CN111564036A - Method, device and system for detecting traffic information reliability and storage medium - Google Patents
Method, device and system for detecting traffic information reliability and storage medium Download PDFInfo
- Publication number
- CN111564036A CN111564036A CN202010209584.9A CN202010209584A CN111564036A CN 111564036 A CN111564036 A CN 111564036A CN 202010209584 A CN202010209584 A CN 202010209584A CN 111564036 A CN111564036 A CN 111564036A
- Authority
- CN
- China
- Prior art keywords
- data
- traffic information
- vehicle
- reliability
- determining
- 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
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
Abstract
The invention discloses a method for detecting the reliability of traffic information, which comprises the following steps: acquiring vehicle sample characteristic data, traffic information space stability data and traffic information time stability data within a preset time length; calculating information entropy according to the vehicle sample characteristic data, the traffic information space stability data and the traffic information time stability data; and determining the reliability of the traffic information based on the information entropy. The method is a comprehensive detection method with rich indexes, has strong result reliability, and can quantitatively detect the reliability of traffic information.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a method, a device and a system for detecting traffic information reliability and a storage medium.
Background
With the great increase of the vehicle holding capacity, the problem of traffic jam becomes the problem that large and medium-sized domestic cities need to be straight, and under the background, people develop dynamic traffic information service and have very important functions in the aspects of guiding public trips, managing traffic jam and the like. The accuracy and reliability of dynamic traffic information is critical to the user of the traffic information. For public trip, the wrong traffic information causes unreasonable route planning and selection, wastes the time of travelers, and aggravates social energy loss and pollution emission; for social management, the wrong traffic information will cause that the government cannot fundamentally find the problem of traffic operation, and further invest limited resources to carry out targeted treatment. Therefore, when the traffic information is released, the reliability of the traffic information needs to be detected so as to improve the accuracy and reliability of the traffic information.
At present, a method for detecting the reliability of traffic information mostly adopts a field test method, actual traffic operation parameters of a given road are faithfully tested to be used as a true value, issued traffic information data of corresponding time periods and road sections are compared with the true value to detect the accuracy and reliability of the road, the reliability detection of the traffic information by the field observation method is visual and effective, but the method has the characteristics of small sampling range and difficulty in covering all traffic information data, and the traffic information is difficult to avoid deviation compared with the actual true value due to the subjective cognition of testers and the difference of an information extraction method.
Disclosure of Invention
The embodiment of the disclosure provides a method and a device for detecting traffic information reliability and a storage medium. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In some optional embodiments, a method for detecting traffic information reliability includes:
acquiring vehicle sample characteristic data, traffic information space stability data and traffic information time stability data within a preset time length;
calculating information entropy according to the vehicle sample characteristic data, the traffic information space stability data and the traffic information time stability data;
and determining the reliability of the traffic information based on the information entropy.
Further, the obtaining of the vehicle sample characteristic data within the preset time period includes:
acquiring preset time and the traveled distance data of all vehicles in a preset road, acquiring the length data of the preset road, and determining the vehicle mileage coverage rate data in the vehicle sample characteristic data based on the traveled distance data of the vehicles and the length data of the road;
uniformly dividing a preset road, counting the total length covered by each road vehicle track, and determining vehicle spatial distribution data in the vehicle sample characteristic data based on the total length covered by each road vehicle track;
uniformly dividing preset duration, counting vehicle data in each time slice, and determining vehicle time distribution data in the vehicle sample characteristic data based on the vehicle data in each time slice;
and calculating the average speed of the vehicle passing through a preset road, and determining vehicle speed distribution data in the vehicle sample characteristic data based on the average speed.
Further, the acquiring traffic information spatial stability data within a preset time includes:
searching continuous road sections with consistent traffic trends between upstream and downstream with the current road section as a starting point, and determining traffic information continuous length data in the traffic information space stability data based on the length of the continuous road sections;
calculating the average passing speed of the current road section, respectively calculating the average passing speeds of all downstream road sections of the current road section, respectively calculating the average passing speeds of all upstream road sections of the current road section, and determining upstream and downstream change rate data in the traffic information space stability data based on the average passing speeds of the current road section and all upstream and downstream adjacent road sections.
Further, the acquiring of the time stability data of the traffic information within the preset time duration includes:
calculating the vehicle passing time in all equal-length continuous time intervals of a preset road, sequentially calculating the change rate of the passing time of adjacent intervals, and determining the traffic information change rate data in the traffic information time stability data based on the mean value of all the change rates.
Further, calculating information entropy according to the vehicle sample characteristic data, the traffic information space stability data and the traffic information time stability data, and the method comprises the following steps:
carrying out normalization processing on the data to obtain normalized data;
establishing a detection matrix according to the normalized data;
and calculating the information entropy of each data according to the detection matrix.
Further, determining the reliability of the traffic information based on the information entropy comprises:
determining weights of the vehicle sample characteristic data, the traffic information space stability data and the traffic information time stability data based on the information entropy;
and determining the reliability of the traffic information according to the weight of each data.
Further, determining the reliability of the traffic information based on the information entropy comprises:
determining the reliability of the real-time traffic information based on the information entropy;
and determining the credibility of the historical traffic information based on the information entropy.
In some optional embodiments, a device for detecting the reliability of traffic information includes:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring vehicle sample characteristic data, traffic information space stability data and traffic information time stability data within a preset time length;
the calculation module is used for calculating information entropy according to the vehicle sample characteristic data, the traffic information space stability data and the traffic information time stability data;
and the determining module is used for determining the reliability of the traffic information based on the information entropy.
In some optional embodiments, a system for detecting traffic information reliability includes:
one or more processors, storage devices storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement a method for detecting the reliability of traffic information provided by the above-described embodiments.
In some optional embodiments, a computer-readable storage medium has a computer program stored thereon, and when the computer program is executed by a processor, the method for detecting the reliability of traffic information provided in the above embodiments is implemented.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
the invention provides a method for detecting the reliability of traffic information, which is characterized in that reliability basic detection data of three aspects of vehicle sample characteristics, traffic information space stability and traffic information time stability are obtained, the information entropy of the data is calculated, the reliability of the traffic information is obtained through the information entropy, and the quantitative detection of the reliability of the traffic information is completed. By the method, the traffic information data can be integrally detected, the limitation of field observation or road test methods is broken through, the detected data are rich, diversified detection and quantitative detection of the traffic information are realized, and the method can be applied to historical data detection and traffic law mining.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a flow chart diagram illustrating a method for detecting traffic information confidence level according to an example embodiment;
FIG. 2 is a schematic diagram illustrating the logical relationship of various sensed data, according to an exemplary embodiment;
FIG. 3 is a schematic diagram illustrating a method of calculating vehicle mileage coverage data in accordance with one exemplary embodiment;
FIG. 4 is a schematic diagram illustrating a method of detecting a spatial distribution of a vehicle according to an exemplary embodiment;
FIG. 5 is a schematic illustration of detection of a vehicle spatial distribution according to an exemplary embodiment;
FIG. 6 is a schematic diagram illustrating a method for detecting traffic trends upstream and downstream of traffic information, according to an exemplary embodiment;
FIG. 7 is a schematic diagram illustrating an example of traffic trend detection for upstream and downstream traffic information in accordance with an exemplary embodiment;
FIG. 8 is a schematic diagram illustrating a vehicle range coverage data normalization method according to an exemplary embodiment;
fig. 9 is a schematic structural diagram illustrating a traffic information reliability detection apparatus according to an exemplary embodiment;
fig. 10 is a schematic structural diagram illustrating a traffic information reliability detection system according to an exemplary embodiment.
Detailed Description
So that the manner in which the features and elements of the disclosed embodiments can be understood in detail, a more particular description of the disclosed embodiments, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.
Example 1:
the embodiment of the disclosure provides a method for detecting the reliability of traffic information, and fig. 1 is a schematic flow chart of a method for detecting the reliability of traffic information according to an exemplary embodiment.
In an embodiment of the present disclosure, a method for detecting reliability of traffic information includes:
s101, obtaining vehicle sample characteristic data, traffic information space stability data and traffic information time stability data within a preset time length;
specifically, the vehicle sample characteristic data comprises vehicle mileage coverage data, vehicle spatial distribution data, vehicle time distribution data, and vehicle speed distribution data; the traffic information space stability data comprises traffic information continuous length data and upstream and downstream change rate data; the traffic information time stability data includes traffic information change rate data.
And acquiring the data within the preset time length, selecting the corresponding dates of each week, such as the same time periods of all Mondays, and calculating a detection matrix consisting of the corresponding data, according to the regularity of the road traffic.
Specifically, vehicle mileage coverage rate data within a preset time length is acquired, the preset time length and the mileage data of all vehicles within a preset road are acquired within the preset time length, the length data of the preset road is acquired, and the vehicle mileage coverage rate data is determined based on the mileage data of the vehicles and the length data of the road. And defining the ratio of the sum of the driving mileage of all vehicles in a certain road section to the length of the road section as the vehicle mileage coverage rate of the road section. The formula is as follows:
wherein C is the mileage coverage rate of the vehicles on the road section, n is the number of the vehicles, and lkFig. 3 is a schematic diagram illustrating a calculation method of vehicle range coverage data according to an exemplary embodiment, where the kth vehicle travels on a road, and L is a road length, and a calculation formula of the vehicle range coverage of a road AB in fig. 3 is:
the vehicle mileage coverage rate may reflect the quantitative characteristics of the vehicle trajectory data on the current road, and the higher the value, the more reliable the traffic information thereof.
Specifically, the method for acquiring the vehicle spatial distribution data provided by the invention has the advantages that the reliability of the calculated traffic information is higher if the vehicle is distributed on a road section more uniformly and the vehicle track coverage is more complete, and therefore, the method for acquiring the vehicle spatial distribution data comprises the following steps: FIG. 4 is a schematic diagram illustrating a method for detecting a spatial distribution of a vehicle according to an exemplary embodiment, wherein a road is uniformly divided into n parts as shown in FIG. 4, wherein n is a positive integer greater than or equal to 2 and r is a positive integer respectively1,r2,r3...rnCounting the total length l covered by the vehicle track on each small section of road1,l2,l3...lnObtaining the road data, analyzing the distribution of the coverage lengths by a mathematical statistic method, such as a standard deviation method, and obtaining a mean value l of the coverage lengthsaveThe calculation formula of (1):
vehicle spatial distribution data σcovCalculating the formula:
σcovthe discrete degree of the vehicle track coverage length of each small road segment can be measured, and if the discrete degree is large, the vehicle space distribution is considered to be uneven, and the reliability of road condition information is poor. Fig. 5 is a schematic diagram illustrating a detection of a spatial distribution of vehicles according to an exemplary embodiment, where, as shown in fig. 5, the spatial distribution of vehicle trajectories on a road 1 is better than that on a road 2, and the reliability of traffic information is better than that on the road 2.
Specifically, the vehicle time distribution data is acquired, including the time distribution detection mainly detecting the timeliness of the vehicle data, and the method adopts the following steps:
setting a preset time slice of traffic information to be detected as t,uniformly dividing t into n small slices, wherein n is a positive integer greater than or equal to 2, for example, dividing the slices into one slice per minute, and respectively setting the weight p to be 1,2,. m according to the time increasing sequence, wherein each time slice contains n vehicles1,n2,...nm. The time distribution data for each vehicle can be calculated by weighted averaging:
pavethe timeliness of the whole sample can be detected, and the value of the timeliness is in positive correlation with the timeliness. Sample timeliness is important data for detecting current traffic information.
Specifically, the vehicle speed distribution data is acquired, including acquiring the speed information of the vehicle within a preset time, calculating the average speed of each vehicle passing through a road section, and detecting the speed distribution through a vehicle speed standard deviation:
in the formula, σvAs vehicle speed profile data, viIs the average vehicle speed of the ith vehicle,
the average speed of all vehicles on the current road, and n is the number of vehicles on the current road.
Generally, on the same road, the driving behavior of passing vehicles should be convergent due to the same traffic tendency. If the vehicle behavior difference is large and the speed distribution is relatively discrete, the current road traffic condition is relatively complex and the reliability of the traffic information is relatively low. SigmavCan be used for speed profile detection, which is inversely related to traffic information reliability.
Specifically, the traffic information duration length data is acquired, and includes that the larger the traffic trend coverage area is, the higher the stability is, and the lower the probability of occurrence of drastic changes is, so that a continuous range with the same traffic trend can be acquired through upstream and downstream searching, the range includes the current road segment, the length L of the searched range is used as the traffic information duration length data, and the longer the length is, the higher the reliability is.
Specifically, the method for acquiring the upstream and downstream change rate data includes that fig. 6 is a schematic diagram illustrating a method for detecting the upstream and downstream traffic trends of the traffic information according to an exemplary embodiment, as shown in fig. 6, icAcquiring the average passing speed v of the current road section to be detectedc，{lnext1,lnext2,...lnextnIs equal tocAll the sections of the downstream, n in total, correspond to an average traffic speed of { v }next1,vnext2,...vnextn}；{lpre1,lpre2,...lprenIs equal tocAll the road sections at the upstream, m pieces, have corresponding average traffic speed of { v }pre1,vpre2,...vpren}。
Then define lcThe upstream and downstream rates of change for the road segment are:
in the formula, viIs the average traffic speed, v, of the ith downstream road sectionjIs the average traffic speed for the jth upstream road segment.
FIG. 7 is a schematic diagram illustrating an example of traffic trend detection for upstream and downstream traffic information in accordance with an exemplary embodiment; FIG. 7 shows an example of actual calculation, where the road segments AB, BC, CD are three adjacent road segments, and the average traffic speed is v1、v2、v3. The upstream and downstream speed change rates of the road segment BC are respectively
Calculating the upstream and downstream change rate as detection data:
ravecan be used for detecting the deviation degree, r, of the traffic information of the current road section and the traffic information of the topological adjacent road sectionaveThe reliability of traffic information is inversely related.
Specifically, the traffic information change rate data is acquired, the traffic trend is generally stable, the drastic change rarely occurs, the drastic change generally occurs in a traffic disturbance, such as a minimal range near a traffic accident, and the traffic change trend tends to be gentle in the process of outward diffusion from a disturbance point. Therefore, the reliability of the traffic information of the target road section is detected by time stability through time sequence analysis of the traffic information of the target road section. Taking the time t as an abscissa and taking the passing time t of the target road sectionpassFor the ordinate, the transit time data for n equal-length consecutive time intervals are analyzed as follows:
firstly, the slope k of two adjacent time intervals is sequentially obtained:
then, the traffic information change rate k is obtainedave：
kaveThe variation degree of the road section passing time in a period of time is reflected, and the value is negatively related to the stability of the road condition information.
By the method, vehicle mileage coverage rate data, vehicle space distribution data, vehicle time distribution data, vehicle speed distribution data, traffic information duration data, upstream and downstream change rate data and traffic information change rate data can be acquired.
S102, calculating an information entropy according to the vehicle sample characteristic data, the traffic information space stability data and the traffic information time stability data;
specifically, each data is normalized to obtain normalized data;
generally, because the sum of each data unit is too large, normalization processing needs to be performed on each data, taking vehicle mileage coverage rate data and vehicle speed distribution data as examples:
FIG. 8 is a schematic diagram illustrating a vehicle range coverage data normalization method according to an exemplary embodiment; as shown in fig. 8, the vehicle mileage coverage rate of all the road condition data is obtained and counted to obtain the statistical table, and the vehicle mileage coverage rate C corresponding to 95% of the data points of the road section is obtainedmaxAt the maximum, the data exceeding this value is also denoted as CmaxTaking the minimum value of C as Cmin. And (3) carrying out normalization processing on mileage coverage rate data of all vehicles:
similar processing is performed on the vehicle speed distribution data, but note that the calculation formula should be as follows because the data is negatively correlated with the road condition reliability:
by the method, all the acquired vehicle mileage coverage rate data, vehicle space distribution data, vehicle time distribution data, vehicle speed distribution data, traffic information duration data, upstream and downstream change rate data and traffic information change rate data are normalized to obtain normalized data.
Establishing a detection matrix according to the normalized data;
specifically, FIG. 2 is a schematic diagram illustrating the logical relationship of various sensed data, according to an exemplary embodiment; as shown in fig. 2, all data are preprocessed, and all data in a corresponding time period of a certain road section per week are taken out to construct a detection matrix:
wherein n represents n days in totalCorresponding to the time interval data, for the convenience of representation, the seven data are sequentially marked with X1,X2,...,X7And if so, the matrix is represented as:
further, the seven data are further normalized, and the normalized data are Y1,Y2,...,Y7Then:
and calculating the information entropy of each data according to the detection matrix, specifically, according to the definition of the information entropy, a set of calculation formulas of the information entropy of the data is as follows:
wherein the content of the first and second substances,
if p isijWhen 0, then
According to a calculation formula, calculating the information entropy E of 7 data1,E2,...,E7。
S103, the credibility of the traffic information is determined based on the information entropy.
And calculating the reliability R of the road section through the information entropy:
wherein, WjFor the calculated weight of the jth data, the calculation formula is:
the traffic information credibility R of a certain road section is taken as [0,1], and the closer the value is to 1, the higher the reliability is.
By the method, the traffic information data can be integrally detected, the limitation of field observation or road test methods is broken through, the detected data are rich, diversified detection and quantitative detection of the traffic information are realized, and the method can be applied to historical data detection and traffic law mining.
Further, calculating information entropy according to the vehicle sample characteristic data, the traffic information space stability data and the traffic information time stability data, and the method comprises the following steps:
carrying out normalization processing on the data to obtain normalized data;
establishing a detection matrix according to the normalized data;
and calculating the information entropy of each data according to the detection matrix.
Further, determining the reliability of the traffic information based on the information entropy comprises:
determining weights of the vehicle sample characteristic data, the traffic information space stability data and the traffic information time stability data based on the information entropy;
and determining the reliability of the traffic information according to the weight of each data.
And calculating the reliability R of the road section through the information entropy:
wherein, WjFor the calculated weight of the jth data, the calculation formula is:
the traffic information credibility R of a certain road section is taken as [0,1], and the closer the value is to 1, the higher the reliability is.
Further, determining the reliability of the traffic information based on the information entropy comprises:
determining the reliability of the real-time traffic information based on the information entropy;
and determining the credibility of the historical traffic information based on the information entropy.
The method for detecting the reliability of the traffic information disclosed by the invention can be applied to two scenes: and (3) real-time detection, namely, reliability detection of the current traffic information is given in real time based on the characteristics of the vehicle sample, the time stability of the traffic information and the space stability of the traffic information. And historical detection, namely, for historical data in a certain space-time range, reliability detection is given by analyzing the time stability and the space stability of the historical data.
Example 2:
the embodiment of the present disclosure provides a device for detecting the reliability of traffic information, and fig. 9 is a schematic structural diagram of a device for detecting the reliability of traffic information according to an exemplary embodiment.
In an embodiment of the present disclosure, a device for detecting reliability of traffic information includes:
the system comprises an S901 acquisition module, a traffic information space stability module and a traffic information time stability module, wherein the S901 acquisition module is used for acquiring vehicle sample characteristic data, traffic information space stability data and traffic information time stability data within a preset time length;
the S902 computing module is used for computing information entropy according to the vehicle sample characteristic data, the traffic information space stability data and the traffic information time stability data;
and S903 determining module for determining the reliability of the traffic information based on the information entropy.
The device for detecting the traffic information reliability can detect the whole traffic information data, breaks through the limitation of field observation or road test methods, has rich detection data, realizes diversified detection and quantitative detection of the traffic information, and can be applied to historical data detection and traffic law mining.
Example 3:
the embodiment of the present disclosure provides a system for detecting the reliability of traffic information, and fig. 10 is a schematic structural diagram of a system for detecting the reliability of traffic information according to an exemplary embodiment.
In the embodiment of the present disclosure, a system for detecting the reliability of traffic information includes a processor 101 and a memory 102 storing program instructions, and may further include a communication interface 103 and a bus 104. The processor 101, the communication interface 103, and the memory 102 may communicate with each other via the bus 104. The communication interface 103 may be used for information transfer. The processor 101 may call logic instructions in the memory 102 to execute the method for detecting the reliability of traffic information provided by the above-described embodiments.
Furthermore, the logic instructions in the memory 102 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products.
The memory 102 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 101 executes the functional application and data processing by executing the software program, instructions and modules stored in the memory 102, that is, implements the method in the above-described method embodiments.
The memory 102 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal device, and the like. Further, the memory 102 may include high speed random access memory and may also include non-volatile memory.
Example 4:
the embodiment of the disclosure provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor, and implements the method for detecting the reliability of traffic information provided in the above embodiment.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for detecting the reliability of traffic information is characterized by comprising the following steps:
acquiring vehicle sample characteristic data, traffic information space stability data and traffic information time stability data within a preset time length;
calculating information entropy according to the vehicle sample characteristic data, the traffic information space stability data and the traffic information time stability data;
and determining the reliability of the traffic information based on the information entropy.
2. The method of claim 1, wherein the obtaining of the vehicle sample characteristic data within the preset time period comprises:
acquiring preset time and the traveled distance data of all vehicles in a preset road, acquiring the length data of the preset road, and determining the vehicle mileage coverage rate data in the vehicle sample characteristic data based on the traveled distance data of the vehicles and the length data of the road;
uniformly dividing a preset road, counting the total length covered by each road vehicle track, and determining vehicle spatial distribution data in the vehicle sample characteristic data based on the total length covered by each road vehicle track;
uniformly dividing preset duration, counting vehicle data in each time slice, and determining vehicle time distribution data in the vehicle sample characteristic data based on the vehicle data in each time slice;
and calculating the average speed of the vehicle passing through a preset road, and determining vehicle speed distribution data in the vehicle sample characteristic data based on the average speed.
3. The method according to claim 1, wherein the obtaining of the traffic information spatial stability data within a preset time period comprises:
searching continuous road sections with consistent traffic trends between upstream and downstream with the current road section as a starting point, and determining traffic information continuous length data in the traffic information space stability data based on the length of the continuous road sections;
calculating the average passing speed of the current road section, respectively calculating the average passing speeds of all downstream road sections of the current road section, respectively calculating the average passing speeds of all upstream road sections of the current road section, and determining upstream and downstream change rate data in the traffic information space stability data based on the average passing speeds of the current road section and all upstream and downstream adjacent road sections.
4. The method according to claim 1, wherein the obtaining of the time stability data of the traffic information within a preset time period comprises:
calculating the vehicle passing time in all equal-length continuous time intervals of a preset road, sequentially calculating the change rate of the passing time of adjacent intervals, and determining the traffic information change rate data in the traffic information time stability data based on the mean value of all the change rates.
5. The method of claim 1, wherein the calculating information entropy from the vehicle sample characterization data, traffic information spatial stability data, and traffic information temporal stability data comprises:
carrying out normalization processing on the data to obtain normalized data;
establishing a detection matrix according to the normalized data;
and calculating the information entropy of each data according to the detection matrix.
6. The method of claim 1, wherein determining the confidence level of the traffic information based on the information entropy comprises:
determining weights of the vehicle sample characteristic data, the traffic information spatial stability data and the traffic information temporal stability data based on the information entropy;
and determining the reliability of the traffic information according to the weight of each data.
7. The method of claim 1, wherein determining the confidence level of the traffic information based on the information entropy comprises:
determining the reliability of the real-time traffic information based on the information entropy;
and determining the credibility of the historical traffic information based on the information entropy.
8. A device for detecting the reliability of traffic information, comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring vehicle sample characteristic data, traffic information space stability data and traffic information time stability data within a preset time length;
the calculation module is used for calculating information entropy according to the vehicle sample characteristic data, the traffic information space stability data and the traffic information time stability data;
and the determining module is used for determining the reliability of the traffic information based on the information entropy.
9. A system for detecting the reliability of traffic information, comprising:
one or more processors, storage devices storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement a method for detecting traffic information reliability according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements a method for detecting a traffic information reliability according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010209584.9A CN111564036B (en) | 2020-03-23 | 2020-03-23 | Method, device and system for detecting traffic information reliability and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010209584.9A CN111564036B (en) | 2020-03-23 | 2020-03-23 | Method, device and system for detecting traffic information reliability and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111564036A true CN111564036A (en) | 2020-08-21 |
| CN111564036B CN111564036B (en) | 2021-06-15 |
Family
ID=72074131
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010209584.9A Active CN111564036B (en) | 2020-03-23 | 2020-03-23 | Method, device and system for detecting traffic information reliability and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111564036B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112270840A (en) * | 2020-10-23 | 2021-01-26 | 清华大学 | Information credible identification method based on single-vehicle motion characteristics under vehicle-road cooperative environment |
| CN114510997A (en) * | 2021-12-30 | 2022-05-17 | 北京掌行通信息技术有限公司 | Toll station passing mode identification method and system |
| CN116756205A (en) * | 2023-05-12 | 2023-09-15 | 北京建筑大学 | Driving cycle-oriented subdivision speed VKT and VHT distribution construction method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101360053A (en) * | 2008-09-08 | 2009-02-04 | 华南理工大学 | Route interference impact metering method based on information entropy |
| CN101364345A (en) * | 2008-09-25 | 2009-02-11 | 北京航天智通科技有限公司 | Real-time dynamic information processing method based on car detecting technique |
| KR20090094973A (en) * | 2008-03-04 | 2009-09-09 | 서울여자대학교 산학협력단 | Traffic information data frame, apparatus and method producing for the same |
| CN104392094A (en) * | 2014-10-17 | 2015-03-04 | 北京航空航天大学 | Reliability evaluation method of urban road network based on data of floating vehicles |
| CN104484996A (en) * | 2014-12-18 | 2015-04-01 | 江苏省交通规划设计院股份有限公司 | Road segment traffic state distinguishing method based on multi-source data |
| CN107507271A (en) * | 2017-08-09 | 2017-12-22 | 交通运输部科学研究院 | Traffic index drives highway congestion scene simulation and projection sand table methods of exhibiting |
-
2020
- 2020-03-23 CN CN202010209584.9A patent/CN111564036B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20090094973A (en) * | 2008-03-04 | 2009-09-09 | 서울여자대학교 산학협력단 | Traffic information data frame, apparatus and method producing for the same |
| CN101360053A (en) * | 2008-09-08 | 2009-02-04 | 华南理工大学 | Route interference impact metering method based on information entropy |
| CN101364345A (en) * | 2008-09-25 | 2009-02-11 | 北京航天智通科技有限公司 | Real-time dynamic information processing method based on car detecting technique |
| CN104392094A (en) * | 2014-10-17 | 2015-03-04 | 北京航空航天大学 | Reliability evaluation method of urban road network based on data of floating vehicles |
| CN104484996A (en) * | 2014-12-18 | 2015-04-01 | 江苏省交通规划设计院股份有限公司 | Road segment traffic state distinguishing method based on multi-source data |
| CN107507271A (en) * | 2017-08-09 | 2017-12-22 | 交通运输部科学研究院 | Traffic index drives highway congestion scene simulation and projection sand table methods of exhibiting |
Non-Patent Citations (1)
| Title |
|---|
| 周礼节: "大城市中心区交通系统可靠性评价", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112270840A (en) * | 2020-10-23 | 2021-01-26 | 清华大学 | Information credible identification method based on single-vehicle motion characteristics under vehicle-road cooperative environment |
| CN112270840B (en) * | 2020-10-23 | 2021-09-10 | 清华大学 | Information credible identification method based on single-vehicle motion characteristics under vehicle-road cooperative environment |
| CN114510997A (en) * | 2021-12-30 | 2022-05-17 | 北京掌行通信息技术有限公司 | Toll station passing mode identification method and system |
| CN116756205A (en) * | 2023-05-12 | 2023-09-15 | 北京建筑大学 | Driving cycle-oriented subdivision speed VKT and VHT distribution construction method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111564036B (en) | 2021-06-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111564036B (en) | Method, device and system for detecting traffic information reliability and storage medium | |
| CN104751631B (en) | The method that Trip chain mode of transportation is judged based on GPS location and fuzzy theory | |
| Zhang et al. | Understanding taxi service strategies from taxi GPS traces | |
| CN109643485A (en) | A kind of urban highway traffic method for detecting abnormality | |
| US9508257B2 (en) | Road detection logic | |
| CN108198425A (en) | A kind of construction method of Electric Vehicles Driving Cycle | |
| Chen et al. | Clustering vehicle temporal and spatial travel behavior using license plate recognition data | |
| CN108345666A (en) | A kind of vehicle abnormality track-detecting method based on time-space isolated point | |
| CN106530692A (en) | Motor vehicle fuel consumption emission intensity calculating method based on traffic jam evaluation index | |
| CN106845768A (en) | Bus hourage model building method based on survival analysis parameter distribution | |
| CN113988441A (en) | Power wireless network link quality prediction and model training method and device | |
| Cagliero et al. | Modeling correlations among air pollution-related data through generalized association rules | |
| CN109921938A (en) | Fault detection method under a kind of cloud computing environment | |
| Yao et al. | Analyzing the effects of rainfall on urban traffic-congestion bottlenecks | |
| CN110827537B (en) | Method, device and equipment for setting tidal lane | |
| CN109272760B (en) | Online detection method for abnormal data value of SCATS system detector | |
| Yang et al. | Prediction of traffic accident severity based on random forest | |
| CN108053646B (en) | Traffic characteristic obtaining method, traffic characteristic prediction method and traffic characteristic prediction system based on time sensitive characteristics | |
| CN111461460B (en) | Police service inspection route planning method and personnel inspection method | |
| CN114419894B (en) | Method and system for setting and monitoring parking positions in road | |
| Hossain et al. | Modelling road traffic congestion from trajectories | |
| CN114518988A (en) | Resource capacity system, method of controlling the same, and computer-readable storage medium | |
| CN112309129A (en) | OD matrix forming method for vehicle travel characteristic classification based on checkpoint license plate recognition | |
| Wang et al. | Travel time prediction: Empirical analysis of missing data issues for advanced traveler information system applications | |
| CN111292139A (en) | Anti-cheating method based on time-frequency analysis |
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 |