CN102800191B - Traffic evaluation method and device - Google Patents
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- CN102800191B CN102800191B CN201210270626.5A CN201210270626A CN102800191B CN 102800191 B CN102800191 B CN 102800191B CN 201210270626 A CN201210270626 A CN 201210270626A CN 102800191 B CN102800191 B CN 102800191B
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Abstract
The embodiment of the invention discloses a traffic evaluation method and device, relating to the technical field of an intelligent traffic system. The method comprises the following steps of: calculating the RAS of a specific area; storing the RAS and the timestamp information corresponding to the RAS; and extracting the normal traffic trend of the specific area according to the RAS and the timestamp information corresponding to the RAS. The method and device disclosed by the invention are applicable to the area normal traffic evaluation of the city.
Description
Technical field
The present invention relates to intelligent transportation system technical field, particularly a kind of traffic evaluation method and device based under Floating Car dynamic information service system.
Background technology
Along with the raising of people's living standard, in city, the quantity of private car is more and more, has caused that urban transportation is blocked up day by day, the problem such as Frequent Accidents and ecological deterioration, has had a strong impact on people's normal work and life.Traffic Systems itself is very complicated, not only be subject to the impact of comings and goings and various factors, also be subject to the impact of nature accidentalia, cause traffic law mining more difficult, if can grasp the basic law of traffic tendency, just can avoid to a great extent the problems referred to above, improve people's quality of life.
For the problems referred to above, prior art has proposed a kind of traffic information evaluation method, comprising: import map datum, unsteady vehicle data and cellular base station locator data, and carry out organization and administration, obtain basic data; Set up evaluation model; By evaluation model, basic data is carried out to data fusion, obtain traffic integrated information.Above-mentioned traffic information evaluation method is based on float vehicle data and cellular base station locator data, to set up the evaluation model of road network, traffic flow distribution and the flowing law in reflection city.
In realizing process of the present invention, inventor finds that in prior art, at least there are the following problems: on the one hand, because floating car data amount is larger, in addition in computation process, operation is too complicated, cause computing time longer, counting yield is low, usually, calculate an index result and conventionally at least need the time of 120 seconds, and computational solution precision is subject to the restriction of map-matching algorithm and path culculating algorithm, different algorithms is can getable road condition data different, and then also difference to some extent of index result; On the other hand, this traffic information evaluation method is subject to the restriction of map version, different map versions, and evaluation result is difference to some extent, and along with the replacing of version, all historical traffic road condition datas all will recalculate, and cost is larger.
Summary of the invention
Embodiments of the invention provide a kind of traffic evaluation method and device, can solve computation process complexity, the high problem that assesses the cost in existing traffic evaluation method, can utilize system resource still less to obtain higher system performance.
The technical scheme that the embodiment of the present invention adopts is:
A traffic evaluation method, comprising:
Reading configuration file information, described profile information comprises area coordinate, result data memory location, journal file memory location and floating car data file;
Each data extractor section from described floating car data file, described field comprises timestamp information, 02 coordinate system longitude, 02 coordinate system latitude, passenger carrying status and speed;
Described 02 coordinate system longitude and described 02 coordinate system latitude are carried out to standardization, be converted into general longitude and latitude data;
According to described longitude and latitude data, judge whether the vehicle that this floating car data is corresponding travels in specific region, the scope of described specific region is divided according to longitude and latitude, if described Vehicle Driving Cycle, in described specific region, joins this floating car data in the calculating of zone leveling instantaneous velocity RAS; If described vehicle does not travel in described specific region, abandon these data;
Calculate the RAS of described specific region;
Store described RAS and the timestamp information corresponding with described RAS;
According to described RAS and the timestamp information corresponding with described RAS, extract the normality traffic tendency of described specific region.
A Traffic Evaluation device, comprising:
Information reading module, for reading configuration file information, described profile information comprises area coordinate, result data memory location, journal file memory location and floating car data file;
Field abstraction module, for each the data extractor section from described floating car data file, described field comprises timestamp information, 02 coordinate system longitude, 02 coordinate system latitude, passenger carrying status and speed;
Standardized module, for described 02 coordinate system longitude and described 02 coordinate system latitude are carried out to standardization, is converted into general longitude and latitude data;
Judging treatmenting module, for judging according to described longitude and latitude data whether the vehicle that this floating car data is corresponding travels in specific region, the scope of described specific region is divided according to longitude and latitude, when described Vehicle Driving Cycle is in described specific region, this floating car data is joined in the calculating of zone leveling instantaneous velocity RAS; When described vehicle does not travel in described specific region, abandon this data;
Computing module, for calculating the RAS of described specific region;
Memory module, for storing described RAS and the timestamp information corresponding with described RAS;
Extraction module, for according to described RAS and the timestamp information corresponding with described RAS, extracts the normality traffic tendency of described specific region.
Compared with prior art, the embodiment of the present invention has been introduced this new Traffic Evaluation index of average instantaneous velocity RAS of Floating Car, by Screening Treatment floating car data, according to these data, calculate RAS, according to the RAS calculating and timestamp information corresponding to RAS, extract the normality traffic tendency of specific region, based on this normality traffic tendency, the traffic behavior in this region is evaluated.This index of RAS relies on a large amount of historical datas, regular strong, and the larger error that can avoid map match and path culculating to bring, the computation model using when calculating with respect to existing evaluation method not only simply but also efficient, thereby can macroscopic view, accurately hold the traffic behavior of specific region.
Accompanying drawing explanation
In order to be illustrated more clearly in the technical scheme in the embodiment of the present invention, to the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skills, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.
The method flow diagram that Fig. 1 provides for the embodiment of the present invention one;
The method flow diagram that Fig. 2, Fig. 3 provide for the embodiment of the present invention two;
The apparatus structure schematic diagram that Fig. 4, Fig. 5 provide for the embodiment of the present invention three.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, rather than whole embodiment.Embodiment based in the present invention, those of ordinary skills, not making all other embodiment that obtain under creative work prerequisite, belong to the scope of protection of the invention.
For making the advantage of technical solution of the present invention clearer, below in conjunction with drawings and Examples, the present invention is elaborated.
Embodiment mono-
The present embodiment provides a kind of traffic evaluation method, and as shown in Figure 1, described method comprises:
101, reading configuration file information, described profile information comprises area coordinate, result data memory location, journal file memory location and floating car data file.
102, each data extractor section from described floating car data file, described field comprises timestamp information, 02 coordinate system longitude, 02 coordinate system latitude, passenger carrying status and speed.
103, described 02 coordinate system longitude and described 02 coordinate system latitude are carried out to standardization, be converted into general longitude and latitude data.
104, according to described longitude and latitude data, judge whether the vehicle that this floating car data is corresponding travels in specific region, the scope of described specific region is divided according to longitude and latitude, if described Vehicle Driving Cycle, in described specific region, joins this floating car data in the calculating of zone leveling instantaneous velocity RAS; If described vehicle does not travel in described specific region, abandon these data.
105, calculate the RAS of described specific region.
106, store described RAS and the timestamp information corresponding with described RAS.
107, according to described RAS and the timestamp information corresponding with described RAS, extract the normality traffic tendency of described specific region.
Wherein, the zone leveling instantaneous velocity RAS of described calculating specific region comprises:
Utilize computing formula
calculate described RAS, wherein w
ifor the weight of vehicle instantaneous velocity, v
ifor the instantaneous velocity of vehicle, f (s
i) be the passenger carrying status of vehicle, n is the gross vehicle number of effectively being added up in described specific region.
Wherein, described according to described RAS and the timestamp information corresponding with described RAS, the normality traffic tendency of extracting described specific region comprises:
Read described RAS and described timestamp information;
Remove the anomaly trend data in described RAS;
Extract remaining described RAS data as the normality traffic tendency of described specific region.
Wherein, the anomaly trend data in the described RAS of described removal comprise:
Utilize model of fit for the Gaussian function on n rank
described RAS data acquisition is carried out curve fitting, obtain parameter result vector
According to described parameter result vector
obtain the described specific region traffic behavior vector of every day with date d corresponding to described parameter result vector
wherein, k is the k days in a year, d
krepresent the date of k days, p
krepresent the parameter result vector of k days;
The sample set S being comprised of described traffic behavior vector is projected in coordinate system, and construction feature vector distance, obtains mean distance
More described mean distance
with the size of threshold parameter α, determine the mean distance that is less than described threshold parameter α
corresponding detected object outlier, the data corresponding with described outlier are anomaly trend data.
Further, before the anomaly trend data in the described RAS of described removal, also comprise:
Described RAS data are carried out to pretreatment operation, eliminate the noise in described RAS data, and fill up the vacancy value in described RAS data.
Wherein, described timestamp information is for the RAS of each particular point in time is indicated, corresponding one by one with described RAS.
Compared with prior art, the embodiment of the present invention has been introduced this new Traffic Evaluation index of average instantaneous velocity RAS of Floating Car, by Screening Treatment floating car data, according to these data, calculate RAS, according to the RAS calculating and timestamp information corresponding to RAS, extract the normality traffic tendency of specific region, based on this normality traffic tendency, the traffic behavior in this region is evaluated.This index of RAS relies on a large amount of historical datas, regular strong, and the larger error that can avoid map match and path culculating to bring, the computation model using when calculating with respect to existing evaluation method not only simply but also efficient, thereby can macroscopic view, accurately hold the traffic behavior of specific region.
Embodiment bis-
The present embodiment provides a kind of traffic evaluation method, by the analysis of the floating car data to a large amount of, thereby the traffic tendency of specific region is extracted, and as shown in Figure 2, described method comprises:
201, reading configuration file information, obtains parameter information.
For example, parameter information can comprise: area coordinate, result data memory location, journal file memory location and floating car data file etc.
202, search next calculative file destination.
Wherein, these file destinations are that step 201 reads out from raw data, and file destination comprises original Floating Car gps data file.
203, from this file destination, read next vehicle registration, and extract respective field.
Concrete, what read is an original Floating Car gps data file, comprising a lot of bar data, all will stab the fields such as information, 02 coordinate system longitude, 02 coordinate system latitude, passenger carrying status and speed for each data extraction time.
204, the fields such as 02 coordinate system longitude and 02 coordinate system latitude are carried out to standardization, and deposit in internal memory.
Wherein, it is in order to be translated into the longitude and latitude form of standard that 02 coordinate system longitude and 02 coordinate system latitude are carried out to standardization, so that carry out the regional location judgement of vehicle.
205, the data in internal memory are carried out to time unifying.
Concrete, when sorting out for data, according to certain time interval, go to divide, for example, data every 5 minutes are divided into a class, the timestamp of a car shows that it was recorded at 12: 58, and the data that this car is corresponding so will be classified into 12 55-13 points in this time period of 5 minutes.
206, judge that this vehicle is whether in specific region, if this vehicle in specific region, execution step 207; If this vehicle, not in specific region, returns to execution step 203.
For example, the latitude and longitude information district obtaining according to step 204 judges in the special traffic the region whether vehicle of these data need to evaluate in us.
It should be noted that, can also judge that whether this vehicle is in passenger carrying status, certainly, this judgement is only evaluated for taxi, if only according to the index judgement of step 206, be to judge for all vehicles through this region.
207, satisfactory data are joined in the calculating of RAS, obtain RAS data.
For example, utilize computing formula
calculate described RAS, wherein w
ifor the weight of vehicle instantaneous velocity, in the present embodiment, be defaulted as 1, v
ifor the instantaneous velocity of vehicle, f (s
i) be the passenger carrying status of vehicle, if calculate the RAS of all vehicles, passenger carrying status is defaulted as 1, if only calculate the RAS hiring a car, the passenger carrying status of the taxi of carrying is 1, the passenger carrying status of the taxi of carrying is not that 0, n is the gross vehicle number of effectively being added up in described specific region.
208, judge in file destination whether have untreated vehicle registration, if there is untreated vehicle registration, perform step 203; If there is no untreated vehicle registration, perform step 209.
209, judge whether untreated file destination, if there is untreated file destination, performed step 202; If there is no untreated file destination, perform step 210.
210, all satisfactory achievement datas and timestamp corresponding to achievement data are stored.
So far completed the computation process of the RAS achievement data of specific region, will process the RAS data that calculate below, to obtain the normality traffic tendency of specific region.
As shown in Figure 3, the extracting method of normality traffic tendency comprises:
211, read whole RAS data.
Concrete, the total data of step 209 storage is all read into memory, when calculating, can improve computing velocity like this.
212,, according to the timestamp information of RAS data, extract interior on the same day data.
Whether the data that 213, judgement is extracted are complete, if imperfect, execution step 214; If complete, execution step 215.
214, incomplete data division is filled up to operation.
Wherein, due to corresponding at the same time not same date (my god) road chain travel speed almost there is no correlativity, so when filling up operation, only consider that speed corresponding to each adjacent time point of the same area within on the same day judges exceptional value and the value of filling a vacancy.Fill up the trend that operation data afterwards can reflect traffic essence more accurately.
215, judge that whether data are containing noise data, if noise data, execution step 216; If there is no noise data, execution step 217.
216, eliminate the noise data in data.
Wherein, after eliminating noise data, the trend of overall data can be Paint Gloss, and regularity is stronger.
217, by data fitting, carry out data modeling, obtain standardized sample set.
For example, for the time series with temporal correlation, carry out the modeling of data, the method that adopts Gauss curve fitting and fitting of a polynomial to combine is processed, and corresponding modeling parameters is stored, concrete:
When carrying out curve fitting, use the Gaussian function that model of fit is n rank, its expression formula is
n=1,2,3 ..., 9; Parameter result vector after data fitting is
therefore, it is upper that the traffic behavior using a day is mapped to vectorial p, and the traffic behavior vector of every day can be expressed as
wherein, k is the k days in a year, d
krepresent the date of k days, p
krepresent the parameter result vector of k days.Sample data set can be expressed as: Sample={e
k| e
k=e (d
k, p
k).
218, judge whether to also have untreated data, if there are untreated data, execution step 212; If there is no untreated data, execution step 219.
219, sample set is described in coordinate system, the mean distance of construction feature vector.
For example, the sample set S being comprised of described traffic behavior vector is projected in coordinate system, construction feature vector distance, obtains mean distance
220, the size that compares mean distance and threshold parameter α, judges whether current sample object is outlier, if outlier, execution step 221; If not outlier, execution step 222.
For example,, if mean distance
be greater than threshold parameter α, judge that current sample object is outlier; If mean distance
be greater than threshold parameter α, judge that current sample object is not outlier, wherein outlier data must be anomaly trend data, to be subject to the impact of certain undesirable element and the larger data fluctuations phenomenon that produces, but not outlier is the data that can reflect normality basic trend, so normality traffic tendency can be expressed as:
wherein the number of days of n for calculating, can need to go to set voluntarily according to measuring, the RAS data that i measures for every 5 minutes, and within one day, one has 288 groups of data, and wherein, the time interval of RAS DATA REASONING also can be set voluntarily, (RAS
i)
jthe regional average value speed that represents i the time point of j days in a year.
Further, normal data fluctuations scope can reflect by variance:
221, outlier is reverted to raw data.
Wherein, although outlier data are the larger data that fluctuate, its essence is still the reflection of the traffic tendency in a certain region, so we need to process these outlier, is become the normal trend data of fluctuation, and reverts in raw data.
222, the result data of the traffic tendency of acquisition is stored.
Wherein, no matter be the baseline of the normal traffic tendency of reflection, still reflect the outlier data of abnormal traffic tendency, all need to store.
Compared with prior art, the embodiment of the present invention has been introduced this new Traffic Evaluation index of average instantaneous velocity RAS of Floating Car, by Screening Treatment floating car data, according to these data, calculate RAS, according to the RAS calculating and timestamp information corresponding to RAS, extract the normality traffic tendency of specific region, based on this normality traffic tendency, the traffic behavior in this region is evaluated.This index of RAS relies on a large amount of historical datas, regular strong, and the larger error that can avoid map match and path culculating to bring, the computation model using when calculating with respect to existing evaluation method not only simply but also efficient, thereby can macroscopic view, accurately hold the traffic behavior of specific region.
Embodiment tri-
The present embodiment provides a kind of Traffic Evaluation device, and as shown in Figure 4, described device comprises:
Information reading module 31, for reading configuration file information, described profile information comprises area coordinate, result data memory location, journal file memory location and floating car data file;
Field abstraction module 32, for each the data extractor section from described floating car data file, described field comprises timestamp information, 02 coordinate system longitude, 02 coordinate system latitude, passenger carrying status and speed;
Standardized module 33, for described 02 coordinate system longitude and described 02 coordinate system latitude are carried out to standardization, is converted into general longitude and latitude data;
Judging treatmenting module 34, for judging according to described longitude and latitude data whether the vehicle that this floating car data is corresponding travels in specific region, the scope of described specific region is divided according to longitude and latitude, when described Vehicle Driving Cycle is in described specific region, this floating car data is joined in the calculating of zone leveling instantaneous velocity RAS; When described vehicle does not travel in described specific region, abandon this data;
Computing module 35, for calculating the RAS of described specific region;
Memory module 36, for storing described RAS and the timestamp information corresponding with described RAS;
Extraction module 37, for according to described RAS and the timestamp information corresponding with described RAS, extracts the normality traffic tendency of described specific region.
Wherein, described computing module 35 specifically for:
Utilize computing formula
calculate described RAS, wherein w
ifor the weight of vehicle instantaneous velocity, v
ifor the instantaneous velocity of vehicle, f (s
i) be the passenger carrying status of vehicle, n is the gross vehicle number of effectively being added up in described specific region.
Further, as shown in Figure 5, described extraction module 37 comprises:
Reading unit 371, for reading described RAS and described timestamp information;
Removal unit 372, for removing the anomaly trend data of described RAS;
Extraction unit 373, for extracting remaining described RAS data as the normality traffic tendency of described specific region.
Further, as shown in Figure 5, described removal unit 372 comprises:
Curve subelement 3721, for utilizing model of fit for the Gaussian function on n rank
described RAS data acquisition is carried out curve fitting, obtain parameter result vector
Vector obtains subelement 3722, for according to described parameter result vector
obtain the described specific region traffic behavior vector of every day with date d corresponding to described parameter result vector
wherein, k is the k days in a year, d
krepresent the date of k days, p
krepresent the parameter result vector of k days;
Distance is obtained subelement 3723, and for the sample set S being comprised of described traffic behavior vector is projected to coordinate system, construction feature vector distance, obtains mean distance
Determine subelement 3724, for more described mean distance
with the size of threshold parameter α, determine the mean distance that is less than described threshold parameter α
corresponding detected object outlier, the data corresponding with described outlier are anomaly trend data.
Further, as shown in Figure 5, described extraction module 37 also comprises:
Pretreatment unit 374, for described RAS data are carried out to pretreatment operation, eliminates the noise in described RAS data, and fills up the vacancy value in described RAS data.
Wherein, described timestamp information is for the RAS of each particular point in time is indicated, corresponding one by one with described RAS.
Compared with prior art, the embodiment of the present invention has been introduced this new Traffic Evaluation index of average instantaneous velocity RAS of Floating Car, by Screening Treatment floating car data, according to these data, calculate RAS, according to the RAS calculating and timestamp information corresponding to RAS, extract the normality traffic tendency of specific region, based on this normality traffic tendency, the traffic behavior in this region is evaluated.This index of RAS relies on a large amount of historical datas, regular strong, and the larger error that can avoid map match and path culculating to bring, the computation model using when calculating with respect to existing evaluation method not only simply but also efficient, thereby can macroscopic view, accurately hold the traffic behavior of specific region.
The above-mentioned embodiment of the method providing can be provided the Traffic Evaluation device that the embodiment of the present invention provides, and concrete function is realized and referred to the explanation in embodiment of the method, does not repeat them here.The traffic evaluation method that the embodiment of the present invention provides and device go for the region normality Traffic Evaluation in city, but are not limited only to this.
One of ordinary skill in the art will appreciate that all or part of flow process realizing in above-described embodiment method, to come the hardware that instruction is relevant to complete by computer program, described program can be stored in a computer read/write memory medium, this program, when carrying out, can comprise as the flow process of the embodiment of above-mentioned each side method.Wherein, described storage medium can be magnetic disc, CD, read-only store-memory body (Read-Only Memory, ROM) or random store-memory body (Random Access Memory, RAM) etc.
The above; be only the specific embodiment of the present invention, but protection scope of the present invention is not limited to this, is anyly familiar with those skilled in the art in the technical scope that the present invention discloses; the variation that can expect easily or replacement, within all should being encompassed in protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claim.
Claims (10)
1. a traffic evaluation method, is characterized in that, comprising:
Reading configuration file information, described profile information comprises area coordinate, result data memory location, journal file memory location and floating car data file;
Each data extractor section from described floating car data file, described field comprises timestamp information, 02 coordinate system longitude, 02 coordinate system latitude, passenger carrying status and speed;
Described 02 coordinate system longitude and described 02 coordinate system latitude are carried out to standardization, be converted into general longitude and latitude data;
According to described longitude and latitude data, judge whether the vehicle that this floating car data is corresponding travels in specific region, the scope of described specific region is divided according to longitude and latitude, if described Vehicle Driving Cycle, in described specific region, joins this floating car data in the calculating of zone leveling instantaneous velocity RAS; If described vehicle does not travel in described specific region, abandon these data;
Utilize computing formula
calculate described RAS, wherein w
ifor the weight of vehicle instantaneous velocity, v
ifor the instantaneous velocity of vehicle, n is the gross vehicle number of effectively being added up in described specific region, f (s
i) be the passenger carrying status of vehicle, while calculating in described specific region all vehicles, f (s
i) be 1, only calculate while hiring a car the f (s of carrying taxi
i) be 1, the f (s of carrying taxi not
i) be 0;
Store described RAS and the timestamp information corresponding with described RAS;
According to described RAS and the timestamp information corresponding with described RAS, extract the normality traffic tendency of described specific region.
2. method according to claim 1, is characterized in that, described according to described RAS and the timestamp information corresponding with described RAS, the normality traffic tendency of extracting described specific region comprises:
Read described RAS and described timestamp information;
Remove the anomaly trend data in described RAS;
Extract remaining described RAS data as the normality traffic tendency of described specific region.
3. method according to claim 2, is characterized in that, the anomaly trend data in the described RAS of described removal comprise:
Utilize model of fit for the Gaussian function on n rank
described RAS data acquisition is carried out curve fitting, obtain parameter result vector
According to described parameter result vector
obtain the described specific region traffic behavior vector of every day with date d corresponding to described parameter result vector
wherein, k is the k days in a year, d
krepresent the date of k days, p
krepresent the parameter result vector of k days;
The sample set S being comprised of described traffic behavior vector is projected in coordinate system, and construction feature vector distance, obtains mean distance
More described mean distance
with the size of threshold parameter α, determine the mean distance that is less than described threshold parameter α
corresponding detected object outlier, the data corresponding with described outlier are anomaly trend data.
4. method according to claim 2, is characterized in that, before the anomaly trend data in the described RAS of described removal, also comprises:
Described RAS data are carried out to pretreatment operation, eliminate the noise in described RAS data, and fill up the vacancy value in described RAS data.
5. according to the method described in claim 1-4 any one, it is characterized in that, described timestamp information is for the RAS of each particular point in time is indicated, corresponding one by one with described RAS.
6. a Traffic Evaluation device, is characterized in that, comprising:
Information reading module, for reading configuration file information, described profile information comprises area coordinate, result data memory location, journal file memory location and floating car data file;
Field abstraction module, for each the data extractor section from described floating car data file, described field comprises timestamp information, 02 coordinate system longitude, 02 coordinate system latitude, passenger carrying status and speed;
Standardized module, for described 02 coordinate system longitude and described 02 coordinate system latitude are carried out to standardization, is converted into general longitude and latitude data;
Judging treatmenting module, for judging according to described longitude and latitude data whether the vehicle that this floating car data is corresponding travels in specific region, the scope of described specific region is divided according to longitude and latitude, when described Vehicle Driving Cycle is in described specific region, this floating car data is joined in the calculating of zone leveling instantaneous velocity RAS; When described vehicle does not travel in described specific region, abandon this data;
Computing module, for utilizing computing formula
calculate described RAS, wherein w
ifor the weight of vehicle instantaneous velocity, v
ifor the instantaneous velocity of vehicle, n is the gross vehicle number of effectively being added up in described specific region, f (s
i) be the passenger carrying status of vehicle, while calculating in described specific region all vehicles, f (s
i) be 1, only calculate while hiring a car the f (s of carrying taxi
i) be 1, the f (s of carrying taxi not
i) be 0;
Memory module, for storing described RAS and the timestamp information corresponding with described RAS;
Extraction module, for according to described RAS and the timestamp information corresponding with described RAS, extracts the normality traffic tendency of described specific region.
7. device according to claim 6, is characterized in that, described extraction module comprises:
Reading unit, for reading described RAS and described timestamp information;
Removal unit, for removing the anomaly trend data of described RAS;
Extraction unit, for extracting remaining described RAS data as the normality traffic tendency of described specific region.
8. device according to claim 7, is characterized in that, described removal unit comprises:
Curve subelement, for utilizing model of fit for the Gaussian function on n rank
described RAS data acquisition is carried out curve fitting, obtain parameter result vector
Vector obtains subelement, for according to described parameter result vector
obtain the described specific region traffic behavior vector of every day with date d corresponding to described parameter result vector
wherein, k is the k days in a year, d
krepresent the date of k days, p
krepresent the parameter result vector of k days;
Distance is obtained subelement, and for the sample set S being comprised of described traffic behavior vector is projected to coordinate system, construction feature vector distance, obtains mean distance
Determine subelement, for more described mean distance
with the size of threshold parameter α, determine the mean distance that is less than described threshold parameter α
corresponding detected object outlier, the data corresponding with described outlier are anomaly trend data.
9. device according to claim 7, is characterized in that, described extraction module comprises:
Pretreatment unit, for described RAS data are carried out to pretreatment operation, eliminates the noise in described RAS data, and fills up the vacancy value in described RAS data.
10. according to the device described in claim 6-9 any one, it is characterized in that, described timestamp information is for the RAS of each particular point in time is indicated, corresponding one by one with described RAS.
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Families Citing this family (5)
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|---|---|---|---|---|
| CN103035123B (en) * | 2012-12-25 | 2016-01-20 | 中国科学院深圳先进技术研究院 | Abnormal data acquisition methods and system in a kind of traffic track data |
| CN104035985B (en) * | 2014-05-30 | 2017-07-07 | 同济大学 | A kind of method for digging towards Fundamental Geographic Information System abnormal data |
| CN104951893A (en) * | 2015-06-24 | 2015-09-30 | 银江股份有限公司 | Urban-traffic-oriented method for evaluating road police alarm handling efficiency of traffic polices |
| CN107886723B (en) * | 2017-11-13 | 2021-07-20 | 深圳大学 | Traffic travel survey data processing method |
| CN108428342A (en) * | 2018-05-16 | 2018-08-21 | 北京理工大学 | A kind of vehicle density prediction technique, device and storage medium |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4064044B2 (en) * | 2000-08-29 | 2008-03-19 | 三菱電機株式会社 | Traffic information transmission system, traffic information collection and distribution system, and traffic information collection and distribution method |
| ITTO20020814A1 (en) * | 2002-09-19 | 2004-03-20 | Maurizio Morisio | PROCEDURE AND SYSTEM FOR THE DETECTION AND ESTIMATION OF THE |
| US7366606B2 (en) * | 2004-04-06 | 2008-04-29 | Honda Motor Co., Ltd. | Method for refining traffic flow data |
| CN100456335C (en) * | 2006-10-12 | 2009-01-28 | 华南理工大学 | Visual evaluating method for urban traffic system state based on traffic flow phase character istic and its application |
| CN101373559B (en) * | 2007-08-24 | 2010-08-18 | 同济大学 | Method for evaluating city road net traffic state based on floating vehicle data |
| CN101673460B (en) * | 2009-08-25 | 2011-06-15 | 北京世纪高通科技有限公司 | Traffic information quality evaluation method, device and system therefor |
| CN101976505A (en) * | 2010-10-25 | 2011-02-16 | 中国科学院深圳先进技术研究院 | Traffic evaluation method and system |
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