CN109978224A - A method of analysis obtains the Trip Generation Rate of heterogeneity building - Google Patents
A method of analysis obtains the Trip Generation Rate of heterogeneity building Download PDFInfo
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Abstract
The invention discloses a kind of methods that analysis obtains the Trip Generation Rate of heterogeneity building, comprising steps of first, it handles mobile phone signaling data and obtains travel amount, the land investigation data in statistical research area, and the gross floors area of travel amount and heterogeneity building is saved as into tabular form.Secondly, determining the Trip Generation Rate of search time, different trip purposes, different building types according to research purpose.Then, especially by the traffic trip amount for counting the gross floors area of different traffic zone heterogeneity buildings and being obtained based on mobile phone signaling data, different traffic zones is subjected to clustering from traffic accessibility according to the commuting density of population, multivariate statistical regression analysis finally is carried out for every a kind of traffic zone, determines the Trip Generation Rate of different characteristic, different building types.The Trip Generation Rate that the present invention realizes heterogeneity building obtains, and the traffic trip situation in planning year is predicted with this.
Description
Technical field
The present invention relates to traffic programme trip survey technical field, especially a kind of acquisition methods of Trip Generation Rate.
Background technique
Traffic programme cannot be detached from the land use planning in city, and this requires have the reason of science during traffic programme
By system form the basis.Using land using effect as the basis of trip prediction, planning is made to meet following transport need.It hands in city
The logical interactive relationship between urban land use determine different land use arrangement form and intensity can generate different type and
The social activities of intensity, to determine the traffic passenger collector-distributor volume and distribution situation of different zones.Correspondingly, traffic system functional efficiency
Height also directly affect periphery land price, land rent and popularity, whether influencing the realization of periphery land function sufficiently.Therefore, into
Need to further investigate the correlation of urban land use and traffic in row traffic programme, Trip Generation Rate be intuitively reflect it is this
One of important indicator of correlation.
Traffic zone is known as the area OD (OD node), is divided by traffic zone, and traffic flow is divided between several OD points
Traffic flow.The division of OD node is actually the process of a fuzzy clustering, needs the method using fuzzy clustering, will hand over
It is through-flow according to certain degree of membership, be divided into specific traffic zone.At present in actual operation, but since statistics is received
Collect many-sided reason such as difficulty, generally uses the administrative areas such as province (autonomous region), city, county (township, area) transport hub center for unit
Or using slip-road as traffic zone, for specific Highway Construction Project Based, wanted according to the depth etc. of feasibility study
It asks, determines that specific traffic zone divides., can be using county, city as traffic zone such as a national highway, a state highway can
Using Jiang Shi, county, villages (towns) as traffic zone；One country highway, can be using county, villages (towns), village as traffic zone.Together
When, when dividing traffic zone, also by economic and technological development zone, new technological industry area, tourist district, important mine or large-scale list
Position location, important port or transfer Distribution Center etc. are taken into account.
Trip rate model is the decision index and its trip generation for describing the variation of each land use trip generation
Between relationship, between descriptive study object self attributes and traffic generation quantization rule.Meanwhile with the information age
Arrival, the change of working and time for school system, trip generation also occurs that variation.This requires traffic planners to answer
It combines closely social development, pays close attention to influence the relevant institutions factor that trip generates, amendment in time generates prediction model, makes it
More it is close to reality.So solving urban transport problems, it be unable to do without the traffic behavior i.e. resident to the people lived in city
The research of trip characteristics.Big data has certain development in every field, then for going out in traditional transportation planning
The prediction of row rate also proposed new opportunities and challenges.
Travel amount prediction is carried out by resident's survey data in existing method, due to resident trip survey sampling rate is low,
The features such as at high cost make trip data sample size it is small, research precision it is low, be difficult to return out essence by resident trip survey data
High trip rate is spent, simultaneously because the trip rate of different land use type is different, survey data is difficult largely to cover different land use,
Therefore traditional trip rate investigation method has its apparent limitation.For example, the trip rate of commercial building in survey sampling only
The trip rate that commercial building few in city can be covered, therefore obtained cannot embody actual conditions, the trip for the non-coming year
Amount precision of prediction has apparent limitation.In brief, to carry out travel amount estimated accuracy by resident's survey data not high.
Summary of the invention
The technical problem to be solved by the present invention is to overcome the deficiencies in the prior art, and provide a kind of based on mobile phone signaling number
According to the method for obtaining the Trip Generation Rate that heterogeneity is built with land investigation data, the present invention can be calculated relatively accurately
The Trip Generation Rate of different land use, and plan that the land investigation data in year carry out travel amount prediction.
The present invention uses following technical scheme to solve above-mentioned technical problem:
The present invention proposes a kind of method that analysis obtains the Trip Generation Rate of heterogeneity building, comprising the following steps:
Step 1) counts the gross floors area of different traffic zone different land use types；
Step 2) obtains the traffic trip amount of each traffic zone based on mobile phone signaling data；
Different traffic zones is carried out clustering from traffic accessibility according to the commuting density of population by step 3)；
Step 4), according to the analysis of step 3) as a result, carrying out multivariate statistical regression analysis for every a kind of traffic zone, really
Determine the Trip Generation Rate of different characteristic, different residential architecture types.
Further, the method that analysis proposed by the invention obtains the Trip Generation Rate of heterogeneity building, the step
Rapid 1) the middle gross floors area for counting different traffic zone different land use types, specific as follows:
Step 1.1) determines building classifications standard；
Step 1.2) is divided land used within the scope of research area by the boundary of traffic zone according to building classifications standard,
Obtain each traffic zone uses map layer；
Step 1.3), opening steps 1.2) in land used layer properties table, successively add the text field, naming number is defeated
Enter corresponding traffic zone number, all traffic zone figure layers are merged into a figure layer；
Step 1.4) obtains all kinds of gross floors areas by statistic unit of traffic zone boundary.
Further, the method that analysis proposed by the invention obtains the Trip Generation Rate of heterogeneity building, the step
It is rapid 2) in based on mobile phone signaling data obtain traffic trip amount, it is specific as follows:
Step 2.1), the service range in ARCGIS according to base station creates Thiessen polygon, by the trip data of base station
Thiessen polygon is assigned, the attribute list of Thiessen polygon is opened, is associated according to spatial relationship；
Step 2.2) is more divided by each Tyson with the trip population of each Thiessen polygon after obtaining trip demographic data
The area of side shape obtains the trip density of population；
The Thiessen polygon of traffic zone and base station is laid out analysis INTERSECT by step 2.3), and by generation
Stacked result is calculated, and attribute list is opened, and is added double precision field, is calculated pedestrian's number using field calculator；
Step 2.4) finally carries out spatial statistics, obtains the travel amount of each traffic zone.
Further, the method that analysis proposed by the invention obtains the Trip Generation Rate of heterogeneity building, the step
It is rapid 3) in different traffic zones carried out by clustering according to the commuting density of population and traffic accessibility, it is specific as follows:
Step 3.1) chooses classified adaptive factor according to research purpose: by the traffic accessibility of each traffic zone in city, commuting
The density of population is determined as classified adaptive factor；
Step 3.2) determines clustering method: carrying out K-Means cluster according to the classified adaptive factor that step 3.1) determines, finally
It determines and is classified as two, i.e., traffic accessibility is high and the density that commutes is high, traffic accessibility is poor and the traffic zone for the density contrast that commutes.
Further, the method that analysis proposed by the invention obtains the Trip Generation Rate of heterogeneity building, the step
It is rapid 4) specific as follows:
According to clustering as a result, traffic zone is divided into several classes, respectively according to statistical result progress multivariate statistics
Recurrence obtains Trip generation forecast rate；Specifically:
(1) a multiple linear equation is constructed for each traffic zone, forms multiple equations and carries out simultaneous solution,
Y1=β1x1+β2x2+β3x3+…+βnxn+b1
Y2=β1x1+β2x2+β3x3+…+βnxn+b2
……
Yn=β1x1+β2x2+β3x3+…+βnxn+bn
In formula, Yn: the traffic trip amount of each traffic zone；βn: the Trip Generation Rate of every kind of land-use style；Xn: every kind with
The gross floors area of ground type；bn: constant term；N represents the quantity of traffic zone；
(2) traffic zone is grouped according to regulatory control unit again, the statistics of travel amount is carried out to each regulatory control unit,
Specific formula for calculation is as follows:
In formula:
Bi: the travel amount of regulatory control unit i；dik: the kth class construction area of regulatory control unit i；wik: each kth of regulatory control unit i
The trip rate of class building；
(3) Trip Generation Rate of every kind of land-use style is finally obtained.
The invention adopts the above technical scheme compared with prior art, has following technical effect that
(1) firstly, the present invention is based on cellular base station service ranges and traffic zone range statistics travel amount.Pass through the party
Method, the travel amount of can reflect out each traffic zone each period daily, and then the trip for analyzing each traffic zone is special
Sign, provides effective decision support for work such as traffic programme, urban planning drawing-up systems, due to that can carry out to each city in the whole nation
Analysis, therefore the applicability of the method for the present invention is stronger.
(2) secondly, the present invention can carry out the method that classification trip rate calculates according to different traffic zone features, and judge
The specific features of different traffic zones out, this provides the foundation for long-term traffic trip prediction, is also beneficial to quickly calculate
The situation of change of various traffic zone travel amounts；Meanwhile this scheme obtains the trip rate of different traffic zones, improves traffic and goes out
The precision of row rate provides technical guarantee for Accurate Prediction travel amount.
(3) furthermore, the present invention can accurately handle the peak period in urban transportation.Due to data in mobile phone
After the precision of time attribute is high, can be as accurate as the second, therefore utilization big data judges Urban Traffic feature, specific to every city
City carries out the judgement on trip peak, then accurately calculates to the trip rate under peak period, urban congestion is provided
Effective judgment basis.
(4) finally, sample of the present invention amount is huge, more traditional resident's survey data based on individual is obtained for small sample,
It more can comprehensively reflect the trip characteristics of city dweller, while the available traffic trip based on different building types
Rate, the equal important role of volume forecasting of Land arrangement and traffic programme for urban planning.
Detailed description of the invention
Fig. 1 is multivariate statistical regression statistical method schematic diagram proposed by the invention.
Fig. 2 is overall flow schematic diagram of the invention.
Fig. 3 is the gross floors area schematic diagram for counting traffic zone different land use type.
Fig. 4 is based on mobile phone signaling data and obtains travel amount schematic diagram.
Fig. 5 is traffic zone travel amount meaning schematic diagram.
Specific embodiment
Technical solution of the present invention is described in further detail with reference to the accompanying drawing:
Since resident's survey data sample size is few, but the trip data sample size based on mobile phone signaling data is big, can be with machine
Device study carries out data cleansing, and finally obtains the spatial distribution of travel amount.Therefore, with mobile phone signaling data and land investigation number
It will be a kind of practicable method according to the trip rate calculating for carrying out different land use.Emphasis of the present invention is exactly to pass through mobile phone signaling number
Multivariate statistical regression model is established according to land investigation data, to carry out relevant analysis.
Mobile phone signaling data is obtained by cellular service quotient company.By carefully analyzing and mobile phone service provider being investigated
Consulting, show that mobile phone signaling data has the characteristics that following three:
1. passive type, covering are wide, nonrandom.Mobile phone signaling data is the mobile phone user of operator's record in network activity
Location information, belong to involuntary passive type acquisition data, when booting occurs for mobile phone, shutdown, caller, is called, receives and dispatches short message, cuts
When changing base station, mobile switching centre or location updating, handset identity number, signaling time, cell base station number locating at that time are equal
It is stored in mobile phone signaling data.
2. timeliness, dynamic, continuity.Mobile phone signaling data has recorded the daily behavior of each user and to city
The usage mode in space can reflect the feature of user's time-space behavior mechanics, realize the continuous tracking of dynamic in real time and visualization
Expression, for description urban residence, obtain employment, stroll about or have a rest, the movable time-space variation such as traffic provides new approach.
3. reflecting the functional cohesion inside and outside city.In regional level, from resident's space-time trajectory data of trans-city trip
With can identifying permanent residence, trip purpose, reflect that the function between city joins by stream of people's connection between measuring and calculating city
System.In urban inner level, from can identify residence, place of working, the connection between ground of strolling about or have a rest in the space-time trajectory of individual.
From the of mobile phone signaling data 2. a feature, it can be found that: the trip characteristics for determining everyone are a key points.
Meanwhile the base station of only people's switching service can just be recorded primary trip.Therefore, according to mobile phone signaling data the characteristics of, first
It first needs to filter out actual traffic trip amount from mass data, then looks into everyone specific trip characteristics.
Next, establishing the traffic trip amount model based on different building types to study specific trip characteristics: referring to
Attached drawing 1, in figure: A: public administration and public service construction area, B: commerce services industry facility construction area, G: greenery patches and square
Construction area, M: industrial building area, R: residential floor area, S: road and means of transportation construction area, U: public utility is built
Area is built, W: logistic storage construction area, H: town and country construction construction area, by Trip Generation Rate βnIt indicates, i.e. every kind of land used
Trip rate.The algorithm core for calculating different land use trip rate is: the 2. and 3. feature based on mobile phone signaling data once has
The trip of effect has to be recorded twice, can inquire the travel time twice, records the trip characteristics of everyone whole day respectively.That
, determine the trip rate rule of different building types, need the construction area X in conjunction with different kinds of buildingnIt is analyzed, it is different
The construction area of type building is obtained by regional planning agency's land investigation, with Trip Generation Rate βn, different kinds of building building sides
Product Xn, different statistic unit travel amount YnCarry out multivariate statistical regression.
It is noted that above-mentioned Trip Generation Rate βnIt is had in different cities, different periods, different building types
Difference needs to be made a concrete analysis of specific to every city, and observes the tendency conclusion being inferred to by long term data, and
The observing time section of selection has generality, that is, eliminates the interference that the factors such as festivals or holidays go on a journey to passenger flow.Since this is a kind of
Tendency judgement, resulting certain conclusions have the characteristics that probabilistic determination, can be phase although this is not a kind of absolutely judgement
It closes researcher's offer thinking to further investigate with reference to decision, this is also with the direct conclusion of big data generally with tendency and presentation conclusion
The characteristics of being main, is consistent.
In conclusion providing Trip Generation Rate βnConcrete meaning: if βnBe worth more big then unit area traffic trip amount more
Greatly.
Refering to what is shown in Fig. 2, method flow of the invention is specific as follows:
Step 1), referring to attached drawing 3, count the gross floors area of different traffic zone different land use types, such as selection elder brother
Mountain inner city specifically counts the construction area of every kind of building type as research range.
Wherein, the gross floors area of different traffic zone different land use types is counted, specific as follows:
Step 1.1), determine building classifications standard: Administration office building, commercial building, residential architecture, logistic storage are built
It builds, industrial building, means of transportation building, public utility building, the mating building in greenery patches square.
Step 1.2), inquiry will study land used within the scope of area in step 1.1) and divide by the boundary of traffic zone
SPLIT (segmentation), selection city of Kunshan inner city are research range, are split after determining traffic zone boundary.
Step 1.3) adds the text field to attribute list is opened in step 1.2), and it is small to input corresponding traffic for naming number
All traffic zone figure layers are merged into a figure layer MERGE (merging) by area's number.To the traffic zone of city of Kunshan inner city
It is numbered since 1.
Step 1.4) obtains all kinds of gross floors areas by statistic unit of traffic zone boundary.Specifically into city of Kunshan
The construction area of all kinds of buildings of each traffic zone in heart city.
Step 2) obtains Kunshan 2017 days 6 based on the traffic trip amount that mobile phone signaling data obtains referring to attached drawing 4
Whole day mobile phone signaling data, carry out data processing, obtain actual travel amount.
Wherein, the traffic trip amount obtained based on mobile phone signaling data, specific as follows:
Step 2.1), the service range in ARCGIS according to base station creates Thiessen polygon, by the trip data of base station
Thiessen polygon is assigned, the attribute list of Thiessen polygon is opened, is associated according to spatial relationship；By city of Kunshan 2017 6
Months No. 6 trip datas are counted, after obtain the travel amount statistical form of each base station, rear and city of Kunshan, base station, inner city into
Row space correlation obtains the travel amount of each base station.
Step 2.2) is more divided by each Tyson with the trip population of each Thiessen polygon after obtaining trip demographic data
The area of side shape obtains the trip density of population；It is serviced with the travel amount of city of Kunshan, each base station, inner city divided by each base station
After range, the trip density of population of each base station is obtained.
Then traffic zone is laid out analysis INTERSECT (intersection) with the Thiessen polygon of base station by step 2.3),
And calculate the stacked result of generation, attribute list is opened, double precision field is added, is calculated and is gone on a journey using field calculator
Number；The traffic zone boundary of city of Kunshan is intersected with base station service range boundary, base station is renumberd.
Step 2.4) finally carries out spatial statistics.It is renumbered based on city of Kunshan, base station, inner city and carries out spatial statistics, obtained
To the travel amount of each traffic zone.Fig. 5 is traffic zone travel amount meaning schematic diagram.
Different traffic zones is carried out clustering from traffic accessibility according to the commuting density of population by step 3),
Wherein, different traffic zones is carried out from traffic accessibility by clustering according to the commuting density of population, specifically such as
Under:
Step 3.1) is chosen classified adaptive factor, is determined with specific reference to research purpose；It is each to choose city of Kunshan inner city
Accessibility, the commuting density of traffic zone are classified adaptive factor.
Step 3.2) determines clustering method.To the accessibility of city of Kunshan, each traffic zone, inner city, logical in SPSS
Frequently degree is that classified adaptive factor carries out K-Means (K mean value) cluster, and most determining to be classified as two, i.e., traffic accessibility is high and commuting is close
Degree is high, traffic accessibility is poor and the traffic zone for the density contrast that commutes.
Step 4) finally carries out multivariate statistical regression analysis for every a kind of traffic zone according to step 3), determines different
The traffic trip of feature, different residential architecture types.
Wherein, traffic zone is divided into several classes, multivariate statistical regression is carried out according to statistical result respectively and show that traffic is raw
At rate.Multivariate statistical regression is carried out for two class traffic zones of city of Kunshan inner city, obtains the value of specific traffic trip amount.
It is city of Kunshan inner city trip rate regression result schematic diagram below；Wherein, table 1 is that center city different land use goes out
Row rate regressive case, table 2 are peripheral city different land use trip rate regressive case.
Table 1
| Land-use style | Morning peak is set out | Morning peak reaches | Whole day is set out | Whole day reaches |
| A public administration and public service land used | 1.134 | 3.8075 | 7.903** | 8.075** |
| B commerce services industry facilities land | 1.057 | 3.7685 | 16.472** | 17.315** |
| The greenery patches G and land for squares | 5.7805 | 2.05 | 8.551 | 2.303 |
| H land used for urban and rural construction projects | 3.8615 | 0.8745 | 9.905 | 9.617 |
| M industrial land | 2.7885** | 4.5315** | 4.977** | 5.211** |
| R residential estate | 1.986** | 0.7015** | 4.932** | 4.630** |
| S road and means of transportation land used | 14.4195 | 12.2035 | 47.872* | 52.070* |
| U public utility land used | 3.267 | 13.18 | 9.848 | 15.112 |
| W logistic storage land used | 1.634 | 1.468 | 3.121 | 3.596 |
Note: * * indicates that p value indicates p value less than 0.05 less than 0.01, *, remaining p value is between 0.05-0.15.
Table 2
| Land-use style | Morning peak is set out | Morning peak reaches | Whole day is set out | Whole day reaches |
| A public administration and public service land used | 2.019* | 1.060* | 5.243* | 5.099* |
| B commerce services industry facilities land | 1.049** | 0.285** | 1.488* | 1.349* |
| The greenery patches G and land for squares | 1.186 | 0.624 | 2.456 | 2.108 |
| H land used for urban and rural construction projects | 0.181 | 0.140 | 0.605 | 0.538 |
| M industrial land | 0.588** | 1.075** | 1.448** | 1.552** |
| R residential estate | 2.439** | 1.129** | 4.461** | 4.232** |
| S road and means of transportation land used | 1.216 | 1.405 | 5.245 | 5.595 |
| U public utility land used | 1.515 | 0.716 | 8.860 | 8.275 |
| W logistic storage land used | 0.557 | 0.036 | 3.294 | 2.916 |
Note: * * indicates that p value indicates p value less than 0.05 less than 0.01, *, remaining p value is between 0.05-0.15.
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 (5)
1. a kind of method that analysis obtains the Trip Generation Rate of heterogeneity building, which comprises the following steps:
Step 1) counts the gross floors area of different traffic zone different land use types；
Step 2) obtains the traffic trip amount of each traffic zone based on mobile phone signaling data；
Different traffic zones is carried out clustering from traffic accessibility according to the commuting density of population by step 3)；
Step 4) determines not according to the analysis of step 3) as a result, carrying out multivariate statistical regression analysis for every a kind of traffic zone
The Trip Generation Rate of same feature, different residential architecture types.
2. the method that a kind of analysis according to claim 1 obtains the Trip Generation Rate of heterogeneity building, feature exist
In the gross floors area of the different traffic zone different land use types of statistics in the step 1) is specific as follows:
Step 1.1) determines building classifications standard；
Land used within the scope of research area is divided by the boundary of traffic zone, is obtained according to building classifications standard by step 1.2)
Use map layer in each traffic zone；
Step 1.3), opening steps 1.2) in the attribute list with map layer, successively add the text field, naming number, input
All traffic zone figure layers are merged into a figure layer by corresponding traffic zone number；
Step 1.4) obtains all kinds of gross floors areas by statistic unit of traffic zone boundary.
3. the method that a kind of analysis according to claim 2 obtains the Trip Generation Rate of heterogeneity building, feature exist
In, traffic trip amount is obtained based on mobile phone signaling data in the step 2), specific as follows:
Step 2.1), the service range in ARCGIS according to base station create Thiessen polygon, the trip data of base station are assigned
Thiessen polygon is opened the attribute list of Thiessen polygon, is associated according to spatial relationship；
Step 2.2), after obtaining trip demographic data, with the trip population of each Thiessen polygon divided by each Thiessen polygon
Area, obtain the trip density of population；
The Thiessen polygon of traffic zone and base station is laid out analysis INTERSECT, and being stacked generation by step 2.3)
Result calculated, open attribute list, add double precision field, calculate pedestrian's number using field calculator；
Step 2.4) finally carries out spatial statistics, obtains the travel amount of each traffic zone.
4. the method that a kind of analysis according to claim 3 obtains the Trip Generation Rate of heterogeneity building, feature exist
In different traffic zones being carried out clustering according to the commuting density of population and traffic accessibility in the step 3), specifically
It is as follows:
Step 3.1) chooses classified adaptive factor according to research purpose: by the traffic accessibility of each traffic zone in city, commuting population
Density is determined as classified adaptive factor；
Step 3.2) determines clustering method: K-Means cluster is carried out according to the classified adaptive factor that step 3.1) determines, it is final to determine
Two are classified as, i.e., traffic accessibility is high and the density that commutes is high, traffic accessibility is poor and the traffic zone for the density contrast that commutes.
5. the method that a kind of analysis according to claim 4 obtains the Trip Generation Rate of heterogeneity building, feature exist
In the step 4) is specific as follows:
According to clustering as a result, traffic zone is divided into several classes, respectively according to statistical result progress multivariate statistical regression
Obtain Trip generation forecast rate；Specifically:
(1) a multiple linear equation is constructed for each traffic zone, forms multiple equations and carries out simultaneous solution,
Y1=β1x1+β2x2+β3x3+…+βnxn+b1
Y2=β1x1+β2x2+β3x3+…+βnxn+b2
……
Yn=β1x1+β2x2+β3x3+…+βnxn+bn
In formula, Yn: the traffic trip amount of each traffic zone；βn: the Trip Generation Rate of every kind of land-use style；Xn: every kind of land used class
The gross floors area of type；bn: constant term；N represents the quantity of traffic zone；
(2) traffic zone is grouped according to regulatory control unit again, the statistics of travel amount is carried out to each regulatory control unit, specifically
Calculation formula is as follows:
In formula:
Bi: the travel amount of regulatory control unit i；dik: the kth class construction area of regulatory control unit i；wik: each kth class of regulatory control unit i is built
The trip rate built；
(3) Trip Generation Rate of every kind of land-use style is finally obtained.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110322694A (en) * | 2019-07-16 | 2019-10-11 | 青岛海信网络科技股份有限公司 | A kind of method and device of urban traffic control piece Division |
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101976500A (en) * | 2010-10-25 | 2011-02-16 | 中国科学院深圳先进技术研究院 | Method and system for analyzing traffic network |
| CN105760454A (en) * | 2016-02-04 | 2016-07-13 | 东南大学 | Method for dynamically measuring distribution density of city population in real time |
| CN107958031A (en) * | 2017-11-20 | 2018-04-24 | 上海市城市建设设计研究总院（集团）有限公司 | Resident trip OD distribution extracting methods based on fused data |
-
2019
- 2019-01-14 CN CN201910031098.XA patent/CN109978224B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101976500A (en) * | 2010-10-25 | 2011-02-16 | 中国科学院深圳先进技术研究院 | Method and system for analyzing traffic network |
| CN105760454A (en) * | 2016-02-04 | 2016-07-13 | 东南大学 | Method for dynamically measuring distribution density of city population in real time |
| CN107958031A (en) * | 2017-11-20 | 2018-04-24 | 上海市城市建设设计研究总院（集团）有限公司 | Resident trip OD distribution extracting methods based on fused data |
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|---|---|---|---|---|
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| CN112231392A (en) * | 2020-10-29 | 2021-01-15 | 广东机场白云信息科技有限公司 | Civil aviation customer source data analysis method, electronic equipment and computer readable storage medium |
| CN113409570A (en) * | 2021-04-27 | 2021-09-17 | 上海同济城市规划设计研究院有限公司 | Method for evaluating physical traffic accessibility |
| CN114021800A (en) * | 2021-10-29 | 2022-02-08 | 南京大学 | Cumulative model employment reachability analysis method based on multi-source big data |
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| CN114153899A (en) * | 2021-11-30 | 2022-03-08 | 南京大学 | Method and electronic equipment for acquiring employment posts of unit building area of different land types |
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