LU500569B1 - Method and device for acquiring driving test cycle weight factors based on gis large data - Google Patents
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Abstract
The invention relates to a method and a device for acquiring driving test cycle weight factors based on GIS large data. The acquisition method comprises the following steps: S1, supplementing and correcting the GIS data; S2, processing and matching road information, and constructing a typical city all-road network GIS database; S3, establishing a traffic flow model selection model based on support vector machine; S4, calibrating and calculating a traffic flow model to obtain the traffic flow of the all-road network road; S5, calculating a speed interval weight factor. The method can accurately and objectively calculate the weight factor of each speed interval, and provides technical support for policy making, test design and product development of governments, research institutions and enterprises in relevant fields of driving test cycle extraction.
Description
FIELD OF THE INVENTION The present invention relates to the field of transportation, in particular to a method and a device for acquiring driving test cycle weight factors based on GIS large data.
BACKGROUND OF THE INVENTION The automobile driving test cycle is an important common basic technology in the automobile industry, and is the basis of the vehicle energy consumption/emission test method and the limit value standard. In the construction process of the test cycle, how to objectively and scientifically determine the weight factors of each speed interval is an urgent problem to be solved. The traditional weight factor determination method is based on actual fleet collection data, and the calculation results are greatly influenced by the subjectivity of fleet formation, so the obtained weight factors are not accuracy enough and cannot objectively reflect the weight factors of each speed interval. Accordingly, the present invention has been described with particularity.
SUMMARY OF THE INVENTION In a first aspect, the present invention aims to provide a method for acquiring driving test cycle weight factors based on GIS large data, which can accurately and objectively determine the weight factor of each speed interval, and provides technical support for policy making, test design and product development of governments, research institutions and enterprises in relevant fields of driving test cycle extraction. In a second aspect, the present invention aims to provide a device for acquiring driving test cycle weight factors based on GIS large data. 7500568 In a third aspect, it is an object of the present invention to provide an electronic device.
In a fourth aspect, it is an object of the present invention to provide a medium.
In order to achieve the above object, the present invention adopts the following technical solution: The present invention provides a method for acquiring driving test cycle weight factors based on GIS large data, which comprises the following steps: S1, supplementing and correcting the GIS data; S2, processing and matching road information, and constructing a typical city all-road network GIS database; S3, establishing a traffic flow model selection model based on support vector machine or particle swarm optimization based on extreme learning machine; S4, calibrating and calculating a traffic flow model to obtain a traffic flow of the all-road network road; S5, calculating a speed interval weight factor.
As a preferred technical solution, step S1 comprises: calculating a missing rate of the GIS data of each road, and supplementing and correcting the GIS data of each road of which the missing rate is in a certain range; directly deleting the GIS data of the road of which the missing rate is greater than or equal to 0.3; supplementing the GIS data by adopting a speed average value at the same moment of adjacent dates if the missing rate of the road is greater than or equal to 0.1 and less than 0.3; supplementing the GIS data by adopting a linear difference value of average speeds at adjacent moments in the day if the missing rate of the road is less than 0.1; preferably, the adjacent date is five days before that day to five days after that day; preferably, the adjacent moment is the first 15 minutes to the last 15 minutes of the current moment.
As a further preferred technical solution, step S2 comprises: weighting the number of lanes of each road according to the road information to obtain an average number of lanes of the road; matching road GIS data with road information, and constructing a typical city all-road network GIS database; 7500568 preferably, calculating the average lane number of the road by adopting the following formula: CE 4 Wherein n is an average number of lanes; ni is a number of lanes of the i road ; and / is a lane length of the i road.
As a further preferred technical solution, the traffic flow model selection model is established based on support vector machine.
Preferably, the step S3 comprises: S3a, providing traffic flow data of a plurality of roads at different moments; S3b, selecting a plurality of traffic flow models, respectively calculating the traffic flow of the corresponding roads according to an average speed of the roads in the GIS database, determining the traffic flow models of the corresponding roads, and finally forming a traffic flow model sample database in which the roads correspond to the traffic flow models one by one; S3c, adopting a support vector machine, selecting a kernel function and parameters, and constructing a traffic flow model selection model based on the support vector machine.
As a further preferred technical solution, in the step S3a, traffic flow data of a plurality of roads at different moments are obtained by analyzing and processing video data of a typical road traffic camera.
Preferably, in the step S3b, there are 3 traffic flow models.
Preferably, in the step S3b, determining a traffic flow model of a corresponding road by using a least square method.
In the step S3c, a traffic flow model sample database is divided into a training set and a test set, and then a traffic flow model selection model based on support vector machine is constructed.
As a further preferred technical solution, step S4 comprises: S4a, inputting road information into a traffic flow model selection model, calculating a traffic flow model applicable to the road, and calibrating the traffic flow model, including free flow speed calibration and optimal density coefficient calibration: S4b, comparing the calculated traffic flow of the traffic flow model with the real traffic flow, and calculating a relative error average value and an absolute error average value of the traffic flow in each hour; 0500569 S4c, taking the GIS database as input, and calculating the traffic flow of the all-road network road through a traffic flow model.
Preferably, the road information comprises a road length, a road weighted lane number, a road speed limit value and a road grade; preferably, the relative error average is calculated using the following formula: c= EZ -
IiZo% - “is a relative error average value, Qi is an real traffic flow at the i moment, and gi is a traffic flow at the i" moment calculated by traffic flow model:
Preferably, calculating a mean absolute error using the formula: Q= IE IQ; — ay I |
; wherein Q is the absolute error average value, Qi is the real traffic flow at the i moment and gi is the traffic flow at the i" moment calculated by traffic flow model.
As a further preferred technical solution, step S5 comprises: according to the traffic flow of all-road network, obtaining the vehicle running time distribution of different speed intervals of the all-road network, respectively dividing a low-speed interval, a medium-speed interval and a high-speed interval according to a speed interval threshold value, calculating an accumulated vehicle running time of the low-speed interval, the medium-speed interval and the high-speed interval, respectively, and finally obtaining the weight factors of each speed intervals.
The present invention provides a device for acquiring driving test cycle weight factors based on GIS large data, which comprises: a GIS data supplementing and correcting module used for supplementing and correcting the GIS data; a road information processing and matching module used for processing and matching the road information and constructing a typical city all-road network GIS database; a traffic flow model selection construction module based on support vector machine used for constructing a traffic flow model selection model based on the support vector machine;
a traffic flow model calibration and calculation module used for calibrating and 0500569 calculating the traffic flow model; and a speed interval weight factor calculation module used for calculating the speed interval weight factor.
In a third aspect, the present invention further provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the method for acquiring driving test cycle weight factors based on GIS large data.
In a fourth aspect, the present invention further provides a medium having stored thereon computer instructions for causing the computer to perform the above-described method for acquiring driving test cycle weight factors based on GIS large data.
Compared with the prior art, the present invention has the following beneficial effects: According to the method for acquiring driving test cycle weight factors based on GIS large data, the Geographic Information System or Geo-Information system (GIS) traffic big data which can relatively objectively reflect the real driving situation of the vehicle in China is innovatively introduced, a traffic flow model selection model is established, the travel time occupation ratio of the vehicle in the all-road network of a typical city in China at different speed intervals is acquired, and then the weight factor of each speed interval is determined, and the basis for the construction of driving conditions is laid.
The method establishes a traffic flow model selection model based on a support vector mechanism, and determines a traffic flow model applicable to each road by taking road information as input.
Inputting the GIS data of each road into a corresponding traffic flow model, and calculating to obtain the road flow of a typical city all-road network and the driving hours of vehicles, so as to acquire the weight factors of each speed interval.
Compared with the traditional method for acquiring the driving test cycle weight factor through vehicle actual acquisition data, the method has the advantages that the subjective influence of the choice of the vehicle fleet on the determination of 0500569 the weight factor is avoided to a certain extent, the weight factor is more accurate and objective, and technical support can be provided for policy making, test design and product development of governments, research institutions and enterprises in relevant fields of driving test cycle extraction.
BRIEF DESCRIPTION OF THE DRAWINGS In order that the detailed description of the invention or the prior art may be more clearly understood, reference will now be made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration only, and in which is shown by way of illustration only, and in which is shown by way of illustration only, other embodiments of the invention may be devised without departing from the spirit and scope of the present invention. FIG. 1 is a flow chart of a method for acquiring driving test cycle weight factors based on GIS large data, provided in Embodiment 1; FIG. 2 is a flow chart for processing and matching road information in Embodiment 1; FIG. 3 is a graph showing the fitting of measured data to simulated data in Embodiment 1; FIG. 4 is a flow chart for establishing a traffic flow model selection model in Embodiment 1; FIG. 5 is a graph showing the daily variation of traffic flow in Embodiment 1; FIG. 6 is a graph showing the ratio of each speed interval in Embodiment 1.
DETAILED DESCRIPTION OF THE EMBODIMENTS Reference will now be made in detail to the exemplary embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein the various details of the embodiments of the present application are included to facilitate understanding and are to be considered as exemplary only. Accordingly, a person skilled in the art appreciates that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Also,
descriptions of well-known functions and structures are omitted from the 0500569 following description for clarity and conciseness.
With the development of science and technology, the continuous popularization of 4G network and smart phone, through the analysis and processing of driver's vehicle navigation data (GIS big data), the average speed information of typical city road network in different geographical locations at different moments can be acquired, which provides a new method and new ideas for obtaining the weight factors of vehicle driving conditions.
The present invention creatively provides a method for acquiring driving test cycle weight factors based on GIS large data.
The method for acquiring driving test cycle weight factors based on GIS large data comprises the following steps: S1, supplementing and correcting the GIS data; S2, processing and matching road information, and constructing a typical city all-road network GIS database; S3, establishing a traffic flow model selection model based on support vector machine or a traffic flow model selection model based on a particle swarm optimization based on extreme learning machine; S4, calibrating a traffic flow model to obtain a traffic flow of the all-road network road; S5, calculating weight factor of each speed interval.
The method for acquiring driving test cycle weight factors based on GIS large data, the Geographic Information System or Geo-Information system (GIS) traffic big data which can relatively objectively reflect the real driving situation of the vehicle in China is innovatively introduced, a traffic flow model selection model is established, the travel time occupation ratio of the vehicle in the all-road network of a typical city in China at different speed intervals is acquired, and then the weight factor of each speed interval is determined, and the basis for the construction of driving test cycle is laid.
The method established a traffic flow model selection model based on support vector machine or a particle swarm optimization based extreme learning mechanism, and determined a traffic flow model applicable to each road by taking road information as input.
Inputting the GIS data of each road into a corresponding traffic flow model, and calculating to obtain the traffic flow of a 0500569 typical city all-road network and the driving hours of vehicles, so as to acquire the weight factors of each speed interval.
Compared with the traditional method for acquiring the driving test cycle weight factor through vehicle actual acquisition data, the method had the advantages that the subjective influence of the choice of the vehicle fleet on the determination of the weight factor was avoided to a certain extent, the weight factor was more accurate and objective, and technical support could be provided for policy making, test design and product development of governments, research institutions and enterprises in relevant fields of driving test cycle extraction.
In a preferred embodiment, step S1 comprises: calculating a missing rate of the GIS data of each road, and supplementing and correcting the GIS data of each road of which the missing rate is in a certain range.
The road data with GIS data missing rate in a certain range is supplemented and corrected, so that the integrity and accuracy of the data are improved.
The above-mentioned “missing rate” refers to the ratio of the cumulative missing duration of the data to the acquisition period.
Preferably, directly deleting the GIS data of the road of which the missing rate is greater than or equal to 0.3; supplementing the GIS data by adopting a speed average value at the same moment of adjacent dates if the missing rate of the road is greater than 0.1 or equal to and less than 0.3; supplementing the GIS data by adopting a linear difference value of average speeds at adjacent moments in the day if the missing rate of the road is less than 0.1. Preferably, the adjacent date is five days before that day to five days after that day.
For example, if that day is the 15" day in a certain month, its adjacent dates are the 10" day, the 11” day, the 12 day, the13” day, the 14" day, the 16" day, the 17 day, the 18" day, the 19" day, and the 20" day of the month. preferably, the adjacent moment is the first 15 minutes to the last 15 minutes of the current moment.
For example, if the current time is 15:25, then its adjacent moments are 15:10, 15:15, 15:20, 15:30, 15:35 and 15:40. It should be understood that the adjacent moments listed above are data acquired every 5 minutes, and thus every 5 minutes is taken from the first 15 minutes to the last minutes, which is consistent with the lowest refresh frequency of the current 0500569 GIS data, and obviously, the time interval thereof can be adjusted according to the refresh frequency of the GIS data.
In a preferred embodiment, step S2 comprises: weighting the number of lanes of each road according to the road information to obtain an average number of lanes of the road; matching road GIS data with road information, and constructing a typical city all-road network GIS database.
In general, the number of lanes of a road varies in different road sections, so the number of lanes needs to be weighted first to obtain an average number of lanes of the road. preferably, calculating the average lane number of the road by adopting the following formula: Co In Wherein n is an average number of lanes; ni is a number of lanes of the i" road and / is a lane length of the i" road.
In a preferred embodiment, the traffic flow model selection model is established based on support vector machine.
Preferably, step S3 comprises: S3a, providing traffic flow data of a plurality of roads at different moments; S3b, selecting a plurality of traffic flow models, respectively calculating the traffic flow of the corresponding roads according to an average speed of the roads, determining the traffic flow models of the corresponding roads, and finally forming a traffic flow model sample database in which the roads correspond to the traffic flow models one by one; S3c, adopting a support vector machine, selecting a kernel function and parameters, and constructing a traffic flow model selection model based on support vector machine.
In a preferred embodiment, firstly, traffic flow data is provided, then a traffic flow model sample database corresponding to each road is formed, and then a traffic flow model selection model based on support vector machine is constructed according to the sample database.
The term “a plurality of” in “a plurality of roads” refers to more than two roads, e.g. 2, 3, 4, 5, etc.
The above-mentioned “a plurality of” in “a plurality of traffic flow models” refers to more than two traffic flow models, for example, 2, 3, 4,
etc.
LU500569 In a preferred embodiment, in step S3a, traffic flow data of a plurality of roads at different moments are obtained by analyzing and processing video data of a typical road traffic camera.
In a preferred embodiment, in step S3b, there are 3 traffic flow models.
Preferably, in step S3b, the traffic flow model of the corresponding road is determined using a least square method.
The least square method is adopted to determine the traffic flow model which is most suitable for the traffic flow estimation of the road.
In a preferred embodiment, in step S3c, the traffic flow model sample database is divided into a training set and a test set, and then a traffic flow model selection model based on support vector machines is constructed.
The training set can be used for training the support vector machine, and then the test set is used for verifying and testing the constructed support vector machine model so as to ensure the accuracy of the model.
In a preferred embodiment, step S4 comprises: S4a, inputting road information into a traffic flow model selection model, calculating a traffic flow model applicable to the road, and calibrating the traffic flow model, including free flow speed calibration and optimal density coefficient calibration; S4b, comparing the calculated traffic flow of the traffic flow model with the real traffic flow, and calculating a relative error average value and an absolute error average value of the traffic flow in each hour; S4c, taking the GIS database as input, and calculating the traffic flow of the all-road network road through a traffic flow model.
In a preferred embodiment, through the calibration and calculation of the steps S4a and S4b, the accuracy and representativeness of the traffic flow model selection model can be accurately evaluated, and then the traffic flow of the all-road network road is calculated.
Optionally, after obtaining the traffic flow of the all-road network road, obtaining the vehicle running time of the all-road network road; the vehicle running time of all levels of roads can be further obtained.
The running time of the vehicle can be calculated by adopting the following formula: Vil, = a. x I Wherein VHT; represents the vehicle travel time of the road section / at a certain time, qi represents the average traffic flow of the road section / at a certain time, and 7; represents the average travel time of the vehicle on the road section /. Preferably, the road information includes road length, road weighted lane number, road speed limit value, and road grade. Preferably, the relative error average is calculated using the following formula: c= EIRENE - 24 EZ% - “is a relative error average value, Qi is an real traffic flow at the i moment, and gi is a traffic flow at the i" moment calculated by traffic flow model: Preferably, calculating a mean absolute error using the formula: Q=—EÉ;Q;— ql == ; wherein Q is the absolute error average value, Qi is the real traffic flow at the i" moment and gi is the traffic flow at i" moment calculated by the traffic flow model.
In a preferred embodiment, step S5 comprises: according to the traffic flow of all-road network, obtaining the vehicle running time distribution of different speed intervals of the all-road network. Dividing a low-speed interval, a medium-speed interval and a high-speed interval according to a speed interval threshold value, respectively. Calculating an accumulated vehicle running time of the low-speed interval, the medium-speed interval and the high-speed interval, respectively, and finally obtaining the weight factors of the speed intervals.
According to another aspect of the invention, the invention provides a device for acquiring driving test cycle weight factors based on GIS large data, which comprises: a GIS data supplementing and correcting module used for supplementing and correcting the GIS data; a road information processing and matching module used for processing and matching the road information and constructing a typical city all-road network
GIS database; 0500568 a traffic flow model selection construction module based on support vector machine used for constructing a traffic flow model selection model based on support vector machine; a traffic flow model calibration and calculation module used for calibrating and calculating the traffic flow model; a speed interval weight factor calculation module used for calculating the speed interval weight factor.
By adopting a GIS data supplement and correction module, a road information processing and matching module, a traffic flow model selection construction module based on support vector machine, a traffic flow model calibration and calculation module and a speed interval weight factor calculation module, the device can obtain weight factors of different speed intervals objectively and accurately, and provides technical support for policy making, test design and product development of governments, research institutions and enterprises in relevant fields of driving test cycle extraction.
According to another aspect of the present invention, the invention provides an electronic device, including: at least one processor; a memory communicatively connected to the at least one processor; wherein, the memory stores an instruction executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the method for acquiring driving test cycle weight factors based on GIS large data.
The processor in the electronic device is capable of performing the above method and thus has at least the same advantages as the above method.
According to another aspect of the present invention, there is provided a medium having stored thereon computer instructions for causing the computer to perform the method described above.
The computer instructions in the medium enable a computer to perform the methods described above, thus providing at least the same advantages as the methods described above.
The present invention will now be described in further detail with reference to examples.
Embodiment 1
As shown in FIG. 1, an embodiment of the invention provides a method for 0500569 acquiring driving test cycle weight factors based on GIS large data, which comprises the following steps: S1, supplementing and correcting the GIS data: calculating a missing rate of the GIS data of each road, and supplementing and correcting the GIS data of each road of which the missing rate is in a certain range; directly deleting the GIS data of the road if the missing rate is greater than or equal to 0.3; supplementing by adopting a speed average value at the same moment of adjacent dates if the missing rate is greater than or equal to 0.1 and less than
0.3; supplementing the vehicle information by adopting a linear difference value of average speeds at adjacent moments in the day if the missing rate is less than
0.1; S2, road information processing and matching (as shown in FIG. 2): weighting the number of lanes of each road according to the road information to obtain an average number of lanes of the road; matching road GIS data with road information, and constructing a typical city all-road network GIS database; calculating the average lane number of the road by adopting the following formula: sh Wherein n is an average number of lanes; ni is a number of lanes of the i road; and / is a lane length of the i" road. S3, establishing a traffic flow model selection model based on support vector machine: S3a, analyzing and processing video data of a road traffic camera, performing image smoothing processing and image graying processing through Gaussian filtering, and finally calculating the traffic flow of the road at different moments by using mark tracking; S3b, selecting three traffic flow models, respectively calculating the traffic flow of the corresponding roads according to an average speed of the roads in the GIS database, determining the traffic flow models of the corresponding roads by using a least square method, and finally forming a traffic flow model sample database with each road corresponding to the traffic flow models one by one
(as shown in FIG. 3): 7500568 S3c, adopting a support vector machine, dividing the traffic flow model sample database into a training set and a test set, selecting a kernel function and parameters, and constructing a traffic flow model selection model based on support vector machine (as shown in FIG. 4, “establishing an SVM model” in the figure is establishing a traffic flow model selection model based on support vector machine). S4, calibrating and calculating a traffic flow model to obtain a traffic flow of the all-road network road; S4a, inputting the road length, the road weighted lane number, the road speed limit value and the road grade into a traffic flow model selection model, calculating a traffic flow model applicable to the road, and calibrating the traffic flow model, including free flow speed calibration and optimal density coefficient calibration.
The free flow speed generally refers to the theoretical driving speed of the driver, which is not affected by traffic conditions, and is related to the speed limit value of the road, and the free flow speed is generally 80-95% of the speed limit value of the corresponding road, and each grade of road is different as shown in Table 1. Table 1 Proportional Relationship Between Free Flow Velocity and Speed Limit for Each Grade Road Grade of Speed limit Type of road Quantile of velocity me ot Freeway 80 %% | oy Below 80 expressway To Ewes ew Tews I [seme Jew After the free flow speed is determined, the speed data to be calculated are input, and in the model calibration stage, assuming that the speed data are sequentially 1-120 km/h (taking 1 km/h as an interval unit), a density coefficient is arbitrarily defined, the traffic flow data at each speed can be calculated, and 0500569 the maximum flow is the traffic capacity.
The coefficient is continuously adjusted through an iterative method, so that the traffic capacity is infinitely approximate to a recommended value, the coefficient at the moment is an optimal density coefficient, and the recommended value of the traffic capacity is shown in the following Table 2. Table 2 Traffic Capacity (pcu/h) Recommended Values Grade of Medium and small S4b, comparing the calculated flow of the traffic flow model with the real traffic flow, and calculating a relative error average value and an absolute error average value of the traffic flow in each hour; the relative error average is calculated using the following formula: c= L HE al
24 Eizo% - js a relative error average value, Q; is an real traffic flow at the ih moment, and gi is a traffic flow at i" moment calculated by the traffic flow model;
| | Q = ZEIQ — al calculating a mean absolute error using the formula: = ; wherein Q is the absolute error average value, Qi is the real traffic flow at the it moment and gq; is the traffic flow at the i" moment calculated by traffic flow model.
The calculation accuracy of the model is usually required to reach a relative error of + 10% or less.
S4c, taking the GIS database as input, and calculating the traffic flow of the all-road network road through a traffic flow model; After the calibration of the traffic flow model is completed, the traffic flow of each road can be calculated through the GIS data of each road.
To further determine the speed interval weighting factor, a parameter of Vehicle Hours Traveled (VHT) needs to be introduced.
The number of VHT is a product of the average traffic volume on a road segment and the average travel time of the 0500569 vehicle, and contains the double effects of the road length and the road congestion degree; it can not only reflect the traffic congestion, but also reflect the traffic needs of the traffic travelers. In a certain time period, the calculation formula of the Vehicle Hours Traveled of a certain road section is as follows: VHT, = q, XT, . . .
; wherein VHT; represents the vehicle travel time of the road section i at a certain time, and gi represents the average traffic flow of the road section i at a certain time, and 7; represents the average travel time of the vehicle on the road section i.
The cumulative calculation is carried out according to the number of Vehicle Hours Traveled at each average speed of each road, and the distribution of the number of Vehicle Hours Traveled in different speed intervals can be acquired, as shown in FIGS. 5 and 6. S5, calculating a speed interval weight factor: integrating GIS data of a typical city all-road network, finally obtaining vehicle running hours distribution of different speed intervals of the typical city all-road network, respectively dividing a low-speed interval, a medium-speed interval and a high-speed interval according to a speed interval threshold value, further calculating accumulated vehicle running hours of the low-speed interval, the medium-speed interval and the high-speed interval, and finally obtaining a weight factor of each speed interval, wherein the result is shown in Table 3. Table 3 Typical city speed interval occupancy Low speed | Medium speed | High speed
100.00
100.00
100.00 In summary, the method for acquiring driving test cycle weight factors based on GIS large data provided by the invention can conveniently and quickly acquire the weight factors of different speed intervals of vehicle driving in 0500569 typical cities, and provides data support for further energy conservation, emission reduction and intelligent traffic research.
It will be appreciated that the various forms of flow, reordering, adding or removing steps shown above may be used.
For example, the steps recited in the present application may be carried out in parallel or sequentially or may be carried out in a different order, so long as the desired results of the technical solutions disclosed in the present application can be achieved, and no limitation is made herein.
The above-described embodiments are not to be construed as limiting the scope of the present application.
It will be apparent to a person skilled in the art that various modifications, combinations, sub-combinations and substitutions are possible, depending on design requirements and other factors.
Any modifications, equivalents, and improvements within the spirit and principles of this application are intended to be included within the scope of this application.
Claims (10)
1. A method for acquiring driving test cycle weight factors based on GIS large data, characterized by comprising the following steps of: s1, supplementing and correcting the GIS data; s2, processing and matching road information, and constructing a typical city all-road network GIS database; s3, establishing a traffic flow model selection model based on support vector machine or a traffic flow model selection model based on a particle swarm optimization based on extreme learning machine; s4, calibrating and calculating a traffic flow model to obtain a traffic flow of the all-road network road; and s5, calculating a speed interval weight factor.
2. The method for acquiring driving test cycle weight factors based on GIS large data according to claim 1, characterized in that the step s1 comprises: calculating a missing rate of the GIS data of each road, and supplementing and correcting the GIS data of each road of which the missing rate is in a certain range; wherein, directly deleting the GIS data of the road if the missing rate is greater than or equal to 0.3; supplementing by adopting a speed average value at the same moment of adjacent dates if the missing rate is greater than or equal to 0.1 and less than
0.3; supplementing the vehicle information by adopting a linear difference value of average speeds at adjacent moments in the day if the missing rate is less than
0.1; wherein, the adjacent date is five days before that day to five days after that day, wherein, the adjacent moment is the first 15 minutes to the last 15 minutes of the current moment.
3. The method for acquiring driving test cycle weight factors based on GIS large data according to claim 1, characterized in that the step s2 comprises: weighting the number of lanes of each road according to the road information to obtain an average number of lanes of the road; matching road GIS data with road information, and constructing a typical city all-road network GIS database; 7500568 preferably, calculating the average lane number of the road by adopting the following formula: CE 4 wherein n is an average number of lanes; n; is a number of lanes of the i road: and / is a lane length of the i" road.
4. The method for acquiring driving test cycle weight factors based on GIS large data according to claim 1, characterized in that the traffic flow model selection model is established based on support vector machine; the step s3 comprises: s3a, providing traffic flow data of a plurality of roads at different moments; s3b, selecting a plurality of traffic flow models, respectively calculating the traffic flow of the corresponding roads according to an average speed of the roads in the GIS database, determining the traffic flow models of the corresponding roads, and finally forming a traffic flow model sample database in which the roads correspond to the traffic flow models one by one; s3c, adopting a support vector machine, selecting a kernel function and parameters, and constructing a traffic flow model selection model based on support vector machine.
5. The method for acquiring driving test cycle weight factors based on GIS large data according to claim 4, characterized in that in the step s3a, traffic flow data of a plurality of roads at different moments are obtained by analyzing and processing video data of a typical road traffic camera; in the step s3b, there are 3 traffic flow models; in the step s3b, determining a traffic flow model of a corresponding road by using a least square method; in the step s3c, a traffic flow model sample database is divided into a training set and a test set, and then a traffic flow model selection model based on support vector machine is constructed.
6. The method for acquiring driving test cycle weight factors based on GIS large data according to claim 1, characterized in that the step s4 comprises: sda, inputting road information into a traffic flow model selection model, calculating a traffic flow model applicable to the road, and calibrating the traffic flow model, including free flow speed calibration and optimal density coefficient calibration; 0500568 s4b, comparing the calculated flow of the traffic flow model with the real traffic flow, and calculating a relative error average value and an absolute error average value of the traffic flow in each hour; s4c, taking the GIS database as input, and calculating the traffic flow of the all-road network road through a traffic flow model: preferably, the road information comprises a road length, a road weighted lane number, a road speed limit value and a road grade; preferably, the relative error average is calculated using the following formula: 2 LEE rl - BX, ; “ js a relative error average value, Qj is an real traffic flow at the i" moment, and gi is a traffic flow at i" moment calculated by the traffic flow model; preferably, calculating a mean absolute error using the formula: Q = LÉO: — ail 5 = ; wherein Q is the absolute error average value, Qi is the real traffic flow at the i" moment and gi is the traffic flow at the i" moment calculated by traffic flow model.
7. The method for acquiring driving test cycle weight factors based on GIS large data according to any one of claims 1-6, characterized in that the step s5 comprises the following steps: according to the traffic flow of all-road network, obtaining the vehicle running time distribution of different speed intervals of the all-road network, respectively dividing a low-speed interval, a medium-speed interval and a high-speed interval according to a speed interval threshold value, calculating an accumulated vehicle running time of the low-speed interval, the medium-speed interval and the high-speed interval, respectively, and finally obtaining the weight factors of the speed intervals.
8. A device for acquiring driving test cycle weight factors based on GIS large data, characterized by comprising: a GIS data supplementing and correcting module used for supplementing and correcting the GIS data; a road information processing and matching module used for processing and matching the road information and constructing a typical city all-road network
GIS database; 0500568 a traffic flow model selection construction module based on support vector machine used for constructing a traffic flow model selection model based on support vector machine; a traffic flow model calibration and calculation module used for calibrating and calculating the traffic flow model; a speed interval weight factor calculation module used for calculating the speed interval weight factor.
9. An electronic device, characterized by comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the method for acquiring driving test cycle weight factors based on GIS large data according to any one of claims 1 to 7.
10. A medium, characterized in that the medium has stored thereon computer instructions for causing the computer to execute the method for acquiring driving test cycle weight factors based on GIS large data according to any one of claims 1 to 7.
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