CN109559511A - A kind of urban traffic blocking information orientation put-on method - Google Patents
A kind of urban traffic blocking information orientation put-on method Download PDFInfo
- Publication number
- CN109559511A CN109559511A CN201811479705.0A CN201811479705A CN109559511A CN 109559511 A CN109559511 A CN 109559511A CN 201811479705 A CN201811479705 A CN 201811479705A CN 109559511 A CN109559511 A CN 109559511A
- Authority
- CN
- China
- Prior art keywords
- road
- traffic
- data set
- section
- record
- 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
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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- 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
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Analytical Chemistry (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Chemical & Material Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (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)
Abstract
The invention discloses a kind of urban traffic blocking information to orient put-on method, firstly, pre-processing to research internal road；Secondly, forming traffic trip log data set is denoted as GJ；The relational network between urban road is determined further according to GJ；Finally, calculating the primary association road section information in urban traffic blocking section.Urban congestion information can relatively accurately can be transmitted on maximally related section by the present invention, moreover it is possible to traffic congestion information be carried out global and local prioritised manner and transmitted, to provide decision-making foundation for urban planning and traffic administration.
Description
Technical field
The present invention relates to urban planning and urban transportation technical field, especially a kind of urban traffic blocking information orientation is thrown
Put method.
Background technique
Traffic road congestion is a kind of current important urban disease, is related to the physical and mental health of each city dweller, to city
City's development produces serious negative effect.The collection of traffic congestion information, processing and to be delivered to the public in time be to solve city
One important means of city's congestion problems.At the same time, with the arrival of big data era, mankind space is carried out using big data
The research of active characteristics is more and more extensive.In urban traffic blocking field, researcher using mobile phone, go on a journey by mobile, shared automobile
The data such as track, which calculate which road in city, belongs to congestion location, and by congestion segment information is told to city dweller.So
And the scheme that there is problems and its need to solve:
Firstly, general congestion information be supplied to the public it is mostly be congestion information point position, lacking is that the stream of people in which section leads
Certain a road section congestion, i.e. distribution of the source of congested link in city are caused.Secondly, how more accurately congestion information
The public that may enter congested link is sent to there is also deficiency, i.e. the information of congestion is often notified in city extensively,
It can not be conveyed according to certain priority in city different sections of highway.Finally, urban transportation relevance is with global and local
Feature, how to be quickly found the global most related and maximally related section in part of congested link is also to be badly in need of to be solved ask
Topic.Because the time that global maximally related section reaches congested link is longer, this category information is more suitable for city in long-term and gathers around
The scientific reference frame that stifled problem is administered.Part is most related may to be related to the public that will currently enter congested link, need
Extremely quickly to tell congested link information to they.
How traffic trip track relationship is converted into the incidence relation between road section, and current big data, handed over
The emphasis that drift is drawn, smart city is studied.
Summary of the invention
A kind of urban traffic blocking letter is provided the technical problem to be solved by the present invention is to overcome the deficiencies in the prior art
Urban congestion information more accurately can be oriented dispensing by breath orientation put-on method, the present invention, be urban planning and friendship
Siphunculus reason provides decision-making foundation.
The present invention uses following technical scheme to solve above-mentioned technical problem:
A kind of urban traffic blocking information orientation put-on method proposed according to the present invention, comprising the following steps:
Step 1 pre-processes research internal road: doing segment processing to research internal road, and adds to every section of road
Unique identifying number forms roadway segment data set R；
Traffic trip track data is matched on road by step 2, is obtained each traffic trip track in area to be studied and is based on road
The traffic trip of road partitioned data set (PDS) R records, and all traffic trips based on roadway segment data set R record number in area to be studied
GJ is denoted as according to collection；
Obtain each traffic trip track recorded based on the traffic trip of roadway segment data set R it is specific as follows:
Single hand over is obtained using roadway segment data set R to its cutting for each traffic trip track L in area to be studied
Traffic trip track data collection L1 after pass-out row trajectory segment, and according to the space one-to-one relationship with R, it records every in L1
Section route unique identifying number records PL based on the traffic trip of roadway segment data set R to obtain each traffic trip track；
Step 3 determines relational network between urban road according to GJ；
Each traffic trip records PL in step 3.1, traversal set GJ；
Step 3.2, according to identification number sequencing, a record is formed to the different identification number of any two in PL；
Step 3.3, after GJ traversal after the completion of, storage record simultaneously counts every record quantity；
Step 3.4 establishes complex network data set W to the record stored in R and step 3.3 according to Complex Networks Theory；
Step 3.5 divides calculation method according to community in complex network, and W is divided into different communities, is denoted as S, so that data
Each identification number belongs to some community in collection R；
Step 4, the primary association road section information for obtaining urban traffic blocking section；
Step 4.1 selects traffic congestion location to be analyzed from data set R, is denoted as L3；
Step 4.2, the community where finding out L3 in S, are denoted as SR；
The record set containing L3 is filtered out in step 4.3, the record stored from step 3.3, is denoted as record set GListA；Together
When, the section occurred in SR in GListA is extracted to form record set GListB；
Step 4.4, on map, according to numerical values recited is recorded in GListA, to the road in the data set of the section GListA in addition to L3
Duan Jinhang classification visualization；
Step 4.5, on map, according to numerical values recited is recorded in GListB, to the road in the data set of the section GListB in addition to L3
Duan Jinhang classification visualization；
Step 4.6, in the data set of the section GListA or GListB, the descending arrangement of numerical value will be recorded, selection is in record number
The position with L3 relevant road segments is shown on public transport display platform before value ranking on the section of M, M is preset integer.
Scheme, step 1 are advanced optimized as a kind of urban traffic blocking information orientation put-on method of the present invention
Middle roadway segment data set R is single line road data collection.
Scheme, step 2 are advanced optimized as a kind of urban traffic blocking information orientation put-on method of the present invention
Middle traffic trip track data is line composed by the geographical space point that records sequentially in time.
Scheme, step 2 are advanced optimized as a kind of urban traffic blocking information orientation put-on method of the present invention
The unique identifying number sequence that middle L1 is recorded is the chronological order according to traffic trip track.
Scheme, step are advanced optimized as a kind of urban traffic blocking information orientation put-on method of the present invention
L2 is made of 2 identification numbers in 3.2, and first identification number appears in PL before second identification number.
Scheme, step are advanced optimized as a kind of urban traffic blocking information orientation put-on method of the present invention
3.3 is specific as follows:
Dictionary list GList is constructed, obtained all records are added to dictionary list GList after the completion of successively traversing GJ,
Add 1, if it does not exist, then store it in dictionary the quantity of the record if a certain item records existing dictionary list GList
1 is denoted as in list GList and by its quantity.
Scheme, M 3 are advanced optimized as a kind of urban traffic blocking information orientation put-on method of the present invention.
The invention adopts the above technical scheme compared with prior art, has following technical effect that
(1) present invention is associated with the traffic connection information between city road, can be relatively accurately by urban congestion
Information is transmitted on maximally related section, provides science support foundation for urban traffic control and planning design analysis；
(2) traffic congestion information can be carried out global and local prioritised manner and transmits by the present invention, be conducive to from different angles
Degree carries out the analysis of reason congestion correlation.
Detailed description of the invention
Fig. 1 is overall flow schematic diagram of the invention.
Fig. 2 is road and intersection Node distribution schematic diagram.
Fig. 3 is roadway segment result schematic diagram.
Fig. 4 is track data distribution schematic diagram in the road.
Fig. 5 is the statistics schematic diagram of track number between section.
Fig. 6 is suitable for data format schematic diagram required for Complex Networks Analysis.
Fig. 7 is community division result schematic diagram.
Specific embodiment
Technical solution of the present invention is described in further detail with reference to the accompanying drawing:
Global and local maximally related to solve the problems, such as, the present invention is solved using community division method in complex network.Community
Division methods are mainly used to disclose a kind of technology of network aggregation behavior, and practical is exactly a kind of method of network clustering.Here
" community " can be understood as a kind of set with identical property node.If can be first to road network and traffic trip
Track data is created as the complex network between road section, then community division method division can be carried out road section
For the different close communities of internal connection.Just belong to " local correlations " between each community inside, the road in whole communities
Road section then belongs to " holistic correlation ".
The method of the present invention is specific as follows:
Step 1) first pre-processes research internal road referring to attached drawing 1；
Segment processing is done to research internal road, and unique identifying number is added to every section of road, forms roadway segment data set R.
Referring to attached drawing 2, there are 5 roads in case study area, and this 5 roads have 6 crosspoints, respectively point a, b, c, d, e, f.Benefit
5 above-mentioned roads can be interrupted respectively with this 6 points, to form roadway segment data set R.The distribution of R spatially
Referring to attached drawing 3, specific roadway segment title is respectively R1, R2, R3, R4, R5, R6, R7, R8, R9, R10, R11, R12, R13,
R14, R15, R16, R17.
Traffic trip track data is matched on road by step 2, obtains each traffic trip track base in area to be studied
It is recorded in the traffic trip of roadway segment data set R, all traffic trip notes based on roadway segment data set R in area to be studied
Record data set is denoted as GJ；
Obtain each traffic trip track recorded based on the traffic trip of roadway segment data set R it is specific as follows:
Single hand over is obtained using roadway segment data set R to its cutting for each traffic trip track L in area to be studied
Traffic trip track data collection L1 after pass-out row trajectory segment, and according to the space one-to-one relationship with R data collection, record
Every section of route exclusive identification code in L1, to obtain each traffic trip of the traffic trip track based on roadway segment data set R
Record PL.It is successively successively passed through by section R6, R8, R10, traffic trip track Lb referring to attached drawing 4, such as traffic trip track La
Cross section R1, R3, R9, R10, traffic trip track Lc is successively by section R16, R12, R8, R10, then traffic trip track La
Traffic trip record PL be { R6, R8, R10 }, Lb traffic trip record PL be { R1, R3, R9, R10 }, the traffic trip of Lc
Recording PL is { R16, R12, R8, R10 }.So, GJ then includes that these three traffic trips record: { R6, R8, R10 }, R1,
R3, R9, R10 }, { R16, R12, R8, R10 }.
The quantity that the quantity that the quantity for studying La in area is 1, Lb is 4, Lc is 1.
Step 3) determines the relational network between urban road according to GJ；
Step 3.1) traverses each traffic trip in set GJ and records PL；
Step 3.2) is according to identification number sequencing, to the different identification number of any two in PL as a record L2；It is right
For this example, then following a plurality of record: { R6, R8 } can be formed, { R6, R10 }, { R8, R10 }, { R8, R10 }, { R1, R3 },
{ R1, R3 }, { R1, R3 }, { R1, R3 }, { R1, R9 }, { R1, R9 }, { R1, R9 }, { R1, R9 }, { R1, R10 }, { R1, R10 },
{ R1, R10 }, { R1, R10 }, { R3, R9 }, { R3, R9 }, { R3, R9 }, { R3, R9 }, { R3, R10 }, { R3, R10 }, { R3, R10 },
{ R3, R10 }, { R9, R10 }, { R9, R10 }, { R9, R10 }, { R9, R10 }, { R16, R12 }, { R16, R8 }, { R16, R10 },
{ R12, R8 }, { R12, R10 }.
Step 3.3) constructs dictionary list GList, L2 is stored in dictionary list GList, if GList referring to attached drawing 5
In do not include this L2, then the L2 is added in GList, and its quantity is denoted as 1, if in GList include this L2, general
The quantity of L2 adds 1 in this GList.The calculated result of this example are as follows: { R6, R8 } is 1, and { R6, R10 } is 1, R8,
R10 } it is 2, { R1, R3 } is 4, and { R1, R9 } is 4, and { R1, R10 } is 4, and { R3, R9 } is 4, and { R3, R10 } is 4,
{ R9, R10 } is 4, and { R16, R12 } is 1, and { R16, R8 } is 1, and { R16, R10 } is 1, and { R12, R8 } is 1, R12,
R10 } it is 1.
Step 3.4) establishes complex network data set W to R and dictionary list GList according to Complex Networks Theory；Referring to
Attached drawing 6 is the data format of this example, and it is suitable for a kind of common data forms of the Complex Networks Analysis software such as Pajek.
Step 3.5) divides calculation method according to community in complex network, and W is divided into different communities, is denoted as S, so that
Each identification number belongs to some community in data set R.Assuming that there is the road segment segment of the magnitude of traffic flow to be divided into this example
2 communities.Referring to attached drawing 7, it is { R1, R3, R9 } that the composition of each community, which is respectively as follows: S1, S2 be R6, R8, R10, R12,
R16}。
The primary association road section information in step 4) acquisition urban traffic blocking section；
Step 4.1) selects traffic congestion location to be analyzed from data set R, is denoted as L3, this example is R10；
Community SR of the step 4.2) where finding out R10 in S, this example is S2；
Step 4.3) filters out the record set containing R10 from GList, is denoted as data set GListA.Meanwhile in GListA
The section occurred in S2 extracts to form record set GListB；I.e. if certain record contains section in SR in GListA,
It extracts.What these were extracted ultimately forms GListB.
GListA:{ R6, R10 } it is 1, { R8, R10 } is 2, and { R1, R10 } is 4, and { R3, R10 } is 4, R9,
R10 } it is 4, { R16, R10 } is 1, and { R12, R10 } is 1.
GListB:{ R6, R10 } it is 1, { R8, R10 } is 2, and { R16, R10 } is 1, and { R12, R10 } is 1.
Step 4.4) is on map, and according to numerical values recited is recorded in GListA, classification visualizes the section GListA data set
In in addition to R10 section；Classification is to be divided into several ranks according to size, and each rank is shown with different graphic pattern.For this reality
Example, { R1, R10 }, { R3, R10 }, { R9, R10 } is 4, is maximum value in GListA, visualization when with most thick lines into
Row shows these three sections R1, R3 and R9；
Step 4.5), according to numerical values recited is recorded in GListB, is removed on map in the classification visualization section GListB data set
The outer section R10；For this example, { R8, R10 } is 2, is maximum value in GListB, in visualization with the progress of most thick lines
Show the section R8；
Step 4.4 and step 4.5 select to visualize for global and local respectively, one be it is global, one is local.
Classification visualization is to help people's observation maximally related with congested link in section, i.e., other sections of non-congestion
Section is at which, and helping people's observation, at which, this is core purpose with the maximally related section of congested link.
Step 4.6) simultaneously, in the data set of the section GListA or GListB, will record the descending arrangement of numerical value, selection
The position with R10 relevant road segments is shown on public transport display platform before recording numerical ranks on the section of M, M is pre-
If integer, M can be 3.
The above content is a further detailed description of the present invention in conjunction with specific preferred embodiments, and it cannot be said that
Specific implementation of the invention is only limited to these instructions.For those of ordinary skill in the art to which the present invention belongs, exist
Under the premise of not departing from present inventive concept, several simple deductions or substitution can also be made, all shall be regarded as belonging to of the invention
Protection scope.
Claims (7)
1. a kind of urban traffic blocking information orients put-on method, which comprises the following steps:
Step 1 pre-processes research internal road: doing segment processing to research internal road, and adds to every section of road
Unique identifying number forms roadway segment data set R；
Traffic trip track data is matched on road by step 2, is obtained each traffic trip track in area to be studied and is based on road
The traffic trip of road partitioned data set (PDS) R records, and all traffic trips based on roadway segment data set R record number in area to be studied
GJ is denoted as according to collection；
Obtain each traffic trip track recorded based on the traffic trip of roadway segment data set R it is specific as follows:
Single hand over is obtained using roadway segment data set R to its cutting for each traffic trip track L in area to be studied
Traffic trip track data collection L1 after pass-out row trajectory segment, and according to the space one-to-one relationship with R, it records every in L1
Section route unique identifying number records PL based on the traffic trip of roadway segment data set R to obtain each traffic trip track；
Step 3 determines relational network between urban road according to GJ；
Each traffic trip records PL in step 3.1, traversal set GJ；
Step 3.2, according to identification number sequencing, a record is formed to the different identification number of any two in PL；
Step 3.3, after GJ traversal after the completion of, storage record simultaneously counts every record quantity；
Step 3.4 establishes complex network data set W to the record stored in R and step 3.3 according to Complex Networks Theory；
Step 3.5 divides calculation method according to community in complex network, and W is divided into different communities, is denoted as S, so that data
Each identification number belongs to some community in collection R；
Step 4, the primary association road section information for obtaining urban traffic blocking section；
Step 4.1 selects traffic congestion location to be analyzed from data set R, is denoted as L3；
Step 4.2, the community where finding out L3 in S, are denoted as SR；
The record set containing L3 is filtered out in step 4.3, the record stored from step 3.3, is denoted as record set GListA；Together
When, the section occurred in SR in GListA is extracted to form record set GListB；
Step 4.4, on map, according to numerical values recited is recorded in GListA, to the road in the data set of the section GListA in addition to L3
Duan Jinhang classification visualization；
Step 4.5, on map, according to numerical values recited is recorded in GListB, to the road in the data set of the section GListB in addition to L3
Duan Jinhang classification visualization；
Step 4.6, in the data set of the section GListA or GListB, the descending arrangement of numerical value will be recorded, selection is in record number
The position with L3 relevant road segments is shown on public transport display platform before value ranking on the section of M, M is preset integer.
2. a kind of urban traffic blocking information according to claim 1 orients put-on method, which is characterized in that in step 1
Roadway segment data set R is single line road data collection.
3. a kind of urban traffic blocking information according to claim 1 orients put-on method, which is characterized in that in step 2
Traffic trip track data is line composed by the geographical space point that records sequentially in time.
4. a kind of urban traffic blocking information according to claim 1 orients put-on method, which is characterized in that in step 2
The unique identifying number sequence that L1 is recorded is the chronological order according to traffic trip track.
5. a kind of urban traffic blocking information according to claim 1 orients put-on method, which is characterized in that step 3.2
Middle L2 is made of 2 identification numbers, and first identification number appears in PL before second identification number.
6. a kind of urban traffic blocking information according to claim 1 orients put-on method, which is characterized in that step 3.3
It is specific as follows:
Dictionary list GList is constructed, obtained all records are added to dictionary list GList after the completion of successively traversing GJ,
Add 1, if it does not exist, then store it in dictionary the quantity of the record if a certain item records existing dictionary list GList
1 is denoted as in list GList and by its quantity.
7. a kind of urban traffic blocking information according to claim 1 orients put-on method, which is characterized in that M 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811479705.0A CN109559511B (en) | 2018-12-05 | 2018-12-05 | A kind of urban traffic blocking information orientation put-on method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811479705.0A CN109559511B (en) | 2018-12-05 | 2018-12-05 | A kind of urban traffic blocking information orientation put-on method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109559511A true CN109559511A (en) | 2019-04-02 |
| CN109559511B CN109559511B (en) | 2019-10-29 |
Family
ID=65869012
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811479705.0A Active CN109559511B (en) | 2018-12-05 | 2018-12-05 | A kind of urban traffic blocking information orientation put-on method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109559511B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113362488A (en) * | 2021-08-11 | 2021-09-07 | 智联万维科技有限公司 | OBD monitoring data processing system |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20100022151A (en) * | 2008-08-19 | 2010-03-02 | 한국전자통신연구원 | Gis data processing apparatus and method for searching route of a car |
| CN101751777A (en) * | 2008-12-02 | 2010-06-23 | 同济大学 | Dynamic urban road network traffic zone partitioning method based on space cluster analysis |
| CN101866545A (en) * | 2010-05-11 | 2010-10-20 | 中国科学院软件研究所 | Method for acquiring road network matching track of mobile object |
| CN102968901A (en) * | 2012-11-30 | 2013-03-13 | 青岛海信网络科技股份有限公司 | Method for acquiring regional congestion information and regional congestion analyzing device |
| CN106384504A (en) * | 2016-09-06 | 2017-02-08 | 江苏智通交通科技有限公司 | Urban road network jam feature description analysis method based on data visualization |
| CN106981194A (en) * | 2017-05-02 | 2017-07-25 | 北京大学 | A kind of recognition methods of highway network key road segment |
| CN107182034A (en) * | 2017-07-26 | 2017-09-19 | 江苏省城市规划设计研究院 | Urban Transport Terminals point evaluation method based on complex network and mobile phone signaling data |
| CN108074008A (en) * | 2016-11-18 | 2018-05-25 | 腾讯科技（深圳）有限公司 | A kind of method and device in predicted congestion section |
-
2018
- 2018-12-05 CN CN201811479705.0A patent/CN109559511B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20100022151A (en) * | 2008-08-19 | 2010-03-02 | 한국전자통신연구원 | Gis data processing apparatus and method for searching route of a car |
| CN101751777A (en) * | 2008-12-02 | 2010-06-23 | 同济大学 | Dynamic urban road network traffic zone partitioning method based on space cluster analysis |
| CN101866545A (en) * | 2010-05-11 | 2010-10-20 | 中国科学院软件研究所 | Method for acquiring road network matching track of mobile object |
| CN102968901A (en) * | 2012-11-30 | 2013-03-13 | 青岛海信网络科技股份有限公司 | Method for acquiring regional congestion information and regional congestion analyzing device |
| CN106384504A (en) * | 2016-09-06 | 2017-02-08 | 江苏智通交通科技有限公司 | Urban road network jam feature description analysis method based on data visualization |
| CN108074008A (en) * | 2016-11-18 | 2018-05-25 | 腾讯科技（深圳）有限公司 | A kind of method and device in predicted congestion section |
| CN106981194A (en) * | 2017-05-02 | 2017-07-25 | 北京大学 | A kind of recognition methods of highway network key road segment |
| CN107182034A (en) * | 2017-07-26 | 2017-09-19 | 江苏省城市规划设计研究院 | Urban Transport Terminals point evaluation method based on complex network and mobile phone signaling data |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113362488A (en) * | 2021-08-11 | 2021-09-07 | 智联万维科技有限公司 | OBD monitoring data processing system |
| CN113362488B (en) * | 2021-08-11 | 2021-10-15 | 智联万维科技有限公司 | OBD monitoring data processing system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109559511B (en) | 2019-10-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Andrienko et al. | Spatio-temporal aggregation for visual analysis of movements | |
| CN104318324B (en) | Shuttle Bus website and route planning method based on taxi GPS records | |
| Yao et al. | Discovering the homogeneous geographic domain of human perceptions from street view images | |
| CN110298500A (en) | A kind of urban transportation track data set creation method based on taxi car data and city road network | |
| CN108564226A (en) | A kind of public bus network optimization method based on taxi GPS and mobile phone signaling data | |
| Jiang et al. | Large-scale taxi O/D visual analytics for understanding metropolitan human movement patterns | |
| Yu et al. | Understanding spatial-temporal travel demand of free-floating bike sharing connecting with metro stations | |
| CN107195180A (en) | A kind of traffic trip track extraction method and device based on the alert data of electricity | |
| CN108230020B (en) | Method for mining space-time frequent region based on multi-dimensional time granularity | |
| CN107861992A (en) | A kind of running route processing method and apparatus | |
| CN107529135A (en) | User Activity type identification method based on smart machine data | |
| CN109859505A (en) | Early warning processing method, device, server and medium for high-speed station | |
| CN112000755B (en) | Regional travel corridor identification method based on mobile phone signaling data | |
| CN104282142B (en) | Bus station arrangement method based on taxi GPS data | |
| CN105844031B (en) | A kind of urban transportation gallery recognition methods based on mobile phone location data | |
| CN110160538A (en) | A kind of map-matching method based on mobile phone signaling data | |
| CN106547862A (en) | Traffic big data dimension-reduction treatment method based on manifold learning | |
| CN109559511B (en) | A kind of urban traffic blocking information orientation put-on method | |
| CN108022006A (en) | The accessibility probability and Area generation method of a kind of data-driven | |
| Steenberghen et al. | Support study on data collection and analysis of active modes use and infrastructure in Europe | |
| CN106778547B (en) | Method and device for constructing remote sensing image ground feature classification reference library | |
| CN104121917B (en) | A kind of method and apparatus of automatic discovery newly building bridge | |
| Guo et al. | An algorithm for analyzing the city residents' activity information through mobile big data mining | |
| Dadashpoor et al. | Spatial Justice and Regional Inequality: An Interdisciplinary Systematic Review | |
| CN106485921A (en) | A kind of region highway network wagon flow state analysis method based on social data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address | ||
| CP03 | Change of name, title or address |
Address after: 210019 Building B3, No. 1, Yunlongshan Road, Jianye District, Nanjing, Jiangsu Patentee after: Jiangsu urban planning and Design Institute Co.,Ltd. Address before: 210036 Jiangsu Jianshe Building, No. 88, Caochangmen Street, Gulou District, Nanjing, Jiangsu Patentee before: JIANGSU INSTITUTE OF URBAN PLANNING AND DESIGN |