CN107452207B - Floating car data source evaluation method, device and system - Google Patents
Floating car data source evaluation method, device and system Download PDFInfo
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- CN107452207B CN107452207B CN201610383868.3A CN201610383868A CN107452207B CN 107452207 B CN107452207 B CN 107452207B CN 201610383868 A CN201610383868 A CN 201610383868A CN 107452207 B CN107452207 B CN 107452207B
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Abstract
The application provides a floating car data source evaluation method, device and system, wherein the method comprises the following steps: acquiring floating car data from a floating car data source; obtaining index information aiming at the floating car data source according to the floating car data, wherein the index information comprises car number information, record number information, data return cycle information, data integrity and effectiveness information, data coverage rate information, data matching rate information and matched effective car number information; evaluating the floating car data source based on the one or more indicator information. By the method, the evaluation index and the standard of the quality of the floating car data source are provided, so that real valuable data can be accessed conveniently, and a basic data source is provided for real-time traffic accuracy.
Description
Technical Field
The application relates to the technical field of intelligent transportation, in particular to a floating car data source evaluation method, device and system.
Background
In the intelligent transportation field, the real-time and dynamic traffic information service can provide convenient travel routes and traffic guidance for people's travel, thereby achieving the purposes of saving travel time, improving time utilization rate, saving energy consumption and the like.
The floating car technology is one of means for storing road traffic information in an international intelligent traffic system in recent years, and the basic principle is as follows: according to the information of the number, the timestamp, the position coordinate, the direction, the speed, the state and the like of the vehicle regularly recorded by the floating vehicle equipped with the vehicle-mounted global positioning in the driving process of the floating vehicle, the processing is carried out by applying relevant calculation models and algorithms such as map matching, path estimation and the like, so that the position data of the floating vehicle is associated with the urban road in time and space, and finally the information of the vehicle driving speed of the road through which the floating vehicle passes, the driving travel time of the road, the road congestion program and the like is obtained. If enough floating vehicles are deployed in a city, the position data of the floating vehicles are transmitted to a floating vehicle information processing module periodically and in real time through a wireless communication system, and the floating vehicle information processing module comprehensively processes the position data, the dynamic and real-time traffic jam information of the whole city can be obtained.
The floating car data is a basic source of real-time traffic flow data, the data quality provided by different data sources is different, and the acquisition frequency, the road coverage rate and the like are different. Whether a data source has an access value and which indexes need to be met is a problem to be paid urgent attention in the field.
Disclosure of Invention
One of the technical problems to be solved by the application is to provide a method, a device and a system for evaluating a floating car data source, wherein reference basis is provided for a floating car information processing module through evaluation of the floating car data source, so that traffic information processing efficiency is improved.
According to one embodiment of the application, a floating car data source evaluation method is provided, and the method comprises the following steps: acquiring floating car data from a floating car data source; obtaining index information aiming at the floating car data source according to the floating car data, wherein the index information comprises car number information, record number information, data return cycle information, data integrity and effectiveness information, data coverage rate information, data matching rate information and matched effective car number information; evaluating the floating car data source based on the one or more indicator information.
According to another embodiment of the present application, there is provided a floating car data source evaluation apparatus, including: the data acquisition unit is used for acquiring floating car data from a floating car data source; the index information calculation unit is used for obtaining index information aiming at the floating car data source according to the floating car data, and the index information comprises car number information, record number information, data return cycle information, data integrity and effectiveness information, data coverage rate information, data matching rate information and matched effective car number information; and the data source evaluation unit is used for evaluating the floating car data source based on the one or more index information.
By adopting the method, before the data of the floating car is accessed into the information processing module of the floating car, the quality of the data source of the floating car is evaluated and standardized, the quality of the data source can be evaluated before the data is accessed, and reference is provided for the information processing module of the floating car. For example, the floating car information processing module can be used for comparing the existing data quality and coverage rate, preferentially accessing a road with weak coverage at present by real-time traffic, or replacing the original data source with low data quality, so as to provide basic guarantee for high-precision real-time road conditions. The invention provides the evaluation index and standard of the quality of the floating car data source, is convenient to access the real valuable data, and provides a basic data source for real-time traffic accuracy.
It will be appreciated by those of ordinary skill in the art that although the following detailed description will proceed with reference being made to illustrative embodiments, the present application is not intended to be limited to these embodiments. Rather, the scope of the application is broad and is intended to be defined only by the claims that follow.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a flow chart of a floating car data source evaluation method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a floating car data source evaluation device according to an embodiment of the present application.
It will be appreciated by those of ordinary skill in the art that although the following detailed description will proceed with reference being made to illustrative embodiments, the present application is not intended to be limited to these embodiments. Rather, the scope of the application is broad and is intended to be defined only by the claims that follow.
Detailed Description
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel, concurrently, or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
The computer equipment comprises user equipment and network equipment. Wherein the user equipment includes but is not limited to computers, smart phones, PDAs, etc.; the network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of computers or network servers, wherein Cloud Computing is one of distributed Computing, a super virtual computer consisting of a collection of loosely coupled computers. The computer equipment can be independently operated to realize the application, and can also be accessed into a network to realize the application through the interactive operation with other computer equipment in the network. The network in which the computer device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a VPN network, and the like.
It should be noted that the user equipment, the network device, the network, etc. are only examples, and other existing or future computer devices or networks may also be included in the scope of the present application, if applicable, and are included by reference.
The methods discussed below, some of which are illustrated by flow diagrams, may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine or computer readable medium such as a storage medium. The processor(s) may perform the necessary tasks.
Specific structural and functional details disclosed herein are merely representative and are provided for purposes of describing example embodiments of the present application. This application may, however, be embodied in many alternate forms and should not be construed as limited to only the embodiments set forth herein.
It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element may be termed a second element, and, similarly, a second element may be termed a first element, without departing from the scope of example embodiments. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements present. Other words used to describe the relationship between elements (e.g., "between" versus "directly between", "adjacent" versus "directly adjacent to", etc.) should be interpreted in a similar manner.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be noted that, in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed substantially concurrently, or the figures may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
The technical solution of the present application is further described in detail below with reference to the accompanying drawings.
Fig. 1 is a flow chart of a floating car data source evaluation method for evaluating a floating car data source prior to accessing the floating car data to a floating car information processing module, thereby providing a reference to the floating car information processing module for selecting valid data access to the system, according to an embodiment of the present application. The method of the embodiment mainly comprises the following steps:
s101: acquiring floating car data from a floating car data source;
s102: obtaining index information aiming at a floating car data source according to the floating car data, wherein the index information comprises car number information, record number information, data return cycle information, data integrity and effectiveness information, data coverage rate information and data matching rate information;
s103: evaluating the floating car data source based on the one or more indicator information.
For a further understanding of the present application, the following schemes are described in further detail.
In order to ensure the accuracy of relevant information such as vehicle running speed, road running time, road congestion degree and the like, before the floating car information processing module processes floating car data in real time, the effectiveness of the floating car data to be processed needs to be evaluated, the accuracy and the continuity of the floating car data to be processed in space and time are evaluated, effective input data are provided for real-time processing, and the accuracy of the calculated vehicle running speed, the road running time and the like is ensured. The existing floating car information processing module processes floating car data only by performing data preprocessing operation before map matching, the data preprocessing only eliminates local abnormal data such as longitude and latitude errors, vehicle information appearing only once in an acquisition period and the like, and then relevant calculation models and algorithms such as map matching, path speculation and the like are applied for processing to obtain dynamic and real-time traffic jam information of the whole city.
However, the inventor of the present application finds that, in practical situations, different data sources provide different data qualities, and different acquisition frequencies, road coverage rates, and the like. Whether one data source has access value and which indexes need to be met is effectively evaluated before the floating car information processing module is accessed, so that reference is provided for subsequent processing of the floating car information processing module.
The floating car data source refers to a source for acquiring floating car data by deploying a certain scale of floating cars. The floating car generally refers to a bus, a taxi or other vehicles which are provided with a vehicle-mounted GPS positioning device and run on a main road of a city. For example, a taxi company may be a floating car data source that provides floating car data for a certain number of taxis; alternatively, a bus company may also be a floating car data source that provides floating car data for a certain number of buses; as another example, a truck company may also be a floating car data source that provides floating car data for a number of trucks.
Different floating car data sources are suitable for different road traffic information analysis. For example, a taxi floating car data source which often runs on a city center road is more suitable for analyzing city center road traffic information and is not suitable for analyzing highway traffic information around a city. Similarly, the data source of the floating truck which often runs on the highway is more suitable for analyzing the traffic information of the highway around the city but not suitable for analyzing the traffic information of the road in the center of the city. For another example, there may be multiple floating car data sources for the same taxi, and if A, B, C floating car data sources for three taxies exist, and there is a significant difference in the number of floating cars deployed in A, B, C and the data quality of the floating cars, it is necessary to evaluate and discriminate the three floating car data sources, so as to select a better data source for use. It can be seen that for different types of floating car data sources and similar but different characteristics floating car data sources, evaluation is necessary to provide references for data processing for subsequent floating car information processing modules.
In step S101, floating car data may be acquired from at least one floating car data source. Where floating car data is obtained from multiple floating car data sources on a contract, for example, with 5 floating car data sources on a contract, then floating car data is obtained from the 5 floating car data sources. As previously described, each floating car data source in turn deploys a number of floating cars. Thus, the floating car data of a floating car data source refers to the data sent by all floating cars deployed by the floating car data source. Specifically, floating car data can be obtained from each floating car of the floating car data source according to a FCD (floating collection data, floating car collection data protocol).
Since FCD data can be simply considered as generalized GPS receiver data, its data format is relatively similar to the commonly used GPS receiver data format. For example, the data format includes the following fields: unique identification of the vehicle, acquisition time, longitude and latitude coordinates, reliability indexes of GPS coordinates, instant azimuth angle of the GPS, instant speed of the GPS, instant running state of the vehicle and the like.
FCD data acquisition and analysis are well established techniques in the art and are not described in detail herein. The invention carries out integrity analysis on the acquired FCD data of a floating car data source, thereby carrying out effectiveness evaluation on the floating car data source.
Index information needs to be obtained in advance in order to perform overall evaluation on the effectiveness of the floating car data source. Specifically, in step S102, obtaining index information of a floating car data source according to floating car data includes: the data processing method comprises the following steps of vehicle number information, record number information, data returning period information, data integrity and effectiveness information, data coverage rate information and data matching rate information. In specific operation, the FCD data received from a certain data source is analyzed and analyzed, and the numerical value of each index is calculated and recorded.
The calculation process of each index information will be described in detail below.
1. Vehicle number information
The process of obtaining the vehicle number information comprises the following steps: firstly, analyzing FCD data according to a data source uploading protocol to obtain a vehicle ID identification; then, the number of different vehicle IDs is calculated to be the number of vehicles of the data source.
In the subsequent evaluation, the quality reference of the index is as follows: the higher the number of vehicles, the better.
2. Recording number information
The process of obtaining the record number information is as follows: firstly, analyzing FCD data to obtain GPS points according to a data source uploading protocol, and then determining the total number of the GPS points as the total record number of the data source.
In the subsequent evaluation, the quality reference of the index is as follows: the higher the number of recordings, the better.
3. Data return cycle information
In the embodiment of the present invention, the data backhaul period includes an average backhaul period and a reasonable backhaul period ratio, that is, the data backhaul period includes two layers. Therefore, obtaining the data backhaul period information includes: and obtaining average return period information and reasonable return period proportion information.
3.1 averaging of backhaul period information
Since FCD data contains only one sample point per return packet, the return cycle is averaged
The period is equal to the average acquisition frequency. As can be seen, the process of obtaining the average backhaul period information is as follows: obtaining an average acquisition frequency; and determining the average acquisition frequency as the average return period.
In subsequent evaluations, the quality reference for the average backhaul period was: the average backhaul period is required to be less than the average backhaul period threshold, e.g., the average backhaul period is less than or equal to 120 s.
3.2 reasonable feedback of periodic proportional information
The process of obtaining the reasonable return period proportion information is as follows:
firstly, aiming at FCD data of the same vehicle (namely, FCD unique identifiers are the same), calculating a collection time difference value between any two continuous sample points under the condition of strictly ensuring the time sequence of the FCD sample points;
then, counting the occupation ratio of the return period in a section of the longest return period threshold (for example, 180s) and a predetermined unit time (for example, 10s) by taking the vehicle as a unit, wherein the occupation ratio in each section is counted, and more than the longest return period threshold (for example, 180s) is one section;
finally, the vehicle number fraction of the single vehicle pass back period within the optimal pass back period threshold (e.g., 60s) is determined to be a reasonable pass back period.
In subsequent evaluations, the quality references for reasonable backhaul period ratios were: the reasonable backhaul period ratio is greater than or equal to the minimum backhaul period ratio threshold, e.g., the reasonable backhaul period ratio is greater than or equal to 80%.
For example, the preset quality detail rating criteria are as follows:
a: the backhaul period is greater than or equal to the backhaul period minimum ratio threshold within the optimal backhaul period threshold, e.g., more than 80% of the backhaul period within 60 s;
b: the backhaul period is greater than or equal to the backhaul period minimum ratio threshold within the suboptimal backhaul period threshold, e.g., more than 80% of the backhaul period within 90 s;
c: the backhaul period is greater than or equal to the backhaul period minimum ratio threshold within the average backhaul period threshold, e.g., greater than 80% of the backhaul period within 120 s;
d: the backhaul period is less than the minimum backhaul period percentage threshold within the average backhaul period threshold, e.g., less than 80% of the backhaul period within 120 s.
Wherein the optimal backhaul period threshold < the suboptimal backhaul period threshold < the average backhaul period threshold < the longest backhaul period threshold, as in the above example, the optimal backhaul period threshold is 60s, the suboptimal backhaul period threshold is 90s, the average backhaul period threshold is 120s, and the longest backhaul period threshold is 180 s. It is to be understood that the values of the optimal backhaul period threshold, the sub-optimal backhaul period threshold, the average backhaul period threshold, the longest backhaul period threshold, and the backhaul period minimum ratio threshold are not limited to the above examples, but may be set and adjusted according to the actual situation.
4. Data integrity & validity information
In an embodiment of the present invention, the integrity and validity information includes: abnormal point proportion, GPS point delay rate, repeat point proportion, direction angle accuracy rate and/or instant speed accuracy rate, therefore, the process of obtaining data integrity and validity information is as follows: and calculating and counting the abnormal point proportion, the GPS point delay rate, the repeat point proportion, the direction angle accuracy rate and/or the instant speed accuracy rate of the floating car data source, thereby completing the calculation and counting of the integrity and the effectiveness of the floating car data source.
4.1 abnormal Point proportion information
The process of obtaining the abnormal point proportion information comprises the following steps:
first, integrity information is determined
The process of obtaining the integrity information is as follows: and counting whether the FCD data issued by the data source has an item missing condition. For example, FCD data items include: unique identification of the vehicle, acquisition time, longitude and latitude coordinates, reliability indexes of GPS coordinates, instant azimuth angle of the GPS, instant speed of the GPS, instant running state of the vehicle and the like.
Secondly, validity information is determined
The process of obtaining the validity information comprises the following steps: under the condition that a certain field has a numerical value, judging whether the value is reasonable or not, whether the value is in an effective range or not, and whether the value meets the preset specification or not; judging whether the acquisition time accords with a time format or not and whether the GPS time has time delay (see the GPS point time delay rate in detail); judging whether the GPS instant azimuth angle is within the range of 0-360 degrees; and judging whether the GPS instant speed is in a reasonable interval of 0km/h-200 km/h.
In the subsequent evaluation, the quality references for the proportion of abnormal points were: the proportion of outliers is less than or equal to the average threshold of the proportion of outliers, e.g., the proportion of outliers is ≦ 20%.
Quality detail rating criteria may be preset, such as:
a: the proportion of the abnormal points is smaller than the minimum threshold value of the abnormal proportion, for example, the proportion of the abnormal points is between 0 and 5 percent;
b: the abnormal point proportion is between the abnormal proportion minimum threshold value and the abnormal proportion average threshold value, for example, the abnormal point proportion is between 5% and 20%;
c: the abnormal point proportion is between the average abnormal proportion threshold value and the highest abnormal proportion threshold value, for example, the abnormal point proportion is between 20% and 30%;
d: the abnormal point proportion is larger than the maximum abnormal proportion threshold value, for example, the abnormal point proportion is larger than 30%.
Here, the abnormality ratio minimum threshold value < abnormality ratio average threshold value < abnormality ratio maximum threshold value, and in the above example, the abnormality ratio minimum threshold value is 5%, the abnormality ratio average threshold value is 20%, and the abnormality ratio maximum threshold value is 30%. It is to be understood that the values of the abnormality ratio minimum threshold, the abnormality ratio average threshold, and the abnormality ratio maximum threshold are not limited to the above examples, but may be set and adjusted according to actual situations.
4.2GPS Point delay Rate information
The process of obtaining the GPS point delay rate information comprises the following steps:
first, a delay calculation is performed, for example, the delay calculation is system time — acquisition time;
secondly, setting each negative delay interval and each positive delay interval and counting the number of each delay interval: for example, the delay interval includes: -180 seconds or more, (-180 seconds, -120 seconds), (-120 seconds, -60 seconds), (-60 seconds, 0), [0,60 seconds ], [60 seconds, 120 seconds ], [120 seconds, 180 seconds ], [180 seconds, 240 seconds ], [240 seconds, 300 seconds ], [300 seconds, 360 seconds ], [360 seconds, 420 seconds ], [420 seconds, 480 seconds ], 480 seconds or more, etc.;
finally, all dot fractions except the positive delay [0,60 seconds ] are counted.
In the subsequent evaluation, the quality reference of the GPS point delay rate is: the GPS point delay rate is less than or equal to the average delay rate threshold, for example, the GPS point delay rate is less than or equal to 20%.
Quality detail rating criteria may be preset, such as:
a: the GPS point delay rate is less than or equal to the minimum delay rate threshold, for example, the GPS point delay rate is between 0% and 5%;
b: the GPS point delay rate is between the minimum delay rate threshold and the average delay rate threshold, for example, the GPS point delay rate is between 5% and 20%;
c: the GPS point delay rate is between the average delay rate threshold and the highest delay rate threshold, for example, the GPS point delay rate is between 20% and 30%;
d: the GPS point delay rate is larger than the maximum delay rate threshold, for example, the GPS point delay rate is larger than 30%.
Wherein, the minimum delay threshold value < the average delay threshold value < the maximum delay threshold value, as in the above example, the minimum delay threshold value is 5%, the average delay threshold value is 20%, and the maximum delay threshold value is 30%. It is to be understood that the values of the delay rate minimum threshold, the delay rate average threshold, and the delay rate maximum threshold are not limited to the above examples, but may be set and adjusted according to actual situations.
4.3 repeat Point proportion information
The process of obtaining the ratio information of the repetition points comprises the following steps: and (4) regarding the same data source, taking the vehicle ID and the time which are completely the same as each other as a repetition point, and counting the occupation ratio of the data source in the total sample.
In subsequent evaluations, the quality references for the proportion of repeat points were: the proportion of the repetition points is less than or equal to the average threshold value of the proportion of the repetition points, for example, the proportion of the repetition points is less than or equal to 20 percent.
Quality detail rating criteria may be preset, such as:
a: the repetition point ratio is less than or equal to the minimum threshold of the repetition point ratio, for example, the repetition point ratio is between 0% and 5%;
b: the repeat point ratio is between a repeat point ratio minimum threshold and a repeat point ratio average threshold, e.g.,
the ratio of the repetition points is between 5 and 20 percent;
c: the proportion of the repetition points is between the average threshold value of the proportion of the repetition points and the maximum threshold value of the proportion of the repetition points, for example, the proportion of the repetition points is between 20 percent and 40 percent;
d: the proportion of the repetition points is larger than the maximum threshold value of the proportion of the repetition points, for example, the proportion of the repetition points is larger than 30%.
Here, the minimum threshold value of the repetition point ratio < the average threshold value of the repetition point ratio < the maximum threshold value of the repetition point ratio, in the above example, the minimum threshold value of the repetition point ratio is 5%, the average threshold value of the repetition point ratio is 20%, and the maximum threshold value of the repetition point ratio is 30%. It is to be understood that the values of the repetition point ratio lowest threshold, the repetition point ratio average threshold, and the repetition point ratio highest threshold are not limited to the above examples, but may be set and adjusted according to actual circumstances.
4.4 Direction Angle accuracy information
The process of obtaining the direction angle accuracy information comprises the following steps:
firstly, counting the number of FCD sample points corresponding to different azimuth angle values in a time period, thereby obtaining the distribution condition of the azimuth angle;
secondly, determining a calculation method: calculating azimuth angles by taking the line segments (A, B) as directions based on FCD data sequences of the same vehicle and two A and B which are continuously adjacent (A is before B); note: the azimuth angle is taken as the azimuth angle of the point A, the clockwise direction is taken as the positive direction, and the positive north direction is taken as 0 degree;
then, a plurality of error sections are set and the number of vehicles in the error sections is counted, for example, the set error sections include: -30% or more, -15%, [ -15%, + 15% ], (+ 15%, + 30%, (+ 30%), etc.;
finally, the vehicle fraction with statistical error at [ -15%, + 15% ].
In subsequent evaluations, the quality references for the azimuth accuracy were: the heading angle accuracy is greater than or equal to a heading angle accuracy average threshold, e.g., the heading angle accuracy is greater than or equal to 70%.
Quality detail rating criteria may be preset, such as:
a: the proportion of the direction angle error in the minimum error interval is greater than or equal to the direction angle accuracy optimal threshold, for example, the direction angle error [ -15%, + 15% ] accounts for more than 80%;
b: the proportion of the direction angle error within the minimum error interval is between the direction angle accuracy average threshold and the direction angle accuracy optimum threshold, for example, the direction angle error [ -15%, + 15% ] accounts for 70% -80%;
c: the proportion of the direction angle error in the maximum error interval is less than or equal to the direction angle accuracy worst threshold value, for example, the direction angle error [ -30%, + 30% ] accounts for less than 60%;
d: the ratio of the direction angle error within the maximum error interval is greater than or equal to a direction angle accuracy worst threshold, for example, the direction angle error [ -30%, + 30% ] accounts for 60% or more.
In the above example, the maximum error range [ -30%, + 30% ] and the minimum error range [ -15%, + 15% ] are set; also, the direction angle accuracy worst threshold value < the direction angle accuracy average threshold value < the direction angle accuracy optimum threshold value, as in the above example, the direction angle accuracy worst threshold value is 60%, the direction angle accuracy average threshold value is 70%, and the direction angle accuracy optimum threshold value is 80%. It is to be understood that the values of the direction angle accuracy worst threshold value, the direction angle accuracy average threshold value, and the direction angle accuracy optimum threshold value are not limited to the above examples, but may be set and adjusted according to actual situations.
4.5 instant speed accuracy information
The process of obtaining the instant speed accuracy information is as follows:
first, the velocity is calculated, for example: based on the FCD data sequence of the same vehicle, continuously projecting points A and B to a LINK, calculating the road distance between the projected points A and B, and dividing the road distance by the time difference between the points A and B to obtain the calculated speed, wherein the points A and B are two adjacent points A and B (A is before B); note: the speed is used as the calculated speed of the point A;
next, the ratio of the instantaneous speed error to the positive and negative distance thresholds (5km) is determined as the quality reference. The instant speed accuracy is greater than or equal to an instant speed accuracy average threshold, e.g., the instant speed accuracy is greater than or equal to 70%.
In subsequent evaluations, the quality references for instantaneous velocity accuracy were: the instant speed accuracy rate is more than or equal to 70 percent.
Quality detail rating criteria may be preset. For example:
a: the instant speed accuracy is greater than or equal to the maximum instant speed accuracy threshold, e.g., the instant speed accuracy is above 80%;
b: the instant speed accuracy is between the average instant speed accuracy threshold and the highest instant speed accuracy threshold, for example, the instant speed accuracy is 70-80%;
c: the instant speed accuracy is between the instant speed accuracy minimum threshold and the instant speed accuracy average threshold, for example, the instant speed accuracy is 60-70%;
d: the instant speed accuracy is less than the instant speed accuracy minimum threshold, e.g., the instant speed accuracy is below 60%.
Wherein the instant speed accuracy minimum threshold value < the instant speed accuracy average threshold value < the instant speed accuracy maximum threshold value, as in the above example, the instant speed accuracy minimum threshold value is 60%, the instant speed accuracy average threshold value is 70%, and the instant speed accuracy maximum threshold value is 80%. It is to be understood that the values of the instantaneous speed accuracy minimum threshold, the instantaneous speed accuracy average threshold, the instantaneous speed accuracy maximum threshold, and the distance threshold are not limited to the above examples, but may be set and adjusted according to actual circumstances.
5. Data coverage information
The process of obtaining the data coverage rate information comprises the following steps: and counting the total number of the traveled mileage corresponding to the FCD sample data in a time period.
In the subsequent evaluation, the quality references for data coverage were: the higher the coverage rate, the better the quality of the data source.
6. Match rate information
The obtaining process of the matching rate information comprises the following steps: the successful points matched to the road account for the proportion of the total points by using the processing matching logic.
In the subsequent evaluation, the quality references for the matching rates are: the higher the success rate, the better the data source quality is, which is an important quality index.
7. Matched effective vehicle number information
The obtaining process of the matched effective vehicle number information is as follows: the number of successful vehicles is matched using process matching logic.
In the subsequent evaluation, the quality references of the number of matched valid vehicles are: the higher the number of valid vehicles, the better the quality of the data source.
The above describes the obtaining process for each index in step S102 and the specific meaning of each index.
Then, step S103 is executed, namely, the floating car data source is evaluated based on one or more index information. The specific process of evaluation may be:
comparing the vehicle number information, the record number information, the data return period information, the data integrity and effectiveness information, the data coverage rate information, the data matching rate information and the matched effective vehicle number information of the floating vehicle data source with corresponding index thresholds respectively to obtain each index evaluation result;
and if the indexes are multiple, weighting the evaluation results of the indexes of the corresponding items according to the preset weight corresponding to each index to obtain the evaluation results of the floating car data source.
Assuming that there are three floating car data sources A, B, C, the results after evaluation according to the criteria described above are shown in table 1.
TABLE 1
The above criteria 1-7, which can be introduced as above, are: the number of vehicles, the number of records, the data return period, the data integrity and effectiveness, the data coverage rate, the data matching rate and the number of effective vehicles after matching. However, those skilled in the art will understand that the above mentioned number of vehicles, number of records, data return period, data integrity and validity, data coverage rate, data matching rate, and number of valid vehicles after matching are not limited as indicators, and other indicators may be added, and that all of the above seven indicators are not limited, and some of them may be used as evaluation indicators.
The above-mentioned a1-a7 is the evaluation result of each evaluation index of the floating car data source a, that is, the evaluation result of each latitude of the floating car data source a is obtained, then the weight of each index (latitude) can be given, and the final evaluation result of the floating car data source a is obtained comprehensively. Similarly, for the floating car data sources B and C, the final evaluation result is obtained in the same way.
By adopting the method, before the data of the floating car is accessed into the information processing module of the floating car, the quality of the data source of the floating car is evaluated and standardized, the quality of the data source can be evaluated before the data is accessed, and reference is provided for the information processing module of the floating car. For example, the floating car information processing module can be used for comparing the existing data quality and coverage rate, preferentially accessing a road with weak coverage at present by real-time traffic, or replacing the original data source with low data quality, so as to provide basic guarantee for high-precision real-time road conditions. The invention provides the evaluation index and standard of the quality of the floating car data source, is convenient to access the real valuable data, and provides a basic data source for real-time traffic accuracy.
The embodiment of the application provides a floating car data source evaluation device corresponding to the floating car data source evaluation method, as shown in fig. 2, the device is a schematic structural diagram, and the device mainly comprises the following units:
a data acquisition unit 201 for acquiring floating car data from a floating car data source;
the index information calculation unit 202 is configured to obtain index information for the floating car data source according to the floating car data, where the index information includes vehicle number information, record number information, data return cycle information, data integrity and validity information, data coverage rate information, data matching rate information, and matched valid vehicle number information;
and the data source evaluation unit 203 is used for evaluating the floating car data source based on the one or more index information.
Preferably, the data source evaluation unit 203 is specifically configured to compare the vehicle number information, the record number information, the data return period information, the data integrity and validity information, the data coverage rate information, the data matching rate information, and the matched valid vehicle number information of the floating vehicle data source with corresponding index thresholds respectively to obtain each index evaluation result; and if the indexes are multiple, weighting the evaluation results of the indexes of the corresponding items according to the preset weight corresponding to each index to obtain the evaluation results of the floating car data source.
Preferably, the data backhaul period includes: averaging the ratio of the backhaul period to a reasonable backhaul period; wherein the content of the first and second substances,
the index information calculation unit 202 is specifically configured to calculate and count an average collection frequency of floating car data, and obtain the average return period according to the average collection frequency; calculating the sampling time difference between any two continuous sample points according to the floating car data of each car to obtain the return period of each car; counting the ratio of each return period by taking the vehicle as a unit to obtain the vehicle within a preset optimal return period threshold value; the vehicle number proportion in the preset optimal return period threshold is a reasonable return period proportion.
Preferably, the index information calculation unit 202 is specifically configured to calculate and count the integrity and effectiveness of the floating car data source by calculating and counting an abnormal point proportion, a GPS point delay rate, a repeat point proportion, a direction angle accuracy rate, and/or an instant speed accuracy rate of the floating car data source.
Preferably, the apparatus further comprises:
and the average result sending unit 204 is used for sending the evaluation result of the floating car data source to the floating car information processing module.
Similarly, the invention also provides a floating car data source evaluation system, which comprises the floating car data source evaluation device and a floating car information processing module; the floating car information processing module refers to a floating car data source evaluation result provided by the floating car data source evaluation device, and preferentially accesses floating car data of a floating car data source with a better evaluation result, or replaces original floating car data with the floating car data of the floating car data source with the better evaluation result.
It should be noted that the present application may be implemented in software and/or a combination of software and hardware, for example, implemented using Application Specific Integrated Circuits (ASICs), general purpose computers or any other similar hardware devices. In one embodiment, the software programs of the present application may be executed by a processor to implement the steps or functions described above. Likewise, the software programs (including associated data structures) of the present application may be stored in a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. Additionally, some of the steps or functions of the present application may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.
In addition, some of the present application may be implemented as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide methods and/or techniques in accordance with the present application through the operation of the computer. Program instructions which invoke the methods of the present application may be stored on a fixed or removable recording medium and/or transmitted via a data stream on a broadcast or other signal-bearing medium and/or stored within a working memory of a computer device operating in accordance with the program instructions. An embodiment according to the present application comprises an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to perform a method and/or a solution according to the aforementioned embodiments of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Claims (6)
1. An evaluation method of a floating car data source, comprising:
acquiring floating car data from a floating car data source;
obtaining index information aiming at the floating car data source according to the floating car data, wherein the index information comprises car number information, record number information, data return cycle information, data integrity and effectiveness information, data coverage rate information, data matching rate information and matched effective car number information;
evaluating the floating car data source based on the one or more indicator information;
sending the evaluation result of the floating car data source to a floating car information processing module, wherein the floating car information processing module refers to the floating car data source evaluation result and preferentially accesses floating car data of the floating car data source with a better evaluation result;
the data return period comprises: averaging the ratio of the backhaul period to a reasonable backhaul period; wherein the content of the first and second substances,
calculating and counting the average acquisition frequency of floating car data, and obtaining the average return period according to the average acquisition frequency;
calculating the sampling time difference between any two continuous sample points according to the floating car data of each car to obtain the return period of each car; counting the ratio of each return period by taking the vehicle as a unit to obtain the vehicle within a preset optimal return period threshold value; the vehicle number proportion in the preset optimal return period threshold is a reasonable return period proportion.
2. The method of claim 1, wherein said evaluating said floating car data source based on said one or more indicator information comprises:
comparing the vehicle number information, the record number information, the data return period information, the data integrity and effectiveness information, the data coverage rate information, the data matching rate information and the matched effective vehicle number information of the floating vehicle data source with corresponding index thresholds respectively to obtain each index evaluation result;
and if the indexes are multiple, weighting the evaluation results of the indexes of the corresponding items according to the preset weight corresponding to each index to obtain the evaluation results of the floating car data source.
3. The method of claim 1,
and calculating and counting the integrity and effectiveness of the floating car data source by calculating and counting the abnormal point proportion, the GPS point delay rate, the repeat point proportion, the direction angle accuracy rate and/or the instant speed accuracy rate of the floating car data source.
4. A floating car data source evaluation device is characterized by comprising:
the data acquisition unit is used for acquiring floating car data from a floating car data source;
the index information calculation unit is used for obtaining index information aiming at the floating car data source according to the floating car data, and the index information comprises car number information, record number information, data return cycle information, data integrity and effectiveness information, data coverage rate information, data matching rate information and matched effective car number information;
the data source evaluation unit is used for evaluating the floating car data source based on the one or more index information and sending the evaluation result of the floating car data source to the floating car information processing module;
the floating car information processing module is used for referring to a floating car data source evaluation result provided by the floating car data source evaluation device and preferentially accessing floating car data of a floating car data source with a better evaluation result;
the data return period comprises: averaging the ratio of the backhaul period to a reasonable backhaul period; wherein the content of the first and second substances,
the index information calculation unit is specifically used for calculating and counting the average acquisition frequency of floating car data, and obtaining the average return period according to the average acquisition frequency; calculating the sampling time difference between any two continuous sample points according to the floating car data of each car to obtain the return period of each car; counting the ratio of each return period by taking the vehicle as a unit to obtain the vehicle within a preset optimal return period threshold value; the vehicle number proportion in the preset optimal return period threshold is a reasonable return period proportion.
5. The apparatus according to claim 4, wherein the data source evaluation unit is specifically configured to compare vehicle number information, record number information, data return period information, data integrity and validity information, data coverage rate information, data matching rate information, and matched valid vehicle number information of the floating vehicle data source with corresponding index thresholds, respectively, to obtain an evaluation result of each index; and if the indexes are multiple, weighting the evaluation results of the indexes of the corresponding items according to the preset weight corresponding to each index to obtain the evaluation results of the floating car data source.
6. The apparatus according to claim 4, wherein the index information calculation unit is specifically configured to calculate and count the integrity and effectiveness of the floating car data source by calculating and counting an abnormal point ratio, a GPS point delay rate, a repeat point ratio, a direction angle accuracy rate, and/or an instantaneous speed accuracy rate of the floating car data source.
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