CN106627677A - Method and device for predicting arrival time of target train of railway travel service system - Google Patents
Method and device for predicting arrival time of target train of railway travel service system Download PDFInfo
- Publication number
- CN106627677A CN106627677A CN201611266592.7A CN201611266592A CN106627677A CN 106627677 A CN106627677 A CN 106627677A CN 201611266592 A CN201611266592 A CN 201611266592A CN 106627677 A CN106627677 A CN 106627677A
- Authority
- CN
- China
- Prior art keywords
- train
- time
- target train
- website
- target
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 230000008901 benefit Effects 0.000 abstract description 6
- 230000008569 process Effects 0.000 description 7
- 238000012937 correction Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000033001 locomotion Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- GJWAPAVRQYYSTK-UHFFFAOYSA-N [(dimethyl-$l^{3}-silanyl)amino]-dimethylsilicon Chemical compound C[Si](C)N[Si](C)C GJWAPAVRQYYSTK-UHFFFAOYSA-N 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000001035 drying Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000003825 pressing Methods 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
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
- B61L27/14—Following schedules
-
- 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
- B61L27/40—Handling position reports or trackside vehicle data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Mechanical Engineering (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Train Traffic Observation, Control, And Security (AREA)
Abstract
The embodiment of the invention discloses a method and device for predicting the arrival time of a target train of a railway travel service system. The method comprises the steps that when a sent large-area late signal is detected, journey information and train type of the target train are obtained; the predicted arrival time of the target train to arrive at a nearest first station in the advancing direction is obtained according to the journey information; the actual arrival time of the target train arriving at the first station is obtained; according to the train type of the target train, the time error information of a train with the same type as the target train and arriving at the first station is obtained; according to the predicted arrival time, the actual arrival time and the time error information, the mileage from the target train to a next station is calibrated; according to the calibrated mileage, the predicted arrival time of the target train to arrive at the next station is obtained. The arrival time of the target train is predicted based on the actual operating information and predicted operating information of the target train in a self-adaptive mode. Compared with the prior art, the method has the advantage of high prediction accuracy.
Description
Technical field
The present embodiments relate to track traffic service technology field, and in particular to a kind of target column of railway trip dress system
Car arrival time Forecasting Methodology and device.
Background technology
Passenger Service System be arrange Integrated Management Platform, be oriented to announcement, broadcast, monitoring, clock, inquire about, seek help, nothing
The subsystems such as line, deposit.Based on automatic information collecting, to provide full spectrum information service as target as passenger, visitor is realized
The functions such as fortune station information Auto broadcast, guiding are disclosed, monitoring, and the integrated system of the information service of number of ways is provided.Trip
Dress system Integrated Management Platform to the comprehensive production service of staff and passenger's offer and passenger facilities as target, to connect automatically
Enter the external informations such as scheduling, passenger ticket, according to unified interface standard detached broadcast, guiding, video, ticket checking, inquiry, deposit
With the subsystem Deep integrating such as clock, there is provided integrated service is operated, information sharing and function linkage are realized, improve passenger facilities
Information-based and automatization level.
During the embodiment of the present invention is realized, inventor has found the TDMS accuracy of informations when train large area is late
It is relatively low, it is difficult to which that Accurate Prediction goes out the arrival time of train.
The content of the invention
One purpose of the embodiment of the present invention is to solve prior art when train large area is late, and system is based on delivery tube
Manage the low problem of the train arrival time accuracy of the acquisition of information that scheduling system TDMS is provided.
The embodiment of the present invention proposes a kind of target train arrival time Forecasting Methodology of railway trip dress system, including：
Detect send out large area late signal when, obtain the travel information and type of train of the target train；
The target train is obtained according to the travel information and reaches the pre- of the first website closest in direction of advance
Survey arrival time；
Obtain the target train and reach the actual achievement of first website and reach the time；
Previous column is obtained according to the type of train of the target train and reaches institute with the train of the target train same type
State the time difference information of the first website；
The target column car is calibrated apart from next website according to the actual achievement arrival time and the time difference information
Mileage；
The prediction arrival time that the target train reaches next website is obtained according to the mileage after calibration.
Preferably, the travel information includes：The current system of traffic coverage and trip's dress system residing for the target train
The system time；
Correspondingly, it is described the target train is obtained according to the travel information to reach closest the in direction of advance
The step of prediction arrival time of one website, specifically includes：
Traffic coverage according to residing for the target train, obtains the operation that the target train reaches first website
Mileage；
Described in the distance travelled and the present system time for reaching first website according to the target train is obtained
Target train reaches the prediction arrival time of the first website.
Preferably, the travel information also includes：The speed of service parameter of the target train；
Correspondingly, it is described first website is reached according to the target train distance travelled and the current system when
Between obtain the step of the target train reaches prediction arrival time of the first website and specifically include：
According to the speed of service parameter, with reference to formula one, prediction speed of service v of the target train is obtainedi0'；
The distance travelled of first website and described current is reached according to the prediction speed of service, the target train
System time, with reference to formula two, obtains the prediction arrival time TtyD_n1；
Wherein, vi0For the initial operating speed of the target train；For the restriction speed of the target train；VcFor institute
State the average speed of the affiliated type train of target train；α, (γ >=0) is speed weight to beta, gamma, alpha+beta+γ=1 for α >=0, β >=0；
TsysFor present system time；Si1For the distance travelled that the target train reaches first website.
Preferably, reach closest first in direction of advance obtaining the target train according to the travel information
Before the step of prediction arrival time of website, methods described also includes：
Time is generated according to the prediction arrival time and actual achievement arrival time that the target train reaches first website
Error update information；
According to time error fresh information renewal previous column and the train of the target train same type are reached
The time difference information of the first website.
Preferably, the travel information also includes：The time of running table of the target train, the time of running table includes
The bus stop of the target train and process station, and in each bus stop and the berthing time through station；
Correspondingly, the method also includes：
The time of staying of the target train in first website is obtained according to the time of running table of the target train；
The target train is obtained according to the time of staying and the prediction arrival time and sails out of first website
The prediction departure time.
Present invention also offers a kind of target train arrival time prediction meanss of railway trip dress system, including：
First acquisition module, for detect send out large area late signal when, obtain the stroke letter of the target train
Breath and type of train；
First prediction module, distance is reached in direction of advance most for obtaining the target train according to the travel information
The prediction arrival time of the first near website；
Second acquisition module, the actual achievement that first website is reached for obtaining the target train reaches the time；
3rd acquisition module, it is same with the target train for obtaining previous column according to the type of train of the target train
The train of type reaches the time difference information of first website；
Calibration module, for calibrating the target column spacing according to the actual achievement arrival time and the time difference information
From the mileage of next website；
Second prediction module, for obtaining the target train according to the mileage after calibration the pre- of next website is reached
Survey arrival time.
Preferably, the travel information includes：The current system of traffic coverage and trip's dress system residing for the target train
The system time；
Correspondingly, first prediction module, specifically for the traffic coverage according to residing for the target train, obtains institute
State the distance travelled that target train reaches first website；Reached in the operation of first website according to the target train
Journey and the present system time obtain the prediction arrival time that the target train reaches the first website.
Preferably, the travel information also includes：The speed of service parameter of the target train；
Correspondingly, first prediction module, specifically for according to the speed of service parameter, with reference to formula one, obtains
Prediction speed of service v of the target traini0'；
The distance travelled of first website and described current is reached according to the prediction speed of service, the target train
System time, with reference to formula two, obtains the prediction arrival time TtyD_n1；
Wherein, vi0For the initial operating speed of the target train；For the restriction speed of the target train；VcFor institute
State the average speed of the affiliated type train of target train；α, (γ >=0) is speed weight to beta, gamma, alpha+beta+γ=1 for α >=0, β >=0；
TsysFor present system time；Si1For the distance travelled that the target train reaches first website.
Preferably, described device also includes：Update module；
The update module, for reaching prediction arrival time and the actual achievement of first website according to the target train
Arrival time generates time error fresh information；
According to time error fresh information renewal previous column and the train of the target train same type are reached
The time difference information of the first website.
Preferably, the travel information also includes：The time of running table of the target train, the time of running table includes
The bus stop of the target train and process station, and in each bus stop and the berthing time through station；
Correspondingly, the device also includes：3rd prediction module；
3rd prediction module, for obtaining the target train in institute according to the time of running table of the target train
State the time of staying of the first website；The target train is obtained according to the time of staying and the prediction arrival time and sails out of institute
State the prediction departure time of the first website.
As shown from the above technical solution, the target train arrival time of the railway trip dress system that the embodiment of the present invention is proposed is pre-
Actual achievement operation information and prediction operation information of the method and device based on target train is surveyed, adaptively to the arrival of target train
Time is predicted, compared with prior art, with precision of prediction great number advantage.
Description of the drawings
The features and advantages of the present invention can be more clearly understood from by reference to accompanying drawing, accompanying drawing is schematic and should not manage
Solution is to carry out any restriction to the present invention, in the accompanying drawings：
Fig. 1 shows a kind of target train arrival time prediction side of railway trip dress system that one embodiment of the invention is provided
The schematic flow sheet of method；
Fig. 2 shows a kind of target train arrival time prediction dress of railway trip dress system that one embodiment of the invention is provided
The schematic flow sheet put；
Fig. 3 shows a kind of target train arrival time prediction of railway trip dress system that another embodiment of the present invention is provided
The schematic flow sheet of device.
Specific embodiment
To make purpose, technical scheme and the advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
A part of embodiment of the present invention, rather than the embodiment of whole.Based on the embodiment in the present invention, ordinary skill people
The every other embodiment that member is obtained on the premise of creative work is not made, belongs to the scope of protection of the invention.
Fig. 1 shows a kind of prediction side of target train arrival time railway trip dress system that one embodiment of the invention is provided
The schematic flow sheet of method, the target train arrival time Forecasting Methodology, including：
110th, when the late signal of large area is detected, the travel information and type of train of the target train is obtained；
It should be noted that the mode of the late signal of triggering large area has various, illustrate：If trip's dress system is detected
Administrative train occur large area it is late when, the triggering late signal of large area；Or, related personnel is according to expertise in correspondence
Time by way of pressing keys, triggers the late signal of large area；
Correspondingly, in the late signal of occurrence of large-area, trip's dress system stops receiving TDMS periodic plans, only receives TDMS
Actual achievement information, containing the travel information of target train in actual achievement information, is managed collectively by trip's dress system to administrative train；
Understandable to be, preset ratio herein can be 60%, 80% etc., if administrative train has 80% row
Car is all late or staff independently judges big name area occur late, then trigger the late signal of large area.
120th, the target train is obtained according to the travel information and reaches the first website closest in direction of advance
Prediction arrival time；
130th, the actual achievement that the acquisition target train reaches first website reaches the time；
140th, obtain previous column according to the type of train of the target train to arrive with the train of the target train same type
Up to the time difference information of first website；
It is understood that color is, when each train is through the first website, system will record the train in the first website
Prediction arrival time, actual achievement arrival time, and time difference information.
150th, the target column car is calibrated apart from the next stop according to the actual achievement arrival time and the time difference information
The mileage of point；
It should be noted that occur large area it is late when, target train reach the first website actual achievement arrival time and
Prediction arrival time is probably inconsistent.Accordingly, it is possible to trip's dress system occur thinks that target train at a time reaches
One website, and actually target train sails out of already the situation of the first website.Therefore, need to target column car apart from next website
Mileage is calibrated, to improve the degree of accuracy for reaching the time of prediction.
160th, the prediction arrival time that the target train reaches next website is obtained according to the mileage after calibration.
Actual achievement operation information and prediction operation information of the embodiment of the present invention based on target train, adaptively to target column
The arrival time of car is predicted, and compared with prior art, has the advantages that precision of prediction is high.
And, the present embodiment considers the time difference information of the travel information of target train and same type train,
With according to travel information obtain prediction arrival time after, based on time difference information to target column car in next website
Journey is modified, further to improve the degree of accuracy of prediction.
The step 120 in the present embodiment is described in detail below：
The present system time of traffic coverage and trip's dress system according to residing for the target train that travel information includes；
Traffic coverage of trip's dress system according to residing for the target train, obtains the target train and reaches the first stop
The distance travelled of point；
The speed of service parameter of the target train that trip's dress system includes according to travel information, with reference to formula one, obtains described
Prediction speed of service v of target traini0'；
The distance travelled of first website and described current is reached according to the prediction speed of service, the target train
System time, with reference to formula two, obtains the prediction arrival time TtyD_n1；
Wherein, vi0For the initial operating speed of the target train；For the restriction speed of the target train；VcFor institute
State the average speed of the affiliated type train of target train；α, (γ >=0) is speed weight to beta, gamma, alpha+beta+γ=1 for α >=0, β >=0；
TsysFor present system time；Si1For the distance travelled that the target train reaches first website.
In a possible embodiments, present invention also offers the prediction scheme of another kind of prediction arrival time, specific as follows：
Time is generated according to the prediction arrival time and actual achievement arrival time that the target train reaches first website
Error update information；
According to time error fresh information renewal previous column and the train of the target train same type are reached
The time difference information of the first website.
In another possible embodiments, the invention allows for target train slave site prediction departure time, concrete steps
It is as follows：
The time of running table of the target train that trip's dress system includes according to travel information, obtains target train described first
The time of staying of website；
It is understandable to be, described in time of running table the time of staying in bus stop of original plan general objective train；
The target train is obtained according to the time of staying and the prediction arrival time and sails out of first website
The prediction departure time.
The present embodiment, based on prediction arrival time the departure time is further predicted, accurate compared to the original departure time
Du Genggao.
Referring to example, the present invention is described in detail：
After S1, the late signal of triggering large area, trip's dress system records reference information automatically, and gives birth to automatically according to reference information
Into check information；
S2, according to the check information that automatically generates, produce each train reach nearest station it is different to a time, complete
Predict for the first time；
When S3, train reach the nearest station of prediction for the first time, actual achievement arrival time can be produced, and calibration letter is generated with this
Breath；
After the station of S4, train from after arrival is outputed, start second and predict, second prediction process reference time by with
Actual achievement arrival time produces after being corrected；
When S5, train reach the station of second prediction, actual achievement arrival time can be produced, and calibration information is generated with this；
S6, further, if there is the train of same type before train through this traffic coverage, train will be according to previous column
Car is corrected；
S7, triggering stop the late signal of large area, and system returns normal condition, continues to TDMS information.
Below expansion explanation is carried out to above-mentioned steps：
S1 is specifically included：
S101, occur train large area it is late when, trip dress system stops receiving TDMS periodic plans, only receives TDMS actual achievements
Information.Actual achievement packet bus stop containing train and the whole actual achievement information through station.
S102, trip's dress system road bureau central platform are managed collectively according to type of train to the train of approach local exchange to train number.
S103, the late signal of trip's dress system triggering large area, system automatic train arrival time self-adaptative adjustment plan
Slightly.
S104, reference information are comprising as follows：After the late signal of triggering large area, when trip's dress system records current system automatically
Between Tsys, train RiResiding traffic coverage set Inti={ (IntF1,IntN1),(IntF2,IntN2) ..., (IntFn,IntNn)}
((IntFj,IntNj) represent train RiFrom j-th interval, Int after the late signal of triggering large areaFjMiss the stop, IntNjWill arrive
Stand) train RiInitial operating speed vi0, from each train speed limit that TD is obtainedInitial calibration speed weight α, β, γ (α >=0, β
>=0, γ >=0) (wherein alpha+beta+γ=1)；
S105, check information are comprising as follows：Train Ri1st interval (Int after the late signal of large areaF1,IntN1), away from stopping
By the mileage S of the next stopi1, the train average speed V of variant typec, obtain predicted operation speed
It should be noted that mileage Si1Acquisition methods it is as follows：According to train RiSail out of website IntF1Time and train
RiSpeed of service parameter calculate obtain train RiWith website IntF1Between distance, then according to traffic coverage (IntF1,
IntN1), distance, calculate obtain mileage Si1。
S2 is specifically included：
The check information that S201, foundation are automatically generated, produces train RiReach IntN1To a time, train is predicted
Up to time IntN1The time of station is
S202, the prediction time of departure
S203, trip's dress system record predicted time, complete prediction for the first time.
S3 is specifically included：
S301, train reach the Int of prediction for the first timeN1During station, actual achievement arrival time T can be producedsjDt_n1, and given birth to this
Into calibration information.
S302, calibration information generating process are as follows：Train reaches the Int of prediction for the first timeN1During station, produce actual achievement and reach
Time TsjDt_n1, train is from IntN1After station is outputed, actual achievement time T is producedsjFt_n1.And and TtyD_n1、TtyF_n1It is compared product
Raw time error tDy-s、tFy-s.And the incoming next column of this time error will be passed through into the same type train of this segment.
S303, while correct train running interval set Inti(IntF2,IntN2), each train is obtained away from the stop next stop
Mileage Si2。
S4 is specifically included：
S401, train are from IntN1After station is outputed, start second and predict.Second prediction process reference time by with reality
Achievement arrival time TsjDt_n1Produce after being corrected.
S402, second prediction fiducial time：Trip's dress system automatically updates present system time Tsys, residing for each train
Traffic coverage set (IntF2,IntN2), from each train speed limit that TD updatesAccording to TsjDt_n1With Si1, generate actual motion speed
Degree vi1, produce correction rate weight α ', (α ' >=0, β ' >=0 and meet α '+β '+γ '=1 at γ ' >=0) for β ', γ '.
S403, second prediction check information：Each train is away from the mileage S for stopping the next stopi2, obtain verifying speed
S404, second prediction process are as follows：The prediction arrival time Int of trainN2The time of station is
S405, the prediction time of departure
S406, trip's dress system record predicted time, complete second prediction.
S5 is specifically included：
S501, train reach the Int of second predictionN2During station, actual achievement arrival time T can be producedsjDt_n2, and given birth to this
Into calibration information.
S502, calibration information generating process are as follows：Train reaches the Int of second predictionN2During station, produce actual achievement and reach
Time TsjDt_n2、TsjFt_n2, train is from IntN2After station is outputed, actual achievement time T is producedsjFt_n2.And and TtyD_n2、TtyF_n2Carry out
Compare generation time error tDy-s、tFy-s.And the incoming next column of this error will be passed through into the same type train of this segment.
Further, train RiFrom j-th interval (Int after the late signal of triggering large areaFj,IntNj), IntFjCross
Stand, IntNjWill arrive at a station, the prediction arrival time Int of train will be produced the step of repeating S4, S5NjThe time of station isThe prediction time of departure Reach
IntNjAfter can produce actual achievement arrival time TsjDt_nj, actual achievement effect time of departure TsjFt_nj.And and TtyD_nj、TtyF_njIt is compared product
Raw time error tDy-s、tFy-s.And the incoming next column of this error will be passed through into the same type train of this segment.
S6 is specifically included：
If there is the train of same type before S601, train through this traffic coverage, train will be according to S3, S4, S5 step
Carry out, and add that previous train produces and with time error tDy-s、tFy-s, the arrival time for predicting this train is the pre- of train
Survey arrival time IntN2The time of station is
S602, the prediction time of departure(timetable sends out point-timetable to point).
In S7：Repeat step S4, S5, S6 are until exit the late pattern of large area.
For method embodiment, in order to be briefly described, therefore it is all expressed as a series of combination of actions, but ability
Field technique personnel should know that embodiment of the present invention is not limited by described sequence of movement, because according to the present invention
Embodiment, some steps can adopt other orders or while carry out.Secondly, those skilled in the art also should know,
Embodiment described in this description belongs to preferred embodiment, involved action embodiment party not necessarily of the present invention
Necessary to formula.
Fig. 2 shows a kind of target train arrival time prediction dress of railway trip dress system that one embodiment of the invention is provided
The schematic flow sheet put, referring to Fig. 2, the device includes：First acquisition module 21, the first prediction module 22, the second acquisition module
23rd, the 3rd acquisition module 24, calibration module 25, and the second prediction module 26, wherein；
First acquisition module 21, for detect send out large area late signal when, obtain the stroke of the target train
Information and type of train；
First prediction module 22, for obtaining the target train according to the travel information distance in direction of advance is reached
The prediction arrival time of the first nearest website；
Second acquisition module 23, the actual achievement that first website is reached for obtaining the target train reaches the time；
3rd acquisition module 24, for obtaining previous column and the target train according to the type of train of the target train
The train of same type reaches the time difference information of first website；
Calibration module 25, for calibrating the target train according to the actual achievement arrival time and the time difference information
Apart from the mileage of next website；
Second prediction module 26, for obtaining the target train according to the mileage after calibration next website is reached
Prediction arrival time.
It should be noted that when the administrative train of the trip's of detecting dress system has more than the Train delay of preset ratio, first
Acquisition module 21 obtains the travel information and type of train of target train from TMDS systems；And send the travel information for getting
To the first prediction module 22, type of train is sent to the 3rd acquisition module 24, target train is entered by the first prediction module 22
Row is predicted for the first time, and the transmission that will predict the outcome is to correction module 25；By the 3rd acquisition module 24 according to according to the target column
The type of train of car obtains previous column and reaches the time error letter of first website with the train of the target train same type
Breath, and time difference information is sent to correction module 25；After the completion of predicting for the first time, the second acquisition module 23 obtains target column
The actual achievement that car reaches reaches the time, and actual achievement arrival time is sent to calibration module 25；By calibration module 25 according to the actual achievement
Arrival time and the time difference information calibrate mileage of the target column car apart from next website, and by the mileage after calibration
Send to the second prediction module 26, second prediction is carried out to target train by the second prediction module 26.In the same manner, above-mentioned several moulds
Block repeated work can complete the prediction work on the whole stroke of target train.
Thus, actual achievement operation information and prediction operation information of the embodiment of the present invention based on target train, adaptively right
The arrival time of target train is predicted, compared with prior art, with precision of prediction great number advantage.
In the present embodiment, travel information includes：The current system of traffic coverage and trip's dress system residing for the target train
The system time；
Correspondingly, first prediction module 21, specifically for the traffic coverage according to residing for the target train, obtains
The target train reaches the distance travelled of first website；The operation of first website is reached according to the target train
Mileage and the present system time obtain the prediction arrival time that the target train reaches the first website.
In the present embodiment, the travel information also includes：The speed of service parameter of the target train；
Correspondingly, first prediction module 21, specifically for according to the speed of service parameter, with reference to formula one, obtaining
Take prediction speed of service v of the target traini0'；
The distance travelled of first website and described current is reached according to the prediction speed of service, the target train
System time, with reference to formula two, obtains the prediction arrival time TtyD_n1；
Wherein, vi0For the initial operating speed of the target train；For the restriction speed of the target train；VcFor institute
State the average speed of the affiliated type train of target train；α, (γ >=0) is speed weight to beta, gamma, alpha+beta+γ=1 for α >=0, β >=0；
TsysFor present system time；Si1For the distance travelled that the target train reaches first website.
In a possible embodiments, described device also includes：Update module；
The update module, for reaching prediction arrival time and the actual achievement of first website according to the target train
Arrival time generates time error fresh information；
According to time error fresh information renewal previous column and the train of the target train same type are reached
The time difference information of the first website.
Fig. 3 shows a kind of target train arrival time prediction of railway trip dress system that another embodiment of the present invention is provided
The schematic flow sheet of device, referring to Fig. 3, the device includes：First acquisition module 31, the first prediction module 32, second obtain mould
Block 33, calibration module 34, the second prediction module 35, the 3rd acquisition module 36, and the 3rd prediction module 37, wherein；
First acquisition module 31, the first prediction module 32, the second acquisition module 33, calibration module 34, the second prediction module
35th, the 3rd acquisition module 36 the first acquisition module 21, the first prediction module 22, second respectively in embodiment corresponding with Fig. 2
Acquisition module 23, the 3rd acquisition module 24, calibration module 25, and the second prediction module 26 is corresponding, its operation principle is identical,
Therefore, no longer being repeated herein, its concrete function refer to the description of the corresponding embodiments of Fig. 2；
The travel information also includes：The time of running table of the target train, the time of running table includes the mesh
Mark the bus stop of train and through station, and in each bus stop and the berthing time through station；
Correspondingly, the 3rd prediction module 37, the operation of the target train for being included according to travel information
Timetable obtains the time of staying of the target train in first website；Reached according to the time of staying and described predicting
Time obtains the prediction departure time that the target train sails out of first website.
For device embodiments, due to itself and method embodiment basic simlarity, so description is fairly simple,
Related part is illustrated referring to the part of method embodiment.
It should be noted that in all parts of the device of the present invention, according to its function to be realized to therein
Part has carried out logical partitioning, but, the present invention is not only restricted to this, all parts can be repartitioned as needed or
Person combines.
The present invention all parts embodiment can be realized with hardware, or with one or more processor fortune
Capable software module is realized, or is realized with combinations thereof.In this device, PC is by realizing internet to equipment or device
Remotely control, accurately the step of each operation of control device or device.The present invention is also implemented as performing here
(for example, computer program and computer program are produced for some or all equipment of described method or program of device
Product).Being achieved in that the program of the present invention can store on a computer-readable medium, and the file or document tool that program is produced
Having can be statistical, produces data report and cpk reports etc., batch testing can be carried out to power amplifier and is counted.It should be noted that on
The present invention will be described rather than limits the invention to state embodiment, and those skilled in the art are without departing from institute
Replacement embodiment can be designed in the case of the scope of attached claim.In the claims, should not be by between bracket
Any reference symbol be configured to limitations on claims.Word "comprising" does not exclude the presence of unit not listed in the claims
Part or step.Word "a" or "an" before element does not exclude the presence of multiple such elements.The present invention can be borrowed
Help to include the hardware of some different elements and by means of properly programmed computer realizing.If listing equipment for drying
Unit claim in, several in these devices can be embodied by same hardware branch.Word first,
Second and third use do not indicate that any order.These words can be construed to title.
Although being described in conjunction with the accompanying embodiments of the present invention, those skilled in the art can be without departing from this
Various modifications and variations are made in the case of bright spirit and scope, such modification and modification are each fallen within by claims
Within limited range.
Claims (10)
1. the target train arrival time Forecasting Methodology of a kind of railway trip dress system, it is characterised in that include：
Detect send out large area late signal when, obtain the travel information and type of train of the target train；
The target train is obtained according to the travel information and reaches predicting for the first website closest in direction of advance
Up to the time；
Obtain the target train and reach the actual achievement of first website and reach the time；
Previous column and the train of the target train same type are obtained according to the type of train of the target train and reaches described the
The time difference information of one website；
Mileage of the target column car apart from next website is calibrated according to the actual achievement arrival time and the time difference information；
The prediction arrival time that the target train reaches next website is obtained according to the mileage after calibration.
2. method according to claim 1, it is characterised in that the travel information includes：Residing for the target train
The present system time of traffic coverage and trip's dress system；
Correspondingly, it is described that first stop closest in the target train arrival direction of advance is obtained according to the travel information
The step of prediction arrival time of point, specifically includes：
Traffic coverage according to residing for the target train, in obtaining the operation that the target train reaches first website
Journey；
The target is obtained according to the distance travelled and the present system time that the target train reaches first website
Train reaches the prediction arrival time of the first website.
3. method according to claim 2, it is characterised in that the travel information also includes：The fortune of the target train
Row speed parameter；
Correspondingly, the distance travelled and the present system time for reaching first website according to the target train is obtained
Take the step of the target train reaches prediction arrival time of the first website and specifically include：
According to the speed of service parameter, with reference to formula one, prediction speed of service v of the target train is obtainedi0'；
The distance travelled and the current system of first website are reached according to the prediction speed of service, the target train
Time, with reference to formula two, obtain the prediction arrival time TtyD_n1；
Wherein, vi0For the initial operating speed of the target train；For the restriction speed of the target train；VcFor the mesh
The average speed of the affiliated type train of mark train；α, (γ >=0) is speed weight to beta, gamma, alpha+beta+γ=1 for α >=0, β >=0；Tsys
For present system time；Si1For the mileage that the target train reaches first website.
4. the method according to any one of claim 1-3, it is characterised in that methods described also includes：
Time error is generated according to the prediction arrival time and actual achievement arrival time that the target train reaches first website
Fresh information；
Previous column is updated according to the time error fresh information and reaches described first with the train of the target train same type
The time difference information of website.
5. the method according to any one of claim 1-3, it is characterised in that the travel information also includes：The target
The time of running table of train, the time of running table includes the bus stop of the target train and through station, and in each stop
Stand and pass through the berthing time at station；
Correspondingly, the method also includes：
The time of staying of the target train in first website is obtained according to the time of running table of the target train；
The prediction that the target train sails out of first website is obtained according to the time of staying and the prediction arrival time
Departure time.
6. target train arrival time prediction meanss of a kind of railway trip dress system, it is characterised in that include：
First acquisition module, for detect send out large area late signal when, obtain the target train travel information and
Type of train；
First prediction module, reaches closest in direction of advance for obtaining the target train according to the travel information
The prediction arrival time of the first website；
Second acquisition module, the actual achievement that first website is reached for obtaining the target train reaches the time；
3rd acquisition module, for obtaining previous column and the target train same type according to the type of train of the target train
Train reach the time difference information of first website；
Calibration module, for being calibrated under the target column car distance according to the actual achievement arrival time and the time difference information
The mileage of one website；
Second prediction module, for obtaining the target train according to the mileage after calibration predicting for next website is reached
Up to the time.
7. device according to claim 6, it is characterised in that the travel information includes：Residing for the target train
The present system time of traffic coverage and trip's dress system；
Correspondingly, first prediction module, specifically for the traffic coverage according to residing for the target train, obtains the mesh
Mark train reaches the distance travelled of first website；According to the target train reach first website distance travelled and
The present system time obtains the prediction arrival time that the target train reaches the first website.
8. device according to claim 7, it is characterised in that the travel information also includes：The fortune of the target train
Row speed parameter；
Correspondingly, first prediction module, specifically for according to the speed of service parameter, with reference to formula one, obtains described
Prediction speed of service v of target traini0'；
The distance travelled and the current system of first website are reached according to the prediction speed of service, the target train
Time, with reference to formula two, obtain the prediction arrival time TtyD_n1；
Wherein, vi0For the initial operating speed of the target train；For the restriction speed of the target train；VcFor the mesh
The average speed of the affiliated type train of mark train；α, (γ >=0) is speed weight to beta, gamma, alpha+beta+γ=1 for α >=0, β >=0；Tsys
For present system time；Si1For the distance travelled that the target train reaches first website.
9. the device according to any one of claim 6-8, it is characterised in that described device also includes：Update module；
The update module, the prediction arrival time and actual achievement for reaching first website according to the target train reaches
Time generates time error fresh information；
Previous column is updated according to the time error fresh information and reaches described first with the train of the target train same type
The time difference information of website.
10. the device according to any one of claim 6-8, it is characterised in that the travel information also includes：The target
The time of running table of train, the time of running table includes the bus stop of the target train and through station, and in each stop
Stand and pass through the berthing time at station；
Correspondingly, the device also includes：3rd prediction module；
3rd prediction module, for obtaining the target train described the according to the time of running table of the target train
The time of staying of one website；The target train is obtained according to the time of staying and the prediction arrival time and sails out of described the
The prediction departure time of one website.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611266592.7A CN106627677B (en) | 2016-12-31 | 2016-12-31 | The target train arrival time prediction technique and device of railway trip dress system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611266592.7A CN106627677B (en) | 2016-12-31 | 2016-12-31 | The target train arrival time prediction technique and device of railway trip dress system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106627677A true CN106627677A (en) | 2017-05-10 |
| CN106627677B CN106627677B (en) | 2018-09-04 |
Family
ID=58837676
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611266592.7A Active CN106627677B (en) | 2016-12-31 | 2016-12-31 | The target train arrival time prediction technique and device of railway trip dress system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106627677B (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108764526A (en) * | 2018-04-20 | 2018-11-06 | 西南交通大学 | A kind of Train delay artificial neural network recognition methods based on Analysis of Train Operation Order |
| CN109359788A (en) * | 2018-12-06 | 2019-02-19 | 西南交通大学 | A kind of initial late method for building up for influencing prediction model of bullet train |
| CN109508751A (en) * | 2018-12-06 | 2019-03-22 | 西南交通大学 | The deep neural network model modeling method of the late time prediction of High Speed Railway Trains |
| CN110481603A (en) * | 2018-05-14 | 2019-11-22 | 比亚迪股份有限公司 | Train method of adjustment, device and storage medium at equal intervals |
| CN110626391A (en) * | 2019-09-27 | 2019-12-31 | 交控科技股份有限公司 | Information prediction method of passenger information system |
| CN110758493A (en) * | 2019-11-14 | 2020-02-07 | 通号城市轨道交通技术有限公司 | Train arrival time prediction method and system |
| CN111114598A (en) * | 2019-12-31 | 2020-05-08 | 卡斯柯信号有限公司 | Tramcar signal system prediction plan automatic adjustment method |
| CN111768074A (en) * | 2020-05-22 | 2020-10-13 | 北京交通大学 | Novel train operation intelligent adjustment method |
| CN111884694A (en) * | 2020-07-28 | 2020-11-03 | 中国联合网络通信集团有限公司 | Beam forming control method and device, electronic equipment and storage medium |
| CN112580204A (en) * | 2020-12-16 | 2021-03-30 | 同济大学 | Train delay time prediction method under abnormal events in railway section |
| CN112613728A (en) * | 2020-12-19 | 2021-04-06 | 西安银石科技发展有限责任公司 | Locomotive interval operation data analysis system and analysis method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005280524A (en) * | 2004-03-30 | 2005-10-13 | Railway Technical Res Inst | User support system |
| CN101973291A (en) * | 2010-09-27 | 2011-02-16 | 浙江浙大网新众合轨道交通工程有限公司 | Equal interval regulation method of urban rail traffic train |
| JP4768983B2 (en) * | 2004-12-03 | 2011-09-07 | 公益財団法人鉄道総合技術研究所 | Operation organizing result analysis support device and program |
| CN103101560A (en) * | 2011-11-14 | 2013-05-15 | 北京南车时代信息技术有限公司 | Method and device of train operation control and train operation monitor system |
| JP5292202B2 (en) * | 2009-06-29 | 2013-09-18 | 株式会社日立製作所 | Train control system, ground vehicle cooperation control system |
| CN104192177A (en) * | 2014-08-07 | 2014-12-10 | 国电南瑞科技股份有限公司 | Method for automatically adjusting urban rail transit train operation based on discrete event model |
| CN106232454A (en) * | 2014-04-21 | 2016-12-14 | 三菱电机株式会社 | Train driving prediction means and train driving Forecasting Methodology |
-
2016
- 2016-12-31 CN CN201611266592.7A patent/CN106627677B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005280524A (en) * | 2004-03-30 | 2005-10-13 | Railway Technical Res Inst | User support system |
| JP4768983B2 (en) * | 2004-12-03 | 2011-09-07 | 公益財団法人鉄道総合技術研究所 | Operation organizing result analysis support device and program |
| JP5292202B2 (en) * | 2009-06-29 | 2013-09-18 | 株式会社日立製作所 | Train control system, ground vehicle cooperation control system |
| CN101973291A (en) * | 2010-09-27 | 2011-02-16 | 浙江浙大网新众合轨道交通工程有限公司 | Equal interval regulation method of urban rail traffic train |
| CN103101560A (en) * | 2011-11-14 | 2013-05-15 | 北京南车时代信息技术有限公司 | Method and device of train operation control and train operation monitor system |
| CN106232454A (en) * | 2014-04-21 | 2016-12-14 | 三菱电机株式会社 | Train driving prediction means and train driving Forecasting Methodology |
| CN104192177A (en) * | 2014-08-07 | 2014-12-10 | 国电南瑞科技股份有限公司 | Method for automatically adjusting urban rail transit train operation based on discrete event model |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108764526A (en) * | 2018-04-20 | 2018-11-06 | 西南交通大学 | A kind of Train delay artificial neural network recognition methods based on Analysis of Train Operation Order |
| CN110481603A (en) * | 2018-05-14 | 2019-11-22 | 比亚迪股份有限公司 | Train method of adjustment, device and storage medium at equal intervals |
| CN109359788B (en) * | 2018-12-06 | 2021-08-17 | 西南交通大学 | Method for establishing high-speed train initial late point influence prediction model |
| CN109359788A (en) * | 2018-12-06 | 2019-02-19 | 西南交通大学 | A kind of initial late method for building up for influencing prediction model of bullet train |
| CN109508751A (en) * | 2018-12-06 | 2019-03-22 | 西南交通大学 | The deep neural network model modeling method of the late time prediction of High Speed Railway Trains |
| CN109508751B (en) * | 2018-12-06 | 2021-02-09 | 西南交通大学 | Deep neural network model modeling method for high-speed railway train late time prediction |
| CN110626391A (en) * | 2019-09-27 | 2019-12-31 | 交控科技股份有限公司 | Information prediction method of passenger information system |
| CN110758493A (en) * | 2019-11-14 | 2020-02-07 | 通号城市轨道交通技术有限公司 | Train arrival time prediction method and system |
| CN110758493B (en) * | 2019-11-14 | 2021-01-05 | 通号城市轨道交通技术有限公司 | Train arrival time prediction method and system |
| CN111114598A (en) * | 2019-12-31 | 2020-05-08 | 卡斯柯信号有限公司 | Tramcar signal system prediction plan automatic adjustment method |
| CN111768074A (en) * | 2020-05-22 | 2020-10-13 | 北京交通大学 | Novel train operation intelligent adjustment method |
| CN111768074B (en) * | 2020-05-22 | 2024-01-19 | 北京交通大学 | Novel intelligent train operation adjusting method |
| CN111884694A (en) * | 2020-07-28 | 2020-11-03 | 中国联合网络通信集团有限公司 | Beam forming control method and device, electronic equipment and storage medium |
| CN111884694B (en) * | 2020-07-28 | 2023-03-24 | 中国联合网络通信集团有限公司 | Beam forming control method and device, electronic equipment and storage medium |
| CN112580204A (en) * | 2020-12-16 | 2021-03-30 | 同济大学 | Train delay time prediction method under abnormal events in railway section |
| CN112613728A (en) * | 2020-12-19 | 2021-04-06 | 西安银石科技发展有限责任公司 | Locomotive interval operation data analysis system and analysis method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106627677B (en) | 2018-09-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106627677A (en) | Method and device for predicting arrival time of target train of railway travel service system | |
| US9183741B2 (en) | Estimation of arrival times at transit stops | |
| Sun et al. | Real-time and predictive analytics for smart public transportation decision support system | |
| US20140278026A1 (en) | Apparatus and system for monitoring and managing traffic flow | |
| JP6675860B2 (en) | Data processing method and data processing system | |
| CN109426930A (en) | The transport delay forecasting system and method for logistics vehicles | |
| CN110246332B (en) | Rail transit real-time passenger flow monitoring method and system based on multi-source data fusion | |
| WO2014199503A1 (en) | Traffic demand control device | |
| JP2015209050A (en) | Operation control system | |
| CN103310118A (en) | Method for predicting train operation conflicts on high speed railways | |
| CN102953304A (en) | Precision measurement control method of metro track structure construction | |
| CN103377551A (en) | Vehicle terminal-based traffic information sharing method and system | |
| WO2016084197A1 (en) | Train operation interval control system, and train operation interval control device | |
| JP6454222B2 (en) | Data processing system and data processing method | |
| Yue et al. | Traffic signal retiming to improve corridor performance | |
| CN106507406A (en) | A kind of equipment of wireless network accesses the Forecasting Methodology of number and equipment | |
| CN106274994A (en) | A kind of train arrival time Forecasting Methodology and system completely | |
| JP6785632B2 (en) | Vehicle operation business support system | |
| JP2019526092A (en) | Control and management of traffic signal system using VANET | |
| JP2018140683A (en) | Device, method and program of train operation management | |
| JP2014091424A (en) | Travel distance correction system in consideration of application to railway vehicle | |
| JP7295106B2 (en) | System and method for navigating within a track network | |
| Ricci | Punctuality based calibration of railway capacity models | |
| CN106205176B (en) | A kind of vehicle arrives at a station prediction technique and system in real time | |
| JP7082338B2 (en) | A method for supporting taxi dispatch and a taxi dispatch support system using it |
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 |