CN112874586B - Urban rail transit intelligent train timetable matching method and electronic equipment - Google Patents
Urban rail transit intelligent train timetable matching method and electronic equipment Download PDFInfo
- Publication number
- CN112874586B CN112874586B CN202110117284.2A CN202110117284A CN112874586B CN 112874586 B CN112874586 B CN 112874586B CN 202110117284 A CN202110117284 A CN 202110117284A CN 112874586 B CN112874586 B CN 112874586B
- Authority
- CN
- China
- Prior art keywords
- train
- matching
- schedule
- sequence
- weight
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/10—Operations, e.g. scheduling or time tables
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Train Traffic Observation, Control, And Security (AREA)
Abstract
The invention discloses an intelligent train schedule matching method for urban rail transit, which comprises the steps of firstly determining the sequence of all trains according to the sequence position relationship of all trains; and selecting the first train in all the train sequences to match with the schedule travel, sequentially matching the subsequent trains with the schedule travel according to the schedule sequence, and calculating the matching weight. And then, the train sequence is readjusted, the schedule travel is matched, and the matching weight is calculated until all the train sequences are traversed. And finally, comparing the sum of the weights of all train sequence matching schedules, and selecting the train matching schedule scene with the largest sum of the weights. The invention also provides electronic equipment, and the processor realizes the steps of the urban rail transit intelligent train schedule matching method when executing the computer program. The invention realizes the matching of the intelligent train timetable, has less reference data, clear logic, simple realization method, less calculation amount and high train punctuality rate.
Description
Technical Field
The invention belongs to the technical field of rail transit, and particularly relates to an intelligent train schedule matching technology for urban rail transit.
Background
Under normal conditions, the operation adjusting method of the urban rail transit train adopts schedule adjustment; when a large-area late point adjustment method of the train occurs, equal interval adjustment or manual auxiliary adjustment is adopted, and the adjustment method is switched to a schedule adjustment scene when the train basically recovers the running interval; or under the scene of changing the timetable, in the prior art, the dispatcher needs to manually judge and match the timetable, so that the requirement on the professional technology of the dispatcher is high, and the train punctuality rate is influenced.
Disclosure of Invention
Aiming at the application requirement of adjusting the recovery schedule in the operation of the urban rail transit train, the invention aims to solve the technical problem of providing an intelligent train schedule matching method for the urban rail transit, which realizes the optimization of train schedule matching, reduces manual intervention and improves the train punctuality rate.
In order to solve the technical problems, the invention adopts the following technical scheme: an intelligent train schedule matching method for urban rail transit comprises the following steps:
step S1: determining the sequence of all trains according to the sequence position relationship of all trains;
step S2: selecting all train sequences Q j In the first train V 1 Matching the timetable and finding out the matching travel T in the timetable 1 Calculating the train matching weight W according to the preset schedule matching weight value 1 ；
And step S3: calculating the matching weights of all trains in sequence: selecting the ith train V in the train sequence i ，i>1, finding the journey T in the timetable i-1 Next stroke T of i Calculating the train matching weight W according to the preset schedule matching weight value i ；
And step S4: recording schedule travel matched with each train, and calculating the sum of all train matching weights according to the formula (1): SW j ＝∑W i (1) In the formula W i Matching weight for the ith train;
step S5: adjusting all train sequences, adjusting the first train to be the last train, matching schedule travel, calculating matching weight until all train sequences are traversed, and if the train is consistent with the recorded matching schedule travel, not executing the train sequence schedule matching weight calculation;
step S6: and comparing the sum of the weights of all the train sequence matching schedules, and selecting the train matching schedule scene with the largest sum of the weights.
Preferably, the method for finding the matching trip in the schedule comprises: according to the position, the direction and the departure time of the train, sequentially traversing a journey list in the corresponding time period in the timetable to obtain a journey with the minimum time deviation at the morning and evening points; selecting the departure time according to the position of the train, and selecting the predicted departure time of the train on the platform if the train stops at the platform; and if the train is in the interval or in the process of entering the station, selecting the actual departure time of one station on the train.
Preferably, the timetable matching weight value is divided into: a severe weight of 2, a mild weight of 4, a standard weight of 5, a mild weight of 3, and a severe weight of 1.
The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the steps of the urban rail transit intelligent train schedule matching method when executing the computer program.
The method for matching the intelligent schedules of the trains by adopting the technical scheme has the following advantages:
because the maximum train number on the subway line generally does not exceed 120, the sequence list of all trains is traversed by adopting an exhaustion method, the calculation of the same matching result is reduced, the logic is clear, and the implementation method is simple.
The train matching timetable travel fully considers the factors of train position, departure time and predicted departure time, and improves the matching accuracy and the train punctuality rate.
The main calculated amount of the method is that the train sequence used in initialization is matched with the first train in the train sequence, the algorithms of matching of subsequent trains in the train sequence and the weight value are simple, and the calculated amount is small.
The following detailed description of the present invention and the advantages thereof will be described with reference to the accompanying drawings.
Drawings
The invention is further described with reference to the following figures and detailed description:
FIG. 1 is a flow chart of an intelligent train schedule matching method of the present invention.
Fig. 2 is a system state diagram when the intelligent train schedule matching method of the present invention is initialized.
Fig. 3 is a schedule when the intelligent train schedule matching method of the present invention is initialized.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the operation process of the urban rail transit train, after the schedule cannot be continuously adjusted due to line faults, the operation adjusting method is switched from the schedule adjustment to equal-interval adjustment or manual auxiliary adjustment; after the fault is recovered, when the operation adjusting method is recovered to the schedule adjustment, the intelligent train schedule matching method of the invention reduces the participation of the dispatcher, reduces the time for recovering normal operation, and improves the operation quality and the operation capacity.
Example one
The main flow of the urban rail transit intelligent train schedule matching method is shown in figure 1:
firstly, determining the sequence of all trains according to the sequence position relation of all trains.
And selecting the first train in all the train sequences to match with the schedule travel, sequentially matching the subsequent trains with the schedule travel according to the schedule sequence, and calculating the matching weight.
And readjusting the train sequence, matching the schedule travel, and calculating the matching weight until all the train sequences are traversed.
And comparing the sum of the weights of all the train sequence matching schedules, and selecting the train matching schedule scene with the largest sum of the weights.
Fig. 2 and fig. 3 show a system state diagram and a schedule chart when the schedule matching method for 6 trains at 8 platforms at 4 stations is initialized, respectively.
The basic process of the schedule matching method of the present invention is described with reference to fig. 2 and 3:
1) And determining the sequence of all the trains according to the sequence position relation of all the trains. And finding out the sequence of all the trains according to the sequence of the platforms and the sequence of the trains reaching the nearest platform in front.
As shown in fig. 2, the sequence of the stations is ST0101, ST0201, ST0301, ST0401, ST0402, ST0302, ST0202, ST0102; the train sequence reaching the platform ST0101 is train 1; the train sequence reaching the platform ST0201 is train 2; the train arriving at the platform ST0301 is train 3; the train arriving at the station ST0401 is train 4; the train arriving at the station ST0402 is train 5; the train arriving at the platform ST0302 is empty in sequence; the train arriving at the platform ST0202 is in the sequence of 6; the train sequence arriving at the station ST0102 is empty; all trains sequence Q 1 Trains 1, 2, 3, 4, 5 and 6.
2) Selecting all train sequences Q j In the first train V 1 Matching the timetable and finding out the matching travel T in the timetable 1 Calculating the train matching weight W according to the preset schedule matching weight value 1 。
And sequentially traversing the travel list in the corresponding time period in the timetable according to the position, the direction and the departure time of the train to obtain the travel with the minimum time deviation at the early and late points (the time deviation at the early and late points is the absolute value of the difference between the departure time of the train and the planned departure time in the timetable). In order to improve the matching precision of the timetable, the departure time is selected according to the position of the train. When the train stops at the platform, the expected departure time of the train at the platform is selected. And selecting the actual departure time of one station on the train in the interval or station entering process of the train. As shown in fig. 2, in which the train 1 runs in the interval ST02 to ST01, the matching schedule employs the ST0201 station and the actual departure time of the station; the train 6 stops at the ST01 station, and the matching schedule adopts the ST0101 station and the predicted departure time of the station.
The timetable matching weighted value is divided into the following parts according to the early and late point degrees: a severe weight of 2, a mild weight of 4, a standard weight of 5, a mild weight of 3 and a severe weight of 1. Supposing that the late point is less than 30s and the early point is less than 30s as the quasi-point weight value; the early point is more than 30s and less than 60s, which is a light early point weight value; the weight value of the early point is more than 60 s; the weight value of the light evening is that the evening is more than 30s and less than 60 s; the weight value of the severe late is that the late is more than 60 s.
3) Calculating the matching weights of all trains in sequence: selecting the ith (i) in train sequence>1) Train V i Finding the travel T in the timetable i-1 Next stroke T of i Calculating the train matching weight W according to the preset schedule matching weight value i 。
As shown in FIG. 2, assume a train list Q 1 The calculation result of (a) is: timetable trip T of train 1 1 And a matching weight value W 11 Schedule journey T of train 2 2 And a matching weight value W 12 Schedule journey T of train 3 3 And a matching weight value W 13 And so on.
4) Recording schedule travel matched with each train, and calculating the sum of all train matching weights according to the formula (1):
SW j ＝∑W i (1)
in the formula (1), W i Is the matching weight of the ith train.
As shown in FIG. 2, assume the total matching weight value SW of the train list Q1 1 。
5) Adjusting the sequence of all trains, wherein the first train is adjusted to be the last train; and 2) re-executing, if the train is consistent with the recorded matching schedule in journey, not executing the matching weight calculation of the train sequence schedule.
As shown in fig. 2, the intelligent train schedule matching algorithm sequentially traverses the following train sequences: train sequence Q 2 Trains 2, 3, 4, 5, 6, 1; train sequence Q 3 Trains 3, 4, 5, 6, 1, 2; train sequence Q 4 Trains 4, 5, 6, 1, 2, 3; train sequence Q 5 Trains 5, 6, 1, 2, 3, 4; train sequence Q 6 Trains 6, 1, 2, 3, 4, 5.
Suppose a train list Q 2 The calculation result of (a) is: schedule journey T1 and matching weight value W of train 2 21 Schedule journey T of train 3 2 And matching weight value W 22 Schedule journey T of train 3 3 And a matching weight value W 23 Analogize in turn, the total matching weighted value SW 2 。
Suppose a train list Q 3 Matching schedule journey T in first train 3 3 Then the train list Q 3 The calculation result and the train list Q 1 Has the same calculation result, does not calculate the train list Q 3 And (4) weighting values.
6) And comparing the sum of the weights of all the train sequence matching schedules, and selecting the train matching schedule scene with the largest sum of the weights.
According to the technical scheme, the intelligent train schedule matching is realized, the reference data is less, the logic is clear, the realization method is simple, the calculated amount is less, and the train punctuality rate is high.
Example two
An electronic device includes a memory, a processor, and a computer program stored in the memory and operable on the processor, where the processor executes the computer program to implement the steps of the intelligent train schedule matching method for urban rail transit according to the first embodiment.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that the invention is not limited thereto, and may be embodied in other forms without departing from the spirit or essential characteristics thereof. Any modification which does not depart from the functional and structural principles of the present invention is intended to be included within the scope of the claims.
Claims (2)
1. An intelligent train schedule matching method for urban rail transit is characterized by comprising the following steps:
step S1: determining the sequence of all trains according to the sequence position relationship of all trains;
step S2: selecting all train sequences Q j In the first train V 1 Matching the timetable and finding out the matching travel T in the timetable 1 Calculating the train matching weight W according to the preset schedule matching weight value 1 ；
And step S3: calculating the matching weights of all trains in sequence: selecting the ith train V in the train sequence i ，i>1, finding the journey T in the timetable i-1 Next stroke T of i Calculating the train matching weight W according to the preset schedule matching weight value i ；
And step S4: recording schedule travel matched with each train, and calculating the sum of all train matching weights according to the formula (1): SW j ＝∑W i (1) In the formula W i Matching weight for the ith train;
step S5: adjusting all train sequences, adjusting the first train to be the last train, matching schedule travel, calculating matching weight until all train sequences are traversed, and if the train is consistent with the recorded matching schedule travel, not executing the train sequence schedule matching weight calculation;
step S6: comparing the sum of the weights of all train sequence matching timetables, and selecting a train matching timetable scene with the largest sum of the weights;
the method for finding out the matching travel in the timetable comprises the following steps: according to the position, the direction and the departure time of the train, sequentially traversing a journey list in the corresponding time period in the timetable to obtain a journey with the minimum time deviation at the morning and evening points; selecting the departure time according to the position of the train, and selecting the predicted departure time of the train on the platform if the train stops at the platform; if the train is in an interval or in the process of entering the train, selecting the actual departure time of one station on the train, and dividing the time table matching weight value into the following parts according to the early and late point degrees: a severe weight of 2, a mild weight of 4, a standard weight of 5, a mild weight of 3, and a severe weight of 1.
2. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: the processor realizes the steps of the urban rail transit intelligent train schedule matching method 1 when executing the computer program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110117284.2A CN112874586B (en) | 2021-01-28 | 2021-01-28 | Urban rail transit intelligent train timetable matching method and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110117284.2A CN112874586B (en) | 2021-01-28 | 2021-01-28 | Urban rail transit intelligent train timetable matching method and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112874586A CN112874586A (en) | 2021-06-01 |
| CN112874586B true CN112874586B (en) | 2022-10-21 |
Family
ID=76053040
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110117284.2A Active CN112874586B (en) | 2021-01-28 | 2021-01-28 | Urban rail transit intelligent train timetable matching method and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112874586B (en) |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130116865A1 (en) * | 2011-11-03 | 2013-05-09 | Jared COOPER | System and method for changing when a vehicle enters a vehicle yard |
| WO2015162670A1 (en) * | 2014-04-21 | 2015-10-29 | 三菱電機株式会社 | Train travel prediction device and train travel prediction method |
| CN107215361B (en) * | 2017-06-09 | 2018-12-28 | 湖南中车时代通信信号有限公司 | Time-table display device based on Locomotive Running Monitor System |
| JP7066365B2 (en) * | 2017-10-16 | 2022-05-13 | 株式会社日立製作所 | Timetable creation device and automatic train control system |
| CN111539537B (en) * | 2020-03-24 | 2022-08-30 | 卡斯柯信号有限公司 | Subway train receiving and dispatching plan automatic matching method combined with maintenance plan |
-
2021
- 2021-01-28 CN CN202110117284.2A patent/CN112874586B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN112874586A (en) | 2021-06-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2021208484A1 (en) | Dynamic control method for bus signal priority high-frequency multi-application | |
| CN110533242B (en) | Energy-saving optimization method under train interconnection and intercommunication overline operation | |
| CN108647813B (en) | High-speed train dynamic interval energy-saving optimization method based on elastic force adjustment | |
| CN109703606B (en) | High-speed train intelligent driving control method based on historical operation data | |
| JP5680262B1 (en) | Train travel prediction apparatus and train travel prediction method | |
| CN110497943A (en) | A kind of municipal rail train energy-saving run strategy method for on-line optimization based on intensified learning | |
| CN109002625B (en) | Robust optimization-based method for designing connection public transport network | |
| CN110979407B (en) | Urban rail train timetable group adjusting method | |
| CN104575038A (en) | Intersection signal control method considering priority of multiple buses | |
| CN110920700A (en) | High-speed rail scheduling optimization method, system and storage medium | |
| CN110843870A (en) | Method for maintaining fixed capacity of high-speed railway network graph under abnormal event | |
| CN109146758A (en) | Passenger flow-based train operation delay adjustment method and system | |
| BR102015003620B1 (en) | Automatic train operating system | |
| CN114298398B (en) | High-speed train dynamic tracking operation optimization method based on elastic adjustment strategy | |
| CN107545730A (en) | A kind of website based on Based on Bus IC Card Data is got on or off the bus passenger's number estimation method | |
| CN109190811A (en) | A kind of car speed tracking based on adaptive extended kalman filtering | |
| CN110228507A (en) | A kind of automatic train regulation control method based on event triggering | |
| JP2008189180A (en) | Train operation management system | |
| CN112874586B (en) | Urban rail transit intelligent train timetable matching method and electronic equipment | |
| CN113415324A (en) | Dynamic scheduling and operation control collaborative optimization method and system for high-speed train | |
| CN102609689B (en) | A kind of video track background modeling method based on multiframe statistics | |
| CN114493201A (en) | Method for measuring service toughness based on urban rail transit network and application | |
| CN108263400B (en) | High-speed rail train running speed control method, device, storage medium and high-speed rail train | |
| CN114880770B (en) | Train cooperative operation control method based on reference deep reinforcement learning | |
| CN110304117A (en) | A kind of train scheduling method and device solving tide passenger flow |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |