KR20210016100A - Method And Apparatus for Providing Dynamic Train Control - Google Patents

Abstract

Disclosed are an autonomous collaboration-based dynamic train control method and an apparatus thereof. Embodiments of the present invention provide an autonomous collaboration-based dynamic train control method and an apparatus thereof, which calculate severity and cascaded delay time for exceptional circumstances determined via communication between trains and ground communication to select an optimal alternative to allow trains to run autonomously based on the exceptional circumstances. The dynamic train control apparatus comprises a train circumstance recognition unit, a severity determination unit, a cascaded delay time prediction unit, an alternative selection unit, and a train operation unit.

Description

Autonomous collaboration-based dynamic train control method and device {Method And Apparatus for Providing Dynamic Train Control}

The present embodiment relates to a method and apparatus for dynamic train control based on autonomous collaboration.

The contents described below merely provide background information related to the present embodiment and do not constitute the prior art.

In general, in order to operate a train, a plurality of trains are operated according to a preset schedule on a preset route. If there is a delay in the preceding train, it affects the schedule of the following train, so contention between trains may occur or confusion may occur.

Trains running in the same direction as the preceding train must be operated to overtake the preceding train in order to run according to the scheduled arrival time when a delay occurs in the preceding train. Trains must be operated to readjust the order or schedule of trains on routes where multiple routes are merged into one track.

In general, train schedules are generated based on the experience of railroad operators rather than based on optimization algorithms or simulations. If a railroad operator prepares a schedule for train operation in advance, the engineer and the controller will operate the train in accordance with the train schedule as much as possible.

When an engineer and a controller operate a train according to a schedule, unexpected situations such as train breakdown, track breakdown, non-compliance with the schedule, and platform delays occur.Thus, occupying the same track at the same time, deadlock in both directions, and safety test (Headway Constraints) Train contention such as non-compliance occurs.

When train contention occurs, the central control center clears the contention between trains by re-adjusting the route and schedule of the train to ensure the punctuality and safety of the train. The general train schedule creation method is determined by collecting all train-related operation information at the central control center. The central control center delivers the determined train schedule to each train. There is a risk of human error in the process of receiving train schedules and communicating with train controllers and engineers.

However, the central control center needs to collect operation information of all trains in real time to create train schedules for all trains, and there is a real difficulty in collecting operation information from all trains in real time.

When a train contention occurs, the central control center needs to analyze a large-scale and complex railway network, so it takes a lot of time to resolve the train contention. In other words, there is a limit in determining an appropriate response for resolving train contention by a controller that adjusts the train schedule inside the central control center.

The general train schedule preparation method relies on the experience of the controller to resolve train contention. The risk of human error occurs because the controller directly readjusts train routes and schedules to eliminate train contention. In the process of dissolving the train combination, since train contention is resolved based on a uniform standard without considering the current operating state of the train, there is a problem that the overall train operation efficiency is deteriorated.

The present embodiment is a dynamic train control method based on autonomous collaboration that calculates the severity and ripple effect of the unusual situation so that the train runs autonomously based on the unusual situation determined by communication between trains and ground communication, and selects an optimal alternative. The purpose is to provide the device.

According to an aspect of the present embodiment, there is provided a train situation identification unit for checking whether exceptional circumstances have occurred with respect to a preceding train based on a train operating state and a good running state; A severity determination unit that calculates a severity of the unusual situation; A ripple effect prediction unit for predicting a cascaded delay time for trains sequentially affected by the unusual situation among trains located on the same path as the preceding train based on the severity; An alternative selection unit for selecting an optimal alternative that minimizes the ripple effect from among a plurality of previously stored alternatives; And a train operating unit for controlling a train located after the preceding train to operate in a course according to the optimal alternative.

According to another aspect of the present embodiment, there is provided a process of checking whether an unusual situation occurs with respect to a preceding train based on a train operation state and a good operation state; Calculating the severity of the unusual situation; Predicting a ripple effect on trains sequentially affected by the unusual situation among trains located on the same route as the preceding train based on the severity; Selecting an optimal alternative that minimizes the ripple effect from among a plurality of previously stored alternatives; And controlling a train located after the preceding train to run along a course according to the optimal alternative.

As described above, according to the present embodiment, there is an effect of selecting an optimal alternative by calculating the severity and ripple effect of the unusual situation so that the train can autonomously run based on the unusual situation determined by communication between trains and ground communication. have.

1 is a diagram schematically showing a train autonomous driving system according to the present embodiment.
2A is a block diagram schematically showing a train situation recognition module according to the present embodiment.
2B is a block diagram schematically showing a train dynamic control module according to the present embodiment.
3A, 3B, 3C, and 3D are diagrams for explaining a communication interface between trains according to the present embodiment.
4 is a diagram for explaining an unusual situation classification according to the present embodiment.
5 is a diagram for explaining coefficients required for autonomous train travel according to the present embodiment.
6 is a diagram for explaining a method of classifying an unusual situation into a scenario according to the present embodiment.
7 is a block diagram schematically showing a train control learning server according to the present embodiment.
8 is a flowchart for explaining a method of recognizing a train situation according to the present embodiment.
9 is a flowchart illustrating a method of learning an unusual situation determination threshold in the train control server according to the present embodiment.
10 is a flowchart illustrating a dynamic train control method according to the present embodiment.
11 is a flowchart for explaining an alternative application method according to the present embodiment.
12 is a flowchart illustrating a method for determining a severity according to the present embodiment.
13 is an exemplary view showing the ripple effect according to the present embodiment.
14 is an exemplary diagram showing dynamic path setting according to the present embodiment.

Hereinafter, this embodiment will be described in detail with reference to the accompanying drawings.

In this embodiment, ATO (Automatic Train Operation) refers to an automatic driving module mounted in a train for autonomous train driving, and ATP (Automatic Train Protection) refers to an automatic train stop module mounted in a train for autonomous train driving. it means. RM (Resource Manager) refers to a resource management module installed in a train for autonomous driving of a train, and OC (Operation Control) refers to an operation control module mounted in a train for autonomous driving of a train.

ATS (Automatic Train Supervisor) refers to a ground system that periodically manages train conditions, and TCMS (Train Control Management System) refers to a comprehensive train control system that manages train failure or congestion.

1 is a diagram schematically showing a train autonomous driving system according to the present embodiment.

The autonomous train system according to the present embodiment includes a train control device 110, a relay device 120, a train control learning server 130, and an ATS 140. Components included in the autonomous train system are not necessarily limited thereto.

The train control device 110 performs data communication with neighboring trains and ground infrastructure facilities (eg, ATS 140) via a network.

The train control device 110 is a memory for storing a program or protocol for communicating with neighboring trains and ground infrastructure facilities (eg, ATS 140) via a network, and a microprocessor for calculating and controlling by executing the program. Etc.

The train control device 110 includes (i) a communication device such as a communication modem for performing communication with various devices or wired/wireless networks, (ii) a memory for storing various programs and data, and (iii) an operation by executing a program and It is a variety of devices including a microprocessor for controlling. According to at least one embodiment, the memory is a computer such as a random access memory (RAM), a read only memory (ROM), a flash memory, an optical disk, a magnetic disk, or a solid state disk (SSD). It may be a readable recording/storage medium. According to at least one embodiment, the microprocessor may be programmed to selectively perform one or more operations and functions described in the specification. According to at least one embodiment, the microprocessor may be implemented entirely or partially as hardware such as an Application Specific Integrated Circuit (ASIC) having a specific configuration.

Related data and programs are stored in the memory, and the processor reads and processes the related data from the memory. A processor can be implemented so that one processor can perform each of the above functions, but a plurality of processors can be shared and processed. The processor may be implemented in a general-purpose processor, but may be implemented as a separately manufactured chip to perform its function.

The train control device 110 is a device implemented as a separate device from the ATS 140 and the train control learning server 130, and refers to a device that operates as a stand alone. The train control device 110 may operate by mounting a train autonomous driving program mounted in an embedded or installed form.

The train control device 110 includes ATO, ATS, RM, and OC, and recognizes a train situation based on autonomous cooperation with neighboring trains.

The train control device 110 is mounted on a train for autonomous driving of the train, and is implemented in a compact size for controlling each train. The train control device 110 performs autonomous train driving through collaboration between trains based on communication with neighboring trains. Since the train control device 110 is mounted on each train and operates individually, ground infrastructure facilities required for direct communication between the train and the ATS 140 can be minimized.

The train control device 110 receives information on neighboring trains through inter-train communication. The train control device 110 compares the operation information of the target train with the neighboring train information to determine the operation state. The train control device 110 distinguishes whether or not there are exceptional circumstances based on the operating state. The train control device 110 classifies what event the unusual situation is.

The train control apparatus 110 according to the present embodiment checks whether an unusual situation occurs with respect to the preceding train based on the train operation state and the good operation state. Train control device 110 calculates the severity (Severity) for the unusual situation. The train control device 110 predicts a cascaded delay time for trains sequentially affected by an unusual situation among trains located on the same path (course) as the preceding train based on the severity. The train control device 110 selects an optimal alternative that minimizes the ripple effect from among a plurality of previously stored alternatives. The train control device 110 controls the train located after the preceding train to run along a course (path) according to an optimal alternative.

The relay device 120 transmits data between the train control device 110 and the train control learning server 130 and ATS 140 using various wired and wireless communication technologies such as a mobile communication network, a local area network, an Internet network, an intranet network, and a satellite communication network. Send and receive.

The train control learning server 130 includes a hardware module identical to a typical web server or network server in terms of hardware. The train control learning server 130 generally communicates with a plurality of train control devices 110 via an open computer network such as the Internet.

The train control learning server 130 refers to a computer system and a computer software (web server program) that derives and provides a work result corresponding to a request for performing work from the train control device 110. In addition to the above-described web server program, the train control learning server 130 includes a series of application programs running on the web server or various databases built in the device.

When the train enters the garage, the train control learning server 130 transmits the collected information to the ATS 140, and is updated based on the information collected from the ATS 140, and the unusual situation classification information and the unusual situation determination criterion threshold. Receive.

The train control learning server 130 updates the unusual situation occurrence threshold and the unusual situation classification threshold based on the information collected from the train control device 110 and transmits it to the train control device 110.

The train control learning server 130 receives target train information and neighbor train information from a train control device mounted in a plurality of trains when entering a garage. The train control learning server 130 compares the target train information and the neighbor train information with the scheduled operation information, and updates a threshold of occurrence of an unusual situation. The train control learning server 130 transmits an unusual situation occurrence threshold to the target train. The train control learning server 130 learns an unusual situation classification threshold based on the target train information and the operation status information about the neighboring train information, and updates the unusual situation classification threshold. The train control learning server 130 transmits an unusual situation classification threshold to the target train.

The ATS 140 is implemented in the form of a separate server regardless of the train, and manages information related to the train by processing Big Data received by communicating with each train.

2A is a block diagram schematically showing a train situation recognition module according to the present embodiment.

The train control apparatus 110 according to the present embodiment includes a train situation recognition module 210 and a train dynamic control module 220. The train situation recognition module 210 includes a communication unit 212, an unusual situation determination unit 214, and a train situation recognition unit 216. The components included in the train situation recognition module 210 are not necessarily limited thereto.

Each component included in the train situation recognition module 210 is connected to a communication path connecting a software module or a hardware module inside the device so that they can operate organically with each other. These components communicate using one or more communication buses or signal lines.

Each component of the train situation recognition module 210 shown in FIG. 2A refers to a unit that processes at least one function or operation, and may be implemented as a software module, a hardware module, or a combination of software and hardware.

The communication unit 212 communicates between trains (T2T: Train to Train) from neighboring trains (e.g., preceding trains) to neighbor train information (real-time location, speed, course, event, train failure information, cabin congestion level (car congestion)) And receive line status information (line failure occurrence information, platform congestion degree (ground congestion degree)) from the ATS 140.

), 대상열차의 위치값(x_i(t)), 열차 지연 허용 한도 설정값(X₁), 선행열차의 위치값(x_i-1(t)), 대상열차의 위치값(x_i(t)), 열차간 간격 허용 한도 설정값(X₂)을 추출한다.The communication unit 212 is the position value of the target train scheduled from the neighbor train information (

), the location value of the target train (x _i (t)), the train delay allowable limit setting value (X ₁ ), the location value of the preceding train (x _i-1 (t)), the location value of the target train (x _i ( t)), extract the set value (X ₂ ) of the allowable interval between trains.

The unusual situation determination unit 214 checks whether or not an unusual situation has occurred based on at least one of information on neighboring trains and track status.

), 대상열차의 위치값(x_i(t)), 열차 지연 허용 한도 설정값(X₁), 선행열차의 위치값(x_i-1(t)), 대상열차의 위치값(x_i(t)), 열차간 간격 허용 한도 설정값(X₂)을 추출한다. 이례상황 판단부(214)는 스케쥴된 대상열차의 위치값(

)에서 대상열차의 위치값(x_i(t))을 차감한 값이 열차 지연 허용 한도 설정값(X₁) 이상(

)이면, 이례상황이 발생한 것으로 확인한다. 또는 이례상황 판단부(214)는 선행열차의 위치값(x_i-1(t))에서 대상열차의 위치값(x_i(t))을 차감한 값이 열차간 간격 허용 한도 설정값(X₂) 이하(x_i-1(t)-x_i(t)≤X₂)인 경우 이례상황이 발생한 것으로 확인한다.The unusual situation determination unit 214 is the position value of the target train scheduled from the neighbor train information (

), the location value of the target train (x _i (t)), the train delay allowable limit setting value (X ₁ ), the location value of the preceding train (x _i-1 (t)), the location value of the target train (x _i ( t)), extract the set value (X ₂ ) of the allowable interval between trains. The unusual situation determination unit 214 is the position value of the scheduled target train (

) Minus the target train's position value (x _i (t)) is equal to or greater than the train delay tolerance set value (X ₁ ) (

), confirm that an unusual situation has occurred. Alternatively, the unusual situation determination unit 214 subtracts the position value of the target train (x _i (t)) from the position value of the preceding train (x _i-1 (t)) is the set value (X ₂ ) If below (x _i-1 (t)-x _i (t) ≤ X ₂ ), it is confirmed that an unusual situation has occurred.

The unusual situation determination unit 214 extracts train failure occurrence information included in the neighbor train information. The unusual situation determination unit 214 determines that the unusual situation has occurred based on at least one of a faulty train location, a fault time, and a fault type included in the train fault occurrence information.

The unusual situation determination unit 214 extracts passenger information in the train cabin detected when the target train included in the neighbor train information departs from the stop station. The unusual situation determination unit 214 calculates a congestion level (congestion on a vehicle) in a train based on passenger information in the train cabin. The unusual situation determination unit 214 checks that the unusual situation has occurred when the congestion level of the cabin (congestion on the vehicle) in the train exceeds a preset threshold.

The unusual situation determination unit 214 extracts line failure occurrence information included in the line status information. The unusual situation determination unit 214 confirms that the unusual situation has occurred based on the location of the failure on the line, the time of the failure, and the type of failure included in the line failure occurrence information.

The unusual situation determination unit 214 extracts information on waiting passengers of the platform on which the target train will stop next, included in the track status information. The unusual situation determination unit 214 calculates the platform congestion level (ground congestion level) based on the waiting passenger information of the platform to be stopped next. When the platform congestion level (ground congestion level) exceeds a preset threshold, the unusual situation determination unit 214 confirms that an unusual situation has occurred.

The train situation recognition unit 216 classifies the unusual situation into any one of the preset unusual situations when it is confirmed that the unusual situation has occurred as a result of the confirmation of the unusual situation determination unit 214, and based on the specific unusual situation To recognize the train situation.

), 스케쥴된 대상열차의 위치값(

), 선행열차의 위치값(x_i-1(t)), 대상열차의 위치값(x_i(t)), 종착역 종착 이벤트(End_s)를 추출한다.The train situation recognition unit 216 is a preceding train event (e _{i -1} (t)), a target train event (e _i (t)), arrival at a station (s) (Dpt _s ), arrival event ( Arr _s ), course of the preceding train (p _i-1 (t)), course of the target train (p _i (t)), speed of the preceding train (v _i-1 (t)), speed of the target train (v _i (t)) ), the position value of the scheduled preceding train (

), the position value of the scheduled target train (

), the location value of the preceding train (x _i-1 (t)), the location value of the target train (x _i (t)), and the destination station end event (End _s ) are extracted.

The train situation recognition unit 216 adjusts the order of passage according to the contention for simultaneous use of track resources and the scenario for determining the bypass route of the subsequent train when the preceding train's forward path is impeded based on the information extracted from the neighboring train information. Scenario (Scenario 2), or the round-line adjustment scenario (Scenario 3) for delayed rounds.

)에서 선행열차의 위치값(x_i-1(t))을 차감한 값이 열차 지연 허용 한도 설정값(X₁) 이상인지의 여부(

)를 확인한다. 열차상황 인지부(216)는 선행열차의 위치값(x_i-1(t))에서 대상열차의 위치값(x_i(t))을 차감한 값이 열차간 간격 허용 한도 설정값(X₂) 이하인지의 여부(x_i-1(t)-x_i(t)≤X₂)를 확인한다. 열차상황 인지부(216)는 전술한 조건이 만족하는 경우, 선행 열차의 전방 진로 지장시 후속 열차의 우회 경로 판단 시나리오(시나리오1)로 판단한다.The train situation recognition unit 216 checks whether the preceding train event (e _{i - 1} (t)) has arrived at the station (s) (Dpt _s ) (e _{i -1} (t) = Dpt _s ). The train situation recognition unit 216 checks whether the target train event e _i (t) is an arrival event Arr _s (e _i (t) = Arr _s ). The train situation recognition unit 216 determines that the target train route (p _i (t)) and the preceding train route (p _i-1 (t)) are the same (p _i (t) = p _i-1 (t)). Check whether or not. The train situation recognition unit 216 checks whether the preceding train speed (v _i-1 (t)) is 0 (v _i-1 (t) = 0). The train situation recognition unit 216 is the position value of the scheduled preceding train (

Whether or not the value obtained by subtracting the position value (x _i-1 (t)) of the preceding train from) is equal to or greater than the train delay allowable limit (X ₁ ) (

). That the train status unit 216 between the minus position of the target train to the nearest value (x _i-1 (t)) of the preceding train (x _i (t)) value train interval allowance set value (X ₂ ) It is checked whether or not (x _i-1 (t)-x _i (t) _≤ X ₂ ). When the above-described condition is satisfied, the train situation recognition unit 216 determines a detour path determination scenario (scenario 1) of a subsequent train when the preceding train is interrupted.

)에서 선행열차의 위치값(x_i-1(t))을 차감한 값이 열차 지연 허용 한도 설정값(X₁) 이상인지의 여부(

)를 확인한다. 열차상황 인지부(216)는 전술한 조건이 모두 만족하는 경우, 대상 열차의 운행 상태를 선로 자원 동시 사용 경합에 따른 통과 순서 조정 시나리오(시나리오2)로 판단한다.The train situation recognition unit 216 checks whether the preceding train event (e _{i - 1} (t)) has arrived at the station (s) (Dpt _s ) (e _{i -1} (t) = Dpt _s ). The train situation recognition unit 216 checks whether the target train event (e _i (t)) has arrived at the station (s) (Dpt _s ) (e _i (t) = Dpt _s ). The train situation recognition unit 216 determines whether the target train path (p _i (t)) and the preceding train path (p _i-1 (t)) are not the same (p _i (t)≠p _i-1 (t)). ). The train situation recognition unit 216 determines whether the preceding train speed (v _i-1 (t)) and the target train speed (v _i (t)) are 0 (v _i-1 (t) = v _i (t)). = 0). The train situation recognition unit 216 is the position value of the scheduled preceding train (

Whether or not the value obtained by subtracting the position value (x _i-1 (t)) of the preceding train from) is equal to or greater than the train delay allowable limit (X ₁ ) (

). When all of the above-described conditions are satisfied, the train situation recognition unit 216 determines the operating state of the target train as a passage order adjustment scenario (scenario 2) according to contention for simultaneous use of track resources.

)에서 대상열차의 위치값(x_i(t))을 차감한 값이 열차 지연 허용 한도 설정값(X₁) 이상인지의 여부(

₎를 확인한다. 열차상황 인지부(216)는 전술한 조건이 모두 만족하는 경우, 회차 지연 대비 회차선 조정 시나리오(시나리오3)로 판단한다.The train situation recognition unit 216 checks whether the target train event e _i (t) is the destination station end event End _s (e _i (t) = End _s ). Train situation recognition unit 216 is the scheduled position value of the target train (

) Whether the target train's position value (x _i (t)) is equal to or greater than the train delay allowable limit (X ₁ ) (

₎ . When all of the above-described conditions are satisfied, the train situation recognition unit 216 determines the turn delay versus the turn line adjustment scenario (scenario 3).

The train dynamic control module 220 calculates the severity of the unusual situation, and predicts the ripple effect on the trains sequentially affected by the unusual situation among trains located on the same path (course) as the preceding train based on the severity. do. The train dynamic control module 220 selects an optimal alternative that minimizes the ripple effect from among a plurality of previously stored alternatives. The train dynamic control module 220 controls the train located after the preceding train to run in a course (path) according to an optimal alternative.

2B is a block diagram schematically showing a train dynamic control module according to the present embodiment.

The train dynamic control module 220 according to the present embodiment includes a severity determination unit 222, a ripple effect prediction unit 224, an alternative selection unit 226, and a train operation unit 228. Components included in the train dynamic control module 220 are not necessarily limited thereto.

Each component included in the train dynamic control module 220 is connected to a communication path connecting a software module or a hardware module inside the device, so that they can operate organically with each other. These components communicate using one or more communication buses or signal lines.

Each component of the train dynamic control module 220 shown in FIG. 2B refers to a unit that processes at least one function or operation, and may be implemented as a software module, a hardware module, or a combination of software and hardware.

The severity determination unit 222 calculates the severity of the unusual situation received from the train situation recognition module 210.

The severity determination unit 222 receives an unusual situation duration (t _{i -1} ) for the preceding train, an external notification (input) end time (τ _{i -1} ), and a previously learned threshold (T _{i - 1} ). The severity determination unit 222 is a threshold value in which the unusual situation duration (t _{i -1} ) and the external notification (input) end time (τ _{i -1} ) (max (t _i-1 ,τ _i-1 )) are previously learned If it is (T _i-1 ) or higher, the severity is judged to be serious. The severity determination unit 222 is a threshold value in which the unusual situation duration (t _{i -1} ) and the external notification (input) end time (τ _{i -1} ) (max (t _i-1 ,τ _i-1 )) are previously learned If it is less than (T _{i -1} ), the severity is judged to be insignificant.

If the external notification (input) end time (τ _{i -1} ) is not received, the severity determination unit 222 sets an integer multiple of the unusual situation duration (t _{i - 1} ) to the present as an expected notification end time.

The severity determination unit 222 determines that the severity is serious if the predicted notification end time is greater than or equal to the previously learned threshold T _{i -1} . The severity determination unit 222 determines that the severity is insignificant if the predicted notification end time is less than the previously learned threshold T _i-1 .

Severity determination unit 222 is a detour path determination scenario (scenario 1), the passing sequence adjustment scenario (scenario 2), using the previously learned threshold (T _i ) learned differently for each round line adjustment scenario (scenario 3) Yields

The ripple effect prediction unit 224 predicts the ripple effect on trains that are sequentially affected by an unusual situation among trains located on the same path (course) as the preceding train based on the severity.

The ripple effect prediction unit 224 predicts the ripple effect when it is determined that the severity of the unusual situation is serious based on the current point in time. When it is determined that the severity of the unusual situation is minor based on the current point in time, the ripple effect prediction unit 224 does not perform a separate task of predicting the ripple effect.

The ripple effect prediction unit 224 calculates a delayed arrival time for all trains that are sequentially affected by an unusual situation among trains located on the same path (course) as the preceding train, and calculates a congestion level corresponding to the delayed arrival time. . The ripple effect prediction unit 224 is the arrival time of the bypass route when the train located after the preceding train among all trains sequentially affected by the unusual situation is sent to the secondary main line (bypass route) first (dynamically readjusted route). (Ripple effect) is calculated, and the degree of congestion corresponding to the arrival time of the detour route is calculated.

The alternative selection unit 226 selects an optimal alternative that minimizes the ripple effect from among a plurality of previously stored alternatives. The alternative selection unit 226 selects one of a course maintenance plan (alternative 1) and a course change plan (alternative 2) for the train located after the preceding train based on the ripple effect.

The alternative selection unit 226 calculates the number of sequentially affected trains (the number of delayed trains) that occur when an unusual situation is maintained according to the course maintenance plan (alternative 1). The alternative selection unit 226 is the number of sequentially affected trains that occur when the train located after the preceding train is first sent to the sub-main line (bypass route) according to the course change plan (alternative 2) (dynamically readjusted route). The number of delayed trains) is calculated. The alternative selection unit 226 is a train located after the preceding train according to the number of sequentially affected trains (the number of delayed trains) and the course change plan (alternative 2) that occur when an unusual situation is maintained according to the course maintenance plan (alternative 1). Compare the number of sequentially affected trains (the number of delayed trains) that occur when (dynamically re-route) sent to the secondary line (bypass route) first. The alternative selection unit 226 selects one of a career maintenance plan (alternative 1) and a career change plan (alternative 2) according to the comparison result.

If the alternative selection unit 226 sends the train located after the preceding train to the sub-main line (bypass route) earlier than the number of trains that are sequentially affected (the number of delayed trains) if the unusual situation is maintained (dynamically re-route) If the number of trains that are sequentially affected (the number of delayed trains) decreases beyond the threshold, choose a course change plan (alternative 2).

If the alternative selection unit 226 maintains an unusual situation, the number of trains that are sequentially affected and the trains located after the preceding train are first sent to the secondary line (bypass route) (dynamically re-adjusted), which are sequentially affected. If the number of trains is the same or decreases below the threshold, choose the course maintenance option (alternative 1).

The alternative selection unit 226 is the delayed arrival time that occurs when the unusual situation is maintained and the delayed arrival time that occurs when the train located after the preceding train is sent to the secondary line (bypass) earlier than the congestion level (dynamically rerouted). If the degree of congestion and congestion decreases beyond the threshold, choose a course change plan (alternative 2).

The alternative selection unit 226 reduces the delayed arrival time and congestion when maintaining an unusual situation, and the detour route arrival time and congestion that occur when the train located after the preceding train is first sent to the sub-main line (bypass route) or less than the threshold. If so, choose a career maintenance plan (alternative 1).

The alternative selection unit 226 uses different thresholds applied for each detour path determination scenario (scenario 1), passage order adjustment scenario (scenario 2), and round line adjustment scenario (scenario 3) to maintain the course (alternative 1) and the course. Choose one of the amendments (alternative 2).

The train operation unit 228 controls the train located after the preceding train to run in a course (path) according to an optimal alternative.

3A, 3B, 3C, and 3D are diagrams for explaining a communication interface between trains according to the present embodiment.

ATO, ATP, RM, and RM refer to modules mounted in trains for autonomous train driving. ATS and TCMS refer to infrastructure installed on the ground to periodically manage train conditions.

The train control device 110 according to the present embodiment communicates with the train directly or via the ground control system as shown in (a) and (b) of FIG. 3A.

The train control device 110 communicates by forming a direct communication path with another train, as shown in (a) of FIG. 3a, or via a ground control system with another train, as shown in (b) of FIG. 3a. It communicates by forming an indirect communication path.

The train control device 110 performs inter-train communication using a dynamic route interface between trains.

The ATS transmits schedule information including neighbor train information to the ATO. The schedule information includes schedule information of each train centered on a repetition unit of a vehicle (organization). The tag ID set (k) included in the schedule information means a conversion relationship of career information. The ATS additionally transmits information on the other train event and management of its own event information to the ATO. In case of train delay between ATS and ATO, it communicates by designating it in the form of a fault code. Congestion-related communication methods are defined and communicated between ATS and ATO. To communicate between neighboring trains, multiple ATOs (ATO _(i) ~ ATO _(i+1) ) and multiple ATPs (ATP _(i) ~ ATP _(i+1) ) communicate with each other. The resource can be assigned an ID, or can be defined as a range according to the relationship between a fixed time and a relative time.

In FIG. 3B, a general train communication interface will be described. The ATS transmits a resource control request, a resource cancellation request, and a line switcher permission request to the RM. The RM transmits a resource control response, a resource cancellation response, and a line switcher authority response to the ATS. RM transmits version information request, DB request, resource status request, and resource request/release request to ATP. ATP transmits version information response, DB request response, resource status response, and resource request/release response to RM. ATP transmits a line switcher direction control request and a lock of switch request to the OC. OC transmits the line switcher direction control response and lockout response to ATP.

ATS sends train status information (regular) report (including fault code) and train control request response to ATP. ATP sends a train (emergency) control request to the ATS. ATP transmits a route acquisition request to the ATO. ATO transmits ATP status information and route acquisition response to ATP. The ATS is the ATO and transmits schedule information, schedule information modification response, and train (emergency) control request. The ATO transmits a schedule information request, schedule information modification request, and train (emergency) control request response to the ATS.

In Fig. 3C, a train communication interface according to the present embodiment will be described. The ATS transmits a resource control request, a resource cancellation request, and a line switcher permission request to the RM. The RM transmits a resource control response, a resource cancellation response, and a line switcher authority response to the ATS. RM transmits version information request, DB request, resource status request, and resource request/release request to ATP. ATP transmits version information response, DB request response, resource status response, and resource request/release response to RM. ATP transmits a request for direction control of the line switcher and a request for connection to the OC. OC transmits the line switcher direction control response and lockout response to ATP.

ATS sends train status information (regular) report (including fault code) and train control request response to ATP _(i-1) . ATP _(i-1) sends a train (emergency) control request to the ATS. ATS transmits schedule information, schedule information modification response, and train (emergency) control request to ATO _(i-1) . The schedule information modification response information includes a neighboring schedule and a front-end center schedule. The train (emergency) control request includes information on congestion of the platform (front station), and instructions for leaving the train. The ATO _(i-1) transmits a request for schedule information for a neighboring train, a request for modification of schedule information for a neighboring train, a response to a train (emergency) control request, and a train termination and turn report to the ATS.

ATO _(i) transmits a route acquisition request to ATP _(i) . ATP _(i) transmits ATP status information including current speed information, route acquisition response, fault code, and in-vehicle congestion information to ATO _(i) . ATO _(i-1) and ATI _(i+1) transmit the next event, real-time delay time to ATO _(i) . ATP _(i+1) transmits ATP status information including current speed information, route acquisition response, and fault code to ATO _(i) .

The interface between the ATS and the ATO is as shown in FIG. 3D. Protocols and messages communicated between ATS and ATO use the same format. ATS and ATO store and analyze multiple messages using a predefined protocol. ATS and ATO receive and store a message through multiple paths and analyze it.

4 is a diagram for explaining an unusual situation classification according to the present embodiment.

The communication unit 212 receives neighboring train information (real-time location, speed, course, event, train breakdown information, cabin congestion degree (car congestion degree)) from neighboring trains through inter-train communication, and track status information from the ATS 140 ( Receives line failure occurrence information and platform congestion (ground congestion).

The unusual situation determination unit 214 observes the target train information (real-time location, speed, course, event, train breakdown information, cabin congestion level (car congestion level)) of the target train in real time and transmits it to a neighboring train. The unusual situation determination unit 214 determines the train operation status of the neighboring train based on the neighboring train information. The unusual situation determination unit 214 determines the good running status based on the track status information.

The unusual situation determination unit 214 extracts train failure occurrence information included in the neighbor train information. The unusual situation determination unit 214 determines the train operation status of the neighboring train as a train failure based on the location of the failed train, the time of the failure, and the type of the failure included in the train failure occurrence information.

The unusual situation determination unit 214 extracts passenger information in the train cabin detected when the target train included in the neighbor train information departs from the stop station. The unusual situation determination unit 214 calculates a congestion level (congestion on a vehicle) in a train based on passenger information in the train cabin. The unusual situation determination unit 214 determines the train operation status as train congestion when the congestion level of the cabin (congestion on the vehicle) in the train exceeds a preset threshold.

The unusual situation determination unit 214 extracts line failure occurrence information included in the line status information. The unusual situation determination unit 214 determines the line running status as a line failure based on the location of the failure on the line, the time at which the failure occurred, and the type of failure included in the line failure occurrence information. The unusual situation determination unit 214 extracts information on waiting passengers of the platform on which the target train will stop next, included in the track status information. The unusual situation determination unit 214 calculates the platform congestion level (ground congestion level) based on the waiting passenger information of the platform to be stopped next. When the platform congestion level (ground congestion level) exceeds a preset threshold, the unusual situation determination unit 214 determines a good driving state as platform congestion.

A process in which the unusual situation determination unit 214 determines the neighboring train operation status based on the neighboring train information is as follows.

① A separate failure detection unit other than TCMS (Train Control Management System) and ATCS (Automatic Train Control System)) installed on each train detects whether there is a failure in the train.

② When a fault is detected, the fault detection unit generates fault occurrence information and transmits it to the ATO in charge of setting the dynamic path of ATCS in real time.

③ After the ATO receives the breakdown information (a situation in which the breakdown is recognized in real time), it propagates the breakdown information including the breakdown status of the target train to neighboring trains in real time through inter-train communication (T2T).

Since the faulty train transmits its fault occurrence information directly to neighboring trains through inter-train communication (T2T), neighboring trains around the faulty train receive fault occurrence information via the ground control system. You can respond by receiving fault occurrence information faster than when you do.

④ When the target train receives real-time failure information from the broken train, it judges the operation status due to the failure of the neighboring train.

The unusual situation determination unit 214 in the target train determines the operating status of the broken train based on the location of the broken train, the time of occurrence of the breakdown, and the type of the breakdown.

⑤ As a result, the train control device 110 can quickly recognize whether a failure occurred in a neighboring train and use it for dynamic route setting. When the route is dynamically reset, the train control device 110 immediately evokes a subsequent process.

A process in which the unusual situation determination unit 214 determines the good conduct status based on the track status information is as follows.

① ATS always detects the occurrence of a breakdown in the ground line equipment (including obstructions in the middle of the line).

② When ATS detects that a fault has occurred in the ground track device, it selects the subsequent trains that will be affected by the faulty ground track device.

The ATS transmits the track status information including the failure occurrence information to the ATO in the subsequent train, which is nourished by the occurrence of the failure.

③ The train control device 110 in the train determines the line running status after receiving line status information including failure occurrence information from the ATS in real time.

The train control apparatus 110 determines the line running status based on the location of the failure on the track, the time of the failure, and the type of failure based on the failure occurrence information included in the track status information.

④ As a result, the train control device 110 can quickly recognize whether a failure occurred on the track and use it for dynamic route setting. When the route is dynamically reset, the train control device 110 immediately evokes a subsequent process.

A process by which the unusual situation determination unit 214 determines the flow of congestion information in the vehicle is as follows.

① TCMS detects passenger information in the train cabin when the train departs from the station where it stops.

② ATO requests passenger information (re-enter information) in the train cabin through TCMS in order to check the operation status.

③ The ATO calculates the congestion in the train cabin (car congestion) using the passenger information in the train cabin received from TCMS.

The unusual situation determination unit 214 determines the train operation status based on the congestion level of the cabin in the train (congestion on the vehicle).

The process by which the unusual situation determination unit 214 determines the platform congestion information flow is as follows.

① ATS always keeps track of passenger information waiting on the platform.

② The ATO requests information on waiting passengers of the platform to stop next to the ATS in order to check the operation status.

③ The ATO calculates the platform congestion level (ground congestion level) using the platform waiting passenger information received from the ATS. ATO predicts the stop time at the next station using the platform congestion level (ground congestion level).

④ The ATO predicts the stop time by using the congestion of the cabin (congestion on the vehicle) and the congestion of the platform (ground congestion) in the train.

The unusual situation determination unit 214 determines a good running state based on the platform congestion level (ground congestion level).

The unusual situation determination unit 214 checks whether or not an unusual situation has occurred based on the train operating state and the good running state. The unusual situation determination unit 214 determines whether it is normal or abnormal based on the train operation state and the good operation state.

The unusual situation determination unit 214 checks whether or not an unusual situation has occurred based on the train operating state and the good running state. The unusual situation determination unit 214 determines that an unusual situation has occurred when it is determined that the train operation status is a train failure or congestion. The unusual situation determination unit 214 determines that an unusual situation has occurred when it is determined that the good conduct status is a line breakdown or platform congestion.

The unusual situation determination unit 214 checks whether or not the unusual situation has occurred as follows.

① The unusual situation determination unit 214 checks that the unusual situation has occurred when the failure occurrence information is received from another train in operation.

② When the abnormal situation determination unit 214 receives the information on the occurrence of a breakdown of the ground facility from the ATS 140, it checks that the abnormal situation has occurred.

The train situation recognition unit 216 classifies the unusual situation into any one of the preset unusual situations when it is confirmed that the unusual situation has occurred as a result of the confirmation of the unusual situation determination unit 214, and based on the specific unusual situation To recognize the train situation.

The train situation recognition unit 216 adjusts the order of passage according to the contention for simultaneous use of track resources and the scenario for determining the bypass route of the subsequent train when the preceding train's forward path is impeded based on the information extracted from the neighboring train information. Scenario (Scenario 2), or the round-line adjustment scenario (Scenario 3) for delayed rounds.

In other words, the train situation recognition unit 216 classifies the unusual situation as follows.

)지의 여부를 확인한다.① The train situation recognition unit 216 is larger than a preset threshold (difference in real-time position compared to the schedule) of the current train operation delay.

) Or not.

② The train situation recognition unit 216 checks whether the in-vehicle congestion detected in real time is greater than a preset threshold (C _i (t)≥C ₁ ).

③ The train situation recognition unit 216 checks whether the platform congestion degree detected in real time is greater than a preset threshold (C _si (t) ≥ C ₂ ).

When the unusual situation is confirmed, the train control device 110 performs an action (Evoke) to operate the classification function of the unusual situation response scenario to define the unusual situation.

5 is a diagram for explaining coefficients required for autonomous train travel according to the present embodiment.

Shown in the representation of Fig. 5, i means train sequence. The location (x _i (t)) is installed at predetermined intervals (eg, about 60m intervals). The train is equipped with a tachometer, and the tachometer is used to measure the number of revolutions of the wheel and measure the distance from the sensor to the front of the train.

i ∈ I = {1,2,...,N} assumes a situation in which train numbers are operated in the order of a set (integer), (i-1) → (i) → (i+1). N means the maximum number of trains to be considered.

t means a sign representing the (current) time.

g ∈ G means a set of tag numbers (integer) installed on the ground and detecting the location of the train.

s ∈ S={1,2,...,M} means a set of station numbers (integer).

M means the maximum number of stations to be considered. For convenience of explanation, it is assumed that the first stop of the train in any one direction is the first stop (start), and the M-th stop is the stop where the train finally arrives (final), and unless otherwise specified, the start and end stations In the train, it means that it runs repeatedly.

ξ∈π _s means a set of paths that can be set according to the rules of interlocking within the station [s].

은 열차 [i]가 시발역에서 출발하여 종착역에 도착할 때까지 스케쥴링 단계에서 사전에 설정된 전체 진로를 의미한다.

Denotes the entire course set in advance in the scheduling stage until the train [i] departs from the start station and arrives at the final station.

p _i (t) means the course dynamically selected by the train [i] at the (current) time [t].

e _i (t) means (Srt _s , Arr _s , Dpt _s , End _s ) the event that the train [i] will execute first after the (current) time [t].

Srt _s means start at station [s] (usually s = 1).

Arr _s , Dpt _s means arrival at station [s].

End _s means the end of the station [s] (usually s = M).

x _i (t) means the (real time) position of the train [i] at the (current) time [t].

v _i (t) means the (real time) speed of the train [i] at the (current) time [t].

c _i (t) refers to the degree of congestion of the cabin with the highest congestion level among the plurality of cabins constituting the train [i] at the (current) time [t].

c _si (t) refers to the congestion level of the platform [s] of the station where the train [i] will stop at the (current) time [t].

Position (xi(t)), velocity (vi(t)), course (pi(t)), and event (ei(t)) shown in FIG. 5 are essential elements. Congestion, delay, and interval between precedence and after are factors that can be calculated based on essential factors. The next activity is the same element as the event.

6 is a diagram for explaining a method of classifying an unusual situation into a scenario according to the present embodiment.

An unusual situation response scenario classified by the train control device 110 is as shown in FIG. 6.

① Scenario 1 refers to the scenario of determining the bypass route of the subsequent train when the preceding train's forward path is disturbed. For example, scenario 1 means a decision-making situation as to whether or not to change the arrival course of a subsequent train in which a departure delay occurs while a preceding train is stopped on the main line.

② Scenario 2 refers to a scenario for adjusting the order of passage according to contention for simultaneous use of track resources. For example, scenario 2 is the schedule of departure of the main train first, the main train first, and the later departure of the main train after waiting for each train on the main line and the sub-main line. When the main train is delayed, it means that it is in a decision-making situation whether or not to start the submain train in advance.

③ Scenario 3 refers to a scenario for adjusting lane lines versus delays. For example, scenario 3 refers to a decision-making situation as to whether to shorten the rounding time by changing the rounding path so that the rounding path of the arriving train arrives directly to the opposite starting line when the rounding is delayed.

)에서 선행열차의 위치값(x_i-1(t))을 차감한 값이 열차 지연 허용 한도 설정값(X₁) 이상인지의 여부(

)를 확인한다. 열차 제어 장치(110)는 선행열차의 위치값(x_i-1(t))에서 대상열차의 위치값(x_i(t))을 차감한 값이 열차간 간격 허용 한도 설정값(X₂) 이하인지의 여부(x_i-1(t)-x_i(t)≤X₂)를 확인한다. 열차 제어 장치(110)는 전술한 시나리오1 조건(e_{i -1}(t) = Dpt_{s ,} e_i(t) = Arr_{s ,} p_i(t) = p_i-1(t), v_i-1(t) = 0,

, x_i-1(t)-x_i(t)≤X₂)을 모두 만족하는 경우, 선행 열차의 전방 진로 지장시 후속 열차의 우회 경로 판단 시나리오(시나리오1)로 판단한다. The train control device 110 checks whether the preceding train event (e _{i - 1} (t)) has arrived at the station (s) (Dpt _s ) (e _{i -1} (t) = Dpt _s ). The train control device 110 checks whether the target train event (e _i (t)) is an arrival event (Arr _s ) (e _i (t) = Arr _s ). The train control device 110 determines whether the target train path (p _i (t)) and the preceding train path (p _i-1 (t)) are the same (p _i (t) = p _i-1 (t)). Check. The train control device 110 checks whether the preceding train speed (v _i-1 (t)) is 0 (v _i-1 (t) = 0). The train control device 110 is the position value of the scheduled preceding train (

Whether or not the value obtained by subtracting the position value (x _i-1 (t)) of the preceding train from) is equal to or greater than the train delay allowable limit (X ₁ ) (

). The train control device 110 subtracts the position value of the target train (x _i (t)) from the position value of the preceding train (x _i-1 (t)) is the set value of the allowable interval between trains (X ₂ ) It is checked whether or not (x _i-1 (t)-x _i (t) _≤ X ₂ ). Train control device 110 is the above scenario 1 condition (e _{i -1} (t) = Dpt _{s ,} e _i (t) = Arr _{s ,} p _i (t) = p _i-1 (t), v _{i- 1} (t) = 0,

, x _i-1 (t)-x _i (t) _≤ X ₂ ), when all of them are satisfied, it is determined as a detour path determination scenario (scenario 1) of the subsequent train when the forward path of the preceding train is disturbed.

)에서 선행열차의 위치값(x_i-1(t))을 차감한 값이 열차 지연 허용 한도 설정값(X₁) 이상인지의 여부(

)를 확인한다. 열차 제어 장치(110)는 전술한 시나리오2 조건(e_{i -1}(t) = Dpt_{s ,} e_i(t) = Dpt_{s ,} p_i(t)≠p_i-1(t), v_i-1(t) = v_i(t) = 0,

) 조건이 모두 만족하는 경우, 대상 열차의 운행 상태를 선로 자원 동시 사용 경합에 따른 통과 순서 조정 시나리오(시나리오2)로 판단한다.The train control device 110 checks whether the preceding train event (e _{i - 1} (t)) has arrived at the station (s) (Dpt _s ) (e _{i -1} (t) = Dpt _s ). The train control device 110 checks whether the target train event (e _i (t)) arrives at the station (s) (Dpt _s ) (e _i (t) = Dpt _s ). The train control device 110 determines whether the target train route (p _i (t)) and the preceding train route (p _i-1 (t)) are not the same (p _i (t)≠p _i-1 (t)). Check. The train control device 110 determines whether the preceding train speed (v _i-1 (t)) and the target train speed (v _i (t)) are 0 (v _i-1 (t) = v _i (t) = Check 0). The train control device 110 is the position value of the scheduled preceding train (

Whether or not the value obtained by subtracting the position value (x _i-1 (t)) of the preceding train from) is equal to or greater than the train delay allowable limit (X ₁ ) (

). Train control device 110 is the above scenario 2 condition (e _{i -1} (t) = Dpt _{s ,} e _i (t) = Dpt _{s ,} p _i (t)≠p _i-1 (t), v _{i- 1} (t) = v _i (t) = 0,

) If all the conditions are satisfied, the operation status of the target train is determined as a scenario for adjusting the passage order according to contention for simultaneous use of track resources (Scenario 2).

)에서 대상열차의 위치값(x_i(t))을 차감한 값이 열차 지연 허용 한도 설정값(X₁) 이상인지의 여부(

₎를 확인한다. 열차 제어 장치(110)는 전술한 시나리오3 조건(e_i(t)=End_s,

)을 모두 만족하는 경우, 회차 지연 대비 회차선 조정 시나리오(시나리오3)로 판단한다.The train control device 110 checks whether the target train event e _i (t) is the destination station end event End _s (e _i (t) = End _s ). The train control device 110 is the position value of the scheduled target train (

) Whether the target train's position value (x _i (t)) is equal to or greater than the train delay allowable limit (X ₁ ) (

₎ . Train control device 110 is the above-described scenario 3 condition (e _i (t) = End _s ,

)of If all are satisfied, it is judged as a scenario for adjusting lanes versus delays (scenario 3).

7 is a block diagram schematically showing a train control learning server according to the present embodiment.

The train control learning server 130 according to the present embodiment includes an information transmission/reception unit 710, an unusual situation occurrence reference unit 720, and an unusual situation threshold determination unit 730. Elements included in the train control learning server 130 are not necessarily limited thereto.

Each component included in the train control learning server 130 is connected to a communication path connecting software modules or hardware modules inside the device, so that they can operate organically with each other. These components communicate using one or more communication buses or signal lines.

Each component of the train control learning server 130 shown in FIG. 7 refers to a unit that processes at least one function or operation, and may be implemented as a software module, a hardware module, or a combination of software and hardware.

The information transmission/reception unit 710 receives target train information and neighbor train information from a train control device mounted in a plurality of trains when a train enters a garage.

The unusual situation occurrence reference unit 720 compares the target train information and the neighboring train information with the scheduled operation information and updates the unusual situation occurrence threshold. The unusual situation occurrence reference unit 720 transmits an unusual situation occurrence threshold to the target train.

The unusual situation threshold determining unit 730 learns the unusual situation classification threshold based on the target train information and the driving status information about the neighboring train information, and updates the unusual situation classification threshold. The unusual situation threshold determining unit 730 transmits the unusual situation classification threshold to the target train.

8 is a flowchart for explaining a method of recognizing a train situation according to the present embodiment.

Train dynamic control module 220 receives neighboring train information (real-time location, speed, course, event, train failure information, cabin congestion degree (car congestion degree)) from neighboring trains through inter-train communication, and tracks from the ATS 140 Status information (line failure occurrence information, platform congestion degree (ground congestion degree)) is received (S810).

), 대상열차의 위치값(x_i(t)), 열차 지연 허용 한도 설정값(X₁), 선행열차의 위치값(x_i-1(t)), 대상열차의 위치값(x_i(t)), 열차간 간격 허용 한도 설정값(X₂)을 추출한다.In step S810, the train dynamic control module 220 is the position value of the target train scheduled from neighboring train information (

), the location value of the target train (x _i (t)), the train delay allowable limit setting value (X ₁ ), the location value of the preceding train (x _i-1 (t)), the location value of the target train (x _i ( t)), extract the set value (X ₂ ) of the allowable interval between trains.

The train dynamic control module 220 checks whether an unusual situation occurs based on at least one of information on neighboring trains and track status (S820).

)에서 대상열차의 위치값(x_i(t))을 차감한 값이 열차 지연 허용 한도 설정값(X₁) 이상(

)이거나, 선행열차의 위치값(x_i-1(t))에서 대상열차의 위치값(x_i(t))을 차감한 값이 열차간 간격 허용 한도 설정값(X₂) 이하(x_i-1(t)-x_i(t)≤X₂)인 경우 이례상황이 발생한 것으로 확인한다.In step S820, the train dynamic control module 220 is the scheduled position value of the target train (

) Minus the target train's position value (x _i (t)) is equal to or greater than the train delay tolerance set value (X ₁ ) (

), or the value obtained by subtracting the position value of the target train (x _i (t)) from the position value of the preceding train (x _i-1 (t)) is equal to or less than the set value of the allowable interval between trains (X ₂ ) (x _{i If -1} (t)-x _i (t)≤X ₂ ), it is confirmed that an unusual situation has occurred.

The train dynamic control module 220 extracts train failure occurrence information included in neighbor train information. The train dynamic control module 220 determines that an unusual situation has occurred based on at least one of a faulty train location, a fault occurrence time, and a fault type included in the train fault occurrence information.

The train dynamic control module 220 extracts passenger information in a train cabin sensed when a target train included in the neighbor train information departs from a stop station. The train dynamic control module 220 calculates a cabin congestion level (in-vehicle congestion level) in a train based on passenger information in the train cabin. The train dynamic control module 220 checks that an unusual situation has occurred when the cabin congestion level (in-vehicle congestion level) in the train exceeds a preset threshold.

The train dynamic control module 220 extracts line failure occurrence information included in the line status information. The train dynamic control module 220 checks that an unusual situation has occurred based on the location of the failure on the track, the time of the failure, and the type of failure included in the track failure occurrence information.

The train dynamic control module 220 extracts information on waiting passengers of the platform on which the target train will stop next, included in the track status information. The train dynamic control module 220 calculates a platform congestion level (ground congestion level) based on the waiting passenger information of the platform to be stopped next. When the platform congestion level (ground congestion level) exceeds a preset threshold, the train dynamic control module 220 confirms that an unusual situation has occurred.

The train dynamic control module 220, if it is confirmed that an unusual situation has occurred as a result of the confirmation, classifies the unusual situation into any one of the preset unusual situations, and recognizes the train situation based on the specific unusual situation (830). ).

)에서 선행열차의 위치값(x_i-1(t))을 차감한 값이 열차 지연 허용 한도 설정값(X₁) 이상인지의 여부(

)를 확인한다. 열차 동적 제어모듈(220)은 선행열차의 위치값(x_i-1(t))에서 대상열차의 위치값(x_i(t))을 차감한 값이 열차간 간격 허용 한도 설정값(X₂) 이하인지의 여부(x_i-1(t)-x_i(t)≤X₂)를 확인한다. 열차 동적 제어모듈(220)은 전술한 조건이 만족하는 경우, 선행 열차의 전방 진로 지장시 후속 열차의 우회 경로 판단 시나리오(시나리오1)로 판단한다.In step S830, the train dynamic control module 220 is whether the preceding train event (e _{i - 1} (t)) arrives at the station (s) (Dpt _s ) (e _{i -1} (t) = Dpt _s ) Check. The train dynamic control module 220 checks whether the target train event (e _i (t)) is an arrival event (Arr _s ) (e _i (t) = Arr _s ). Train dynamic control module 220 is the same (p _i (t) = p _i-1 (t)) of the target train path (p _i (t)) and the preceding train path (p _i-1 (t)) Check whether or not. The train situation recognition unit 216 checks whether the preceding train speed (v _i-1 (t)) is 0 (v _i-1 (t) = 0). Train dynamic control module 220 is the position value of the scheduled preceding train (

Whether or not the value obtained by subtracting the position value (x _i-1 (t)) of the preceding train from) is equal to or greater than the train delay allowable limit (X ₁ ) (

). Train the dynamic control module 220 includes a gap between the value subtracting the position value (x _i (t)) of the target train to the nearest value of the preceding train (x _i-1 (t)), train allowance set value (X ₂ ) It is checked whether or not (x _i-1 (t)-x _i (t) _≤ X ₂ ). When the above-described condition is satisfied, the train dynamic control module 220 determines a detour path determination scenario (scenario 1) of a subsequent train when the preceding train is obstructed.

)에서 선행열차의 위치값(x_i-1(t))을 차감한 값이 열차 지연 허용 한도 설정값(X₁) 이상인지의 여부(

)를 확인한다. 열차 동적 제어모듈(220)은 전술한 조건이 모두 만족하는 경우, 대상 열차의 운행 상태를 선로 자원 동시 사용 경합에 따른 통과 순서 조정 시나리오(시나리오2)로 판단한다.The train dynamic control module 220 checks whether the preceding train event (e _{i - 1} (t)) has arrived at the station (s) (Dpt _s ) (e _{i -1} (t) = Dpt _s ). The train dynamic control module 220 checks whether the target train event (e _i (t)) arrives at the station (s) (Dpt _s ) (e _i (t) = Dpt _s ). The train dynamic control module 220 determines whether the target train route (p _i (t)) and the preceding train route (p _i-1 (t)) are not the same (p _i (t)≠p _i-1 (t)). ). The train dynamic control module 220 determines whether the preceding train speed (v _i-1 (t)) and the target train speed (v _i (t)) are 0 (v _i-1 (t) = v _i (t)). = 0). Train dynamic control module 220 is the position value of the scheduled preceding train (

Whether or not the value obtained by subtracting the position value (x _i-1 (t)) of the preceding train from) is equal to or greater than the train delay allowable limit (X ₁ ) (

). When all of the above-described conditions are satisfied, the train dynamic control module 220 determines the operating state of the target train as a passage order adjustment scenario (scenario 2) according to contention for simultaneous use of track resources.

)에서 대상열차의 위치값(x_i(t))을 차감한 값이 열차 지연 허용 한도 설정값(X₁) 이상인지의 여부(

₎를 확인한다. 열차 동적 제어모듈(220)은 전술한 조건이 모두 만족하는 경우, 회차 지연 대비 회차선 조정 시나리오(시나리오3)로 판단한다.The train dynamic control module 220 checks whether the target train event (e _i (t)) is the destination station end event (End _s ) or not (e _i (t) = End _s ). Train dynamic control module 220 is the scheduled position value of the target train (

) Whether the target train's position value (x _i (t)) is equal to or greater than the train delay allowable limit (X ₁ ) (

₎ . When all of the above-described conditions are satisfied, the train dynamic control module 220 determines the turn delay versus the turn line adjustment scenario (scenario 3).

The train dynamic control module 220 dynamically controls the train according to the train situation (S840).

In step S840, the train dynamic control module 220 calculates the severity of the unusual situation, based on the severity, for trains sequentially affected by the unusual situation among trains located on the same path (course) as the preceding train. Predict the ripple effect. The train dynamic control module 220 selects an optimal alternative that minimizes the ripple effect from among a plurality of previously stored alternatives. The train dynamic control module 220 controls the train located after the preceding train to run in a course (path) according to an optimal alternative.

In FIG. 8, steps S810 to S840 are described as sequentially executing, but are not limited thereto. In other words, since it is possible to change and execute the steps illustrated in FIG. 8 or execute one or more steps in parallel, FIG. 8 is not limited to a time-series order.

9 is a flowchart illustrating a method of learning an unusual situation determination threshold in the train control server according to the present embodiment.

The train control learning server 130 receives target train information and neighbor train information from the train control device 110 mounted in a plurality of trains when the train enters the garage (S910).

The train control learning server 130 compares the target train information and the neighbor train information with the scheduled operation information, and updates a threshold of occurrence of an unusual situation. The train control learning server 130 transmits an unusual situation occurrence threshold to the target train (S920).

The train control learning server 130 learns an unusual situation classification threshold based on the target train information and the operation status information about the neighboring train information, and updates the unusual situation classification threshold. The train control learning server 130 transmits an unusual situation classification threshold to the target train (S930).

In FIG. 9, it is described that steps S910 to S930 are sequentially executed, but are not limited thereto. In other words, since it is possible to change and execute the steps illustrated in FIG. 9 or execute one or more steps in parallel, FIG. 9 is not limited to a time-series order.

10 is a flowchart illustrating a dynamic train control method according to the present embodiment.

The train control device 110 checks whether an unusual situation occurs with respect to the preceding train based on the train operation state and the good operation state (S1010).

), 스케쥴된 대상열차의 위치값(

), 선행열차의 위치값(x_i-1(t)), 대상열차의 위치값(x_i(t)), 종착역 종착 이벤트(End_s)를 추출한다.The train control device 110 includes a preceding train event (e _{i -1} (t)), a target train event (e _i (t)), arrival at a station (s) (departure) (Dpt _s ), and arrival from neighboring train information. Event (Arr _s ), course of the preceding train (p _i-1 (t)), course of the target train (p _i (t)), speed of the preceding train (v _i-1 (t)), speed of the target train (v _i ( t)), the position value of the scheduled preceding train (

), the position value of the scheduled target train (

), the location value of the preceding train (x _i-1 (t)), the location value of the target train (x _i (t)), and the destination station end event (End _s ) are extracted.

The train control device 110 uses a plurality of pieces of information extracted from neighboring train information to determine an unusual situation when the preceding train's forward path is impeded, a scenario for determining a bypass route of a subsequent train (scenario 1), and a passing sequence according to contention for simultaneous use of track resources. It is classified as a specific anomalous situation in any one of the adjustment scenario (scenario 2) and the round line adjustment scenario (scenario 3) compared to the round delay (S1020).

The train control device 110 calculates the severity of scenarios 1, 2, and 3, respectively, for classifying the unusual situation (S1030).

The train control device 110 checks whether the severity of the scenarios 1,2, and 3 classified the unusual situation is serious (S1040). In step S1040, the train control device 110 is the duration of the unusual situation for the preceding train (t _{i -1} ), the external notification (input) end time (τ _{i -1} ), the previously learned threshold (T _{i - 1} ) Receive. The train control device 110 has a pre _- learned threshold for an unusual situation duration (t _{i -1} ) and an external notification (input) end time (τ _i-1 ) (max (t _{i - 1} ,τ _{i -1} )) If it is (T _{i -1} ) or more, it is judged that the severity is serious. The train control device 110 has a pre-learned threshold for an unusual situation duration (t _{i -1} ) and an external notification (input) end time (τ _{i -1} ) (max (t _{i - 1} ,τ _{i -1} )) If it is less than (T _{i -1} ), the severity is judged to be insignificant.

As a result of the confirmation of step S1040, if it is determined that the severity of the scenarios 1,2, and 3 classified the unusual situation is serious, the train control device 110 is among trains located on the same path (course) as the preceding train based on the severity. The ripple effect on trains sequentially affected by the unusual situation is predicted (S1050).

In step S1050, the train control device 110 generates a course maintenance plan (alternative 1) and a course change plan (alternative 2) for the train located after the preceding train based on the ripple effect.

The train control device 110 determines whether or not a course change plan (alternative 2) can be selected as an optimal alternative that minimizes the ripple effect among the course maintenance plan (alternative 1) and course change plan (alternative 2) for trains located after the preceding train. Confirmation (S1060).

As a result of checking in step S1060, if a course change plan (alternative 2) can be selected as the optimal alternative that minimizes the ripple effect, the train control device 110 transfers the train located after the preceding train according to the course change plan (alternative 2) to the secondary line ( The number of sequentially affected trains (the number of delayed trains) generated when the route is first sent to the bypass route (dynamically re-adjusted) is calculated (S1070).

The train control device 110 calculates the number of sequentially affected trains (the number of delayed trains) that occur when an unusual situation is maintained according to the course maintenance plan (alternative 1).

The train control device 110 is a train located after the preceding train according to the number of sequentially affected trains (the number of delayed trains) and the course change plan (alternative 2) that occur when an unusual situation is maintained according to the course maintenance plan (alternative 1). In the result of comparing the number of sequentially affected trains (the number of delayed trains) that occurred when (dynamically rerouted) sent to the secondary line (bypass route) first, it was determined whether the course change proposal (alternative 2) was advantageous. Confirm (S1080).

In step S1080, when maintaining the unusual situation, the train control device 110 sends the train located after the preceding train to the secondary main line (the bypass route) before the number of trains sequentially affected (the number of delayed trains) If the number of trains affected sequentially (the number of delayed trains) decreases beyond the threshold, the course change plan (alternative 2) is selected.

The train control device 110 is the delayed arrival time that occurs when the unusual situation is maintained and the delayed arrival time that occurs when the train located after the preceding train is sent to the secondary line (bypass) earlier than the congestion level (dynamically rerouted). If the degree of congestion and congestion decreases beyond the threshold, choose a course change plan (alternative 2).

As a result of the confirmation of step S1080, as a result of confirming that the course change plan (alternative 2) is advantageous, the train control device 110 applies the course change plan (alternative 2) (S1090).

In FIG. 10, it is described that steps S1010 to S1090 are sequentially executed, but are not limited thereto. In other words, since the steps described in FIG. 10 may be changed and executed or one or more steps may be executed in parallel, FIG. 10 is not limited to a time series order.

As described above, the dynamic train control method according to the present embodiment illustrated in FIG. 10 may be implemented as a program and recorded on a computer-readable recording medium. A program for implementing the dynamic train control method according to the present embodiment is recorded and the computer-readable recording medium includes all types of recording devices storing data that can be read by a computer system.

11 is a flowchart for explaining an alternative application method according to the present embodiment.

The train control device 110 sets a course maintenance plan (alternative 1) for a train located after the preceding train to minimize the ripple effect among a plurality of previously stored alternatives (S1110). The train control device 110 sets a course change plan (alternative 2) for a train located after the preceding train to minimize the ripple effect among a plurality of previously stored alternatives (S1112).

The train control device 110 predicts (calculates) the number of sequentially affected trains (the number of delayed trains) that occur when an unusual situation is maintained according to the course maintenance plan (alternative 1) (S1120). The train control device 110 is the number of sequentially affected trains that occur when the train located after the preceding train is first sent to the sub-main line (bypass route) according to the course change plan (alternative 2) (dynamically readjusted route). The number of delayed trains) is predicted (calculated) (S1122).

The train control device 110 is a train located after the preceding train according to the number of sequentially affected trains (the number of delayed trains) and the course change plan (alternative 2) that occur when an unusual situation is maintained according to the course maintenance plan (alternative 1). Compare the number of sequentially affected trains (the number of delayed trains) that occur when (dynamically re-route) sent to the submain line (bypass route) first, and according to the comparison result, It is checked whether the change proposal (alternative 2) is advantageous (S1130).

As a result of the confirmation of step S1130, if the course change plan (alternative 2) is advantageous, the train control device 110 communicates with the ATS 140 to check whether or not the train schedule change to the course of the course change plan (alternative 2) is possible ( S1140).

As a result of checking in step S1140, if it is possible to change the train schedule to the course of the course change plan (alternative 2), the train control device 110 executes the course change plan (alternative 2) (S1150). As a result of checking in step S1130, if the course change plan (alternative 2) is not advantageous, the train control device 110 executes the course maintenance plan (alternative 1) (S1152).

In FIG. 11, steps S1110 to S1152 are described as sequentially executing, but the present invention is not limited thereto. In other words, since it is possible to change and execute the steps illustrated in FIG. 11 or execute one or more steps in parallel, FIG. 11 is not limited to a time series order.

12 is a flowchart illustrating a method for determining a severity according to the present embodiment.

The train control apparatus 110 extracts a preset threshold T _i that can determine the severity of the scenario [i] from a previously stored database. The train control device 110 receives an unusual situation duration (t _i ) and an external notification end time (τ _i ) for the unusual situation classified as a past event [i] from the ATS 140 (S1210).

In step S1210, the train control device 110 also grasps the operating environment/condition (level of time, distance between stations, congestion, etc.) at the corresponding past point and compares it with the operating environment/condition of the current situation, and then a threshold with high suitability ( T _i ) is extracted. The train control device 110 is an unusual situation duration (t _i ) and an external notification end time (τ _i ) for an unusual situation classified as a past event [i] in order to learn T _i and set a default (Default). Acquires, stores and manages information on and.

The train control device 110 determines whether the maximum anomalous event duration (t _i ) and the maximum external notification end time (τ _i ) (max (t _i, τ _i )) are greater than or equal to a preset threshold (T _i ). Check ((max(t _i, τ _i )≥T _i ) (S1220).

As a result of the confirmation of step S1220, the maximum anomalous event duration (t _i ) and the maximum external notification end time (τ _i ) (max(t _i, τ _i )) are equal to or greater than the preset threshold (T _i ) ((max( If t _i, τ _i )≥T _i ), the train control apparatus 110 determines that the severity is serious (S1230).

As a result of the confirmation of step S1220, the maximum anomalous event duration (t _i ) and the maximum external notification end time (τ _i ) (max (t _i, τ _i )) are less than a preset threshold (T _i ) ((max( When t _i, τ _i ) <T _i ), the train control device 110 determines that the severity is insignificant (S1230).

In FIG. 12, steps S1210 to S1230 are described as sequentially executing, but are not limited thereto. In other words, since it is possible to change and execute the steps illustrated in FIG. 12 or execute one or more steps in parallel, FIG. 12 is not limited to a time-series order.

13 is an exemplary view showing the ripple effect according to the present embodiment.

Severity refers to the duration/delay time of the event of the train. The ripple effect refers to the delay time and congestion of all trains sequentially affected by the event.

Train control device 110 spreads Use past learning data to predict effects and judge severity.

When the unusual situation is classified into scenarios 1, 2, and 3, the train control device 110 extracts a minimum threshold value for the duration of an event that can determine that the event is serious. When the unusual situation is classified into scenarios 1,2,3, the train control device 110 may apply a differentiated threshold value according to the operating environment/condition (time interval, distance between stations, levels of congestion, etc.).

When the unusual situation is classified as scenario i, the train control device 110 extracts a minimum threshold value for the duration of an event that can be determined to be serious. When the unusual situation is classified as scenario i, the train control device 110 may apply a threshold value differentiated according to the operating environment/condition (time interval, distance between stations, levels of congestion, etc.).

Train control device 110 spreads Information received through inter-train communication (T2T) at the current time is used to predict the effect and determine the severity.

When the unusual situation is classified into scenarios 1 and 2, the train control device 110 receives the classification of the event and the corresponding event duration (initial time point to present) through inter-train communication. When the unusual situation is classified as scenario 3, the train control device 110 receives the classification of the event and the corresponding event (operation delay) time (initial time to present) through inter-train communication.

When the unusual situation is classified as scenario i, the train control device 110 receives the classification of the event and the corresponding event duration (the initial point in time to the present time) through inter-train communication. The train control device 110 communicates an end prediction time of a corresponding event from an external (ground control system) to the onboard device.

The train control device 110 operates to determine the severity as follows.

When the unusual situation is classified into scenarios 1 and 2, the train control device 110 performs an additional adjustment task for predicting the ripple effect when it is determined that the recognized unusual event of the present time is serious. The train control device 110 does not perform a separate operation when it is determined that the unusual event is insignificant.

① If the duration of the event up to the present time is greater than the learned time (default setting), the train control device 110 determines that the severity of the unusual event is serious.

② If the duration of the event considering the end point given from the outside is greater than the learned time, the train control device 110 determines that the severity of the unusual event is serious.

③ The train control device 110 determines whether or not there is a severity, assuming that it ends after an integral multiple of the duration until now, if information on the end time is not given from the current time. Here, the preset value of the integer multiple is updated by learning.

When the unusual situation is classified as scenario 3, the train control device 110 performs an additional adjustment task for predicting the ripple effect when it is determined that the recognized unusual event of the present time is serious. The train control device 110 does not perform a separate operation when it is determined that the unusual event is insignificant.

① The train control device 110 determines that the severity of the unusual event is serious if the operation delay time to date is greater than the learned time (default setting).

② When there is no additional factor, the train control device 110 handles the severity under the premise that the current level of delay is maintained.

When the unusual situation is classified as scenario i, the train control device 110 performs an additional adjustment task for predicting the ripple effect when it is determined that the recognized unusual situation event is of a serious degree when determined at the present time point. The train control device 110 does not perform a separate operation when it is determined that the unusual event is insignificant.

① If the duration of the event up to the present time is greater than the learned time (default setting), the train control device 110 determines that the severity of the unusual event is serious.

② If the duration of the event considering the end point given from the outside is greater than the learned time, the train control device 110 determines that the severity of the unusual event is serious.

③ The train control device 110 determines whether or not there is a severity, assuming that it ends after an integral multiple of the duration until now, if information on the end time is not given from the current time. Here, the preset value of the integer multiple is updated by learning. The operation of determining the severity of the train control device 110 is shown in FIG. 12.

The train control device 110 uses T _i (a threshold value capable of determining the severity of the scenario [i]) for learning. When the unusual situation is classified as scenario i, the train control device 110 detects the duration of the event (t _i ) to the passing neighboring train through communication such as T2T, and uses the information transmitted from the ground device to the onboard device. The end prediction time (τ _i ) is selectively delivered.

The train control device 110 is an unusual situation duration (t _i ), an external notification end time (τ _i ) for an unusual situation classified as a past event [i] in order to learn T _i and set a default (Default) And it acquires, stores, and manages information on the threshold value T _i set at the time.

The train control device 110 checks the operating environment/condition (level of time interval, station-to-station distance, congestion, etc.) at a corresponding past point and compares it with the operating environment/condition of the current situation, and then calculates a high-fit threshold (T _i ). to provide.

The train control device 110 performs an operation for predicting the ripple effect as follows.

When the unusual situation is classified into scenarios 1,2,3, the train control device 110 predicts the expected ripple effect in the current operating environment (for a given event duration) (the number of delayed trains, the number of delayed trains, passengers). Congestion/travel time increase estimate). The train control device 110 uses a method of calculating a train delay time for a subsequent train in the concept of First Come First Serve (No Control) as a prediction method.

14 is an exemplary diagram showing dynamic path setting according to the present embodiment.

The train control device 110 sets that there is no change in the train operation sequence and route as a prerequisite for Alternative 1.

When the external (ground control system) end time information is given in the temporal (review) range for Alternative 1, the train control device 110 considers the current event of the corresponding train until the time when the external end time information is provided. Calculate the chain delay. When the external end time information is not given in the temporal (review) range for Alternative 1, the train control device 110 calculates a chain delay between trains by considering the time point multiplied by an integer multiple of the duration up to now as a default.

The train control device 110 dynamically adjusts the train operation sequence/route for each scenario as a prerequisite for Alternative 2.

The train control device 110 directly reflects the alternative 2 in the temporal (review) range for the alternative 2 and calculates the chain delay. The train control device 110 sets up to the last train calculated as a chain delay as a temporal (review) range for Alternative 2 as a temporal range.

In order to calculate the chain delay, the train control device 110 sequentially sets the main line train interval to a minimum value, which can be set based on the braking distance calculated by the ATCS based on the current train speed.

In order to calculate the chain delay, the train control device 110 predicts the increase in the number of arriving passengers in the sequence of delayed train → increased arrival passengers → increased boarding time → increased stopping time → delayed train departure time → vicious cycle repetition, and increased stopping time (stop stop time). ) Is calculated. The train control device 110 calculates the number of trains related to the chain delay, the delay time for each train, and the total delay time.

The above description is merely illustrative of the technical idea of the present embodiment, and those of ordinary skill in the technical field to which the present embodiment belongs will be able to make various modifications and variations without departing from the essential characteristics of the present embodiment. Accordingly, the present exemplary embodiments are not intended to limit the technical idea of the present exemplary embodiment, but are illustrative, and the scope of the technical idea of the present exemplary embodiment is not limited by these exemplary embodiments. The scope of protection of this embodiment should be interpreted by the following claims, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the present embodiment.

110: train control device 120: relay device
130: train control learning server 140: ATS
210: train situation recognition module
212: communication unit 214: operation status determination unit
216: unusual situation determination unit 218: train situation recognition unit
220: train dynamic control module
222: severity judgment unit 224: ripple effect prediction unit
226: Alternative Selection Department 228: Train Operations Department
710: information transmitting and receiving unit
720: Unusual situation classification standard
730: unusual situation threshold determination unit

Claims (14)

A train situation identification unit that checks whether exceptional circumstances have occurred for the preceding train based on the train operation status and the good running condition;
A severity determination unit that calculates a severity of the unusual situation;
A ripple effect prediction unit for predicting a cascaded delay time for trains sequentially affected by the unusual situation among trains located on the same path as the preceding train based on the severity;
An alternative selection unit for selecting an optimal alternative that minimizes the ripple effect from among a plurality of previously stored alternatives; And
A train operating unit that controls the train located after the preceding train to run along the course according to the optimal alternative
Train dynamic control device comprising a. The method of claim 1,
The severity determination unit,
Receiving an unusual situation duration (t _{i -1} ), an external notification end time (τ _{i -1} ), and a previously learned threshold (T _{i - 1} ) for the preceding train,
The unusual situation duration (t _{i -1} ) and the external notification end time (τ _{i -1} ) (max (t _i-1 ,τ _i-1 )) are equal to or greater than the previously learned threshold (T _{i -1} ). In case, it is determined that the severity is serious,
If the unusual situation duration (t _{i -1} ) and the external notification end time (τ _{i -1} ) (max (t _i-1 ,τ _i-1 )) are less than the previously learned threshold (T _{i -1} ) , The train dynamic control device, characterized in that it is determined that the severity is insignificant. The method of claim 2,
The severity determination unit
When the external notification end time (τ _i-1 ) is not received, an integer multiple of the unusual situation duration time (t _i-1 ) to date is set as the estimated end time of notification,
The notification ends estimated time of the group learning the threshold value (T _{i -1)} or more, and if it is determined that the severity of the acute and the end notification estimated time when the group is less than the learning threshold value (T _i-1) is the slight severity Train dynamic control device, characterized in that to determine that. The method of claim 2,
The ripple effect prediction unit,
If the severity of the unusual situation is determined to be serious based on the current point in time, the ripple effect is predicted, and when the severity of the unusual situation is determined to be minor, a separate task of predicting the ripple effect is not performed. Train dynamic control device. The method of claim 4,
The ripple effect prediction unit,
Calculate a delayed arrival time for all trains sequentially affected by the unusual situation among trains located on the same route as the preceding train, and calculate a congestion level corresponding to the delayed arrival time,
The arrival time of the bypass route is calculated when the train located after the preceding train among all trains sequentially affected by the unusual situation is sent to the secondary line first, and the congestion level corresponding to the arrival time of the bypass route is calculated. Train dynamic control device. The method of claim 5,
The alternative selection unit,
Based on the ripple effect, the train dynamic control device, characterized in that for selecting one of an alternative of a course maintenance plan and a course change plan for a train located after the preceding train. The method of claim 6,
The alternative selection unit,
According to the course maintenance plan, the number of trains that are sequentially affected when the abnormal situation is maintained and the number of trains that are sequentially affected when the train located after the preceding train is first sent to the secondary line according to the course change plan. Comparing and selecting one of the course maintenance plan and the course change plan according to a comparison result. The method of claim 7,
The alternative selection unit,
In the case of maintaining the unusual situation, if the number of trains located after the preceding train is sent to the sub-main line before the number of trains that are sequentially affected, and the number of trains that are sequentially affected decreases by more than a threshold value, select the course change plan, and the If the unusual situation is maintained, if the number of trains sequentially affected and the train located after the preceding train are sent to the secondary line first, and if the number of trains sequentially affected is the same or decreases below the threshold, selecting the course maintenance plan is recommended. Train dynamic control device characterized by. The method of claim 7,
The alternative selection unit,
If the delayed arrival time and congestion that occurs when the train located after the preceding train is sent to the sub-main line earlier than the delayed arrival time and congestion that occurs when the unusual situation is maintained, select the course change plan,
If the above-mentioned unusual situation is maintained, the delayed arrival time and the congestion degree, and the detour route arrival time and congestion that occurs when the train located after the preceding train is sent to the secondary line first, are the same or decrease below the threshold, selecting the course maintenance plan. Train dynamic control device, characterized in that. The method of claim 1,
The train situation awareness section
If it is confirmed that the unusual situation has occurred, the train dynamic control device, characterized in that for classifying the unusual situation into any one of the preset unusual situation. The method of claim 10,
The train situation awareness section
The preceding train event extracted from the neighboring train information (e _{i -1} (t)), the target train event (e _i (t)), the arrival at the station (s) (Dpt _s ), the arrival event (Arr _s ), the preceding Train course (p _i-1 (t)), target train path (p _i (t)), preceding train speed (v _i-1 (t)), target train speed (v _i (t)), scheduled advance Train position value (

), the position value of the scheduled target train (

), based on the location value of the preceding train (x _i-1 (t)), the location value of the target train (x _i (t)), and the destination event (End _s )
The unusual situation is classified into any one of a scenario for determining a bypass route of a subsequent train when the preceding train has a path forward, a scenario for adjusting the order of passage according to contention for simultaneous use of track resources, and a scenario for adjusting the turnaround line versus delay. Train dynamic control device. The method of claim 11,
The severity determination unit,
The train dynamic control apparatus, characterized in that calculating the severity by using a pre-learned threshold (T _i ) learned differently for each of the detour route determination scenario, the passage order adjustment scenario, and the turn line adjustment scenario. The method of claim 7,
The alternative selection unit,
And selecting one of the course maintenance plan and the course change plan by using a threshold value applied differently for each of the detour path determination scenario, the passage order adjustment scenario, and the turn line adjustment scenario. The process of checking whether an unusual situation has occurred for the preceding train based on the train operation condition and the good operation condition;
Calculating the severity of the unusual situation;
Predicting a ripple effect on trains sequentially affected by the unusual situation among trains located on the same route as the preceding train based on the severity;
Selecting an optimal alternative that minimizes the ripple effect from among a plurality of previously stored alternatives; And
The process of controlling the train located after the preceding train to run along the course according to the optimal alternative
Dynamic train control method comprising a.

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