KR20200051448A - System for scheduling of express train and method thereof - Google Patents

Abstract

The present invention relates to a scheduling system for an express train and a method thereof. The scheduling method for an express train, which is performed through the scheduling system for an express train for controlling train operation through the scheduling of a train station, includes the following steps of: (a) providing passage record data by statistically processing passage input data including station group data, past inter-station passage record data and past inter-station passage time data in regard to a target route; (b) receiving user input data setting a stop attribute parameter in regard to stop patterns of an express train operated for the target route, and setting a train operation analysis scenario based on the user input data; (c) determining a first stop pattern and a second stop pattern for the express train operated for the target route based on the user input data and the passage record data in accordance with the set train operation analysis scenario; and (d) conducting an optimization passage model for deriving an optimal stop pattern through a pattern combination between the first and second stop patterns, and then, providing schedule information for the express train by applying direct-trip passenger information or transferred-trip passenger information to the optimization passage model.

Description

 SCHEDULING OF EXPRESS TRAIN AND METHOD THEREOF

The present invention relates to a scheduling system and method of an express train that can provide scheduling information of an express train optimized to selectively stop at a high-demand station using a pattern combination method.

Many domestic and international trains operating in large cities at home and abroad adopt each station stop (All-Stop) method, which has the disadvantage that the average (expressed) speed of the train is low.

1 is a view for explaining a rapid classification system for improving the average speed of a city rail according to an embodiment of the prior art.

As illustrated in FIG. 1, in order to solve the problem of low average speed of a wide-area railway operating in a metropolitan area, including an urban railway, the express train operation method may be largely divided into a dedicated line operation method and a mixed operation method.

First, the operation method of the dedicated line is to install and operate the lines operated by the general trains that stop at all stations and the express trains that selectively stop, independently of each other, thereby preventing interoperability caused by the difference in speed between the express trains and the general trains. It shows how to remove.

This method of operation of the dedicated line has the advantage of significantly increasing the number of times of express trains and general trains compared to other types of operation, but it is inevitable to increase the construction cost because additional independent lines must be installed, and additional dedicated lines are installed. There is a disadvantage in that it requires space (land) to increase. The disadvantage of such a dedicated line operation method is that it is a factor that makes it difficult to apply the technology when a project to speed up urban railway lines in a large city or urban area, which is initially operated by each station stop, is to be promoted in the future.

Next, the mixed driving method is a method in which an express train and a general train are mixed and operated on the same track. When express trains and general trains operate in a mixture on the same track, operational difficulties occur due to differences in speed between each other. That is, in the mixed driving method, it is necessary for the express train to pass the general train after driving a certain distance.

The method of utilizing the main bus is the most representative mixed operation method, and it is a method of using the main train required for evacuation of general trains so that the express train can pass. The method of using the sub-main line has the advantage of reducing the initial construction cost compared to the exclusive line operation method, since the express train can be additionally operated by installing the sub-main line at some necessary stops.

As mentioned above, the method of utilizing the sub-main line is also likely to be limited to areas outside the city center, where the line section where additional sub-main lines can be constructed, if the original train-oriented route is to be promoted in the future. It has a limitation in that it is difficult to promote future rapid projects for pioneering sections in downtown areas where buildings and civil engineering facilities are concentrated.

In the case of using the crossing line in the intestine, the express train uses the crossing line installed at the entry and exit of the station, and the express train is operated by overtaking the general train by using the opposite line (for example, when operating a lower line.) The utilization method has the advantage that the restrictions of the downtown line (tunnel) improvement work of the sub-main line utilization method is eased, but the congestion of the train operation system in the case of congestion by the express train that overtakes the other side of the line on a dense train route There is a risk of occurrence, and in the event of an unexpected accident, the aftermath of the accident may appear largely.

The pattern combination method is a method of improving the average speed by reducing the stop time of a train by simply setting a stop to selectively stop, and it has the advantage of reducing the project cost by not requiring additional facilities. In addition, the pattern combination method is highly applicable to rapid urbanization projects because it does not require complicated civil / construction improvement even in the case of future rapidization projects. However, the pattern combination method has the disadvantage that passengers may experience inconvenience due to the inability to board at some stops or increase waiting time as the train selectively stops.

2 is a diagram illustrating an A / B pattern combination method according to an embodiment of the prior art.

Referring to FIG. 2, for trains operating in the A / B pattern combination method, five round marks are called stops, and if it is called stops 1-5 from the right, trains of pattern A are (1, 3, 5) stops Stop at, and trains of pattern B stop at (2, 4) stops.

It is assumed that an example of traffic performance between stations is given as Table 1 below. In Table 1, the total number of passengers is 360.

[Table 1]

When the stop pattern is determined as shown in (a) of FIG. 2, the sum of the number of people who can be transported among the traffic performances between stations given in Table 1 is 220 trains of A pattern and B pattern as shown at the top of FIG. The train of 100 is capable of transporting a total of 320 people, and the remaining 40 passengers are displaced (Lost Passengers), so it can be judged as a relatively good result in terms of transportation personnel.

In order to further improve the average speed of the train, when the stop pattern is changed as shown at the bottom of FIG. 2 (b), that is, the train of the A pattern stops at the (2, 4) stop, and the train of the B pattern (1, 3, 5) If you stop at the station, only 40 passengers can be transported, and the remaining 320 passengers will be separated, which can be judged as a result of insufficient transportation.

In order to further improve the effect of the transportation personnel side, as shown at the bottom of FIG. 2 (c), trains of the A pattern stop at (1, 3, 4, 5) stops, and trains of the B pattern (2, 4) If you stop at the station and change the stop pattern from station 4 to the transfer station, you can transport all passengers with traffic performance between stations given in Table 1. However, in the case of the A pattern, the number of stops increases, so the effect in terms of average speed decreases.

In this way, the A / B pattern combination method is in an inevitable situation that needs to be resolved between the departure criteria of the passengers and the judgment criteria that conflict with each other in terms of average speed.

FIG. 3 is a diagram illustrating a method for deriving a theoretical optimal solution to the objective functions of the A / B pattern combination method according to an embodiment of the prior art.

As shown in FIG. 3, in the A / B pattern combination method having mutually conflicting judgment criteria, the priority of one judgment criterion (eg, passenger departure loss) is different from the other judgment criterion (eg, shortening the travel time). There is a problem that it is difficult to objectively know how much the difference is quantitatively compared to the loss of wealth. In general, there are limits that are bound to subjective priorities.

In addition, in the case of A / B pattern combination method with mutually opposed judgment criteria, when considering two judgment criteria at the same time, the size of the mathematical model of the problem, that is, the number of determinants and constraints increases, and the time required to obtain the optimal solution There is a problem of this rapidly increasing.

Republic of Korea Registered Patent No. 10-1881606 (Name of invention: Synchronization of express and slow trains in urban commuter rail systems)

In order to solve the above-described problem, the present invention selectively stops at a high-demand stop according to an embodiment of the present invention, and improves the average speed by passing a train without stopping at a stop where stop is unnecessary, and planning The purpose of this is to provide a healthy control of train operation, which is affected by various unusual situations on the site when actually operating an express train based on schedule information.

However, the technical problem to be achieved by the present embodiment is not limited to the technical problem as described above, and other technical problems may exist.

A method of scheduling an express train according to an embodiment of the present invention as a technical means for achieving the above technical problem is a method of scheduling an express train performed by a scheduling system of an express train that controls train operation through scheduling of a station. The method of claim 1, further comprising: a) statistically processing traffic input data including stop aggregation data for a target route, past station traffic performance data, and past station traffic time data to provide traffic performance data; b) receiving user input data having a stop attribute parameter set for a stop pattern of an express train running on the target route, and setting a train operation analysis scenario based on the user input data; c) determining a first stop pattern and a second stop pattern for an express train operating the target route based on the traffic performance data and user input data according to the set train operation analysis scenario; And d) performing an optimized traffic model for deriving an optimal stop pattern through a pattern combination between the first stop pattern and the second stop pattern, and reflecting the passenger information or transfer passenger information in the optimized traffic model to express the express train. It includes the step of providing the schedule information.

The scheduling system of an express train according to an embodiment of the present invention is a scheduling system of an express train that controls train operation through scheduling of a stop station, scheduling an express train to stop by selectively setting a stop station for a target route A memory in which a program for performing the method is recorded; And a processor for executing the program, wherein the processor statistically inputs traffic input data including stop aggregation data for the target route, past station traffic performance data, and past station traffic time data by executing the program. To provide traffic performance data, receive user input data that set a stop attribute parameter for a stop pattern of an express train operating the target route, and set a train operation analysis scenario based on the user input data The first stop pattern and the second stop pattern for the express train running the target route are determined based on the traffic performance data and user input data according to the set train operation analysis scenario, and the first stop pattern and the second stop pattern are determined. 2 Optimal stop loss through pattern combination between stop patterns An optimized traffic model for deriving a turn is performed, and schedule information of the express train is provided by reflecting passenger information or transit passenger information in the optimized traffic model.

According to the problem solving means of the present invention described above, by using a pattern combination method for different stop patterns for high-density urban rail lines operating in downtown areas where it is difficult to improve railway facilities, express train schedule information having optimized stop patterns is provided. In order to solve the congestion / delay situation that occurs when the express train operates on the same day, real-time dynamic re-adjustment of the stop pattern at the adjacent transit station may be possible.

In addition, the present invention adjusts the number of stops per stop pattern, sets a preferred stop for a specific stop preferred by a user, sets a stop for a specific stop not preferred by a user, and sets a continuous stop pattern at each stop pattern. Various user input data such as settings can be reflected in the optimization traffic model.

In addition, according to the present invention, the expected level of the inevitable passengers, which is inevitably caused by the combination of patterns of different stop patterns, may be reflected in advance through the user input, so that the total number of departure passengers can be maintained above the expected level.

The present invention can reduce the time for solving an optimal solution by separating an optimized traffic model into a transfer model and a straight model to derive an optimized stop pattern.

1 is a view for explaining a rapid classification system for improving the average speed of a city rail according to an embodiment of the prior art.
2 is a diagram illustrating an A / B pattern combination method according to an embodiment of the prior art.
FIG. 3 is a diagram illustrating a method for deriving a theoretical optimal solution to the objective functions of the A / B pattern combination method according to an embodiment of the prior art.
4 is a diagram showing the configuration of a scheduling system for an express train according to an embodiment of the present invention.
5 is a flowchart illustrating a method of scheduling an express train according to an embodiment of the present invention.
FIG. 6 is a diagram illustrating a process of setting a scenario for analyzing a train operation of FIG. 5.
7 is a diagram illustrating a concept of a traffic pattern by an optimized traffic model according to an embodiment of the present invention.
8 is an operation flowchart illustrating an optimization traffic model according to an embodiment of the present invention.
9 is an operation flowchart illustrating a dynamic adjustment process of first and second stop patterns according to an embodiment of the present invention,
FIG. 10 is an exemplary view illustrating a result of a dynamic adjustment process of first and second stop patterns of FIG. 9.
11A and 11B are exemplary views illustrating a stop pattern of an express train derived by applying a scheduling method of an express train according to an embodiment of the present invention to a domestic urban railway.
12 is an exemplary view for explaining a predicted result of expected travel time and departure passenger when the scheduling method of an express train according to an embodiment of the present invention is applied to a domestic urban railway.
13 is an exemplary view for explaining the numerical value of the breakdown analysis in the result of FIG. 12.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art to which the present invention pertains may easily practice. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. In addition, in order to clearly describe the present invention in the drawings, parts irrelevant to the description are omitted, and like reference numerals are assigned to similar parts throughout the specification.

Throughout the specification, when a part is "connected" to another part, this includes not only "directly connected" but also "electrically connected" with another element in between. . Also, when a part is said to "include" a certain component, it means that the component may further include other components, not exclude other components, unless specifically stated otherwise. However, it should be understood that the existence or addition possibilities of numbers, steps, actions, components, parts or combinations thereof are not excluded in advance.

The following examples are detailed descriptions to aid the understanding of the present invention, and do not limit the scope of the present invention. Therefore, the same scope of the invention performing the same function as the present invention will also belong to the scope of the present invention.

Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

4 is a diagram showing the configuration of a scheduling system for an express train according to an embodiment of the present invention.

Referring to FIG. 4, the scheduling system 100 of the express train includes a communication module 110, a memory 120, a processor 130, and a database 140.

In detail, the communication module 110 provides a communication interface necessary to provide a signal transmitted / received to and from the scheduling system 100 of the express train in packet data form in conjunction with the communication network 300. Here, the communication module 110 may be a device including hardware and software necessary for transmitting and receiving a signal such as a control signal or a data signal through a wired or wireless connection with another network device.

In the memory 120, a program for performing a scheduling method of an express train that controls train operation through selective scheduling of a stop is recorded. In addition, it performs a function of temporarily or permanently storing data processed by the processor 130. Here, the memory 120 may include volatile storage media or non-volatile storage media, but the scope of the present invention is not limited thereto.

The processor 130 controls the entire process of providing the scheduling method of the express train. Each step performed by the processor 130 will be described later with reference to FIG. 5.

Here, the processor 130 may include all kinds of devices capable of processing data, such as a processor. Here, a 'processor' may mean a data processing device embedded in hardware having physically structured circuits, for example, to perform functions represented by codes or instructions included in a program. As an example of such a data processing device embedded in hardware, a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, and an application-specific integrated ASIC circuit, a field programmable gate array (FPGA), or the like, but the scope of the present invention is not limited thereto.

The database 140 stores data accumulated while performing the scheduling method of the express train. For example, the database 140 may store station aggregation data, past station traffic performance data, past station traffic time data, traffic performance data, stop pattern information, scheduling information, and the like.

5 is a flowchart illustrating a method of scheduling an express train according to an embodiment of the present invention.

Referring to FIG. 5, in the method of scheduling an express train according to an embodiment of the present invention, in the processor, set data included in the target route and the target route, past station-to-station performance data, and target station-to-station past time The traffic input data including the data is stored in the database, and the past traffic time data and the traffic performance data of the past station are extracted from the database and statistically processed to calculate traffic performance data (S110).

The processor receives user input data having a stop attribute parameter set for a stop pattern of an express train operating a target route (S120), and sets a train operation analysis scenario based on the user input data (S130).

The processor determines a first stop pattern and a second stop pattern for the express train operating the target route based on the traffic performance analysis scenario and the user input data (S140).

The processor performs an optimized traffic model to derive an optimal stop pattern through a pattern combination between the first stop pattern and the second stop pattern (S150), and reflects the passenger information or the transfer passenger information in the optimized traffic model to express the train. Provide scheduling information (S160). At this time, the processor stores the scheduling information of the express train and outputs the scheduling information to the screen so that it can be reflected in the driving plan by the administrator.

The scheduling method of the express train of the present invention can be classified as part of the pattern combination method in the rapid classification system shown in FIG. 1, but the pattern combination method itself can be divided into several types, but the express train of the present invention The scheduling method of is targeted at a method for optimally determining two types of stop patterns (eg, A / B type).

Meanwhile, steps S110 to S160 of FIG. 5 may be divided into additional steps or combined into fewer steps according to an embodiment of the present invention. In addition, some steps may be omitted if necessary, and the order between the steps may be changed.

FIG. 6 is a diagram illustrating a process of setting a scenario for analyzing a train operation of FIG. 5.

Referring to FIG. 6, the processor performs a parameter-based scenario analysis technique. When the total number of stops is set as a stop attribute parameter for stop set data for a target route by user input (S210), a pattern of minimizing passenger departure and The train operation analysis scenario is calculated based on user satisfaction (S220, S2230).

At this time, the total number of stops input by the user may be reached by reflecting the constraints of Equation 1 below in the optimization traffic model.

[Equation 1]

인 경우 제1 정차패턴이 정거장 번호 i에서 정차역으로 설정되고,

인 경우 제1 정차 패턴이 정거장 번호 i에서 무정차역으로 설정된다. 또한,

인 경우 제2 정차패턴이 정거장 번호 i에서 정차역으로 설정되고,

인 경우, 제2 정차 패턴이 정거장 번호 i에서 무정차역으로 설정된다. In Equation 1, S is a set of station numbers to set a stop, n is the total number of stops,

In the case of, the first stop pattern is set to the stop at stop number i,

In the case of, the first stop pattern is set to the stop stop at stop number i. In addition,

In the case of, the second stop pattern is set to the stop at stop number i,

In the case of, the second stop pattern is set to the stop stop at stop number i.

As described above, when the processor applies the parameter-based scenario analysis method according to the user input data, the size of the hydraulic model generated when simultaneously handling the criteria for determining the loss of passengers and the loss of wealth due to the reduction in travel time increases significantly. It is possible to solve the problem of increasing the time for obtaining the optimal solution.

Based on the parameter-based scenario analysis method according to the user input data, the processor sets the user-preferred stop for the target route data when the target stop set is set as the first stop, and then optimizes the solution through the optimized traffic model for the remaining stops. Can be derived.

To this end, the stop attribute parameter by the user input may be achieved by reflecting the constraints of Equation 2 below.

[Equation 2]

는 사용자 선호 정차 설정 대상 정거장 번호의 부분 집합이며,

인 경우, 제1 정차패턴이 정거장 번호 i에서 정차역으로 설정되고,

인 경우, 제2 정차패턴이 정거장 번호 i에서 정차역으로 설정된다. In Equation 2, S is a set of station numbers to set a stop,

Is a subset of the stop number to which the user preferred stop is set,

In the case of, the first stop pattern is set to the stop station at stop number i,

In the case of, the second stop pattern is set to a stop at stop number i.

Based on the parameter-based scenario analysis method according to the user input data, the processor optimizes the remaining stops through an optimized traffic model when the user-preferred stop setting target stop set for the target route is set to first stop stop. Can be derived.

To this end, the stop attribute parameter by user input may be achieved by reflecting the constraints of Equation 3 below.

[Equation 3]

는 사용자 비선호 무정차 설정 대상 정거장 번호의 부분 집합이며,

인 경우, 제1 정차 패턴이 정거장 번호 i에서 무정차역으로 설정되고,

인 경우, 제2 정차 패턴이 정거장 번호 i에서 무정차역으로 설정된다.In Equation 3, S is a set of stop numbers to set a stop,

Is a subset of the stop number for which the user has no preference, and

In case of, the first stop pattern is set to the stop stop at stop number i,

In the case of, the second stop pattern is set to the stop stop at stop number i.

On the other hand, the processor is based on the parameter-based scenario analysis technique according to the user input data, one stop pattern for the target route through the user input data continuously stops at a certain number of stops or more, causing a problem in the driving interval between trains Can be prevented in advance.

Therefore, the processor may limit the continuous stop through user input data reflecting the constraints of Equation 4 below.

[Equation 4]

는 제1 정차패턴의 정거장 번호 i이고,

는 제2 정차패턴의 정거장 번호 i이며,

으로 정의될 수 있다. In Equation 4, S is a set of station numbers to set a stop, m is the maximum for consecutive stop limits,

Is the stop number i of the first stop pattern,

Is the stop number i of the second stop pattern,

Can be defined as

Based on the parameter-based scenario analysis method according to the user input data, the processor enables the total number of departure passengers to be maintained above a user-determined expected level through the user input data.

The processor may keep the total number of departure passengers above the expected level by reflecting the constraints of Equation 5 below in the optimized traffic model.

[Equation 5]

는 정차역 i에서 정차역 j까지 하나의 패턴을 통해 이동한 일간 통행량을 나타내는 결정변수이며,

는 정차역 i에서 정차역 j까지 서로 다른 패턴간 환승을 통해 이동한 일간 통행량을 나타내는 결정변수이고,

는 상기 대상 노선의 일간 총 통행량에 대한 통계값이며, θ는 사용자 입력에 의한 총 이탈 승객수(%)에 대한 기대 수준을 나타내는 파라미터 값이다.In Equation 5, S is a set of station numbers to set a stop,

Is a determinant variable representing the daily traffic volume traveled through one pattern from stop i to stop j,

Is a determinant variable representing the daily traffic volume traveled through transfer between different patterns from stop i to stop j,

Is a statistical value for the total daily traffic volume of the target route, and θ is a parameter value representing an expected level for the total number of passengers leaving by user input (%).

7 is a diagram illustrating a concept of a traffic pattern by an optimized traffic model according to an embodiment of the present invention.

)는 양의 값을 가질 수 없고, 직승에 관련된 변수(

)만이 양의 값을 가질 수 있다.As shown in (a) of FIG. 7, there is no transfer structure between the first stop pattern (A) and the second stop pattern (B).

) Cannot have a positive value, and the variable related to the square (

) Can only have a positive value.

,

,

으로 각각 설정된다. Since the first stop pattern (A) and the second stop pattern (B) stop at the stops (1, 3) and (4, 6), the variable related to the straight line is set to a positive value, and the stops (8, 17) The pair cannot have a positive value because it is a pair of stations that cannot be transported in a straight pattern. In other words,

,

,

Is set respectively.

이 된다. As shown in (b) of FIG. 7, since there is one transfer structure between the first stop pattern (A) and the second stop pattern (B), it is related to the stops (5, 16) that exist before and after the transfer structure. The transfer variable is a positive value,

It becomes.

)와 환승 승객을 나타내는 결정 변수(

)를 하기 수학식 6과 같은 제약식으로 정식화할 수 있다.As such, the processor is a determinant variable representing the occupant in the optimization model (

) And determinant variables (

) Can be formulated in the following equation (6).

[Equation 6]

는 정거장(I, j )을 통행하는 일간 통행량의 통계값이다. In Equation 6,

Is a statistical value of the daily traffic volume through the station (I, j).

However, the equation (6) becomes a quadratic constraints programming model including a nonlinear constraint in which two or more decision variables take the form of multiplication.

)는 하기 수학식 7~수학식 19와 같은 방법으로 선형화하여 일반적인 혼합정수 계획 모델의 알고리즘을 이용하여 해를 구할 수 있도록 한다.When included in such a nonlinear constraint model, the solution cannot be solved using an algorithm of a general mixed integer programming model. Therefore, the processor

) Is linearized in the same manner as in Equation 7 to Equation 19, so that the solution can be solved using an algorithm of a general mixed integer planning model.

[Equation 7]

[Equation 8]

[Equation 9]

[Equation 10]

[Equation 11]

[Equation 12]

[Equation 13]

[Equation 14]

[Equation 15]

[Equation 16]

[Equation 17]

[Equation 18]

[Equation 19]

) 는 하기 수학식 20~수학식 42와 같은 방법으로 선형화하여 일반적인 혼합정수 계획 모델의 알고리즘을 이용하여 해를 구할 수 있도록 한다.Also, the processor

) Is linearized in the same manner as in Equation 20 to Equation 42 below, so that a solution can be solved using an algorithm of a general mixed integer planning model.

[Equation 20]

[Equation 21]

[Equation 22]

[Equation 23]

[Equation 24]

[Equation 25]

[Equation 26]

[Equation 27]

[Equation 28]

[Equation 29]

[Equation 30]

[Equation 31]

[Equation 32]

[Equation 33]

[Equation 34]

[Equation 35]

[Equation 36]

[Equation 37]

[Equation 38]

[Equation 39]

[Equation 40]

[Equation 41]

[Equation 42]

The optimization traffic model may represent a mathematical model that can be derived by combining optimal stop patterns in consideration of a parameter-based scenario analysis method according to user input data, as Equation 43 below.

[Equation 43]

8 is an operation flowchart illustrating an optimization traffic model according to an embodiment of the present invention.

Referring to FIG. 8, in the case of a general urban rail line, even if the size of the hydraulic model increases, that is, the total number of variables and constraints increases, the optimal traffic model is divided into a transfer model and a direct train model in order to obtain an optimal solution within a short time (S310). .

)를 도출한다(S320). After the relaxation of Equation 42, the transfer model is an optimal solution using the constraints of Equation 44 according to Equations 1 to 4 and Equations 20 to 41 (

) Is derived (S320).

[Equation 44]

The processor derives the transfer structure applied to the optimal solution of the transfer model, utilizes the derived transfer structure as an input value of the direct transfer model (S330), and the equations 1 to 4, and 7 to 19 The optimal solution of the square model is derived using the following equation (45) according to S340.

 [Equation 45]

는 대상 노선의 일간 총 통행량에 대한 통계값이고, θ는 사용자 입력에 의한 총 이탈 승객수(%)에 대한 기대 수준을 나타내는 파라미터 값이며, B는 환승 모델의 최적 해로부터 도출한 환승역 번호의 집합이다.In Equation 45,

Is a statistical value for the total daily traffic volume of the target route, θ is a parameter value indicating the expected level for the total number of passengers leaving by user input (%), and B is a set of transfer station numbers derived from the optimal solution of the transfer model to be.

The processor calculates schedule information of the express train by summing the optimal solution of the transfer model and the optimal solution of the direct train model (S350).

As described above, the processor can derive an optimized stop pattern through an optimized traffic model and reflect it in the train's operation plan, but in urban trains operating at high density in downtown areas, due to the schedule information of the express train originally planned by numerous waiting passengers There is a high possibility of crosstalk when operating trains.

When such crosstalk occurs, the processor may dynamically adjust the first stop pattern and the second stop pattern to minimize congestion and delay between trains operating in the first stop pattern and the second stop pattern.

9 is an operation flowchart illustrating the dynamic adjustment process of the first and second stop patterns according to an embodiment of the present invention, and FIG. 10 illustrates the result of the dynamic adjustment process of the first and second stop patterns of FIG. 9 This is an example diagram.

Referring to FIGS. 9 and 10, the processor may include a congestion level, a stop structure (for example, wiring, a platform, and the like) of a vehicle for an express train running in a first stop pattern and a second stop pattern, and a vehicle structure (for example, , Room, door, etc.), and train operation (delay, congestion), including real-time monitoring of the operation status information (S410). At this time, the processor fetches schedule information for the unstopped (SKIP) / stopped (STOP) of the first stop pattern and the second stop pattern and reflects the schedule information (S420).

In addition, the processor statistically analyzes traffic performance data, driving status information, and schedule information of the express train to provide a prediction model for congestion or driving time (S430), and real-time recognition of stop / run times predicted through the prediction model The collected operation status information is synthesized to determine whether the current congestion status is greater than or equal to the management target value (S440). At this time, the processor may set a management target value by performing a reinforcement learning algorithm with input values of traffic performance data, driving status information, schedule information of the express train, stop and vehicle structure information, and driving delay / congestion information in the database. (S450).

When the processor increases over the current congestion situation management target value, the first stop pattern and the second stop pattern are adjusted in real time at the adjacent transfer station (S460), and the delay and waiting time information (t _d ) of the train due to the real time adjustment of the stop pattern = t + Δt), and the process of real-time recognition of the train's operating condition is repeated by reflecting the reduced train delay and waiting time information (S480).

For example, as illustrated in FIG. 10 (a), the processor may recognize in real time a situation in which the train of the first stop pattern has high in-car congestion and the train of the second stop pattern has a low in-car congestion.

As illustrated in (b) of FIG. 10, the processor may adjust the stop plan or stop time for the congested and non-congested stations in real time by adding and reflecting the congestion situation of the scheduled forward stop to the current operation status information.

The processor may provide guide broadcasting information for real-time adjustment of the first stop pattern and the second stop pattern as in-vehicle broadcast or in-stop broadcast, and may increase the stop time for congested stations.

11A and 11B are exemplary views illustrating a stop pattern of an express train derived by applying a scheduling method of an express train according to an embodiment of the present invention to a domestic urban railway.

11A and 11B, when the scheduling method of the express train according to an embodiment of the present invention is applied to Seoul Line 7 in Korea, the first stop pattern (A) and the second stop pattern are utilized by using an optimized traffic model based on user input data. 2 A variety of train operation analysis scenarios are drawn from 26 to 34 stops for the stop pattern (B), and an optimal stop pattern can be derived according to various train operation analysis scenarios.

12 is an exemplary view for explaining a predicted result of expected travel time and departure passengers when the scheduling method of an express train according to an embodiment of the present invention is applied to a domestic urban railway, and FIG. It is an exemplary diagram explaining the numerical value of the breakdown analysis.

12 and 13, the scheduling method of the express train according to an embodiment of the present invention is expected to reduce the expected travel time when applied to the schedule information of the express trains of lines 2, 5 and 7 in Seoul, Korea. In order to minimize the number of escaped passengers, the optimal number of stops for Lines 2 and 5 can be analyzed to be 29 stations and the optimal stop number for Line 7 to 34 stations.

The method of scheduling an express train according to the embodiment of the present invention described above may also be implemented in the form of a recording medium including instructions executable by a computer, such as a program module executed by a computer. Such recording media include computer readable media, which can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. In addition, computer readable media includes computer storage media, which are volatile and nonvolatile implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. , Removable and non-removable media.

The above description of the present invention is for illustration only, and those skilled in the art to which the present invention pertains can understand that the present invention can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope of the present invention is indicated by the following claims rather than the above detailed description, and it should be interpreted that all changes or modified forms derived from the meaning and scope of the claims and equivalent concepts thereof are included in the scope of the present invention. do.

100: express train scheduling system
110: communication module 120: memory
130: processor 140: database
300: communication network

Claims (27)

In the scheduling method of the express train performed by the scheduling system of the express train to control the train operation through the scheduling of the station,
a) statistically processing traffic input data including stop aggregation data, past station traffic performance data, and past station traffic time data for a target route to provide traffic performance data;
b) receiving user input data having a stop attribute parameter set for a stop pattern of an express train running on the target route, and setting a train operation analysis scenario based on the user input data;
c) determining a first stop pattern and a second stop pattern for an express train operating the target route based on the traffic performance data and user input data according to the set train operation analysis scenario; And
d) Performing an optimized traffic model to derive an optimal stop pattern through a pattern combination between the first stop pattern and the second stop pattern, and reflecting the passenger information or transfer passenger information in the optimized traffic model to determine the speed of the express train. And providing the schedule information. According to claim 1,
In step b), a parameter-based scenario analysis technique according to user input data is applied,
The parameter-based scenario analysis technique,
When the total number of stops for the stop set data for the target route is set as the stop attribute parameter, a train operation analysis scenario is calculated based on a passenger departure minimization pattern and user satisfaction. According to claim 2,
The total number of stops is reached by reflecting the constraints of Equation 1 below in the optimized traffic model.
[Equation 1]

S is the set of station numbers for which to set stops
n is the total number of stops,

If is, the first stop pattern is set to stop station at stop number i

If, the first stop pattern is set to stop-stop at station number i

In case of, the second stop pattern is set to stop station at stop number i

If is, the second stop pattern is set to stop no at stop number i According to claim 1,
Step b),
When the stop attribute parameter is set by setting the user-preferred stop set as the preferred stop in the stop set data for the target route, the constraints of Equation 2 below are reflected in the optimized traffic model. Scheduling method.
[Equation 2]

S is the set of station numbers for which to set stops

Is a subset of the stop number to which the user preferred stop is set

If is, the first stop pattern is set to stop station at stop number i

In case of, the second stop pattern is set to stop station at stop number i According to claim 1,
Step b),
When the stop attribute parameter is set by setting a stop set of a user-preferred stop without a stop in the stop set data for the target route, the constraint of Equation 3 below is reflected in the optimized traffic model. Scheduling method.
[Equation 3]

S is the set of station numbers for which to set stops

Is a subset of the user-preferred stop setting target stop number

If, the first stop pattern is set to stop-stop at station number i

If is, the second stop pattern is set to stop no at stop number i According to claim 1,
Step b),
When the stop attribute parameter is set by limiting continuous stops at a predetermined number of stops or more in the stop set data for the target route, express constraints of Equation 4 below are reflected in the optimized traffic model. Scheduling method.
[Equation 4]

S is the set of station numbers for which to set stops
m is the maximum for continuous stop limits

Is the stop number of the first stop pattern i

Is the stop number of the second stop pattern i

According to claim 1,
Step b),
When the expected level for the total number of departed passengers is set as the stop attribute parameter in the stop set data for the target route, the constraint of Equation 5 below is reflected in the optimized traffic model. .
[Equation 5]

S is the set of station numbers for which to set stops

Is a determinant variable representing the daily traffic volume traveled through one pattern from station i to station j

Is a determinant variable that represents the daily traffic volume traveled through transfer between different patterns from stop i to stop j

Is a statistical value for the total daily traffic volume of the target route.
θ is a parameter value indicating the expected level for the total number of passengers leaving by user input (%) According to claim 1,
The optimization traffic model is to apply the mathematical science model of Equation 6 below, which linearizes the nonlinear constraint so that the solution can be derived by using the algorithm of the mixed integer planning model.
[Equation 6]

S is the set of station numbers for which to set stops

Is a determinant variable that represents the passenger who is in a straight line from stop i to stop j

Is a determinant variable representing the transit passenger from stop i to stop j The method of claim 8,
remind

Wow

Is formulated by the following equation (7), the scheduling method of the express train.
[Equation 7]

Is the statistical value of the daily traffic volume through the station (I, j).

Is the stop number of the first stop pattern i

Is the stop number of the second stop pattern i The method of claim 8,
Step d),
d-1) separating the optimized traffic model into a transfer model and a direct transfer model;
d-2) Optimal solution of the transfer model using the constraints of Equation 8 below, which mitigates Equation 7

Deriving);
d-3) deriving a transfer structure applied to the optimal solution of the transfer model, and then deriving an optimal solution of the transfer model using Equation (9) below using the derived transfer structure as an input value of the direct transfer model; And
d-4) calculating the schedule information of the express train by summing the optimal solution of the transfer model and the optimal solution of the direct train model, the scheduling method of the express train.
[Equation 8]

[Equation 9]

Is a statistical value for the total daily traffic volume of the target route.
θ is a parameter value indicating the expected level for the total number of passengers leaving by user input (%)
B is a set of transfer station numbers derived from the optimal solution of the transfer model According to claim 1,
e) Real-time monitoring of driving status information including congestion in the car for express trains operating in the first stop pattern and the second stop pattern, and when the current congestion status is higher than a preset management target value through the operation status information Further comprising the step of dynamically real-time re-adjusting the first stop pattern and the second stop pattern at the transfer station, the scheduling method of the express train. The method of claim 11,
Step e),
e-1) real-time recognition of current operation status information using train operation information including stop structure information, vehicle structure information, delay or congestion;
e-2) Statistical analysis of the traffic performance data, the operation status information, or the schedule information of the express train to provide a predictive model for congestion or driving time, and predict stop / run time through the predictive model ;
e-3) Real-time adjustment of the first stop pattern and the second stop pattern at adjacent transfer stations when the current congestion situation increases above the management target value based on the predicted stop / run time and current operation status information The method comprising the steps of scheduling an express train. The method of claim 12,
In step e-2), the management target value is set by performing a reinforcement learning algorithm based on the traffic performance data, the driving condition information, or the schedule information of the express train, to set the management target value. The method of claim 12,
Step e-3) is a method for scheduling an express train by adding a congestion situation of a station to be stopped in front to the operation status information and adjusting a stop plan or a stop time for a congestion and a non-congestion in real time. The method of claim 12,
In step e-3),
The real-time adjusted first stop pattern and the second stop pattern to provide information about the broadcast, the rapid train scheduling method. In the scheduling system of the express train to control the train operation through the scheduling of the stop,
A memory in which a program for performing a scheduling method of an express train for stopping by selectively setting a stop for a target route is recorded; And
It includes a processor for executing the program,
The processor, by the execution of the program,
Provides traffic performance data by statistically processing traffic input data including stop aggregation data, past station traffic performance data, and past station traffic time data for the target route,
Receiving user input data with a stop attribute parameter set for a stop pattern of an express train running on the target route, setting a train operation analysis scenario based on the user input data,
The first stop pattern and the second stop pattern for the express train operating the target route are determined based on the traffic performance data and user input data according to the set train operation analysis scenario,
Optimized traffic model for deriving an optimal stop pattern through a pattern combination between the first stop pattern and the second stop pattern, and the schedule information of the express train by reflecting passenger information or transfer passenger information in the optimized traffic model To provide, the scheduling system of the express train. The method of claim 16,
When the total number of stops is set as the stop attribute parameter for the stop set data for the target route, the processor is based on parameters according to user input data to calculate a train operation analysis scenario based on a passenger departure minimization pattern and user satisfaction. Scheduling system of express train, applying scenario analysis technique. The method of claim 17,
The processor,
A system for scheduling an express train in which the user input data set with the stop attribute parameter is reflected in the optimized traffic model by setting a user-preferred stop set as a preferred stop in the stop set data for the target route. The method of claim 17,
The processor,
The user's non-preferred stop setting target stop set in the stop set data for the target route is a stop stop, and the user input data set with the stop attribute parameter is reflected in the optimized traffic model. The method of claim 17,
The processor,
The system for scheduling an express train is to limit the continuous stops at a predetermined number of stops or more from the stop set data for the target route to reflect the user input data set with the stop attribute parameter in the optimized traffic model. The method of claim 17,
The processor,
Scheduling system of the express train, which reflects the user input data in which the expected level for the total number of passengers leaving the station aggregate data for the target route is set as the stop attribute parameter in the optimized traffic model. The method of claim 16,
The optimization passage model is a scheduling system for an express train that applies the mathematical science model of Equation 6 below, which linearizes a nonlinear constraint to enable solution derivation by using an algorithm of a mixed integer planning model.
[Equation 6]

S is the set of station numbers for which to set stops

Is a determinant variable that represents the passenger who is in a straight line from stop i to stop j

Is a determinant variable representing the transit passenger from stop i to stop j The method of claim 16,
The processor separates the optimized traffic model into a transfer model and a direct transfer model, and then an optimal solution of the transfer model (

),
After deriving the transfer structure applied to the optimal solution of the transfer model, the optimal solution of the direct transfer model is derived by using the derived transfer structure as an input value of the direct transfer model,
The scheduling system of the express train is to calculate schedule information of the express train by summing the optimal solution of the transfer model and the optimal solution of the direct train model. The method of claim 16,
The processor monitors in real time the operation status information including the congestion level in the vehicle for the express train running in the first stop pattern and the second stop pattern, and the current congestion status is greater than or equal to a preset management target value through the operation status information. In the case of an adjacent transit station, the first stop pattern and the second stop pattern are dynamically real-time readjusted. The method of claim 24,
The processor,
Scheduling system of the express train, to set the management target value by performing a reinforcement learning algorithm based on the traffic performance data, the driving status information or the schedule information of the express train. The method of claim 24,
The processor is to adjust the stop time or stop time for the congestion and non-congestion in real time by adding the congestion status of the scheduled stop ahead to the operation status information, the scheduling system of the express train. The method of claim 24,
The processor is to provide the broadcast information for the first stop pattern and the second stop pattern adjusted in real time, the scheduling system of the express train.

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