CN109272166A - Subway train trajectory predictions method - Google Patents
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Abstract
The present invention relates to a kind of subway train trajectory predictions method, includes the following steps: the plan operating parameter first according to each train, generate the topology diagram of Rail traffic network；It is based on topology diagram again, analyzes the controllability and sensibility of train flow；Further according to the plan operating parameter of each train, multiple row vehicle Lothrus apterus running track is generated；The advanced positions at certain following moment of train are predicted based on the current operating status of train and historical position observation sequence in each sampling instant again.This method is higher to the trajectory predictions precision of subway train.
Description
The application is application No. is 201510150289.X, and invention and created name is " the real-time prediction of subway train track
Method ", the applying date are as follows: the divisional application of the application for a patent for invention on March 31st, 2015.
Technical field
The present invention relates to a kind of real-time predicting method of subway train track more particularly to a kind of ground based on Robust Strategies
The real-time predicting method of iron train track.
Background technique
With being growing for China big and medium-sized cities scale, Traffic Systems are faced with the increasing pressure, energetically
Feasibility of developing track transportation system becomes the important means for solving urban traffic congestion.National Eleventh Five-Year Plan guiding principle is it is to be noted, that there is item
The big city and group of cities area of part are using rail traffic as Priority setting.China is just undergoing a unprecedented rail
Road transport development peak period, some cities have been turned to the construction of net by the construction of line, urban mass transit network gradually shape
At.In the complex region that Rail traffic network and train flow are intensive, is still combined using train operation plan and be based on subjective experience
Train interval dispensing mode gradually show its backwardness, be in particular in: (1) formulation of train operation plan timetable is simultaneously
The influence for not considering various enchancement factors be easy to cause the management of traffic flow tactics crowded, reduces the safety of traffic system operation
Property；(2) train scheduling work lays particular emphasis on the personal distance for keeping single row workshop, not yet rises to and carries out strategic pipe to train flow
The macroscopic aspect of reason；(3) train allocation process depends on the subjective experience of a line dispatcher more, and the selection for deploying opportunity is random
Property it is larger, lack scientific theory support；(4) the less shadow in view of external interference factor of the allotment means that dispatcher is used
It rings, the robustness and availability of train programs are poor.
The discussion object spininess of existing documents and materials is to long-distance railway transportation, and for big flow, high density and closely-spaced
The Scientific Regulation scheme of city underground traffic system under service condition still lacks system design.Under complicated road network service condition
Train Coordinated Control Scheme needed on strategic level in region in transportation network the operating status of single vehicles carry out calculate and
Optimization, and collaborative planning is implemented to the traffic flow being made of multiple trains；And the operation conflict Resolution of multiple row vehicle is based on over the ground
On the basis of the prediction of iron train track, the operating status of train often not exclusively belongs to a certain specific motion state, at present
It there is no the real-time predicting method of effective subway train track.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of robustness and the preferable subway train trajectory predictions of availability
Method, this method are higher to the trajectory predictions precision of subway train.
It realizes that the technical solution of the object of the invention is to provide a kind of subway train trajectory predictions method, includes the following steps:
Step A, according to the plan operating parameter of each train, the topology diagram of Rail traffic network is generated；
Step B, the topology diagram based on Rail traffic network constructed by step A, analyze train flow controllability and
Two class feature of sensibility；
Step C, according to the plan operating parameter of each train, on the basis of constructing Modeling Method for Train Dynamics, according to column
Vehicle operation conflict Coupling point establishes train running conflict and deploys model in advance, generates multiple row vehicle Lothrus apterus running track；
Step D, in each sampling instant t, based on the current operating status of train and historical position observation sequence, to train
The advanced positions at certain following moment are predicted；Detailed process is as follows for it:
Step D1, train track data pre-process, using train initiating station stop position as coordinate origin, adopted each
The sample moment, according to the acquired original discrete two-dimensional position sequence x=[x of train1,x2,...,xn] and y=[y1,y2,...,
yn], processing is carried out to it using first-order difference method and obtains new train discrete location sequence △ x=[△ x1,△x2,...,△
xn-1] and △ y=[△ y1,△y2,...,△yn-1], wherein △ xi=xi+1-xi,△yi=yi+1-yi(i=1,2 ..., n-1)；
Step D2, train track data is clustered, to train discrete two-dimensional position sequence △ x and △ y new after processing, is led to
Setting cluster number M' is crossed, it is clustered respectively using genetic algorithm for clustering；
Step D3, parameter training is carried out using Hidden Markov Model to the train track data after cluster, by that will locate
Train operation track data △ x and △ y after reason is considered as the aobvious observation of hidden Markov models, by setting hidden state number
N' and parameter update period τ ', roll the newest hidden Ma Erke of acquisition according to T' nearest position detection value and using B-W algorithm
Husband's model parameter λ '；Specifically: since train track sets data length obtained is dynamic change, in order in real time with
The state change of track train track, it is necessary to initial track Hidden Markov Model parameter lambda '=(π, A, B) on the basis of it is right
It is readjusted, more accurately to speculate train in the position at certain following moment；T' every period τ ', according to newest acquisition
A observation (o1,o2,...,oT') to track Hidden Markov Model parameter lambda '=(π, A, B) reevaluated；
Step D4, it according to Hidden Markov Model parameter, is obtained corresponding to current time observation using Viterbi algorithm
Hidden state q；
Step D5, every the periodAccording to the Hidden Markov Model parameter lambda of newest acquisition '=(π, A, B) and nearest H
A history observation (o1,o2,...,oH), the hidden state q based on train current time predicts time domain by setting in moment t
H' obtains the position prediction value O of future time period train, arranges to roll supposition in each sampling instant to subway in future time period
The track of vehicle.
Further, detailed process is as follows by step A:
Step A1, the website letter stopped in each train travelling process is extracted from the database of subway transportation control centre
Breath；
Step A2, classify according to the site information that positive and negative two traffic directions stop each train, and will be same
Same site on one traffic direction merges；
Step A3, according to website amalgamation result, the multiple websites in front and back are connected according to the space layout form straight line of website.
Further, detailed process is as follows by step B:
Step Bl, the Traffic flux detection model in single subsegment is constructed；Detailed process is as follows for it:
Step Bl.1, state variable Ψ, input variable u and output variable Ω are introduced, wherein Ψ indicates phase link between website
Train quantity existing for certain moment in section, it includes single channel section and Multiple Sections two types, and u indicates that rail traffic dispatcher is directed to
The Operation Measures that certain section is implemented, such as adjustment train speed or change train in the station time, Ω indicates certain period section
On the train quantity left；
Step B1.2, by establishing time discretization shaped like Ψ (t+ △ t)=A1Ψ(t)+B1U (t) and Ω (t)=C1
Ψ(t)+D1Discrete time Traffic flux detection model in the single subsegment of u (t), wherein △ t indicates sampling interval, Ψ (t) table
Show the state vector of t moment, A1、B1、C1And D1Respectively indicate the state-transition matrix, input matrix, output measurement square of t moment
Battle array and direct transmission matrix；
Step B2, the Traffic flux detection model in more subsegments is constructed；Detailed process is as follows for it:
Step B2.1, according to route space layout form and train flow historical statistical data, each son of cross link is obtained
Flow proportional parameter beta in section；
Step B2.2, according to the discrete time Traffic flux detection model in flow proportional parameter and single subsegment, shape is constructed
Such as Ψ (t+ △ t)=A1Ψ(t)+B1U (t) and Ω (t)=C1Ψ(t)+D1Discrete time traffic flow control in more subsegments of u (t)
Simulation；
Step B3, according to the controllable factor matrix [B of Controlling model1,A1B1,...,A1 n-1B1] order and numerical value n relationship,
Its controllability of qualitative analysis, according to the sensitivity coefficient matrix [C of Controlling model1(zI-A1)-1B1+D1], its input of quantitative analysis is defeated
Sensibility out, wherein n indicates that the dimension of state vector, I indicate that unit matrix, z are indicated to original discrete time Traffic flux detection
The element factor that model is converted.
Further, detailed process is as follows by step C:
Step C1, train status transfer modeling, train are shown as between website along the process that rail traffic road network is run
Switching at runtime process is arranged according to the website in train operation plan, establishes single train switched and transferred between different websites
Petri net model: E=(g, G, Pre, Post, m) is train section metastasis model, and wherein g indicates each sub- section between website, G table
Show that the transfer point of train running speed state parameter, Pre and Post respectively indicate the front and back between each sub- section and website to connection
Relationship,Indicate operation section locating for train, wherein m indicates model identification, Z+Indicate Positive Integer Set；
Step C2, the full operation profile hybrid system modeling of train, is considered as continuous process for operation of the train between website, from
The stress situation of train is set out, and derives kinetics equation of the train in the different operation phase according to energy model, dry in conjunction with the external world
Factor is disturbed, is established about train in a certain operation phase speed vGMapping function vG=λ (T1,T2, H, R, α), wherein T1、T2、
H, R and α respectively indicates tractive force of train, braking force of train, train resistance, train gravity and train status random fluctuation parameter；
Step C3, speculated by the way of mixing emulation and solve train track, it is continuous using state by by time subdivision
The characteristic Recursive Solution any time train of variation in distance of a certain operation phase away from initial rest position point,Wherein J0Voyage for initial time train away from initial rest position point, △ τ are time window
Numerical value, J (τ) are distance of the τ moment train away from initial rest position point, thereby it is assumed that obtain single-row wheel paths；
Step C4, train is in station time probability distribution function modeling, for specific run route, by transferring train each
The dwell time data at station obtain the dwell time probability distribution of different route difference website condition Trains；
Step C5, the Lothrus apterus robust track allotment of multiple row vehicle coupling, reaches the time of conflict point in advance according to each train, passes through
Time segments division, it is discontented nearby to conflict point according to scheduling rule under the premise of incorporating random factor in each sampling instant t
Implement robust secondary planning in the train track that sufficient personal distance requires.
Further, in step D, the value of cluster number M' is 4, and the value of hidden state number N' is 3, and parameter updates period τ '
It is 30 seconds, T' 10,It is 30 seconds, H 10, prediction time domain h' is 300 seconds.
The present invention has the effect of positive: (1) subway train trajectory predictions method of the invention is meeting rail traffic pipe
Under the premise of personal distance processed, the specific fortune of setting train based on the real-time position information of train rather than before prediction is implemented
Row state, maintenance data excavate means dynamic and speculate train track.(2) the present invention is based on the rollings of constructed train operation track
Prediction scheme can incorporate all kinds of disturbing factors in train real time execution in time, improve the accuracy of train trajectory predictions, gram
Take the not high disadvantage of Conventional Off-line prediction scheme accuracy.(3) the present invention is based on the controllabilitys of Rail traffic network topological structure
With sensitivity analysis as a result, scientific basis can be provided for subway transportation stream trajectory predictions, the randomness of prediction scheme selection is avoided.
Detailed description of the invention
Fig. 1 is train flow analysis on Operating figure；
Fig. 2 is that Lothrus apterus 3D robust track speculates figure.
Specific embodiment
(embodiment 1)
A kind of flow-optimized control system of subway transportation, including it is wire topologies generation module, data transmission module, vehicle-mounted
Terminal module, controlling terminal module and track monitoring module, track monitoring module are collected the status information of train and are supplied to
Controlling terminal module.
The controlling terminal module includes following submodule:
Lothrus apterus track generation module before train operation: according to Train operation plan running schedule, train dynamics are initially set up
Model is learned, then train running conflict is established according to train running conflict Coupling point and deploys model in advance, ultimately produces Lothrus apterus column
Vehicle running track.
Train operation middle or short term track generation module: the train real time status information provided according to track monitoring module, benefit
With data mining model, thus it is speculated that the running track of train in future time period.
Train operation situation monitoring module: in each sampling instant t, the track estimation result based on train, when between train
It is possible that its dynamic behaviour implementing monitoring and providing warning information when being in the presence of violating safety regulation for controlling terminal.
Train collision avoidance track optimizing module: when train operation situation monitoring module issues warning information, meeting train
Under the premise of physical property, region hold stream constraint and rail traffic scheduling rule, by setting optimizing index function, use is adaptive
It answers control theory method to carry out robust dual layer resist to train operation track by controlling terminal module, and passes through data transmission module
Program results are transferred to car-mounted terminal module to execute.Train collision avoidance track optimizing module includes internal layer planning and outer layer planning two
Class planning process.
Using the subway train trajectory predictions method of the flow-optimized control system of above-mentioned subway transportation, comprising the following steps:
Step A, according to the plan operating parameter of each train, the topology diagram of Rail traffic network is generated；It is specific
Process is as follows:
Step A1, the website letter stopped in each train travelling process is extracted from the database of subway transportation control centre
Breath；
Step A2, classify according to the site information that positive and negative two traffic directions stop each train, and will be same
Same site on one traffic direction merges；
Step A3, according to website amalgamation result, the multiple websites in front and back are connected according to the space layout form straight line of website.
Step B, the topology diagram based on Rail traffic network constructed by step A, analyze train flow controllability and
Two class feature of sensibility；Detailed process is as follows for it:
Step Bl, see Fig. 1, construct the Traffic flux detection model in single subsegment；Detailed process is as follows for it:
Step Bl.1, state variable Ψ, input variable u and output variable Ω are introduced, wherein Ψ indicates phase link between website
Train quantity existing for certain moment in section, it includes single channel section and Multiple Sections two types, and u indicates that rail traffic dispatcher is directed to
The Operation Measures that certain section is implemented, such as adjustment train speed or change train in the station time, Ω indicates certain period section
On the train quantity left；
Step B1.2, by establishing time discretization shaped like Ψ (t+ △ t)=A1Ψ(t)+B1U (t) and Ω (t)=C1
Ψ(t)+D1Discrete time Traffic flux detection model in the single subsegment of u (t), wherein △ t indicates sampling interval, Ψ (t) table
Show the state vector of t moment, A1、B1、C1And D1Respectively indicate the state-transition matrix, input matrix, output measurement square of t moment
Battle array and direct transmission matrix；
Step B2, the Traffic flux detection model in more subsegments is constructed；Detailed process is as follows for it:
Step B2.1, according to route space layout form and train flow historical statistical data, each son of cross link is obtained
Flow proportional parameter beta in section；
Step B2.2, according to the discrete time Traffic flux detection model in flow proportional parameter and single subsegment, shape is constructed
Such as Ψ (t+ △ t)=A1Ψ(t)+B1U (t) and Ω (t)=C1Ψ(t)+D1Discrete time traffic flow control in more subsegments of u (t)
Simulation；
Step B3, according to the controllable factor matrix [B of Controlling model1,A1B1,...,A1 n-1B1] order and numerical value n relationship,
Its controllability of qualitative analysis, according to the sensitivity coefficient matrix [C of Controlling model1(zI-A1)-1B1+D1], its input of quantitative analysis is defeated
Sensibility out, wherein n indicates that the dimension of state vector, I indicate that unit matrix, z are indicated to original discrete time Traffic flux detection
The element factor that model is converted；
Step C, see Fig. 2, according to the plan operating parameter of each train, on the basis of constructing Modeling Method for Train Dynamics,
Train running conflict is established according to train running conflict Coupling point and deploys model in advance, generates multiple row vehicle Lothrus apterus running track；Its
Detailed process is as follows:
Step C1, train status transfer modeling, train are shown as between website along the process that rail traffic road network is run
Switching at runtime process is arranged according to the website in train operation plan, establishes single train switched and transferred between different websites
Petri net model: E=(g, G, Pre, Post, m) is train section metastasis model, and wherein g indicates each sub- section between website, G table
Show that the transfer point of train running speed state parameter, Pre and Post respectively indicate the front and back between each sub- section and website to connection
Relationship,Indicate operation section locating for train, wherein m indicates model identification, Z+Indicate Positive Integer Set；
Step C2, the full operation profile hybrid system modeling of train, is considered as continuous process for operation of the train between website, from
The stress situation of train is set out, and derives kinetics equation of the train in the different operation phase according to energy model, dry in conjunction with the external world
Factor is disturbed, is established about train in a certain operation phase speed vGMapping function vG=λ (T1,T2, H, R, α), wherein T1、T2、
H, R and α respectively indicates tractive force of train, braking force of train, train resistance, train gravity and train status random fluctuation parameter；
Step C3, speculated by the way of mixing emulation and solve train track, it is continuous using state by by time subdivision
The characteristic Recursive Solution any time train of variation in distance of a certain operation phase away from initial rest position point,Wherein J0Voyage for initial time train away from initial rest position point, △ τ are the number of time window
Value, J (τ) are distance of the τ moment train away from initial rest position point, thereby it is assumed that obtain single-row wheel paths；
Step C4, train is in station time probability distribution function modeling, for specific run route, by transferring train each
The dwell time data at station obtain the dwell time probability distribution of different route difference website condition Trains；
Step C5, the Lothrus apterus robust track allotment of multiple row vehicle coupling, reaches the time of conflict point in advance according to each train, passes through
Time segments division, it is discontented nearby to conflict point according to scheduling rule under the premise of incorporating random factor in each sampling instant t
Implement robust secondary planning in the train track that sufficient personal distance requires.
Step D, in each sampling instant t, based on the current operating status of train and historical position observation sequence, to train
The advanced positions at certain following moment are predicted；Detailed process is as follows for it:
Step D1, train track data pre-process, using train initiating station stop position as coordinate origin, adopted each
The sample moment, according to the acquired original discrete two-dimensional position sequence x=[x of train1,x2,...,xn] and y=[y1,y2,...,
yn], processing is carried out to it using first-order difference method and obtains new train discrete location sequence △ x=[△ x1,△x2,...,△
xn-1] and △ y=[△ y1,△y2,...,△yn-1], wherein △ xi=xi+1-xi,△yi=yi+1-yi(i=1,2 ..., n-1)；
Step D2, train track data is clustered, to train discrete two-dimensional position sequence △ x and △ y new after processing, is led to
Setting cluster number M' is crossed, it is clustered respectively using genetic algorithm for clustering；
Step D3, parameter training is carried out using Hidden Markov Model to the train track data after cluster, by that will locate
Train operation track data △ x and △ y after reason is considered as the aobvious observation of hidden Markov models, by setting hidden state number
N' and parameter update period τ ', roll the newest hidden Ma Erke of acquisition according to T' nearest position detection value and using B-W algorithm
Husband's model parameter λ '；Specifically: since train track sets data length obtained is dynamic change, in order in real time with
The state change of track train track, it is necessary to initial track Hidden Markov Model parameter lambda '=(π, A, B) on the basis of it is right
It is readjusted, more accurately to speculate train in the position at certain following moment；T' every period τ ', according to newest acquisition
A observation (o1,o2,...,oT') to track Hidden Markov Model parameter lambda '=(π, A, B) reevaluated；
Step D4, it according to Hidden Markov Model parameter, is obtained corresponding to current time observation using Viterbi algorithm
Hidden state q；
Step D5, every the periodAccording to the Hidden Markov Model parameter lambda of newest acquisition '=(π, A, B) and nearest H
A history observation (o1,o2,...,oH), the hidden state q based on train current time predicts time domain by setting in moment t
H' obtains the position prediction value O of future time period train, arranges to roll supposition in each sampling instant to subway in future time period
The track of vehicle；
The value of above-mentioned cluster number M' is 4, and the value of hidden state number N' is 3, and it is 30 seconds that parameter, which updates period τ ', and T' is
10,It is 30 seconds, H 10, prediction time domain h' is 300 seconds.
Obviously, the above embodiment is merely an example for clearly illustrating the present invention, and is not to of the invention
The restriction of embodiment.For those of ordinary skill in the art, it can also be made on the basis of the above description
Its various forms of variation or variation.There is no necessity and possibility to exhaust all the enbodiments.And these belong to this hair
The obvious changes or variations that bright spirit is extended out are still in the protection scope of this invention.
Claims (2)
1. a kind of subway train trajectory predictions method, it is characterised in that include the following steps:
Step A, according to the plan operating parameter of each train, the topology diagram of Rail traffic network is generated；
Step B, the topology diagram based on Rail traffic network constructed by step A analyzes the controllability and sensitivity of train flow
Two class features of property；Detailed process is as follows:
Step Bl, the Traffic flux detection model in single subsegment is constructed；Detailed process is as follows for it:
Step Bl.1, state variable Ψ, input variable u and output variable Ω are introduced, wherein Ψ indicates to be connected on section between website
Train quantity existing for certain moment, it includes single channel section and Multiple Sections two types, and u indicates that rail traffic dispatcher is directed to certain road
The Operation Measures implemented of section, such as adjust train speed or change train in the time of standing, Ω indicate on certain period section from
The train quantity opened；
Step B1.2, by establishing time discretization shaped like Ψ (t+ △ t)=A1Ψ(t)+B1U (t) and Ω (t)=C1Ψ
(t)+D1Discrete time Traffic flux detection model in the single subsegment of u (t), wherein △ t indicates the sampling interval, and Ψ (t) indicates t
The state vector at moment, A1、B1、C1And D1Respectively indicate the state-transition matrix of t moment, input matrix, output calculation matrix and
Direct transmission matrix；
Step B2, the Traffic flux detection model in more subsegments is constructed；Detailed process is as follows for it:
Step B2.1, it according to route space layout form and train flow historical statistical data, obtains in each subsegment of cross link
Flow proportional parameter beta；
Step B2.2, it according to the discrete time Traffic flux detection model in flow proportional parameter and single subsegment, constructs shaped like Ψ
(t+ △ t)=A1Ψ(t)+B1U (t) and Ω (t)=C1Ψ(t)+D1Discrete time Traffic flux detection mould in more subsegments of u (t)
Type；
Step B3, according to the controllable factor matrix [B of Controlling model1,A1B1,...,A1 n-1B1] order and numerical value n relationship, it is qualitative
Its controllability is analyzed, according to the sensitivity coefficient matrix [C of Controlling model1(zI-A1)-1B1+D1], its input and output of quantitative analysis are quick
Perception, wherein n indicates that the dimension of state vector, I indicate that unit matrix, z are indicated to original discrete time Traffic flux detection model
The element factor converted；
Step C, it according to the plan operating parameter of each train, on the basis of constructing Modeling Method for Train Dynamics, is transported according to train
Row conflict Coupling point establishes train running conflict and deploys model in advance, generates multiple row vehicle Lothrus apterus running track；
Step D, in each sampling instant t, based on the current operating status of train and historical position observation sequence, to train future
The advanced positions at certain moment are predicted；Detailed process is as follows for it:
Step D1, train track data pre-process, using train initiating station stop position as coordinate origin, in each sampling
It carves, according to the acquired original discrete two-dimensional position sequence x=[x of train1,x2,...,xn] and y=[y1,y2,...,yn], it adopts
Processing is carried out to it with first-order difference method and obtains new train discrete location sequence △ x=[△ x1,△x2,...,△xn-1] and
△ y=[△ y1,△y2,...,△yn-1], wherein △ xi=xi+1-xi,△yi=yi+1-yi(i=1,2 ..., n-1)；
Step D2, train track data is clustered, to train discrete two-dimensional position sequence △ x and △ y new after processing, by setting
Surely number M' is clustered, it is clustered respectively using genetic algorithm for clustering；
Step D3, parameter training is carried out using Hidden Markov Model to the train track data after cluster, after it will handle
Train operation track data △ x and △ y be considered as the aobvious observations of hidden Markov models, by set hidden state number N' and
Parameter updates period τ ', rolls the newest Hidden Markov mould of acquisition according to T' nearest position detection value and using B-W algorithm
Shape parameter λ '；Specifically: since train track sets data length obtained is dynamic change, for real-time tracking column
The state change of wheel paths, it is necessary to initial track Hidden Markov Model parameter lambda '=(π, A, B) on the basis of it is heavy to its
New adjustment, more accurately to speculate train in the position at certain following moment；The T' sight every period τ ', according to newest acquisition
Measured value (o1,o2,...,oT') to track Hidden Markov Model parameter lambda '=(π, A, B) reevaluated；
Step D4, it according to Hidden Markov Model parameter, is obtained using Viterbi algorithm hidden corresponding to current time observation
State q；
Step D5, every the periodAccording to the Hidden Markov Model parameter lambda of newest acquisition '=(π, A, B) and nearest H history
Observation (o1,o2,...,oH), the hidden state q based on train current time predicts time domain h' by setting, obtains in moment t
The position prediction value O of future time period train, to roll the rail speculated to subway train in future time period in each sampling instant
Mark.
2. subway train trajectory predictions method according to claim 1, it is characterised in that: in step D, cluster number M''s
Value is 4, and the value of hidden state number N' is 3, and it is 30 seconds, T' 10 that parameter, which updates period τ ',It is 30 seconds, H 10, predicts time domain
H' is 300 seconds.
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| CN112884190A (en) * | 2019-11-29 | 2021-06-01 | 杭州海康威视数字技术股份有限公司 | Flow prediction method and device |
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| CN114007251B (en) * | 2020-07-28 | 2023-11-10 | 中国电信股份有限公司 | Method, device and system for saving energy of base station |
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| CN102509475B (en) * | 2011-10-26 | 2013-11-06 | 南京航空航天大学 | Air traffic control system and method for four-dimensional (4D)-trajectory-based operation |
| CN103118427B (en) * | 2011-11-16 | 2015-01-07 | 北京百度网讯科技有限公司 | Location method and system based on location prefetching |
| CN103310118B (en) * | 2013-07-04 | 2016-02-10 | 中国铁路总公司 | High speed rail train operation conflict prediction method |
| CN103326903B (en) * | 2013-07-05 | 2016-01-20 | 华北电力大学 | Based on the Internet network latency prediction method of Hidden Markov |
| CN107480424A (en) * | 2014-12-30 | 2017-12-15 | 江苏理工学院 | Ship conflict method for early warning based on Rolling Planning strategy |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112884190A (en) * | 2019-11-29 | 2021-06-01 | 杭州海康威视数字技术股份有限公司 | Flow prediction method and device |
| CN112884190B (en) * | 2019-11-29 | 2023-11-03 | 杭州海康威视数字技术股份有限公司 | Flow prediction method and device |
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| CN105095983B (en) | 2018-11-23 |
| CN105095983A (en) | 2015-11-25 |
| CN109255494A (en) | 2019-01-22 |
| CN109255495A (en) | 2019-01-22 |
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