the train detected by the train detecting unit 2, the actual result value of the delay time when the train arrives at the next station, and stores the updated learning value. The delay detecting unit 5 compares the running condition of the train detected by the train detecting unit 2 with a standard running pattern or a train schedule, and when the train is delayed, the train is compared with the standard running pattern or the train schedule. Calculate the current delay time.

The operation predicting unit 6 determines a train schedule in the train schedule based on the data in the data storage unit 3, the actual value of the delay time in the running result storage unit 4, and the current delay time of the train calculated by the delay detecting unit 5. For example, a predicted delay time is obtained as a predicted delay time with respect to the next station arrival time, and a predicted diagram is created based on the predicted delay time. Here, the operation prediction unit 6 constitutes a prediction unit and a prediction diagram creation unit.

The prediction schedule storage unit 7 stores the prediction schedule created by the operation prediction unit 6. A data storage unit 3, a driving result storage unit 4, a delay detection unit 5, an operation prediction unit 6,
The prediction timetable storage unit 7 is specifically realized by a computer, and is provided, for example, at a central command center or the like that centrally manages the operation of all trains in a line section. The delay notification unit 8
With reference to the current delay time calculated by the delay detection unit 5 and the prediction timetable stored in the prediction timetable storage unit 7, information relating to the current delay time, the estimated time of arrival at the next station, etc. To the train driver. Specifically, for example, it is realized by a wireless device provided on the ground side such as a central command center or each station. Here, the delay reporting unit 8 constitutes reporting means.

Here, the operation of the train operation management device 1 according to the present embodiment will be described with reference to the flowchart of FIG. In step 1, the current position and speed of the train are detected based on the train detection signal of the train detection unit 2. The train speed may be obtained, for example, from the amount of change in the detected train position with respect to time.

In step 2, the delay detecting section 5 determines whether or not the train is stopped at the station based on the train position and the train speed detected in step 1. When the train speed is 0 and the detection that the train position is within the predetermined area of the station is continued, the determination is YES, and the process proceeds to step 10 described later. On the other hand, in the case of NO, the operation proceeds to Step 3.

In step 3, the delay detecting section 5 determines whether the train has arrived at the station based on the train position and the train speed detected in step 1. If the train speed is 0 and it is detected for the first time that the train position is within the predetermined area of the station, it is determined to be YES and the operation proceeds to step 11 described below. On the other hand, in the case of NO, it is determined that the train is running, and the operation proceeds to step 4.

In step 4, the delay detecting section 5 determines whether or not the speed in the standard running pattern corresponding to the current position of the running train is equal to the current speed of the train. If the result of determination is NO, the operation proceeds to the operation of step 5, and if YES, the operation proceeds to an operation of step 13 described later. In step 5, the delay detection unit 5 refers to the temporary speed limited travel section stored in the data storage unit 3 and determines whether the train is in the temporary speed limited travel. If the result of the determination is YES, in step 6, the operation prediction unit 6 calculates the train-to-station travel time of the train in the temporary speed limit section as described below, and proceeds to the operation of step 8. On the other hand, in the case of NO, in step 7, the operation prediction unit 6 determines that the current speed of the train is different from the speed in the standard running pattern, and if the train is delayed from the current position of the train, the running performance The learning value of the delay time stored in the storage unit 4 is read out as described later, and the process proceeds to step S8.

In step 8, the operation prediction unit 6 obtains a predicted delay time of the train when arriving at the next station as described later, and prepares a predicted schedule. Note that the method of creating the prediction diagram is the same as the conventional method, and thus the description is omitted. In step 9, information relating to the train delay situation is reported to the train driver via the delay report unit 8.

If it is determined in step 2 that the train is stopped at the station, in step 10, the delay detecting unit 5 determines the current time and the station departure time in the train diagram stored in the data storage unit 3. If the current time is past the departure time on the train schedule, the current delay time of the train is calculated, and the process proceeds to the operation of step 14 described below.

If the train detection unit 2 determines in step 3 that the train has arrived at the station, the delay detection unit 5 stores the time at which the train actually arrived at the station and the data storage unit 3 in step 11. Then, if the actual station arrival time has passed the station arrival time on the train schedule, the current delay time of the train is calculated.

In step 12, the delay time calculated in step 11 is stored and learned as described later, and the operation proceeds to step 14 described later. In step 4,
If it is determined that the train is traveling at the same speed as the standard traveling pattern, in step 13, the delay detection unit 5
Compares the current running state of the train with the standard running pattern as described later, finds the current delay time if the train is delayed, and proceeds to the operation of step 14.

In step 14, the operation prediction unit 6
It is determined whether the current delay time obtained in steps 10, 11, and 13 is equal to or longer than a predetermined time. As a result of the determination, YE
In the case of S, the process proceeds to the operation of step 8 to obtain a predicted delay time, and a predicted diagram is created. On the other hand, in the case of NO, the process returns to Step 1. Next, a specific example of the method of calculating the predicted delay time will be described with reference to FIGS. Note that the method of calculating the predicted delay time when arriving at the station and when stopping at the station is the same as that in the related art, and a description thereof will be omitted.

FIG. 3 is a diagram showing, by solid lines and broken lines, a standard traveling pattern and an actual traveling state of a train, for example, expressed by a relationship between a traveling time and a traveling distance of a train traveling between adjacent stations. is there. In FIG. 3, for example, the current position of the train when the time t has elapsed from the departure of the station is Pc before the position P when the train travels in the standard traveling pattern.
Let's say it is a point. That is, FIG. 3 shows a case where the train is running with a delay of Δt from the standard running pattern. The delay detection unit 5 compares the speed A indicated by the arrow in the figure and the current speed B of the train in the standard traveling pattern at the point Pc. As a result of the comparison, when the speed A and the speed B are the same, the operation prediction unit 6 considers that the subsequent operation of the train follows the standard traveling pattern, and sets the current delay time Δt as the predicted delay time when arriving at the next station. .

On the other hand, when the speed A based on the standard traveling pattern is different from the current speed B of the train, the operation prediction unit 6
Reads out the learning value of the delay time corresponding to the current traveling state of the train from the running result storage unit 4, and sets the learning value of the delay time as the predicted delay time at the arrival at the next station. FIG.
FIG. 3 is a diagram showing a storage structure of a running result storage unit 4.

In FIG. 4, the running record storage unit 4 stores train position nodes that store the train positions P 1 to Pn detected by the train detecting unit 2, and the actual speed of the train at the train position stored in each train position node. And a relative speed node storing relative speeds V1 to Vn with the speed at the corresponding position in the standard running pattern, and running at each relative speed V1 to Vn at each train position P1 to Pn detected by the train detector 2 by the train. Learning value D11-
A candidate list for storing a performance value node that stores Dnn and a performance value node corresponding to the traveling situation detected by the train detection unit 2 are provided.

More specifically, for example, in FIG. 4, the train detection unit 2 detects that the position of the train is P1 and the relative speed is V3, and the learning value of the delay time when the train arrives at the next station is calculated. In the case of D13, the train position node in which the train position P1 is stored, the relative speed node in which the relative speed V3 is stored, and the actual value node in which the learning value D13 of the delay time is stored respectively correspond to each other. . When the train detection unit 2 detects that the train position is P1 and the relative speed is V3, the learning value D of the delay time corresponding to these is detected.
The result value node in which 13 is stored is stored in the candidate list.

The value stored in each node may be normalized in the minimum unit. Such a delay detection unit 5 for each node
The data write operation according to the above is as shown in the flowchart of FIG. In step 21, it is determined whether or not the train position node storing the current position of the train detected by the train detection unit 2 is in the memory. As a result of the determination, N
In the case of O, it is determined that there is no running record, and the operation proceeds to step 22. On the other hand, in the case of YES, a later-described step 24 for searching for a relative speed node corresponding to the train position node.
Proceed to the operation.

In step 22, a train position node is newly created in the memory, and the current position of the train detected by the train detector 2 is written. In step 23, a relative speed node and an actual value node are newly created in the memory in correspondence with the train position node created in step 22, and the relative speed at the current position of the train detected by the train detector 2 is calculated as the relative speed. The data is written to the node, and the operation proceeds to step 25 described below. In this case, since there is no arrival record at the next station, the result value node has no data.

If it is determined in step 21 that the train position node is in the memory, it is determined in step 24 whether there is a relative speed node corresponding to the train position node. If the result of the determination is NO, it is determined that there is no running record, and the operation proceeds to step 23, where the relative speed node and the record value node are created as described above. On the other hand, Y
In the case of ES, it is determined that there is a running record, and the operation proceeds to step 25.

In step 25, the actual value node newly created in step 23, or in step 21,
The actual value nodes already created corresponding to the train position node and the relative speed node searched in the operation 24 are stored in the candidate list. In step 26, it is determined whether the train has arrived at the station. As a result of the determination, if the determination is YES, the process proceeds to step S27. If the determination is NO, the process returns to step S21.

In step 27, if the actual value of the delay time is not stored in the actual value node listed in the candidate list, the actual value of the delay time upon arrival at the station is written and stored. On the other hand, when the actual value of the delay time stored in the actual value node listed in the candidate list is different from the current delay time at the time of arrival at the station, the actual value of the delay time is changed to the current value at the time of arrival at the station. Learn by updating to the delay time.

As described above, the train detecting section 2, the running record storing section 4 and the delay detecting section 5 constitute learning means. The operation of reading the learning value of the delay time written in the actual value node by the operation prediction unit 6 is as shown in the flowchart of FIG. In step 31, it is determined whether or not the train position node storing the train position detected by the train detection unit 2 is in the memory. If the result of determination is YES, the train location node is called and the operation proceeds to step 32. On the other hand, in the case of NO, in step 33, the train position node closest to the train position detected by the train detection unit 2 is called, and the operation proceeds to step 32.

In step 32, it is determined whether or not any of the relative speed nodes corresponding to the train position nodes called in step 31 or 33 stores the relative speed detected by the train detector 2. judge.
If the result of determination is YES, the relative speed node is called and the operation proceeds to step 34. On the other hand, in the case of NO, in step 35, the relative speed node storing the relative speed closest to the relative speed detected by the train detection unit 2 is called, and the operation proceeds to step 34.

In step 34, the learning value of the delay time stored in the actual value node corresponding to the called train position node and relative speed node is read. The learning value of the delay time is the predicted delay time at the time of arrival at the next station at that time. Further, the learning value is read out each time the traveling state is detected, and a prediction diagram is sequentially created based on the read out learning value.

Next, a specific example of a method of calculating a predicted delay time upon arrival at the next station when the train travels in the temporary speed limit section stored in the data storage unit 3 in advance will be described.
In this embodiment, the temporary speed limit section is defined as the entire area between stations, and the contents of the temporary speed limit include a case where a speed reduction is instructed at a fixed rate over the entire area between stations and a case where the maximum speed between stations is limited. The case instructed shall be handled.

First, the operation of calculating the predicted delay time upon arrival at the next station when speed reduction is instructed at a fixed rate over the entire area between stations will be described with reference to FIG. FIG.
Is a solid line and a standard traveling pattern of a train traveling between stations represented by the relationship between the traveling time and the traveling distance, and a traveling pattern of the train when there is an instruction to reduce the train speed at a fixed rate, respectively. It is the figure shown with the broken line.

In FIG. 7, when a train travels between stations with the α% speed reduced compared to the standard traveling pattern, the train-to-station travel time Tmin ′ is given by: Tmin ′ = Tmin {1 / (1−α / 100)} (1) However, Tmin is the minimum operation time between stations.

Therefore, when there is an instruction to reduce the train speed at a fixed rate, the predicted delay time upon arrival at the next station is calculated as Tmin'-Tmin. Next, the operation of calculating the predicted delay time upon arrival at the next station when the limitation of the maximum speed between stations is instructed will be described with reference to FIG. FIG. 8 is a diagram showing traveling patterns when the train travels between stations at the maximum speed Vs according to the standard traveling pattern and when the train travels at the speed limit Vd (<Vs). FIG. FIG. 2B is a diagram showing the relationship between the running speed and the running distance, and FIG. 2B is a diagram showing the relationship between the running speed and the running time.

The conditions for acceleration and deceleration when the train runs at the maximum speed Vs and when it runs at the speed limit Vd are assumed to be the same. In FIG. 8, the inter-station traveling time T _d0 when the train travels while limiting the maximum speed Vs in the standard traveling pattern to the speed limit Vd is represented by [ _Equation 1] as follows.

[0048]

(Equation 1) It is expressed as Where T _S0 is the time between stations when traveling at the maximum speed Vs, T _VS1 is the acceleration end time when traveling at the maximum speed Vs, T _VS2 is the deceleration start time when traveling at the maximum speed Vs, K is the maximum speed Vs and the speed limit Vd
(= Vd / Vs).

Therefore, when the limitation of the maximum speed between the stations is instructed, the predicted delay time upon arrival at the next station is calculated as T _d0 −T _S0 . As described above, the delay time is predicted according to the current running status of the train, and a sequential prediction diagram is created. However, it becomes possible to create a prediction diagram that appropriately reflects the actual driving situation.
As a result, it is possible to quickly make a decision for traffic control.

Further, since the configuration is such that the delay state of the train is reported to the train side at any time, the train side can perform the driving operation according to the delay state at an early stage. As a result, even when the train stops between stations, the delay of the train can be promptly recovered, and an increase in operation delay can be suppressed. Since the actual value of the delay time at the time of arrival at the station is stored and learned, it is not necessary to prepare data in which various operation situations are assumed in advance.

Further, since the present embodiment is configured to obtain the predicted delay time upon arrival at the next station by comparing the current traveling state of the train with the standard traveling pattern, it is possible to perform highly accurate operation prediction. Further, when traveling in an extraordinary speed limited section in which traveling conditions are defined in advance, a configuration is used in which a simple calculation using parameters included in a standard traveling pattern is performed to calculate a predicted delay time at the time of arrival at the next station. Therefore, it is easy to calculate the predicted delay time when traveling in the temporary speed limit section.

In this embodiment, the prediction schedule is created when a delay of a predetermined time or more occurs.
A configuration in which a prediction diagram is created immediately after delay detection may be used. In addition, when the train is delayed, the predicted delay time is calculated as the predicted delay time with respect to the arrival time of the next station in the train schedule, but it is also predicted when the train is operating faster than the standard traveling pattern or train schedule. A configuration for obtaining the shift time may be adopted. In this case, in the learning function, the learning value to be written to the performance value node may be set to 0 or a negative value.

[0053]

As described above, according to the first aspect of the present invention, since the prediction schedule is sequentially created using the prediction shift time predicted based on the current running condition of the train,
Predicted schedules that appropriately reflect actual operating conditions can be created not only when a train arrives at a station but also when a train exists between stations. As a result, it is possible to quickly make a decision for traffic control.

According to the second aspect of the present invention, since the predicted deviation time is obtained by comparing the actual traveling state of the train with the standard traveling pattern, a highly accurate operation prediction can be performed. According to the third and fourth aspects of the present invention, since the stored value of the shift time is learned and the learned value is used, it is not necessary to prepare data in which various operation situations are assumed in advance.

According to the fifth aspect of the present invention, when the train travels in the temporary speed limit section, the predicted deviation time is predicted from a predetermined arithmetic expression, so that the calculation of the predicted deviation time becomes easy. According to the invention according to claim 6, since the information related to the train deviation situation is notified to the train side, the train driver:
The driving operation according to the deviation situation can be performed early.
As a result, the train can be quickly recovered from the delay, and the increase in the operation delay can be suppressed.

[Brief description of the drawings]

FIG. 1 is a block diagram of an embodiment of a train operation management device of the present invention.

FIG. 2 is an operation flowchart of the operation management device of the embodiment.

FIG. 3 is a diagram showing a standard traveling pattern of a train represented by a relationship between a traveling time and a traveling distance and an actual traveling state;

FIG. 4 is a diagram showing a memory structure of an increase delay prediction unit according to the embodiment;

FIG. 5 is an operation flowchart of the delay detection unit according to the embodiment when learning the actual value of the delay time;

FIG. 6 is an operation flowchart of the operation prediction unit of the embodiment when the learning value of the delay time is read.

FIG. 7 is a diagram showing a standard running pattern of a train represented by a relationship between a running time and a running distance, and a running pattern of a train when there is an instruction to reduce the train speed at a fixed rate.

FIG. 8 is a diagram showing a traveling pattern when the train travels at the maximum speed Vs and a traveling pattern when traveling at the speed limit Vd.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Train operation management device 2 Train detection part 4 Running result storage part 5 Delay detection part 6 Operation prediction part 8 Delay report part

Claims (6)

[Claims] 1. A detecting means for detecting a current running condition of a train, and a predicted time lag with respect to an arrival time at a next station in a predetermined train schedule based on a current running condition of the train detected by the detecting means. A train operation management device, comprising: a prediction unit for predicting; and a prediction diagram creation unit for creating a prediction diagram based on the predicted deviation time predicted by the prediction unit. 2. The method according to claim 1, wherein the predicting means compares a standard running pattern used for creating the train schedule with a current running state of the train detected by the detecting means, and calculates the running pattern corresponding to a current train position. The train operation management device according to claim 1, wherein when the speed in the train is the same as the current train speed, a current shift time is set as the predicted shift time. 3. The system according to claim 1, further comprising the step of storing, for each running condition of the train detected by said detecting means, an actual time lag occurring when the train arrives at the next station, and storing a previously stored value corresponding to the running condition of the train when the train arrives at the next station. The train operation management device according to claim 1, further comprising a learning unit configured to learn by updating to a latest value. 4. The prediction means compares a standard traveling pattern used for creating the train schedule with a current traveling state of the train, and calculates a speed in the traveling pattern corresponding to a current train position and a current train pattern. 4. The train operation management device according to claim 3, wherein when the speed is different, a learning value of the learning unit corresponding to a current driving situation is set as the predicted deviation time. 5. 5. When a train travels on a temporary speed limit section,
The train operation management according to any one of claims 1 to 4, wherein the prediction unit is configured to predict the predicted deviation time from a predetermined calculation formula predetermined based on a speed limit condition. apparatus. 6. A notifying means for notifying a train side of information relating to a shift condition based on a predicted shift time of a train predicted by said predicting means and a predicted timetable created by said predicted timetable creating means. 6. The method according to claim 1, wherein
The train operation management device according to any one of the above.