CN106971537A - For the congestion in road Forecasting Methodology and system of accident - Google Patents
For the congestion in road Forecasting Methodology and system of accident Download PDFInfo
- Publication number
- CN106971537A CN106971537A CN201710261614.9A CN201710261614A CN106971537A CN 106971537 A CN106971537 A CN 106971537A CN 201710261614 A CN201710261614 A CN 201710261614A CN 106971537 A CN106971537 A CN 106971537A
- Authority
- CN
- China
- Prior art keywords
- section
- congestion
- length
- accident
- prediction
- 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 27
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 206010039203 Road traffic accident Diseases 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000005192 partition Methods 0.000 description 2
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000000151 deposition Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
-
- 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"
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Traffic Control Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention provides the congestion in road Forecasting Methodology and system for accident, including：Road is divided into multiple sections；The history vehicle flowrate data and section starting point in each section are obtained to the road section length of road segment end, and obtain the connected relation between section；In the case where occurring accident, position, accident occupancy track quantity and total track quantity ratios and accident time of origin and predicted congestion duration occur for the accident for obtaining incident section, the incident section that accident occurs；The prediction theory congestion length in incident section is calculated according to congestion length computation rule；Combining road situation and the history vehicle flowrate data section situation and the history vehicle flowrate data and current data in incident section, Accurate Prediction jam situation in incident section, make up prior art missing.
Description
Technical field
The present invention relates to transport information processing technology field, more particularly to the congestion in road prediction side for accident
Method and system.
Background technology
With the gradually increase of vehicle flowrate on present road, traffic pressure is more and more heavier, and what congestion in road became can not keep away
Exempt from.Traffic administration personnel are relieved traffic congestion by predicted congestion section, are gone out administrative staff and are gone on a journey by congestion prediction selection road, in advance
Survey is influenceed the demand in section increasing by congestion.Traditional following road condition predicting is all manually intuition, or slightly information-based
The mode of any be it is direct utilize history congestion length data, obtain history congestion length average value, such prediction it is simple but
Accuracy rate is not high, runs under accident (traffic accident, curb parking, track take etc.) is even more unpredictable have no way of temporarily
Hand, can not also dredge road and make support certainly.
The content of the invention
The shortcoming of prior art in view of the above, it is an object of the invention to provide the congestion in road for accident
Forecasting Methodology and system, for solving the problems of the prior art.
In order to achieve the above objects and other related objects, the present invention provides a kind of congestion in road for accident and predicted
Method, including：Road is divided into multiple sections；The history vehicle flowrate data and section starting point in each section are obtained to section
The road section length of terminal, and obtain the connected relation between section；In the case where occurring accident, the accident is obtained
The accident in the incident section, the incident section of generation occurs position, accident and takes track quantity and total track quantity
Ratio and accident time of origin and predicted congestion duration；The incident is calculated according to the first congestion length computation rule
The prediction theory congestion length in section, it includes：The history vehicle flowrate data and accident time of origin in the incident section
To calculate total vehicle flowrate data in predicted congestion duration, and by total vehicle flowrate and the accident take track quantity with
Total track quantity ratios are multiplied to obtain predicted congestion vehicle flowrate, and by the total of the predicted congestion vehicle flowrate divided by incident section
Number of track-lines simultaneously is multiplied by default vehicle commander's parameter to obtain prediction theory congestion length.
In one embodiment of the invention, the described congestion in road Forecasting Methodology for accident, in addition to：Perform
Prediction actual congestion length of the actual congestion length rule of first prediction to obtain incident section, it includes：Judge incident section
Prediction theory congestion length whether be more than incident section section starting point to accident occur position part way length,
To obtain the first judged result；If the first judged result is no, the prediction theory congestion length is regard as incident section
The actual congestion length of prediction；If the first judged result is yes, the actual congestion length of prediction for making incident section is the part
Road section length.
In one embodiment of the invention, the described congestion in road Forecasting Methodology for accident, including：If first
Judged result is yes, then judges that the jam situation in the incident section influences whether the forehearth section that is connected, and it is affected to obtain this
Connected forehearth section is used as the first impacted section；The first impacted section is calculated according to the second congestion length computation rule
Prediction theory congestion length, wherein, the second congestion length computation rule, including：By the prediction theory in the incident section
Congestion length subtracts the part way length and obtains remaining theoretical congestion length；By the vehicle flowrate in the described first impacted section
Be multiplied by the remaining theoretical congestion length with the ratio of total vehicle flowrate, and be multiplied by the number of track-lines in the incident section again divided by
The number of track-lines in the first impacted section is to obtain the prediction theory congestion length in the described first impacted section.
In one embodiment of the invention, the described congestion in road Forecasting Methodology for accident, in addition to：Perform
Prediction actual congestion length of the second actual congestion length judgment rule of prediction to obtain the first impacted section, it includes：Sentence
Whether the prediction theory congestion length in disconnected first impacted section is more than the road section length in the first impacted section, to obtain second
Judged result；It is if the second judged result is no, the prediction theory congestion length in the first impacted section is real as its prediction
Border congestion length；If the second judged result is yes, make the actual congestion length of prediction in the first impacted section for first by shadow
Ring the road section length in section.
In one embodiment of the invention, the described congestion in road Forecasting Methodology for accident, in addition to：If depositing
It is prime in the section of jam situation, and sets the connected forehearth section influenceed by the jam situation as rear class, it is impacted to first
Section and its rear class section at different levels perform predetermined iterative step untill afterbody rear class section；The predetermined iterative step
Including：Using the described second actual congestion length judgment rule of prediction obtain the current road segment prediction theory congestion length and its
The judged result that road section length compares, determines whether there is rear class section according to this；If being not present, the prediction in sections at different levels is obtained
Actual congestion length；If in the presence of passing through the prediction theory in the rear class section of the second congestion length computation rule meter presence
Congestion length.
In one embodiment of the invention, the section, which is divided, to be performed by default section division rule, is preset section and is drawn
Divider then includes：(1) section is unidirectional, and the direction in each section is identical；(2) the vehicle flowrate phase in each unit interval in section
Closely；(3) fork on the road is not contained in every section.
In order to achieve the above objects and other related objects, the present invention provides a kind of congestion in road for accident and predicted
System, including：Section division module, for road to be divided into multiple sections；Road section information acquisition module, it is each for obtaining
The history vehicle flowrate data and section starting point in section and obtain the connected relation between section to the road section length of road segment end；It is prominent
Send out event information acquisition module, in the case where occurring accident, obtain incident section that the accident occurs,
The accident in the incident section occurs position, accident and takes track quantity and total track quantity ratios and accident
Time of origin and predicted congestion duration；First congestion length computation module, by according to the first congestion length computation rule come based on
The prediction theory congestion length in the incident section is calculated, it includes：The history vehicle flowrate data and burst thing in the incident section
Part time of origin takes total vehicle flowrate and the accident to calculate total vehicle flowrate data in predicted congestion duration
Track quantity and total track quantity ratios are multiplied to obtain predicted congestion vehicle flowrate, and by the predicted congestion vehicle flowrate divided by thing
Send out total number of track-lines in section and be multiplied by default vehicle commander's parameter to obtain prediction theory congestion length.
In one embodiment of the invention, the described congestion in road forecasting system for accident, in addition to：First
Congestion influences judge module, for performing prediction actual congestion of the actual congestion length rule of the first prediction to obtain incident section
Length, including：Judge whether the prediction theory congestion length in incident section is more than the section starting point in incident section to accident
Occurs the part way length of position, to obtain the first judged result；If the first judged result is no, by the prediction theory
Congestion length as incident section the actual congestion length of prediction；If the first judged result is yes, the prediction in incident section is made
Actual congestion length is the part way length.
In one embodiment of the invention, the described congestion in road forecasting system for accident, including：Second gathers around
Stifled length computation module, during for being in first judged result, judges that the jam situation in the incident section is influenced whether
Be connected forehearth section, and obtains the affected connected forehearth section as the first impacted section；And for according to the second congestion
Length computation rule calculates the prediction theory congestion length in the first impacted section, wherein, the second congestion length gauge
Rule is calculated, including：The prediction theory congestion length in the incident section is subtracted into the part way length and obtains remaining theory
Congestion length；The ratio of the vehicle flowrate in the described first impacted section and total vehicle flowrate is multiplied by the remaining theoretical congestion
Length, and be multiplied by the number of track-lines in the incident section again divided by the first impacted section number of track-lines to obtain described first
The prediction theory congestion length in impacted section.
In one embodiment of the invention, the described congestion in road forecasting system for accident, in addition to：Second
Congestion influences judge module, predicts actual congestion length judgment rule to obtain the pre- of the first impacted section for execution second
Actual congestion length is surveyed, it includes：Judge whether the prediction theory congestion length in the first impacted section is impacted more than first
The road section length in section, to obtain the second judged result；If the second judged result is no, by the prediction in the first impacted section
Theoretical congestion length predicts actual congestion length as it；If the second judged result is yes, the pre- of the first impacted section is made
Survey the road section length that actual congestion length is the first impacted section.
In one embodiment of the invention, the described congestion in road forecasting system for accident, in addition to：Logic
Performing module；If the section that there is jam situation is prime, and sets the connected forehearth section influenceed by the jam situation as rear class；
The logic performing module, for influenceing judge module to first using the second congestion length computation module and the second congestion
Impacted section and its rear class section at different levels perform predetermined iterative step untill afterbody rear class section；Wherein, it is described
Predetermined iterative step includes：The prediction theory of the current road segment is obtained using the described second actual congestion length judgment rule of prediction
The judged result that congestion length is compared with its road section length, determines whether there is rear class section according to this；If being not present, obtain each
The actual congestion length of prediction in level section；If in the presence of passing through the rear class road of the second congestion length computation rule meter presence
The prediction theory congestion length of section.
In one embodiment of the invention, the section, which is divided, to be performed by default section division rule, is preset section and is drawn
Divider then includes：(1) section is unidirectional, and the direction in each section is identical；(2) the vehicle flowrate phase in each unit interval in section
Closely；(3) fork on the road is not contained in every section.
As described above, the present invention provides a kind of congestion in road Forecasting Methodology and system for accident, including：By road
K-path partition is multiple sections；The history vehicle flowrate data and section starting point in each section are obtained to the road section length of road segment end,
And obtain the connected relation between section；In the case where occurring accident, the incident section of the acquisition accident generation,
The accident in the incident section occurs position, accident and takes track quantity and total track quantity ratios and accident
Time of origin and predicted congestion duration；The prediction theory that the incident section is calculated according to the first congestion length computation rule is gathered around
Stifled length, it includes：The history vehicle flowrate data and accident time of origin in the incident section are calculated during predicted congestion
Total vehicle flowrate data in length, and total vehicle flowrate is taken into track quantity and total track quantity ratios phase with the accident
It is multiplied by and obtains predicted congestion vehicle flowrate, and by the predicted congestion vehicle flowrate divided by total number of track-lines in incident section and is multiplied by default
Vehicle commander's parameter is to obtain prediction theory congestion length；Combining road situation and the history vehicle flowrate data and current number in incident section
According to Accurate Prediction jam situation makes up prior art missing.
Brief description of the drawings
Fig. 1 is shown as calculating prediction in the congestion in road Forecasting Methodology for accident of the present invention in an embodiment
The schematic flow sheet of theoretical congestion length.
Fig. 2 is shown as obtaining prediction in the congestion in road Forecasting Methodology for accident of the present invention in an embodiment
The schematic flow sheet of actual congestion length.
Fig. 3 is shown as the structural representation of multiple connected forehearth sections of the present invention in an embodiment.
Fig. 4 is shown as the module signal of congestion in road forecasting system for accident of the present invention in an embodiment
Figure.
Component label instructions
401 section division modules
402 road section information acquisition modules
403 emergency information acquisition modules
404 first congestion length computation modules
S101~S104 method and steps
S201~S207 method and steps
Embodiment
Illustrate embodiments of the present invention below by way of specific instantiation, those skilled in the art can be by this specification
Disclosed content understands other advantages and effect of the present invention easily.The present invention can also pass through specific realities different in addition
The mode of applying is embodied or practiced, the various details in this specification can also based on different viewpoints with application, without departing from
Various modifications or alterations are carried out under the spirit of the present invention.It should be noted that, in the case where not conflicting, following examples and implementation
Feature in example can be mutually combined.
It should be noted that the diagram provided in following examples only illustrates the basic structure of the present invention in a schematic way
Think, then in schema only display with relevant component in the present invention rather than according to component count, shape and the size during actual implement
Draw, it is actual when implementing, and kenel, quantity and the ratio of each component can be a kind of random change, and its assembly layout kenel
It is likely more complexity.
Technical scheme, applied to traffic management technology field；Vehicle supervision department can utilize the present invention's
Technical scheme precisely is predicted because of the influence for the congestion in road situation that accident is produced.
As shown in figure 1, the present invention provides the embodiment of the congestion in road Forecasting Methodology for accident, this method bag
Include：
Step S101：Road is divided into multiple sections；
Step S102：The history vehicle flowrate data and section starting point in each section are obtained to the road section length of road segment end,
And obtain the connected relation between section.
In one embodiment of the invention, the section, which is divided, to be performed by default section division rule, is preset section and is drawn
Divider then includes：
(1) section is unidirectional, and the direction in each section is identical；If a road is two-way, it is divided into two by direction difference
Different sections of highway；
(2) vehicle flowrate in each unit interval in section is close；The unit interval is, for example, one hour, and the section exists
Vehicle flowrate in multiple unit interval is essentially identical；
(3) fork on the road is not contained in every section；If there is fork on the road, cut by bifurcation and be divided into different sections of highway.
The connected relation refers to the relation connected between road, and the afterbody in such as A sections connects the stem in B sections,
There is certain relation in the section being connected on vehicle flowrate, if for example A sections are only connected with B sections, and both number of track-lines are identical,
Then A vehicle flowrate is B vehicle flowrate.
Step S103：In the case where occurring accident, incident section, the incident that the accident occurs are obtained
When position, accident occupancy track quantity occur for the accident in section with total track quantity ratios and accident generation
Between and predicted congestion duration.
The accident is, for example, traffic accident etc., divided in the case that setting completed in section；In burst thing
When part occurs, road can be navigated to by road monitoring camera and then specific incident section is navigated to, and burst can be positioned
Event incident section occur position and take track quantity, and total track quantity in section when marking off section i.e.
Can be known, so as to try to achieve the ratio；Time by monitoring is the time of origin of can be known accident, and can be according to example
Predicted congestion duration as described in inferring excluded the approximate time of accident generation obstacle.
Step S104：The prediction theory congestion that the incident section is calculated according to the first congestion length computation rule is long
Degree, it includes：The history vehicle flowrate data and accident time of origin in the incident section are calculated in predicted congestion duration
Total vehicle flowrate data, and total vehicle flowrate and the accident taken into track quantity are multiplied to total track quantity ratios
Predicted congestion vehicle flowrate is obtained, and by the predicted congestion vehicle flowrate divided by total number of track-lines in incident section and is multiplied by default vehicle commander
Parameter is to obtain prediction theory congestion length.
The prediction theory congestion length has reference value, is available for traffic department's reference to use.
, can be by being analyzed and processed the history vehicle flowrate to obtain corresponding road section in one embodiment of the invention
Some unit interval in average vehicle flow used again, the unit interval can pass through equipment in units of hour
Or the mode of people's number records the vehicle flowrate that the section each hour passes through, due to different and/or the time is different based on section
Vehicle flowrate is also different, then needs to make data reference value higher by average value, for example, 24 hours will be divided into, each for one day
In seven days weeks, divided by Monday to Sunday, by multiple vehicle flowrate samples of the same unit interval of the same day in multiple weeks
(such as 5) average, and the average value is designated as the vehicle flowrate in this day unit interval, for example, set on certain section Monday
7 points to 8 points of average vehicle flow of noon is 2000, and the morning on Sunday, 7 points to 8 points of average vehicle flow was 700 etc..
Certainly, the vehicle flowrate in a certain section can also be obtained based on the section being connected, as it was previously stated, the tail in A sections
The stem in portion connection B sections, there is certain relation in the section being connected on vehicle flowrate, if for example A sections are only connected with B sections,
And both number of track-lines are identical, then A vehicle flowrate is B vehicle flowrate；Or, the anterior bifurcated in A sections is B sections and C roads
Section, then the vehicle flowrate in A sections is C sections and B sections vehicle flowrate sum.
In one embodiment of the invention, vehicle commander's parameter can be set to 6 meters, and its foundation is：Common vehicle length is big
General 4.6 meters, general 1.4 meters apart from front vehicles during parking, both sums are taken as vehicle commander's parameter.
Illustrate the detailed process for calculating prediction theory congestion length：Provided with A sections, it is 3 tracks, and road section length is
100 (rice), if accident occupies 1 track, calculate the prediction theory congestion length that accident is caused in A sections：300
(vehicle flowrate in the unit interval)/2 (prediction half an hour)/3 (total number of track-lines) * 1 (occupancy number of track-lines)/3 (total number of track-lines, it is whole
The congestion of body section) * 6 (vehicle commander's parameters)=100 meters.
Further, it is more accurately to predict actual jam situation, the described congestion in road for accident is predicted
Method, in addition to：
Step S104：Perform prediction actual congestion of the actual congestion length rule of the first prediction to obtain incident section long
Degree, as shown in Fig. 2 it is specifically included：
Step S201：Judge whether the prediction theory congestion length in incident section is more than the section starting point in incident section to prominent
The part way length of position occurs for hair event, to obtain the first judged result；
Step S202：If the first judged result is no, the prediction theory congestion length is regard as the pre- of incident section
Actual congestion length is surveyed, the actual congestion length of the prediction is final result；
Step S203：If the first judged result is yes, the actual congestion length of prediction for making incident section is the part
Road section length；
Step S204：If the first judged result is yes, judge that the jam situation in the incident section influences whether to be connected
Section, and the affected connected forehearth section is obtained as the first impacted section；According to the second congestion length computation rule
The prediction theory congestion length in the described first impacted section is calculated, wherein, the second congestion length computation rule, including：
The prediction theory congestion length in the incident section is subtracted into the part way length and obtains remaining theoretical congestion length；By institute
The ratio of the vehicle flowrate and total vehicle flowrate of stating the first impacted section is multiplied by the remaining theoretical congestion length, and is multiplied by institute
State the number of track-lines in incident section again divided by the first impacted section number of track-lines to obtain the described first impacted section
Prediction theory congestion length.
Optionally, methods described also includes：The second actual congestion length judgment rule of prediction is performed to obtain first by shadow
The actual congestion length of prediction in section is rung, it includes：
Step S205：Judge whether the prediction theory congestion length in the first impacted section is more than the first impacted section
Road section length, to obtain the second judged result；
Step S206：If the second judged result is no, using the prediction theory congestion length in the first impacted section as
It predicts actual congestion length；
Step S207：If the second judged result is yes, it is the to make the actual congestion length of prediction in the first impacted section
The road section length in one impacted section.
If the section that there is jam situation is prime, and the connected forehearth section influenceed by the jam situation is rear class, right
First impacted section and its rear class section at different levels perform predetermined iterative step untill afterbody rear class section；It is described pre-
Determining iterative step includes：The prediction theory for obtaining the current road segment using the described second actual congestion length judgment rule of prediction is gathered around
The judged result that stifled length is compared with its road section length, determines whether there is rear class section according to this；If being not present, obtain at different levels
The actual congestion length of prediction in section；If in the presence of passing through the rear class section of the second congestion length computation rule meter presence
Prediction theory congestion length.
Specifically, it is assumed that the first impacted section be S1, and the rear class sections at different levels being affected by it be S2,
S3....Sn, then replace with S2, S3....Sn (n is more than or equal to 1) by the S1 in step S201~S207 and be iterated one by one
Calculate, iteration is terminated when judging that Sn does not have rear class section.
Illustrate as one example, as shown in figure 3, setting B, C subsections mergence is communicated to A sections, wagon flow direction be respectively from
B, C are to A；
A sections, the vehicle flowrate for example shown in following table of each unit interval of B sections and C sections：
A sections
| Hours span | Monday vehicle flowrate | Tuesday vehicle flowrate | Wednesday vehicle flowrate | …… |
| 06-07 | 100 | 80 | 85 | …… |
| 07-08 | 300 | 200 | 220 | …… |
| …… | …… | …… | …… | …… |
B sections
| Hours span | Monday vehicle flowrate | Tuesday vehicle flowrate | Wednesday vehicle flowrate | …… |
| 06-07 | 55 | 40 | 45 | …… |
| 07-08 | 170 | 110 | 120 | …… |
| …… | …… | …… | …… | …… |
C sections
| Hours span | Monday vehicle flowrate | Tuesday vehicle flowrate | Wednesday vehicle flowrate | …… |
| 06-07 | 45 | 40 | 40 | …… |
| 07-08 | 130 | 90 | 100 | …… |
| …… | …… | …… | …… | …… |
In the present embodiment, if the number of track-lines in A sections is 3 tracks, section a length of 100；The number of track-lines in B sections is 2 tracks,
The number of track-lines in C sections be 2 tracks, using the vehicle flowrate data in upper table, if on Monday 7 points of morning in A sections apart from road tail
Accident occurs for 40 meters of position, occupies 1 track, and the congestion after prediction half an hour, calculation formula is as follows：(1)
Calculate the prediction theory congestion length caused in A sections：300 (vehicle flowrate of one hour)/2 (prediction half an hour)/3 (total car
Road number) * 1 (occupancy number of track-lines)/3 (total number of track-lines, overall section congestion) * 6 (vehicle commander's parameters)=100 meters, it is pre- due to what is caused
Survey theoretical congestion length and be more than 60 meters (100 meters of road length subtracts 40 meters of event location)；So congestion may proceed to have influence on B sections
With the vehicle flowrate of C sections (2) according to B sections and C sections, the prediction theory congestion length in B sections is calculated：170 (the cars in B sections
Flow) (the prediction theory congestion length in incident section subtracts the actual congestion length of prediction to/300 (total vehicle flowrate) * 40, obtains shadow
Ring to the total congestion length in B and C sections) * 3 (number of track-lines in A sections)/2 (number of track-lines in B sections)=34 meters；Calculate C sections
Prediction theory congestion length：130 (vehicle flowrate in C sections)/300 (total vehicle flowrate) * 40 (have influence on the total congestion in B and C sections long
Degree) * 3 (number of track-lines in A sections)/2 (number of track-lines in C sections)=26 meters.
If C sections, the road section length in B sections are more than respective prediction theory congestion length, and prediction theory congestion is long
Degree is as actual congestion length is predicted, it is 60 meters of A sections congestion, 34 meters of B sections congestion and the congestion of C sections to obtain final result
26 meters；Otherwise, using C sections, B sections road section length to predict actual congestion length, and analogize with similarly by C sections and B
The actual congestion length of prediction in the rear class sections at different levels of section congestion influence.
It is preferred that can reflect that these predict actual congestion length data on the electronic map, for example, carried out by different colours
Mark etc., so more intuitively sees the congestion influence that accident is caused.
As shown in figure 4, the congestion in road forecasting system embodiment for accident that the displaying present invention is provided, due to it
Principle and above method embodiment are essentially identical, therefore technical characteristic generally applicable between embodiment is not repeated and repeated, the system
System includes：Section division module 401, for road to be divided into multiple sections；Road section information acquisition module 402, for obtaining
The history vehicle flowrate data and section starting point in each section and obtain the pass of the connection between section to the road section length of road segment end
System；Emergency information acquisition module 403, in the case where occurring accident, obtaining what the accident occurred
Incident section, the accident in the incident section occur position, accident take track quantity and total track quantity ratios and
Accident time of origin and predicted congestion duration；First congestion length computation module 404, for according to the first congestion length gauge
Calculate rule to calculate the prediction theory congestion length in the incident section, it includes：The history vehicle flowrate number in the incident section
According to calculating total vehicle flowrate data in predicted congestion duration with accident time of origin, and by total vehicle flowrate with it is described prominent
Hair event takes track quantity and total track quantity ratios and is multiplied to obtain predicted congestion vehicle flowrate, and by the predicted congestion car
Flow divided by total number of track-lines in incident section simultaneously are multiplied by default vehicle commander's parameter to obtain prediction theory congestion length.
In one embodiment of the invention, the described congestion in road forecasting system for accident, in addition to：First
Congestion influences judge module, for performing prediction actual congestion of the actual congestion length rule of the first prediction to obtain incident section
Length, including：Judge whether the prediction theory congestion length in incident section is more than the section starting point in incident section to accident
Occurs the part way length of position, to obtain the first judged result；If the first judged result is no, by the prediction theory
Congestion length as incident section the actual congestion length of prediction；If the first judged result is yes, the prediction in incident section is made
Actual congestion length is the part way length.
In one embodiment of the invention, the described congestion in road forecasting system for accident, including：Second gathers around
Stifled length computation module, during for being in first judged result, judges that the jam situation in the incident section is influenced whether
Be connected forehearth section, and obtains the affected connected forehearth section as the first impacted section；And for according to the second congestion
Length computation rule calculates the prediction theory congestion length in the first impacted section, wherein, the second congestion length gauge
Rule is calculated, including：The prediction theory congestion length in the incident section is subtracted into the part way length and obtains remaining theory
Congestion length；The ratio of the vehicle flowrate in the described first impacted section and total vehicle flowrate is multiplied by the remaining theoretical congestion
Length, and be multiplied by the number of track-lines in the incident section again divided by the first impacted section number of track-lines to obtain described first
The prediction theory congestion length in impacted section.
In one embodiment of the invention, the described congestion in road forecasting system for accident, in addition to：Second
Congestion influences judge module, predicts actual congestion length judgment rule to obtain the pre- of the first impacted section for execution second
Actual congestion length is surveyed, it includes：Judge whether the prediction theory congestion length in the first impacted section is impacted more than first
The road section length in section, to obtain the second judged result；If the second judged result is no, by the prediction in the first impacted section
Theoretical congestion length predicts actual congestion length as it；If the second judged result is yes, the pre- of the first impacted section is made
Survey the road section length that actual congestion length is the first impacted section.
In one embodiment of the invention, the described congestion in road forecasting system for accident, in addition to：Logic
Performing module；If the section that there is jam situation is prime, and sets the connected forehearth section influenceed by the jam situation as rear class；
The logic performing module, for influenceing judge module to first using the second congestion length computation module and the second congestion
Impacted section and its rear class section at different levels perform predetermined iterative step untill afterbody rear class section；Wherein, it is described
Predetermined iterative step includes：The prediction theory of the current road segment is obtained using the described second actual congestion length judgment rule of prediction
The judged result that congestion length is compared with its road section length, determines whether there is rear class section according to this；If being not present, obtain each
The actual congestion length of prediction in level section；If in the presence of passing through the rear class road of the second congestion length computation rule meter presence
The prediction theory congestion length of section.
In one embodiment of the invention, the section, which is divided, to be performed by default section division rule, is preset section and is drawn
Divider then includes：(1) section is unidirectional, and the direction in each section is identical；(2) the vehicle flowrate phase in each unit interval in section
Closely；(3) fork on the road is not contained in every section.
In summary, the present invention provides a kind of congestion in road Forecasting Methodology and system for accident, including：By road
K-path partition is multiple sections；The history vehicle flowrate data and section starting point in each section are obtained to the road section length of road segment end,
And obtain the connected relation between section；In the case where occurring accident, the incident section of the acquisition accident generation,
The accident in the incident section occurs position, accident and takes track quantity and total track quantity ratios and accident
Time of origin and predicted congestion duration；The prediction theory that the incident section is calculated according to the first congestion length computation rule is gathered around
Stifled length, it includes：The history vehicle flowrate data and accident time of origin in the incident section are calculated during predicted congestion
Total vehicle flowrate data in length, and total vehicle flowrate is taken into track quantity and total track quantity ratios phase with the accident
It is multiplied by and obtains predicted congestion vehicle flowrate, and by the predicted congestion vehicle flowrate divided by total number of track-lines in incident section and is multiplied by default
Vehicle commander's parameter is to obtain prediction theory congestion length；Combining road situation and the history vehicle flowrate data and current number in incident section
According to Accurate Prediction jam situation makes up prior art missing.
The present invention effectively overcomes various shortcoming of the prior art and has high industrial utilization.
The above-described embodiments merely illustrate the principles and effects of the present invention, not for the limitation present invention.It is any ripe
Know the personage of this technology all can carry out modifications and changes under the spirit and scope without prejudice to the present invention to above-described embodiment.Cause
This, those of ordinary skill in the art is complete without departing from disclosed spirit and institute under technological thought such as
Into all equivalent modifications or change, should by the present invention claim be covered.
Claims (10)
1. a kind of congestion in road Forecasting Methodology for accident, it is characterised in that including：
Road is divided into multiple sections；
The history vehicle flowrate data and section starting point in each section are obtained to the road section length of road segment end, and are obtained between section
Connected relation；
In the case where occurring accident, incident section, the burst thing in the incident section that the accident occurs are obtained
Part occurs position, accident and takes track quantity and total track quantity ratios and accident time of origin and predicted congestion
Duration；
The prediction theory congestion length in the incident section is calculated according to the first congestion length computation rule, it includes：It is described
The history vehicle flowrate data and accident time of origin in incident section calculate total vehicle flowrate data in predicted congestion duration,
And be multiplied to obtain predicted congestion car with total track quantity ratios by total vehicle flowrate and accident occupancy track quantity
Flow, and by the predicted congestion vehicle flowrate divided by total number of track-lines in incident section and default vehicle commander's parameter is multiplied by be predicted
Theoretical congestion length.
2. the congestion in road Forecasting Methodology according to claim 1 for accident, it is characterised in that also include：Hold
Row first predicts prediction actual congestion length of the actual congestion length rule to obtain incident section, and it includes：
Judge incident section prediction theory congestion length whether be more than incident section section starting point to accident occur position
The part way length put, to obtain the first judged result；
It is if the first judged result is no, the actual congestion of prediction in prediction theory congestion length as the incident section is long
Degree；
If the first judged result is yes, the actual congestion length of prediction for making incident section is the part way length.
3. the congestion in road Forecasting Methodology according to claim 2 for accident, it is characterised in that including：
If the first judged result is yes, judge that the jam situation in the incident section influences whether the forehearth section that is connected, and acquisition should
Affected connected forehearth section is used as the first impacted section；
The prediction theory congestion length in the first impacted section is calculated according to the second congestion length computation rule, wherein, institute
The second congestion length computation rule is stated, including：The prediction theory congestion length in the incident section is subtracted into the part way
Length obtains remaining theoretical congestion length；The ratio of the vehicle flowrate in the described first impacted section and total vehicle flowrate is multiplied by
The remaining theoretical congestion length, and be multiplied by the number of track-lines in the incident section again divided by the first impacted section track
Count to obtain the prediction theory congestion length in the described first impacted section.
4. the congestion in road Forecasting Methodology according to claim 3 for accident, it is characterised in that also include：Hold
Row second predicts the actual congestion length of prediction of the actual congestion length judgment rule to obtain the first impacted section, and it includes：
Judge whether the prediction theory congestion length in the first impacted section is more than the road section length in the first impacted section, with
To the second judged result；
If the second judged result is no, actual congestion is predicted using the prediction theory congestion length in the first impacted section as it
Length；
If the second judged result is yes, the actual congestion length of prediction for making the first impacted section is the first impacted section
Road section length.
5. the congestion in road Forecasting Methodology according to claim 4 for accident, it is characterised in that also include：
If the section that there is jam situation is prime, and the connected forehearth section influenceed by the jam situation is set as rear class, to the
One impacted section and its rear class section at different levels perform predetermined iterative step untill afterbody rear class section；
The predetermined iterative step includes：
Using the described second actual congestion length judgment rule of prediction obtain the current road segment prediction theory congestion length and its
The judged result that road section length compares, determines whether there is rear class section according to this；
If being not present, the actual congestion length of prediction in sections at different levels is obtained；
If in the presence of passing through the prediction theory congestion length in the rear class section of the second congestion length computation rule meter presence.
6. a kind of congestion in road forecasting system for accident, it is characterised in that including：
Section division module, for road to be divided into multiple sections；
Road section information acquisition module, for obtaining the history vehicle flowrate data and section starting point in each section to road segment end
Road section length, and obtain the connected relation between section；
Emergency information acquisition module, in the case where occurring accident, obtaining the thing that the accident occurs
Send out section, the accident in the incident section occurs position, accident and takes track quantity and total track quantity ratios and prominent
Send out Time To Event and predicted congestion duration；
First congestion length computation module, the prediction for calculating the incident section according to the first congestion length computation rule
Theoretical congestion length, it includes：The history vehicle flowrate data and accident time of origin in the incident section calculate prediction
Total vehicle flowrate data in congestion duration, and total vehicle flowrate and the accident are taken into track quantity and total track quantity
Ratio is multiplied to obtain predicted congestion vehicle flowrate, and by the predicted congestion vehicle flowrate divided by total number of track-lines in incident section and multiplies
With default vehicle commander's parameter to obtain prediction theory congestion length.
7. the congestion in road forecasting system according to claim 6 for accident, it is characterised in that also include：The
One congestion influences judge module, and for performing, prediction of the actual congestion length rule of the first prediction to obtain incident section is actual to gather around
Stifled length, including：Judge whether the prediction theory congestion length in incident section is more than the section starting point in incident section to the thing that happens suddenly
The part way length of position occurs for part, to obtain the first judged result；If the first judged result is no, the prediction is managed
By prediction actual congestion length of the congestion length as incident section；If the first judged result is yes, the pre- of incident section is made
It is the part way length to survey actual congestion length.
8. the congestion in road forecasting system according to claim 7 for accident, it is characterised in that including：
Second congestion length computation module, during for being in first judged result, judges the congestion feelings in the incident section
Condition influences whether the forehearth section that is connected, and obtains the affected connected forehearth section as the first impacted section；And for root
The prediction theory congestion length in the first impacted section is calculated according to the second congestion length computation rule, wherein, described second
Congestion length computation rule, including：The prediction theory congestion length in the incident section is subtracted into the part way length to obtain
To remaining theoretical congestion length；The ratio of the vehicle flowrate in the described first impacted section and total vehicle flowrate is multiplied by described surplus
Remaining theoretical congestion length, and be multiplied by the number of track-lines in the incident section again divided by the first impacted section number of track-lines to obtain
Obtain the prediction theory congestion length in the first impacted section.
9. the congestion in road forecasting system according to claim 8 for accident, it is characterised in that also include：The
Two congestions influence judge module, predict actual congestion length judgment rule to obtain the first impacted section for execution second
The actual congestion length of prediction, it includes：Judge whether the prediction theory congestion length in the first impacted section is more than first by shadow
The road section length in section is rung, to obtain the second judged result；If the second judged result is no, by the pre- of the first impacted section
Theoretical congestion length is surveyed as it and predicts actual congestion length；If the second judged result is yes, the first impacted section is made
The actual congestion length of prediction is the road section length in the first impacted section.
10. the congestion in road forecasting system according to claim 9 for accident, it is characterised in that also include：Patrol
Collect performing module；If the section that there is jam situation is prime, and sets the connected forehearth section influenceed by the jam situation as after
Level；The logic performing module, for utilizing the second congestion length computation module and the second congestion influence judge module pair
First impacted section and its rear class section at different levels perform predetermined iterative step untill afterbody rear class section；
Wherein, the predetermined iterative step includes：
Using the described second actual congestion length judgment rule of prediction obtain the current road segment prediction theory congestion length and its
The judged result that road section length compares, determines whether there is rear class section according to this；
If being not present, the actual congestion length of prediction in sections at different levels is obtained；
If in the presence of passing through the prediction theory congestion length in the rear class section of the second congestion length computation rule meter presence.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710261614.9A CN106971537B (en) | 2017-04-20 | 2017-04-20 | For the congestion in road prediction technique and system of emergency event |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710261614.9A CN106971537B (en) | 2017-04-20 | 2017-04-20 | For the congestion in road prediction technique and system of emergency event |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106971537A true CN106971537A (en) | 2017-07-21 |
| CN106971537B CN106971537B (en) | 2019-07-26 |
Family
ID=59332252
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710261614.9A Expired - Fee Related CN106971537B (en) | 2017-04-20 | 2017-04-20 | For the congestion in road prediction technique and system of emergency event |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106971537B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108281000A (en) * | 2018-02-05 | 2018-07-13 | 北京交通大学 | A kind of accident of data-driven is to Regional Road Network impact analysis system and method |
| CN108320553A (en) * | 2018-04-04 | 2018-07-24 | 大陆汽车投资（上海）有限公司 | Road condition predicting method based on driving path event |
| CN109615852A (en) * | 2018-11-20 | 2019-04-12 | 上海海事大学 | A kind of method of road traffic delay distribution quick predict under burst accident |
| CN111402585A (en) * | 2020-03-25 | 2020-07-10 | 中南大学 | Detection method for sporadic congestion path |
| CN111723169A (en) * | 2020-04-09 | 2020-09-29 | 腾讯科技（深圳）有限公司 | Map display method and device, electronic equipment and storage medium |
| CN112530163A (en) * | 2020-11-23 | 2021-03-19 | 腾讯科技（深圳）有限公司 | Traffic accident prediction method, traffic accident prediction device, electronic device, and storage medium |
| CN112863172A (en) * | 2020-12-29 | 2021-05-28 | 千方捷通科技股份有限公司 | Highway traffic running state judgment method, early warning method, device and terminal |
| CN113257002A (en) * | 2021-05-11 | 2021-08-13 | 青岛海信网络科技股份有限公司 | Peak start time prediction method, device, equipment and medium |
| CN116229727A (en) * | 2023-05-10 | 2023-06-06 | 深圳市城市交通规划设计研究中心股份有限公司 | Road congestion induction method and management and control system based on online simulation |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09297898A (en) * | 1996-04-30 | 1997-11-18 | Matsushita Electric Ind Co Ltd | Traffic jam information device |
| JP2009075981A (en) * | 2007-09-21 | 2009-04-09 | Toyota Infotechnology Center Co Ltd | Traffic information providing system, program for center side computer, retrieval program, recording medium, and traffic information providing method |
| CN102034353A (en) * | 2010-11-04 | 2011-04-27 | 东南大学 | Method for measuring and calculating queuing length caused by traffic accidents on urban road based on fixed detectors |
| CN102419905A (en) * | 2011-08-12 | 2012-04-18 | 北京航空航天大学 | Traffic-wave theory-based traffic influence area determining method of expressway accidents |
| CN104657199A (en) * | 2015-02-10 | 2015-05-27 | 交通运输部科学研究院 | Method for forecasting influence range of highway traffic incident on basis of microscopic simulation |
| CN105185117A (en) * | 2015-10-09 | 2015-12-23 | 山东高速信息工程有限公司 | Road travel time predicting method, system, and device |
-
2017
- 2017-04-20 CN CN201710261614.9A patent/CN106971537B/en not_active Expired - Fee Related
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09297898A (en) * | 1996-04-30 | 1997-11-18 | Matsushita Electric Ind Co Ltd | Traffic jam information device |
| JP2009075981A (en) * | 2007-09-21 | 2009-04-09 | Toyota Infotechnology Center Co Ltd | Traffic information providing system, program for center side computer, retrieval program, recording medium, and traffic information providing method |
| CN102034353A (en) * | 2010-11-04 | 2011-04-27 | 东南大学 | Method for measuring and calculating queuing length caused by traffic accidents on urban road based on fixed detectors |
| CN102419905A (en) * | 2011-08-12 | 2012-04-18 | 北京航空航天大学 | Traffic-wave theory-based traffic influence area determining method of expressway accidents |
| CN104657199A (en) * | 2015-02-10 | 2015-05-27 | 交通运输部科学研究院 | Method for forecasting influence range of highway traffic incident on basis of microscopic simulation |
| CN105185117A (en) * | 2015-10-09 | 2015-12-23 | 山东高速信息工程有限公司 | Road travel time predicting method, system, and device |
Non-Patent Citations (2)
| Title |
|---|
| 张敖木翰: "突发事件下非重复***通拥堵传播规律与控制策略研究", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 王俊骅 等: "高速公路网突发交通事件时空影响预测模型", 《交通信息与安全》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108281000A (en) * | 2018-02-05 | 2018-07-13 | 北京交通大学 | A kind of accident of data-driven is to Regional Road Network impact analysis system and method |
| CN108281000B (en) * | 2018-02-05 | 2020-08-14 | 北京交通大学 | System and method for analyzing influence of data-driven emergency on regional road network |
| CN108320553A (en) * | 2018-04-04 | 2018-07-24 | 大陆汽车投资（上海）有限公司 | Road condition predicting method based on driving path event |
| CN109615852A (en) * | 2018-11-20 | 2019-04-12 | 上海海事大学 | A kind of method of road traffic delay distribution quick predict under burst accident |
| CN109615852B (en) * | 2018-11-20 | 2021-03-30 | 上海海事大学 | Method for rapidly predicting road traffic flow distribution under sudden accident |
| CN111402585B (en) * | 2020-03-25 | 2021-02-02 | 中南大学 | Detection method for sporadic congestion path |
| CN111402585A (en) * | 2020-03-25 | 2020-07-10 | 中南大学 | Detection method for sporadic congestion path |
| CN111723169A (en) * | 2020-04-09 | 2020-09-29 | 腾讯科技（深圳）有限公司 | Map display method and device, electronic equipment and storage medium |
| CN111723169B (en) * | 2020-04-09 | 2024-04-30 | 腾讯科技（深圳）有限公司 | Map display method and device, electronic equipment and storage medium |
| CN112530163A (en) * | 2020-11-23 | 2021-03-19 | 腾讯科技（深圳）有限公司 | Traffic accident prediction method, traffic accident prediction device, electronic device, and storage medium |
| CN112530163B (en) * | 2020-11-23 | 2022-03-01 | 腾讯科技（深圳）有限公司 | Traffic accident prediction method, traffic accident prediction device, electronic device, and storage medium |
| CN112863172A (en) * | 2020-12-29 | 2021-05-28 | 千方捷通科技股份有限公司 | Highway traffic running state judgment method, early warning method, device and terminal |
| CN112863172B (en) * | 2020-12-29 | 2022-10-28 | 千方捷通科技股份有限公司 | Highway traffic running state judgment method, early warning method, device and terminal |
| CN113257002A (en) * | 2021-05-11 | 2021-08-13 | 青岛海信网络科技股份有限公司 | Peak start time prediction method, device, equipment and medium |
| CN116229727A (en) * | 2023-05-10 | 2023-06-06 | 深圳市城市交通规划设计研究中心股份有限公司 | Road congestion induction method and management and control system based on online simulation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106971537B (en) | 2019-07-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106971537A (en) | For the congestion in road Forecasting Methodology and system of accident | |
| CN104715610B (en) | A kind of traffic index computational methods towards urban transportation | |
| Lu et al. | Freeway micro-simulation calibration: Case study using aimsun and VISSIM with detailed field data | |
| CN101593424B (en) | Intelligent combination forecasting method for short-term traffic flow | |
| Arafat et al. | A data-driven approach to calibrate microsimulation models based on the degree of saturation at signalized intersections | |
| CN111292533B (en) | Method for estimating flow of arbitrary section of highway at any time period based on multi-source data | |
| CN104657199B (en) | The Forecasting Methodology of freeway traffic event coverage based on microscopic simulation | |
| CN104123833A (en) | Road condition planning method and device thereof | |
| CN110766211A (en) | Method for creating vehicle path planning problem model under real-time road condition | |
| CN102592447A (en) | Method for judging road traffic state of regional road network based on fuzzy c means (FCM) | |
| CN101739822B (en) | Sensor network configuring method for regional traffic state acquisition | |
| CN107368931A (en) | Logistics distribution path dynamic programming method and system based on big data analytical technology | |
| CN104933859A (en) | Macroscopic fundamental diagram-based method for determining bearing capacity of network | |
| CN109409713A (en) | Road network appraisal procedure based on Bayesian model and three times standard deviation criterion | |
| CN105574613A (en) | Bus arrival time prediction method and apparatus | |
| Mannini et al. | On the short-term prediction of traffic state: an application on urban freeways in Rome | |
| CN109544967A (en) | A kind of public transport network running state monitoring method based on low frequency AVL data | |
| CN104331746B (en) | A kind of dynamic path optimization system and method for separate type | |
| Anusha et al. | Dynamical systems approach for queue and delay estimation at signalized intersections under mixed traffic conditions | |
| Guan et al. | Traffic incident duration prediction based on artificial neural network | |
| Liu et al. | Developments and applications of simulation-based online travel time prediction system: traveling to Ocean City, Maryland | |
| Tu et al. | Macroscopic travel time reliability diagrams for freeway networks | |
| CN105243848A (en) | Real-time road condition prediction method and system | |
| Ryu et al. | Path flow estimator for planning applications in small communities | |
| CN103280109A (en) | Obtaining method, obtaining device and prediction system of travel time |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 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 | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20220627 Address after: 22 / F, Qilu traffic information industrial park, building 2, union Fortune Plaza, 2177 Tianchen Road, Jinan high tech Industrial Development Zone, Shandong Province Patentee after: Shandong high speed information Group Co.,Ltd. Address before: No.143, Jiangjun Road, Licheng District, Jinan City, Shandong Province, 250108 Patentee before: SHANDONG HI-SPEED INFORMATION ENGINEERING Co.,Ltd. |
|
| TR01 | Transfer of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20190726 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |