CN109191846A - A kind of traffic trip method for predicting - Google Patents
A kind of traffic trip method for predicting Download PDFInfo
- Publication number
- CN109191846A CN109191846A CN201811189434.5A CN201811189434A CN109191846A CN 109191846 A CN109191846 A CN 109191846A CN 201811189434 A CN201811189434 A CN 201811189434A CN 109191846 A CN109191846 A CN 109191846A
- Authority
- CN
- China
- Prior art keywords
- data
- parameter
- traffic
- relevance
- ambient weather
- 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 47
- 238000004458 analytical method Methods 0.000 claims abstract description 22
- 238000010276 construction Methods 0.000 claims abstract description 4
- 238000013139 quantization Methods 0.000 claims abstract 2
- 230000000875 corresponding effect Effects 0.000 claims description 17
- 238000010220 Pearson correlation analysis Methods 0.000 claims description 9
- 238000013459 approach Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000012417 linear regression Methods 0.000 claims description 7
- 238000012352 Spearman correlation analysis Methods 0.000 claims description 6
- 230000002596 correlated effect Effects 0.000 claims description 6
- 230000004907 flux Effects 0.000 claims 1
- 238000011835 investigation Methods 0.000 description 6
- 241001269238 Data Species 0.000 description 4
- 238000013461 design Methods 0.000 description 4
- 108010014173 Factor X Proteins 0.000 description 2
- 238000003646 Spearman's rank correlation coefficient Methods 0.000 description 2
- 210000001520 comb Anatomy 0.000 description 2
- 238000005206 flow analysis Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 239000000758 substrate Substances 0.000 description 2
- 206010033799 Paralysis Diseases 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 239000006227 byproduct Substances 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000047 product Substances 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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
-
- 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"
-
- G06Q50/40—
Abstract
The present invention relates to a kind of traffic trip method for predicting, the construction method includes the following steps: step 1, external traffic data on flows and ambient weather data are obtained and obtain initial data, using initial data respectively obtain the ambient weather parameter after quantization and with ambient weather parameter corresponding traffic flow parameter in real time, step 2, ambient weather parameter and traffic flow parameter are handled and stored to database, step 3, it is analyzed according to the difference of the data type in database using different being associated property of analysis method and obtains the biggish parameter of relevance, step 4, Method Modeling is carried out using the biggish parameter combination algorithm of relevance, following magnitude of traffic flow is predicted.
Description
Technical field
The present invention relates to field of traffic, especially a kind of traffic trip method for predicting.
Background technique
With the improvement of people's living standards, the automobile volume of holding is also stepping up per capita, when urban municipal road is built
When speed is lower than automobile increased number, urban road vehicle flowrate will be significantly increased, when sudden accident especially occurs, four crossway
The traffic of mouth will become state of paralysis moment, need a large amount of police strength that scene is gone to keep order at this time, cause greatly to people's lives
Ground is inconvenient.Traditional traffic trip method for predicting is that the vehicle flowrate based on usual traffic intersection does an average treatment, so
After provide prediction result, such prediction mode is not bound with the other influences factor for influencing traffic trip.Therefore, comprehensively consider shadow
The each influence factor of traffic trip is rung, by mathematical modeling to the Accurate Prediction of vehicular traffic trip flow, to be Ministry of Communications
Door provides an accurately dispatch coordination scheme, and facilitating people's trip is present urgent problem to be solved.
Summary of the invention
The present invention provides a kind of traffic trip method for predicting to solve the deficiency of above-mentioned technology.
In order to solve the above-mentioned technical problem, a kind of traffic trip method for predicting, it is characterised in that: the construction method
Include the following steps: step 1, external traffic data on flows and ambient weather data are obtained and obtained initial data, benefit
The ambient weather parameter after quantifying is respectively obtained with initial data and the corresponding magnitude of traffic flow is joined in real time with ambient weather parameter
Ambient weather parameter and traffic flow parameter are handled and are stored to database, step 3, according to number by number, step 2
The biggish parameter of relevance is analyzed and obtains using different being associated property of analysis method according to the difference of the data type in library,
Step 4 carries out Method Modeling using the biggish parameter combination algorithm of relevance, predicts following magnitude of traffic flow.
After the above method, a kind of core concept of traffic trip method for predicting is obtained based on big data
Under background, establish Database Systems, then the data in Database Systems carry out analysis comparison, obtain relevance it is biggish because
Element, then these factors are modeled, prediction chart is made, road where future can be thus speculated according to previous data
The traffic trip flow of mouth, and data acquiring mode mainly includes business investigation, related system combing and dependency number in step 1
According to acquisition three parts content.Business investigation is mainly unfolded in a manner of interview, forms investigation scheme by discussion, and determining finally needs
It asks.Related system combing is mainly based upon road traffic flow real current situation, lists needed for prediction road traffic flow analysis
Related data detail combs out road traffic flow related data information system.Related data obtains mainly from road traffic
Road traffic flow related data is obtained in flow relevant information system, and is arranged and formed road traffic flow data acquisition system.Step
Database is established in rapid two mainly based on computer hardware, is divided into data acquisition, database design and Database three
Big step.Data acquisition: data are exported based on road traffic flow related system, database is imported in the form of Excel, realize system
System is docked with the artificial of library.Database design: using data derived from system as substrate, in conjunction with corresponding professional knowledge, to data
Library carries out corresponding Table Design.Database: data and designed table are combined, and data is made to can be realized increasing
It revises and looks into, realize the foundation of database.
As a further improvement of the present invention, the external traffic data on flows includes capturing place, and bayonet title is captured
Time, the ambient weather data include the highest temperature, lowest temperature, weather, wind direction, wind-force.
After the above method, above-mentioned data are possible be with the data class that influences the relevant property of road traffic travel amount
Type, by obtaining the modeling datas such as external traffic data on flows and ambient weather data from channels such as traffic department, weather sites.
As a further improvement of the present invention, by external traffic data on flows and ambient weather data respectively with obtain ring
Corresponding Real-Time Traffic Volume carries out pearson correlation analysis when the weather data of border, obtains correlation coefficient r related with the degree of association,
The value range of correlation coefficient r are as follows: -1≤r≤1, it is stronger closer to ± 1 relevance, it is weaker closer to 0 relevance, it is positive and negative
Indicate related direction, positive to indicate to be positively correlated, bearing indicates negatively correlated.
After the above method, since the embodiment mode of relevance is varied, the status based on data, this item number
It also include numeric type variable according to both including classification type variable, therefore, with pearson correlation analysis and Spearman correlation analysis two
Kind method can be adapted to different types of variable format comprehensively, it is possible thereby to seek out the Main Factors for influencing the magnitude of traffic flow, realize
Information characteristics identification provides data basis for traffic trip prediction.
After the above method, the Pearson correlation coefficients r in pearson correlation analysis quantifies to measure gap scale
Two spacing variables degree of correlation, be to establish that the relevance that a related coefficient both is embodied is strong and weak, and the coefficient is exhausted
To value closer to 1, show that the relevance of the two is stronger, closer to 0, indicates weaker, related coefficient is based on product moment method
It calculates, is equally to be multiplied by two deviations based on the deviation of two variables and respective average value to reflect phase between two variables
Pass degree generally represents related coefficient with r.
As a further improvement of the present invention, the relevance obtained after the analysis of pearson correlation analytic approach is analyzed,
The weaker parameter of relevance is subjected to secondary analysis using Spearman analytic approach.
After the above method, for the variable using different scale, the index for measuring its degree of correlation is different,
For the degree of correlation between two spacing variables being quantified with gap scale, Pearson correlation coefficients measurement is generallyd use, and it is right
Degree of correlation between two ordinal datas quantified with order scale, generallys use Spearman rank correlation coefficient to survey
Amount.So carrying out secondary analysis after the relevance result obtained after the analysis of pearson correlation analytic approach is to guarantee variable
Relationship between variable can be excavated thoroughly.
As a further improvement of the present invention, correlation coefficient r is provided in the pearson correlation analysis method, setting becomes
Measuring x indicates the external environment factor, and setting variable y indicates the average value of corresponding traffic trip amount and variableAndRoot
According to pearson correlation analysis method calculation formula it can be concluded thatThe Spearman phase
It closes in analysis method and is provided with ordinal data degree of association parameter, and it is R that ordinal data degree of association parameter, which is arranged, and variable x is arranged
Indicate the external environment factor, setting variable y indicate corresponding traffic trip amount and variable x incremental arrangement grade with it is corresponding
The difference Di and number of samples n of variable y incremental arrangement grade, according to the calculation formula of Spearman correlation analysis method it can be concluded that
It, can be to some using the degree of association that the analysis method calculation formula of Spearman obtains after the above method
Ordinal data is analyzed.
As a further improvement of the present invention, the model in the step 4 is multiple linear regression model, and with polynary
Regression parameter fitting is carried out using least squares estimate based on linear regression model (LRM), forms traffic trip volume forecasting mould
Type, wherein regression coefficient βiAnd regression constant β0Parameter fitting target component y=β is obtained by least square method0+β1x1+ei,xiIndicate the sample data of factor X, yiIndicate factor Y's
Sample data, ξ are random error.
After the above method, least square method is a kind of mathematical optimization techniques.It passes through square for minimizing error
With the optimal function matching for finding data.Unknown data can be easily acquired using least square method, and these are asked
The quadratic sum of error is minimum between the data and real data obtained.Pass through model and substitutes into external environment weather data come to friendship
Through-current capacity is predicted.
Specific embodiment
The construction method includes the following steps: step 1, and external traffic data on flows and ambient weather data are carried out
Initial data is obtained and obtains, initial data is handled and stored to database by step 2, step 3, according to data
The biggish parameter of relevance is analyzed and obtained to the difference of data type in library using different being associated property of analysis method, walks
Rapid four, Method Modeling is carried out using the biggish parameter combination algorithm of relevance, following magnitude of traffic flow is predicted.
A kind of core concept of traffic trip method for predicting is to establish database under the background obtained based on big data
System, then data in Database Systems carry out analysis comparison, obtain the biggish factor of relevance, then carry out to these factors
Modeling, is made prediction chart, the traffic trip flow at crossing where future can be thus speculated according to previous data, and
Data acquiring mode mainly includes that business investigation, related system combing and related data obtain three parts content in step 1.Industry
Business investigation is mainly unfolded in a manner of interview, is formed investigation scheme by discussion, is determined final demand.Related system combing is main
It is based on road traffic flow real current situation, related data detail needed for listing prediction road traffic flow analysis combs out
Road traffic flow related data information system.Related data, which obtains, mainly to be obtained from road traffic flow relevant information system
Road traffic flow related data is taken, and arranges and forms road traffic flow data acquisition system.It is main that database is established in step 2
Based on computer hardware, it is divided into data acquisition, database design and the big step of Database three.Data acquisition: it is based on
Road traffic flow related system exports data, and database is imported in the form of Excel, and the system of realization is docked with the artificial of library.Number
It is designed according to library: using data derived from system as substrate, in conjunction with corresponding professional knowledge, corresponding table being carried out to database and is set
Meter.Database: data and designed table are combined, and so that data is can be realized additions and deletions and is changed and are looked into, and realize database
Foundation.
The external traffic data on flows includes capturing place, and bayonet title captures time, the ambient weather data packet
Include the highest temperature, lowest temperature, weather, wind direction, wind-force.Above-mentioned data are possible property relevant with road traffic travel amount is influenced
Data type is built by obtaining external traffic data on flows and ambient weather data etc. from channels such as traffic department, weather sites
Modulus evidence.
By external traffic data on flows and ambient weather data respectively with obtain ambient weather data when it is corresponding in real time
The magnitude of traffic flow carries out pearson correlation analysis, obtains correlation coefficient r related with the degree of association, the value range of correlation coefficient r
Are as follows: -1≤r≤1, closer to 0 relevance weaker, positive and negative expression related direction stronger closer to ± 1 relevance are positive to indicate
It is positively correlated, bearing indicates negatively correlated.Since the embodiment mode of relevance is varied, the status based on data, this project data was both
It also include numeric type variable comprising classification type variable, therefore, with pearson correlation analysis and two kinds of sides of Spearman correlation analysis
Method can be adapted to different types of variable format comprehensively, it is possible thereby to seek out the Main Factors for influencing the magnitude of traffic flow, realize information
Feature identification provides data basis for traffic trip prediction.Pearson correlation coefficients r in pearson correlation analysis measures difference
The degree of correlation of two spacing variables of carpenters square metrization is that establish the relevance that a related coefficient both is embodied strong
Weak, for the absolute coefficient closer to 1, the relevance both shown is stronger, closer to 0, indicates weaker, related coefficient be by
Product moment method calculates, and is equally to be multiplied by two deviations based on the deviation of two variables and respective average value to reflect two
Degree of correlation between variable generally represents related coefficient with r.
The relevance obtained after the analysis of pearson correlation analytic approach is analyzed, the weaker parameter of relevance is used this
Joseph Pearman analytic approach carries out secondary analysis.For the variable using different scale, the index for measuring its degree of correlation is different,
For the degree of correlation between two spacing variables being quantified with gap scale, Pearson correlation coefficients measurement is generallyd use, and it is right
Degree of correlation between two ordinal datas quantified with order scale, generallys use Spearman rank correlation coefficient to survey
Amount.So carrying out secondary analysis after the relevance result obtained after the analysis of pearson correlation analytic approach is to guarantee variable
Relationship between variable can be excavated thoroughly.
It is provided with ordinal data degree of association parameter in the Spearman correlation analysis method, and ordinal data pass is set
Connection degree parameter is R, and setting variable x indicates the external environment factor, and setting variable y indicates corresponding traffic trip amount and variable x
The difference Di and number of samples n of incremental arrangement grade and corresponding variable y incremental arrangement grade, according to Spearman correlation analysis side
The calculation formula of method it can be concluded thatIt is obtained using the analysis method calculation formula of Spearman
The degree of association some ordinal datas can be analyzed.Model in the step 4 is multiple linear regression model, and with
Regression parameter fitting is carried out using least squares estimate based on multiple linear regression model, forms traffic trip volume forecasting
Model, wherein regression coefficient βiAnd regression constant β0Parameter fitting target component y=β is obtained by least square method0+β1x1+
ei,xiIndicate the sample data of factor X, yiIndicate factor Y's
Sample data, ξ are random error.Least square method is a kind of mathematical optimization techniques.It is sought by minimizing the quadratic sum of error
The optimal function of data is looked for match.Unknown data can be easily acquired using least square method, and these are acquired
The quadratic sum of error is minimum between data and real data.Pass through model and substitutes into external environment weather data come to traffic flow
Amount is predicted.
Claims (7)
1. a kind of traffic trip method for predicting, it is characterised in that: the construction method includes the following steps: step 1, will
External traffic data on flows and ambient weather data are obtained and are obtained initial data, respectively obtain quantization using initial data
Rear ambient weather parameter and corresponding traffic flow parameter, step 2 join ambient weather in real time with ambient weather parameter
It is several to be handled and stored to database with traffic flow parameter, step 3, according to the difference of the data type in database
It is analyzed using different being associated property of analysis method and obtains the biggish parameter of relevance, step 4 is larger using relevance
Parameter combination algorithm carry out Method Modeling, following magnitude of traffic flow is predicted.
2. a kind of traffic trip method for predicting according to claim 1, it is characterised in that: the external traffic flow
Data include capturing place, and bayonet title captures the time, and the ambient weather data include the highest temperature, lowest temperature, weather, wind
To wind-force.
3. a kind of traffic trip method for predicting according to claim 2, it is characterised in that: by external traffic flow number
Accordingly and ambient weather data carry out pearson correlation point with Real-Time Traffic Volume corresponding when obtaining ambient weather data respectively
Analysis, obtains correlation coefficient r related with the degree of association, the value range of correlation coefficient r are as follows: -1≤r≤1, closer to ± 1 association
Property stronger, positive and negative expression related direction weaker closer to 0 relevance, positive to indicate to be positively correlated, bearing indicates negatively correlated.
4. a kind of traffic trip method for predicting according to claim 3, it is characterised in that: analyze pearson correlation
The relevance obtained after method analysis is analyzed, and the weaker parameter of relevance is carried out secondary point using Spearman analytic approach
Analysis.
5. a kind of traffic trip method for predicting according to claim 3, it is characterised in that: the pearson correlation point
Correlation coefficient r is provided in analysis method, setting variable x indicates the external environment factor, and setting variable y indicates corresponding traffic trip
The average value of amount and variableAndAccording to the calculation formula of pearson correlation analysis method it can be concluded that
6. a kind of traffic trip method for predicting according to claim 5, it is characterised in that: the Spearman is related
Ordinal data degree of association parameter is provided in analysis method, and it is R that ordinal data degree of association parameter, which is arranged, and variable x table is arranged
Show the external environment factor, setting variable y indicates corresponding traffic trip amount and variable x incremental arrangement grade and corresponding change
Measure y incremental arrangement grade difference Di and number of samples n, according to the calculation formula of Spearman correlation analysis method it can be concluded that
7. a kind of traffic trip method for predicting according to claim 6, it is characterised in that: the mould in the step 4
Type is multiple linear regression model, and carries out regression parameter using least squares estimate based on multiple linear regression model
Fitting forms traffic trip flux prediction model, wherein regression coefficient βiAnd regression constant β0Parameter fitting by least square
Method obtains target component y=β0+β1x1+ei,xiIndicate because
The sample data of sub- X, yiIndicate the sample data of factor Y, ξ is random error.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811189434.5A CN109191846B (en) | 2018-10-12 | 2018-10-12 | Traffic travel flow prediction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811189434.5A CN109191846B (en) | 2018-10-12 | 2018-10-12 | Traffic travel flow prediction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109191846A true CN109191846A (en) | 2019-01-11 |
| CN109191846B CN109191846B (en) | 2021-03-09 |
Family
ID=64948142
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811189434.5A Active CN109191846B (en) | 2018-10-12 | 2018-10-12 | Traffic travel flow prediction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109191846B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110517481A (en) * | 2019-07-26 | 2019-11-29 | 厦门卫星定位应用股份有限公司 | Vehicle flowrate prediction technique, medium, equipment and device |
| CN110728841A (en) * | 2019-10-23 | 2020-01-24 | 江苏广宇协同科技发展研究院有限公司 | Traffic flow acquisition method, device and system based on vehicle-road cooperation |
| CN110751102A (en) * | 2019-10-22 | 2020-02-04 | 天津财经大学 | Kyojin Ji three-ground airport passenger flow correlation analysis method and device |
| CN111862583A (en) * | 2019-04-24 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | Traffic flow prediction method and device |
| CN113554213A (en) * | 2021-06-11 | 2021-10-26 | 国网内蒙古东部电力有限公司电力科学研究院 | Natural gas demand prediction method, system, storage medium and equipment |
| CN115242663A (en) * | 2022-07-29 | 2022-10-25 | 西安电子科技大学 | Virtual network flow prediction method based on time correlation diagram convolution |
| CN115909748A (en) * | 2023-01-07 | 2023-04-04 | 深圳市城市交通规划设计研究中心股份有限公司 | Festival and holiday road traffic volume prediction method, electronic device and storage medium |
| CN117408393A (en) * | 2023-12-06 | 2024-01-16 | 华中科技大学 | Prediction method and system for comprehensive passenger transportation hub traffic flow under abnormal event |
Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6577946B2 (en) * | 2001-07-10 | 2003-06-10 | Makor Issues And Rights Ltd. | Traffic information gathering via cellular phone networks for intelligent transportation systems |
| US7202816B2 (en) * | 2003-07-22 | 2007-04-10 | Microsoft Corporation | Utilization of the approximate location of a device determined from ambient signals |
| CA2756916A1 (en) * | 2011-11-01 | 2013-05-01 | University Of New Brunswick | A bayesian method for improving group assignment and aadt estimation accuracy of short-term traffic counts |
| CN103177570A (en) * | 2012-12-25 | 2013-06-26 | 北京交通发展研究中心 | Method for predicting traffic jam indexes for rush hours in morning and evening |
| CN103198672A (en) * | 2013-03-27 | 2013-07-10 | 大连海事大学 | Method for laying urban road network traffic flow detectors |
| CN103455132A (en) * | 2013-08-20 | 2013-12-18 | 西安电子科技大学 | Embedded system power consumption estimation method based on hardware performance counter |
| CN103813355A (en) * | 2014-02-21 | 2014-05-21 | 厦门大学 | Identification method for anomalous points of cooperative synchronization in distributed network |
| CN104134349A (en) * | 2014-08-07 | 2014-11-05 | 北京航空航天大学 | Bus road condition processing system and method based on traffic multi-source data fusion |
| CN104378219A (en) * | 2013-08-12 | 2015-02-25 | 中兴通讯股份有限公司 | Intelligent analysis method and device for router flow data |
| CN104750919A (en) * | 2015-03-16 | 2015-07-01 | 同济大学 | Road capacity influence factor recognizing method |
| CN105451016A (en) * | 2015-12-07 | 2016-03-30 | 天津大学 | No-reference video quality evaluation method suitable for video monitoring system |
| CN105788249A (en) * | 2014-12-16 | 2016-07-20 | 高德软件有限公司 | Traffic flow prediction method, prediction model generation method and device |
| CN106779222A (en) * | 2016-12-20 | 2017-05-31 | 中国人民解放军空军装备研究院雷达与电子对抗研究所 | Airport ground stand-by period Forecasting Methodology and device |
| CN106846805A (en) * | 2017-03-06 | 2017-06-13 | 南京多伦科技股份有限公司 | A kind of dynamic road grid traffic needing forecasting method and its system |
| CN107194505A (en) * | 2017-05-10 | 2017-09-22 | 华中科技大学 | A kind of method and system that bus travel amount is predicted based on city big data |
| JP2017225025A (en) * | 2016-06-16 | 2017-12-21 | 日本電信電話株式会社 | Network management system, network management method, and program |
| CN107563596A (en) * | 2017-08-03 | 2018-01-09 | 清华大学 | A kind of evaluation index equilibrium state analysis method based on Bayes's causal network |
| CN107591001A (en) * | 2017-09-07 | 2018-01-16 | 山东大学 | Expressway Traffic Flow data filling method and system based on on-line proving |
| CN107798871A (en) * | 2017-10-27 | 2018-03-13 | 云南大学 | A kind of freeway toll station traffic flow forecasting method and system |
-
2018
- 2018-10-12 CN CN201811189434.5A patent/CN109191846B/en active Active
Patent Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6577946B2 (en) * | 2001-07-10 | 2003-06-10 | Makor Issues And Rights Ltd. | Traffic information gathering via cellular phone networks for intelligent transportation systems |
| US7202816B2 (en) * | 2003-07-22 | 2007-04-10 | Microsoft Corporation | Utilization of the approximate location of a device determined from ambient signals |
| CA2756916A1 (en) * | 2011-11-01 | 2013-05-01 | University Of New Brunswick | A bayesian method for improving group assignment and aadt estimation accuracy of short-term traffic counts |
| CN103177570A (en) * | 2012-12-25 | 2013-06-26 | 北京交通发展研究中心 | Method for predicting traffic jam indexes for rush hours in morning and evening |
| CN103198672A (en) * | 2013-03-27 | 2013-07-10 | 大连海事大学 | Method for laying urban road network traffic flow detectors |
| CN104378219A (en) * | 2013-08-12 | 2015-02-25 | 中兴通讯股份有限公司 | Intelligent analysis method and device for router flow data |
| CN103455132A (en) * | 2013-08-20 | 2013-12-18 | 西安电子科技大学 | Embedded system power consumption estimation method based on hardware performance counter |
| CN103813355A (en) * | 2014-02-21 | 2014-05-21 | 厦门大学 | Identification method for anomalous points of cooperative synchronization in distributed network |
| CN104134349A (en) * | 2014-08-07 | 2014-11-05 | 北京航空航天大学 | Bus road condition processing system and method based on traffic multi-source data fusion |
| CN105788249A (en) * | 2014-12-16 | 2016-07-20 | 高德软件有限公司 | Traffic flow prediction method, prediction model generation method and device |
| CN104750919A (en) * | 2015-03-16 | 2015-07-01 | 同济大学 | Road capacity influence factor recognizing method |
| CN105451016A (en) * | 2015-12-07 | 2016-03-30 | 天津大学 | No-reference video quality evaluation method suitable for video monitoring system |
| JP2017225025A (en) * | 2016-06-16 | 2017-12-21 | 日本電信電話株式会社 | Network management system, network management method, and program |
| CN106779222A (en) * | 2016-12-20 | 2017-05-31 | 中国人民解放军空军装备研究院雷达与电子对抗研究所 | Airport ground stand-by period Forecasting Methodology and device |
| CN106846805A (en) * | 2017-03-06 | 2017-06-13 | 南京多伦科技股份有限公司 | A kind of dynamic road grid traffic needing forecasting method and its system |
| CN107194505A (en) * | 2017-05-10 | 2017-09-22 | 华中科技大学 | A kind of method and system that bus travel amount is predicted based on city big data |
| CN107563596A (en) * | 2017-08-03 | 2018-01-09 | 清华大学 | A kind of evaluation index equilibrium state analysis method based on Bayes's causal network |
| CN107591001A (en) * | 2017-09-07 | 2018-01-16 | 山东大学 | Expressway Traffic Flow data filling method and system based on on-line proving |
| CN107798871A (en) * | 2017-10-27 | 2018-03-13 | 云南大学 | A kind of freeway toll station traffic flow forecasting method and system |
Non-Patent Citations (2)
| Title |
|---|
| 唐荷: "公共交通出行受降雨影响的规律挖掘", 《中国优秀硕士学位论文全文数据库 工程科技II辑》 * |
| 王惠文，孟洁: "多元线性回归的预测建模方法", 《北京航空航天大学学报》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111862583A (en) * | 2019-04-24 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | Traffic flow prediction method and device |
| CN110517481A (en) * | 2019-07-26 | 2019-11-29 | 厦门卫星定位应用股份有限公司 | Vehicle flowrate prediction technique, medium, equipment and device |
| CN110751102A (en) * | 2019-10-22 | 2020-02-04 | 天津财经大学 | Kyojin Ji three-ground airport passenger flow correlation analysis method and device |
| CN110751102B (en) * | 2019-10-22 | 2023-12-22 | 天津财经大学 | Beijing Ji three-place airport passenger flow correlation analysis method and device |
| CN110728841A (en) * | 2019-10-23 | 2020-01-24 | 江苏广宇协同科技发展研究院有限公司 | Traffic flow acquisition method, device and system based on vehicle-road cooperation |
| CN110728841B (en) * | 2019-10-23 | 2022-05-06 | 江苏广宇协同科技发展研究院有限公司 | Traffic flow acquisition method, device and system based on vehicle-road cooperation |
| CN113554213A (en) * | 2021-06-11 | 2021-10-26 | 国网内蒙古东部电力有限公司电力科学研究院 | Natural gas demand prediction method, system, storage medium and equipment |
| CN115242663A (en) * | 2022-07-29 | 2022-10-25 | 西安电子科技大学 | Virtual network flow prediction method based on time correlation diagram convolution |
| CN115909748A (en) * | 2023-01-07 | 2023-04-04 | 深圳市城市交通规划设计研究中心股份有限公司 | Festival and holiday road traffic volume prediction method, electronic device and storage medium |
| CN117408393A (en) * | 2023-12-06 | 2024-01-16 | 华中科技大学 | Prediction method and system for comprehensive passenger transportation hub traffic flow under abnormal event |
| CN117408393B (en) * | 2023-12-06 | 2024-03-19 | 华中科技大学 | Prediction method and system for comprehensive passenger transportation hub traffic flow under abnormal event |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109191846B (en) | 2021-03-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109191846A (en) | A kind of traffic trip method for predicting | |
| Kamarianakis et al. | Real‐time road traffic forecasting using regime‐switching space‐time models and adaptive LASSO | |
| CN109697854B (en) | Multi-dimensional urban road traffic state evaluation method | |
| Bhaskar et al. | Fusing loop detector and probe vehicle data to estimate travel time statistics on signalized urban networks | |
| CN110471024A (en) | A kind of online remote checking method of intelligent electric meter based on metric data analysis | |
| Chan et al. | On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and taguchi method | |
| JP2008123474A (en) | Travel-time prediction apparatus, travel-time prediction method, and traffic information providing system and program | |
| CN105651939A (en) | Concentration detection precision correction method based on projection onto convex set in electron nose system | |
| CN106600037B (en) | Multi-parameter auxiliary load prediction method based on principal component analysis | |
| JP5017437B2 (en) | TRAVEL TIME ESTIMATION METHOD, TRAVEL TIME ESTIMATION DEVICE, TRAVEL TIME ESTIMATION PROGRAM IMPLEMENTING THE METHOD AND MEDIUM CONTAINING THE PROGRAM, TRAVEL TIME PREDICTION METHOD, TRAVEL TIME PREDICTION DEVICE, TRAVEL TIME PREDICTION PROGRAM INCLUDING THE METHOD, AND ITS PROGRAM Stored media | |
| CN111301426A (en) | Method for predicting energy consumption in future driving process based on GRU network model | |
| CN113496314B (en) | Method for predicting road traffic flow by neural network model | |
| CN116739376A (en) | Highway pavement preventive maintenance decision method based on data mining | |
| CN116013426A (en) | Site ozone concentration prediction method with high space-time resolution | |
| CN108053646B (en) | Traffic characteristic obtaining method, traffic characteristic prediction method and traffic characteristic prediction system based on time sensitive characteristics | |
| Anacleto et al. | Forecasting multivariate road traffic flows using Bayesian dynamic graphical models, splines and other traffic variables | |
| Zhang et al. | Method of speed data fusion based on Bayesian combination algorithm and high‐order multi‐variable Markov model | |
| JP4598798B2 (en) | TRAVEL TIME ESTIMATION METHOD, TRAVEL TIME ESTIMATION DEVICE, TRAVEL TIME ESTIMATION PROGRAM MOUNTING THE METHOD AND RECORDING MEDIUM CONTAINING THE PROGRAM, TRAVEL TIME PREDICTION METHOD, TRAVEL TIME PREDICTION DEVICE, TRAVEL TIME PREDICTION PROGRAM MOUNTING THE METHOD, AND ITS PROGRAM Recording medium | |
| Moral et al. | Mapping and hazard assessment of atmospheric pollution in a medium sized urban area using the Rasch model and geostatistics techniques | |
| CN112529299B (en) | Short traffic flow prediction method based on ARIMA and LSTM mixed neural network | |
| CN116205530A (en) | Urban intelligent parking planning method and system | |
| CN112785044B (en) | Real-time full-load rate prediction method, device, equipment and medium for public transport means | |
| CN113808393A (en) | Method for eliminating influence of hybrid control object | |
| De Ona et al. | Bilevel fuzzy optimization to pre-process traffic data to satisfy the law of flow conservation | |
| CN114550454B (en) | Traffic flow prediction method based on traffic flow matrix combination model |
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 | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20210303 Address after: 325000 Jinxiu Road, Wenzhou City, Zhejiang Province Applicant after: WENZHOU POWER SUPPLY COMPANY OF STATE GRID ZHEJIANG ELECTRIC POWER Co.,Ltd. Applicant after: HANGZHOU YUSHU INFORMATION TECHNOLOGY Co.,Ltd. Applicant after: Wenzhou Science and Technology Branch of Zhejiang Tusheng Transmission and Transfer Engineering Co.,Ltd. Address before: 325000 Jinxiu Road, Wenzhou City, Zhejiang Province Applicant before: WENZHOU POWER SUPPLY COMPANY OF STATE GRID ZHEJIANG ELECTRIC POWER Co.,Ltd. Applicant before: HANGZHOU YUSHU INFORMATION TECHNOLOGY Co.,Ltd. Applicant before: WENZHOU TUSHENG SCIENCE & TECHNOLOGY Co.,Ltd. |