CN102097006A - Shortest testing mileage acquisition method and device - Google Patents
Shortest testing mileage acquisition method and device Download PDFInfo
- Publication number
- CN102097006A CN102097006A CN 201110048469 CN201110048469A CN102097006A CN 102097006 A CN102097006 A CN 102097006A CN 201110048469 CN201110048469 CN 201110048469 CN 201110048469 A CN201110048469 A CN 201110048469A CN 102097006 A CN102097006 A CN 102097006A
- Authority
- CN
- China
- Prior art keywords
- road condition
- condition data
- data
- variance
- rate
- 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
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention discloses a shortest testing mileage acquisition method and a shortest testing mileage acquisition device, which relate to the field of traffic information processing. Aiming to acquire the shortest testing mileage with relatively higher accuracy, the method provided by the invention is technically characterized by comprising the following steps of: dividing collected road condition data into different types of road condition data according to specified classification conditions; acquiring the road condition data of corresponding types from each type of road condition data until the change rate of a variance of the road condition data of the corresponding types is smaller than a given value; taking the road condition data, acquired when the change rate of the variance of the road condition data of the corresponding types, of the corresponding types as the final sample data of the corresponding types; and acquiring the shortest testing mileage according to the acquired final sample data of each type. The method and the device are applied to the counting and publishing of road traffic information.
Description
Technical field
The present invention relates to the transport information process field, relate in particular to a kind of acquisition methods and device of the shortest test mileage.
Background technology
Floating Car (Float Car Data) technology is one of the advanced technology means of Traffic Information of obtaining that adopted in the international in recent years intelligent transportation system (ITS).Its ultimate principle is: the Floating Car by dense distribution continuous equipment vehicle-bone global positioning system in road network traffic flow is come image data, and according to the data such as vehicle location, direction and velocity information of Floating Car periodic logging in its driving process of gathering, using relevant computation model and algorithm such as map match, path culculating handles, the position data and the urban road of Floating Car are associated on time and space, finally obtain the traffic congestion information such as driving hourage of the Vehicle Speed of road that Floating Car is passed through and road.
Before adopting relevant computation model and algorithm that the data of gathering are handled, need to determine supplemental characteristics such as the shortest test mileage.At present, obtain the stratified random smapling method that the shortest test mileage is adopted, its principle is: for one overall, its variance be outwardness and can't change, if but total body unit is classified, it is overall promptly to be divided into plurality of sub, between the unit in each subpopulation is more similar, and the variance of each subpopulation is diminished, and so only needs to extract in subpopulation a small amount of sample unit, just can represent the feature of subpopulation well, thereby improve precision whole overall estimation.Its basic procedure is: according to certain bigger feature of observation index influence, the road condition data that the Floating Car of totally collecting is issued is divided into some classes; From each class road condition data, randomly draw the road condition data of some; Calculate average, variance, the standard deviation of this road condition data of randomly drawing; When average, the variance of this road condition data of randomly drawing, when standard deviation begins to tend towards stability, obtain the shortest test mileage.
In realizing process of the present invention, the inventor finds that there are the following problems at least in the prior art: the initial moment that the average of the road condition data of randomly drawing, variance and standard deviation tend towards stability may have nothing in common with each other, the finish time of extracting is not obvious, the different road condition data differences of constantly being extracted, can not determine that the road condition data that extracts constantly according to which can access the shortest accurately test mileage this moment, reduces the statistical accuracy of the shortest test mileage.
Summary of the invention
Embodiments of the invention provide a kind of acquisition methods and device of the shortest test mileage, can obtain accuracy the shortest higher test mileage.
For achieving the above object, embodiments of the invention adopt following technical scheme:
A kind of acquisition methods of the shortest test mileage comprises:
Class condition is divided into different classes of road condition data with the road condition data of collecting according to the rules;
The road condition data that from the road condition data of each classification, obtains described classification respectively until the rate of change of the variance of the road condition data of the described classification of obtaining less than setting;
The road condition data of the described classification that the rate of change of the variance of the road condition data of described classification is obtained during less than setting is as the final sample data of described classification;
Obtain the shortest test mileage according to the final sample data of obtaining of all categories.
A kind of deriving means of the shortest test mileage comprises:
The data qualification unit, being used for according to the rules, class condition is divided into different classes of road condition data with the road condition data of collecting;
Data capture unit, the road condition data that is used for obtaining described classification respectively from the road condition data of each classification until the rate of change of the variance of the road condition data of the described classification of obtaining less than setting;
The sample determining unit, the road condition data of the described classification of being obtained when being used for rate of change with the variance of the road condition data of described classification less than setting is as the final sample data of described classification;
The mileage acquiring unit is used for obtaining the shortest test mileage according to the final sample data of obtaining of all categories.
The acquisition methods and the device of the shortest test mileage that the embodiment of the invention provides, by class condition according to the rules the road condition data of collecting is divided into different classes of road condition data, the road condition data that from the road condition data of each classification, obtains described classification respectively until the rate of change of the variance of the road condition data of the described classification of obtaining less than setting, and the road condition data of the described classification that the rate of change of the variance of the road condition data of described classification is obtained during less than setting obtains the shortest test mileage as the final sample data of described classification according to the final sample data of obtaining of all categories.Therefore, when the rate of change of the variance of data satisfies rated condition, just can obtain the shortest test mileage exactly, thereby can carry out the traffic information system assessment more exactly, for the public provides Traffic Information more accurately according to the road condition data of current extraction.
Description of drawings
In order to be illustrated more clearly in the technical scheme of the embodiment of the invention, the accompanying drawing of required use is done an introduction simply in will describing embodiment below, apparently, accompanying drawing in describing below only is some embodiments of the present invention, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain other accompanying drawing according to these accompanying drawings.
The schematic flow sheet of the acquisition methods of the shortest a kind of test mileage that Fig. 1 provides for the embodiment of the invention;
The formation synoptic diagram of the deriving means of the shortest a kind of test mileage that Fig. 2 provides for the embodiment of the invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the invention, the technical scheme in the embodiment of the invention is clearly and completely described, obviously, described embodiment only is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, those of ordinary skills belong to the scope of protection of the invention not making the every other embodiment that is obtained under the creative work prerequisite.
In order to obtain accuracy the shortest higher test mileage, the embodiment of the invention provides a kind of acquisition methods of the shortest test mileage, as shown in Figure 1, comprising:
101, class condition is divided into different classes of road condition data with the road condition data of collecting according to the rules;
For example, can from database, obtain the road condition data that Floating Car is collected, according to road chain rank the road condition data that obtains is divided into other road condition data of chain level of not going the same way according to data characteristic.For example, the road condition data that will extract from database according to road chain rank is divided into road condition datas such as one-level road, secondary road
102, the road condition data that from the road condition data of each classification, obtains described classification respectively until the rate of change of the variance of the road condition data of the described classification of obtaining less than setting;
For example, such other road condition data of difference random extraction from the road condition data of each classification, the rate of change that satisfies its variance at such other road condition data of current random extraction is during less than the condition of setting, at this moment, it is stable that the average of road condition data, variance, standard deviation all reach, be that average, the variance of road condition data, the variation of standard deviation all tend towards stability, stop the calculating and the correlated judgment of the rate of change of extraction, variance such other road condition data.Wherein, this setting can obtain by long-term simulated experiment.
For example, current from the road condition data of first category the road condition data of random extraction some, wherein, this some can be to set with reference to obtaining the road condition data amount that the shortest test fare register extracts in the past, with the road condition data of current random extraction as first road condition data.Then, from the road condition data of first category, continue the random extraction road condition data, with continue the road condition data of random extraction and before first road condition data of random extraction as second road condition data.According to formula
Obtain the isoparametric average of travel speed, running time of first road condition data and second road condition data, again according to formula
Obtain the variance var (i) of first road condition data and the isoparametric variance var of travel speed, running time (i+1) of second road condition data.Then, according to formula
Obtain the travel speed of second road condition data, the rate of change of the isoparametric variance of running time.Whether the rate of change of variance of judging this second road condition data is less than setting.Be not less than setting if judge the rate of change of the variance of this second road condition data, then continue random extraction road condition data from the road condition data of first category, to continue the road condition data of random extraction and second road condition data as Third Road condition data, calculate the rate of change of the variance of Third Road condition data according to mode mentioned above, and whether the rate of change of variance of judging these Third Road condition data is less than setting.And the like, during less than setting, stop to extract the road condition data of this first category up to the rate of change of the variance of the road condition data of current all random extraction, finish correlation computations such as variance.
In addition, can set suitable step-length according to the difference of historical data amount, with suitable minimizing computation period.For example, can determine step-length according to the data volume of the road condition data of Floating Car collection in the database, the data volume that promptly each continuation is extracted from road condition data of all categories.Then, the road condition data that from the road condition data of each classification, obtains described classification respectively at random according to the step-length of determining until the rate of change of the variance of the road condition data of the described classification of obtaining at random less than setting.For example, can be behind random extraction first road condition data from the road condition data of first category, from the road condition data of first category, continue the random extraction road condition data according to the step-length of setting, will continue the random extraction road condition data and first road condition data as second road condition data.
103, the road condition data of the described classification that the rate of change of the variance of the road condition data of described classification is obtained during less than setting is as the final sample data of described classification;
For example, at the rate of change of the variance of second road condition data less than setting, when promptly the rate of change of the variance of obtaining according to described first road condition data and described second road condition data is less than setting, determine that second road condition data is the final sample data of first category.
In addition, in order to guarantee the computational accuracy of variance rate of change, obtain the shortest accurately test mileage, after data extract that can also be in step 102 and correlation computations finish, continue the random extraction road condition data, and carry out the calculating of the rate of change of variance, if the rate of change of the variance of calculating is still less than setting, then stop the extraction of road condition data and the correlation computations of variance rate of change, with the road condition data of random extraction in the step 102 as such other final sample data.
For example, at the rate of change of the variance of described second road condition data less than setting, when promptly the rate of change of the variance of obtaining according to described first road condition data and described second road condition data is less than setting, from the road condition data of first category, continue the random extraction road condition data, the road condition data that this continuation is obtained at random and second road condition data are as Third Road condition data, calculate the rate of change of the variance of Third Road condition data according to method mentioned above, at the rate of change of the variance of Third Road condition data during still less than setting, with the final sample data of second road condition data as first category.If the rate of change of the variance of Third Road condition data is not less than setting, then continue the rate of change of random extraction road condition data, calculating variance from the road condition data of first category, until the satisfied condition of the rate of change of the variance of calculating less than setting.
104, obtain the shortest test mileage according to the final sample data of obtaining of all categories.
For example, randomly drawing repeatedly road condition data, when obtaining final sample data of all categories, the data volume summation of final sample data of all categories is the minimum of the road condition data of Floating Car collection and extracts sample size, calculates the shortest test mileage according to the travel speed in the final sample data of obtaining of all categories, running time etc.
Present embodiment carries out process of compilation under the Matlab environment, present embodiment is based on Monte Carlo (Monte Carlo) method to carry out.
The acquisition methods of the shortest test mileage that the embodiment of the invention provides, by class condition according to the rules the road condition data of collecting is divided into different classes of road condition data, the road condition data that from the road condition data of each classification, obtains described classification respectively until the rate of change of the variance of the road condition data of the described classification of obtaining less than setting, and the road condition data of the described classification that the rate of change of the variance of the road condition data of described classification is obtained during less than setting obtains the shortest test mileage as the final sample data of described classification according to the final sample data of obtaining of all categories.Therefore, when the rate of change of the variance of data satisfies rated condition, just can obtain the shortest test mileage exactly, thereby can carry out the traffic information system assessment more exactly, for the public provides Traffic Information more accurately according to the road condition data of current extraction.And in the acquisition process of the shortest test mileage, the calculating that independently repeats in a large number respectively increases the simulation number of times simultaneously until thinking that the analog sample data that produced can reflect the feature of simulation system under certain precision.And, can reduce manpower and material resources that actual drive test is spent, reduce the time of total system in test process.
With said method accordingly, the embodiment of the invention also provides a kind of deriving means of the shortest test mileage, as shown in Figure 2, comprising:
Further, described data qualification unit 201 specifically is used for according to road chain rank the road condition data of collecting being divided into other road condition data of chain level of not going the same way.
Further, described data capture unit 202 specifically is used for the variance var (i) according to first road condition data, variance var of second road condition data (i+1) and formula
Obtain the rate of change of the variance of described second road condition data; The road condition data of described first road condition data for from the road condition data of described classification, obtaining, the road condition data of described second road condition data for from the road condition data of described classification, obtaining, described second road condition data comprises described first road condition data.
Further, described sample determining unit 203 specifically comprises:
Rate of change obtains subelement, when being used for rate of change in the variance of described second road condition data less than setting, obtains the rate of change of variance according to the variance of the variance of Third Road condition data and described second road condition data, with the rate of change that obtains as second rate of change; Described Third Road condition data comprise described second road condition data;
Sample is determined subelement, is used at described second rate of change during less than setting, with the final sample data of described second road condition data as described classification.
Further, described data capture unit 202 specifically comprises:
Step-length is determined subelement, is used for determining step-length according to the data volume of the road condition data of described collection;
Data are obtained subelement, the road condition data that is used for obtaining described classification respectively from the road condition data of each classification according to described step-length until the rate of change of the variance of the road condition data of the described classification of obtaining less than setting.
The method of work of the deriving means of the shortest test mileage of present embodiment specifically referring to the described method of Fig. 1, does not repeat them here.
The deriving means of the shortest test mileage that the embodiment of the invention provides, by class condition according to the rules the road condition data of collecting is divided into different classes of road condition data, the road condition data that from the road condition data of each classification, obtains described classification respectively until the rate of change of the variance of the road condition data of the described classification of obtaining less than setting, and the road condition data of the described classification that the rate of change of the variance of the road condition data of described classification is obtained during less than setting obtains the shortest test mileage as the final sample data of described classification according to the final sample data of obtaining of all categories.Therefore, when the rate of change of the variance of data satisfies rated condition, just can obtain the shortest test mileage exactly, thereby can carry out the traffic information system assessment more exactly, for the public provides Traffic Information more accurately according to the road condition data of current extraction.And in the acquisition process of the shortest test mileage, the calculating that independently repeats in a large number respectively increases the simulation number of times simultaneously until thinking that the analog sample data that produced can reflect the feature of simulation system under certain precision.And, can reduce manpower and material resources that actual drive test is spent, reduce the time of total system in test process.
One of ordinary skill in the art will appreciate that all or part of flow process that realizes in the foregoing description method, be to instruct relevant hardware to finish by computer program, described program can be stored in the computer read/write memory medium, this program can comprise the flow process as the embodiment of above-mentioned each side method when carrying out.Wherein, described storage medium can be magnetic disc, CD, read-only storage memory body (Read-Only Memory, ROM) or at random store memory body (Random Access Memory, RAM) etc.
The above; only be the specific embodiment of the present invention, but protection scope of the present invention is not limited thereto, anyly is familiar with those skilled in the art in the technical scope that the present invention discloses; can expect easily changing or replacing, all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion by described protection domain with claim.
Claims (10)
1. the acquisition methods of the shortest test mileage is characterized in that, comprising:
Class condition is divided into different classes of road condition data with the road condition data of collecting according to the rules;
The road condition data that from the road condition data of each classification, obtains described classification respectively until the rate of change of the variance of the road condition data of the described classification of obtaining less than setting;
The road condition data of the described classification that the rate of change of the variance of the road condition data of described classification is obtained during less than setting is as the final sample data of described classification;
Obtain the shortest test mileage according to the final sample data of obtaining of all categories.
2. method according to claim 1 is characterized in that, described class condition according to the rules is divided into different classes of road condition data with the road condition data of collecting and comprises:
According to road chain rank the road condition data of collecting is divided into other road condition data of chain level of not going the same way.
3. method according to claim 1 is characterized in that, the rate of change of variance that obtains the road condition data of described classification comprises:
According to the variance var (i) of first road condition data, variance var of second road condition data (i+1) and formula
Obtain the rate of change of described variance for second road condition data; The road condition data of described first road condition data for from the road condition data of described classification, obtaining, the road condition data of described second road condition data for from the road condition data of described classification, obtaining, described second road condition data comprises described first road condition data.
4. method according to claim 3 is characterized in that, the road condition data of the described classification that the rate of change of the variance of described road condition data with described classification is obtained during less than setting comprises as the final sample data of described classification:
At the rate of change of the variance of described second road condition data during less than setting, obtain the rate of change of variance according to the variance of the variance of Third Road condition data and described second road condition data, with the rate of change that obtains as second rate of change; Described Third Road condition data comprise described second road condition data;
At described second rate of change during less than setting, with the final sample data of described second road condition data as described classification.
5. method according to claim 1 is characterized in that, the described road condition data that obtains described classification from the road condition data of each classification respectively comprises less than setting until the rate of change of the variance of the road condition data of the described classification of obtaining:
Data volume according to the road condition data of described collection is determined step-length;
The road condition data that from the road condition data of each classification, obtains described classification according to described step-length respectively until the rate of change of the variance of the road condition data of the described classification of obtaining less than setting.
6. the deriving means of the shortest test mileage is characterized in that, comprising:
The data qualification unit, being used for according to the rules, class condition is divided into different classes of road condition data with the road condition data of collecting;
Data capture unit, the road condition data that is used for obtaining described classification respectively from the road condition data of each classification until the rate of change of the variance of the road condition data of the described classification of obtaining less than setting;
The sample determining unit, the road condition data of the described classification of being obtained when being used for rate of change with the variance of the road condition data of described classification less than setting is as the final sample data of described classification;
The mileage acquiring unit is used for obtaining the shortest test mileage according to the final sample data of obtaining of all categories.
7. device according to claim 6 is characterized in that, described data qualification unit specifically is used for according to road chain rank the road condition data of collecting being divided into other road condition data of chain level of not going the same way.
8. device according to claim 6 is characterized in that, described data capture unit specifically is used for the variance var (i) according to first road condition data, variance var of second road condition data (i+1) and formula
Obtain the rate of change of the variance of described second road condition data; The road condition data of described first road condition data for from the road condition data of described classification, obtaining, the road condition data of described second road condition data for from the road condition data of described classification, obtaining, described second road condition data comprises described first road condition data.
9. device according to claim 8 is characterized in that, described sample determining unit comprises:
Rate of change obtains subelement, when being used for rate of change in the variance of described second road condition data less than setting, obtains the rate of change of variance according to the variance of the variance of Third Road condition data and described second road condition data, with the rate of change that obtains as second rate of change; Described Third Road condition data comprise described second road condition data;
Sample is determined subelement, is used at described second rate of change during less than setting, with the final sample data of described second road condition data as described classification.
10. device according to claim 6 is characterized in that, described data capture unit comprises:
Step-length is determined subelement, is used for determining step-length according to the data volume of the road condition data of described collection;
Data are obtained subelement, the road condition data that is used for obtaining described classification respectively from the road condition data of each classification according to described step-length until the rate of change of the variance of the road condition data of the described classification of obtaining less than setting.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011100484699A CN102097006B (en) | 2011-02-28 | 2011-02-28 | Shortest testing mileage acquisition method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011100484699A CN102097006B (en) | 2011-02-28 | 2011-02-28 | Shortest testing mileage acquisition method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102097006A true CN102097006A (en) | 2011-06-15 |
| CN102097006B CN102097006B (en) | 2012-11-28 |
Family
ID=44130079
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2011100484699A Active CN102097006B (en) | 2011-02-28 | 2011-02-28 | Shortest testing mileage acquisition method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102097006B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107798875A (en) * | 2017-11-07 | 2018-03-13 | 上海炬宏信息技术有限公司 | Method based on the Floating Car gps data optimization of road joints traffic capacity |
| CN107990909A (en) * | 2016-10-27 | 2018-05-04 | 千寻位置网络有限公司 | A kind of test method and its system of simulated roadway position data |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101286270A (en) * | 2008-05-26 | 2008-10-15 | 北京捷讯畅达科技发展有限公司 | Traffic flow forecasting method combining dynamic real time traffic data |
| CN101286269A (en) * | 2008-05-26 | 2008-10-15 | 北京捷讯畅达科技发展有限公司 | Traffic flow forecasting system combining dynamic real time traffic data |
| CN101673463A (en) * | 2009-09-17 | 2010-03-17 | 北京世纪高通科技有限公司 | Traffic information predicting method based on time series and device thereof |
| CN101950477A (en) * | 2010-08-23 | 2011-01-19 | 北京世纪高通科技有限公司 | Method and device for processing traffic information |
-
2011
- 2011-02-28 CN CN2011100484699A patent/CN102097006B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101286270A (en) * | 2008-05-26 | 2008-10-15 | 北京捷讯畅达科技发展有限公司 | Traffic flow forecasting method combining dynamic real time traffic data |
| CN101286269A (en) * | 2008-05-26 | 2008-10-15 | 北京捷讯畅达科技发展有限公司 | Traffic flow forecasting system combining dynamic real time traffic data |
| CN101673463A (en) * | 2009-09-17 | 2010-03-17 | 北京世纪高通科技有限公司 | Traffic information predicting method based on time series and device thereof |
| CN101950477A (en) * | 2010-08-23 | 2011-01-19 | 北京世纪高通科技有限公司 | Method and device for processing traffic information |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107990909A (en) * | 2016-10-27 | 2018-05-04 | 千寻位置网络有限公司 | A kind of test method and its system of simulated roadway position data |
| CN107990909B (en) * | 2016-10-27 | 2021-05-25 | 千寻位置网络有限公司 | Test method and system for simulating road position data |
| CN107798875A (en) * | 2017-11-07 | 2018-03-13 | 上海炬宏信息技术有限公司 | Method based on the Floating Car gps data optimization of road joints traffic capacity |
| CN107798875B (en) * | 2017-11-07 | 2020-11-06 | 上海炬宏信息技术有限公司 | Method for optimizing intersection traffic capacity based on floating car GPS data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102097006B (en) | 2012-11-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107195180B (en) | Traffic travel track extraction method and device based on electric police data | |
| CN110738856B (en) | Mobile clustering-based urban traffic jam fine identification method | |
| US8554473B2 (en) | Energy efficient routing using an impedance factor | |
| CN105138779A (en) | Vehicle-mounted GPS space-time trajectory big data optimization method and system | |
| CN105513354A (en) | Video-based urban road traffic jam detecting system | |
| CN109166317B (en) | Urban traffic route passing time determination method based on state characteristics | |
| CN106610981A (en) | Verification and upgrading method and system for road information in electronic map | |
| CN106960210A (en) | The method and apparatus of target detection | |
| CN110798805B (en) | Data processing method and device based on GPS track and storage medium | |
| CN104504099A (en) | Position-trajectory-based travel state splitting method | |
| CN110807924A (en) | Multi-parameter fusion method and system based on full-scale full-sample real-time traffic data | |
| CN104916133B (en) | Road altitude information extraction method and system based on traffic track data | |
| CN101950477A (en) | Method and device for processing traffic information | |
| CN109615853A (en) | Identify the method and apparatus of the doubtful illegal operation vehicle of highway | |
| CN106529765A (en) | Performance evaluation method and device for collection operation | |
| CN110827537B (en) | Method, device and equipment for setting tidal lane | |
| CN102800191B (en) | Traffic evaluation method and device | |
| CN105574313A (en) | Road grade determining method and device | |
| CN109489679A (en) | A kind of arrival time calculation method in guidance path | |
| CN102097006B (en) | Shortest testing mileage acquisition method and device | |
| CN114912689A (en) | Map grid index and XGBOST-based over-limit vehicle destination prediction method and system | |
| CN106980029B (en) | Vehicle overspeed judgment method and system | |
| CN112562311B (en) | Method and device for obtaining working condition weight factor based on GIS big data | |
| CN115081505A (en) | Pedestrian network incremental generation method based on walking track data | |
| CN115691140B (en) | Analysis and prediction method for space-time distribution of automobile charging demand |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |