CN115457764A - Road section traffic density estimation method, device and medium based on vehicle track data - Google Patents
Road section traffic density estimation method, device and medium based on vehicle track data Download PDFInfo
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- G08—SIGNALLING
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- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
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Abstract
The invention discloses a road section traffic density estimation method, a road section traffic density estimation device and a road section traffic density estimation medium based on vehicle track data, wherein the method comprises the following steps: acquiring vehicle track data, and preprocessing the vehicle track data; obtaining an estimated time interval, and slicing the vehicle track data; extracting the characteristics of vehicle track data, constructing an original characteristic matrix of each time slice, and performing normalization processing to obtain an input characteristic matrix; inputting the input characteristic matrix into a plurality of preset neural network models, and training and outputting the original traffic density estimation value of each neural network model for each time slice; correcting the estimation result of the traffic density of each time slice; and fusing a plurality of neural network model estimation results. The invention fully excavates the characteristic value of the vehicle track data, integrates various intelligent algorithms, can realize more accurate road section traffic density estimation and provides important parameters for traffic control and management. The invention can be widely applied to the field of traffic information intelligent acquisition.
Description
Technical Field
The invention relates to the field of traffic information intelligent acquisition, in particular to a road section traffic density estimation method, a road section traffic density estimation device and a road section traffic density estimation medium based on vehicle track data.
Background
The traffic density is an important parameter in the traffic field, and can be widely applied to traffic control and management. By combining with the signal timing scheme, the traffic density has wide application prospect in expressway ramp control and urban road active control. At present, traffic jam problems exist in expressways and main roads in cities during specific peak periods. Part of navigation companies propose intelligent traffic control schemes based on indexes such as queuing length, but the method has poor control effect on road sections without queuing; the ramp control, active control and other modes essentially control the total number of vehicles on the road section, namely control the traffic density, so that the control method has a good control effect on various road traffic states.
The existing traffic density acquisition technology mainly comprises a fixed type and a movable type: a fixed acquisition mode is usually based on hardware facilities, has the problems of easy environmental influence, visual field blind areas, low precision, high cost, inconvenient maintenance and the like, and is unsustainable in method due to the fact that a helicopter is needed for acquisition; the mobile acquisition mode is mostly based on floating car data, namely part of vehicle track data collected by a vehicle-mounted GPS. The increasing popularity of navigation software facilitates the collection of floating car data sets. The mobile acquisition cost is low, the applicability is wide, the advantages are obvious, and the research of estimating the traffic density by using the track data of the floating car has higher application value.
At present, in the field of implementing traffic information intelligent acquisition based on floating car data, most mainstream scholars use a machine learning model in the field of artificial intelligence to research traffic estimation. However, as an important parameter for traffic management and control, the traffic density has few related research results, and the research results at home and abroad have certain disadvantages, such as that the feature value extraction is only limited to the "macro-macro" level, and the feature value traffic characteristics are not fully mined. Even though the models proposed by some scholars have certain competitiveness in accuracy, the high requirement on the quality of the data set is still difficult to adapt to the current situation of urban traffic density collection.
Disclosure of Invention
To solve at least one of the technical problems in the prior art to some extent, an object of the present invention is to provide a method, an apparatus and a medium for estimating road section traffic density based on vehicle trajectory data.
The technical scheme adopted by the invention is as follows:
a road section traffic density estimation method based on vehicle track data comprises the following steps:
acquiring vehicle track data and preprocessing the vehicle track data;
obtaining an estimated time interval, and slicing the vehicle track data according to the estimated time interval;
extracting the characteristics of vehicle track data, constructing an original characteristic matrix of each time slice and carrying out normalization processing to obtain an input characteristic matrix;
inputting the input characteristic matrix into a plurality of preset neural network models, and training and outputting the original traffic density estimation value of each neural network model for each time slice by combining a sliding time window principle;
correcting the estimation result of the traffic density of each time slice to obtain a corrected traffic density estimation value;
and fusing a plurality of neural network model estimation results to improve the accuracy of the traffic density estimation value of each time slice.
Further, the vehicle track data comprises a vehicle number, recording time, relative coordinates, a vehicle speed and acceleration;
the preprocessing the vehicle trajectory data includes:
acquiring the corresponding moment, X coordinates and Y coordinates of a first vehicle recorded by a data set as reference values;
and subtracting the recording time, the X coordinate and the Y coordinate in the rest vehicle track data from the reference value to obtain the preprocessed vehicle track data.
Further, the slicing the vehicle trajectory data according to the estimated time interval includes:
slicing the vehicle track data according to the estimated time interval, recording the serial number of each time slice as i, and further dividing the time slices into n time intervals;
the features comprise distance features, motion features, contrast features and traffic features, and an input feature matrix M' i of the time slice i is constructed in the following way n，m ：
Constructing an original feature matrix Mi of a time slice i n，m Comprises the following steps:
wherein, the jth row and kth column element P j，k Denotes the jth (j =1,2.. Times.), n) original feature values of a k (k =1,2.... Multidot.m) number of time intervals; extracting m characteristic values from each time interval j;
the original characteristic value P is measured j，k Normalizing to obtain an input feature matrix M' i of the time slice i n，m 。
Further, the input feature matrix M' i n，m The expression of (a) is:
wherein the content of the first and second substances,
the average value of the k-th feature element representing n time intervals in the time slice i, i.e. the original feature matrix Mi n，m Mean value of the k-th column; s k Representing the standard deviation of the kth feature element of n time intervals in a time slice i, i.e. the original feature matrix Mi n，m Column k standard deviation; p' j，k For the original characteristic value P j，k Normalized values, representing the j (j =1, 2...), n) input feature values of a k (k =1,2.... M) number of time intervals; m' i n，m An input feature matrix representing a time slice i.
Further, the m feature values extracted from each time interval j include:
the characteristic value of the distance characteristic comprises: average distance between adjacent vehicle coordinate points
Coordinate point distance standard deviation s of adjacent vehicles h Distance range R between coordinate points of adjacent vehicles h Distance squared average of adjacent vehicle coordinate points
Distance squared standard deviation of coordinate points of adjacent vehicles
Distance square pole difference between coordinate points of adjacent vehicles
The characteristic value of the motion characteristic comprises: average value of speed
Standard deviation of speed s v Extremely poor speed R v Average value of acceleration
Standard deviation of acceleration s a Acceleration extreme difference R a ；
The characteristic value of the comparison characteristic comprises: difference between the j +1 th and j th time interval speed average value
Difference in standard deviation of velocity
Difference of speed difference
Difference between average values of acceleration
Difference of standard deviation of acceleration
Difference of acceleration pole difference
The characteristic value of the traffic characteristic comprises: lane number W, traffic volume V;
wherein, the calculation mode of the traffic volume V is as follows:
in the formula, t 0 Representing the length L of the link and the average value of the speed
T represents the link length L and the maximum speed v max A ratio; a. β and c are both constants.
Further, the neural network model comprises a long-term and short-term memory neural network model, an extreme gradient boosting neural network model, a multilayer perceptron neural network model and a random forest neural network model;
the method for inputting the input feature matrix into a plurality of preset neural network models and training and outputting the original traffic density estimation value of each neural network model for each time slice by combining the sliding time window principle comprises the following steps:
input feature matrix M' i time sliced i n，m Inputting the data into a p-th neural network model, and combining a sliding time window principle to obtain a more accurate estimation value by utilizing the smoothness of a time sequence;
training and outputting the traffic density estimation values of the time slice i and the time slice i +1 under the input of the input feature matrix;
the traffic density estimation value of the time slice i +1 is cut off, and the traffic density estimation value d of the time slice i is taken p，i As an estimate of the original traffic density of the input feature matrix.
Further, the correcting the estimation result of the traffic density of each time slice to obtain a corrected traffic density estimation value includes:
correcting the original traffic density estimation value d of the time slice i output by the p-th neural network model by using filtering p，t To obtain a corrected density estimation value d 'with a smaller error from the true value' p，t The calculation formula is as follows:
d′ p，i ＝f(d p，i-2 ，d p，i-1 ，d p，i )
wherein, d i-2 ，d i-1 ，d i Representing the raw traffic density estimates for time slices i-2, i-1, i, respectively; f () is a mapping function.
Further, the fusing the plurality of neural network model estimation results comprises:
a Bayesian algorithm is used for fusing a plurality of neural network model estimation results, and the calculation formula is as follows:
D i ＝g(d′ 1，i ，......，d′ q，i )
wherein, d' p，i Indicates the application of the p-th speciesA corrected time slice i density estimate obtained by the neural network model, p =1,2.... Q; d i Represents the fused time slice i density estimate; the function g represents the operation of a bayesian neural network.
The other technical scheme adopted by the invention is as follows:
a road section traffic density estimation apparatus based on vehicle trajectory data, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method described above.
The other technical scheme adopted by the invention is as follows:
a computer readable storage medium in which a processor executable program is stored, which when executed by a processor is for performing the method as described above.
The invention has the beneficial effects that: the invention fully excavates the characteristic value of the vehicle track data, integrates various intelligent algorithms, can realize more accurate road section traffic density estimation and provides important parameters for traffic control and management.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description is made on the drawings of the embodiments of the present invention or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating a method for estimating road traffic density based on vehicle trajectory data according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a concept for estimating road traffic density using vehicle trajectory data according to an embodiment of the present invention;
FIG. 3 is a schematic input/output diagram of an LSTM neural network model in an embodiment of the present invention;
FIG. 4 is a schematic diagram of the filtering correction of the original traffic density estimate according to an embodiment of the present invention;
fig. 5 is a diagram illustrating the estimation result of the fuzhou data set section density according to the embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, a plurality of means is one or more, a plurality of means is two or more, and greater than, less than, more than, etc. are understood as excluding the essential numbers, and greater than, less than, etc. are understood as including the essential numbers. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.
As shown in fig. 1, the present embodiment provides a road section traffic density estimation method based on vehicle trajectory data, which fully utilizes the advantages of a floating car data set, and integrates various intelligent algorithms to achieve more accurate road section traffic density estimation, thereby providing important parameters for traffic control and management. The method specifically comprises the following steps:
s1, vehicle track data are obtained, and the vehicle track data are preprocessed.
The vehicle track data comprises vehicle numbers, recording time, relative coordinates, vehicle speed and acceleration, and the data preprocessing comprises the following steps:
appointing the corresponding time, X coordinate and Y coordinate of the first vehicle recorded by the data set as reference values;
and the recording time, the X coordinate and the Y coordinate in the rest vehicle track data are different from the reference value, so as to obtain the track data after preprocessing.
As an alternative embodiment, step S1 includes the following S11-S12:
and S11, acquiring a data set for estimating the traffic density of the road section, wherein the data set comprises parameters such as vehicle numbers, recording time, relative coordinates, vehicle speed, acceleration and the like. In this embodiment, a floating car data set in the central urban area of 5 months and 5 days in 2018 fuzhou is selected, and comprises 6,402,027 GPS sample points of 7073 taxis in the central area of fuzhou within 1 day, wherein data intervals are unequal (5-20 seconds), density is high, and tide phenomenon is obvious.
As shown in fig. 2, the idea of the present invention is to estimate the overall traffic density of a road section by using the trajectory data uploaded by a part of vehicles. In this embodiment, a proportion of 15% of the vehicles are randomly selected as part of the vehicles for estimation, the trajectory data is used to estimate the traffic density, and then the estimated value is compared with the real value of the full sample to obtain the estimation accuracy.
S12, preprocessing the track data of the part of vehicles, and comprising the following steps:
appointing a corresponding moment, an X coordinate and a Y coordinate of a first vehicle recorded by a data set as reference values;
and (3) recording time, X coordinates and Y coordinates in the rest vehicle track data and making difference with the reference value to obtain track data after preprocessing, wherein the track data are shown in a table 1.
TABLE 1 post-preprocessing partial track data
And S2, obtaining an estimated time interval, and slicing the vehicle track data according to the estimated time interval.
Selecting an estimated time interval, slicing the overall data according to the estimated time interval, and further dividing the time slices into n time intervals.
As an alternative implementation, step S2 includes the following S21-S23:
s21, comprehensively considering the sample interval situation of the data set and the traffic density estimation requirement, and selecting the estimation time interval to be 3 minutes.
And S22, slicing the overall data according to the estimated time interval, wherein the serial number of each time slice is marked as i.
S23, dividing the time slice into n time intervals, where n =6, that is, each time interval is 30 seconds.
And S3, extracting the characteristics of the vehicle track data, constructing an original characteristic matrix of each time slice, and carrying out normalization processing to obtain an input characteristic matrix.
And extracting characteristic values contained in the track data, constructing and normalizing the original characteristic matrix of each time slice, and obtaining an input characteristic matrix. Wherein the characteristic values are classified into distance characteristics, motion characteristics, contrast characteristics, traffic characteristics and the like.
As an alternative embodiment, step S3 includes the following steps S31-S33:
and S31, extracting original characteristic values contained in the track data, and dividing the original characteristic values into distance characteristics, motion characteristics, comparison characteristics, traffic characteristics and the like.
The distance features include an average of adjacent vehicle coordinate points distance
Coordinate point distance standard deviation s of adjacent vehicles h Distance range R between coordinate points of adjacent vehicles h Mean square of distance between adjacent vehicle coordinate points
Distance squared standard deviation of coordinate points of adjacent vehicles
Distance square pole difference between coordinate points of adjacent vehicles
The motion characteristics include a velocity average
Standard deviation of speed s v Extremely poor speed R v Mean value of acceleration
Standard deviation of acceleration s a Acceleration extreme difference R a ；
The comparison feature comprises the difference between the j +1 th time interval and the average speed value of the j time interval
Difference in standard deviation of velocity
Difference of speed difference
Difference between average values of acceleration
Difference of acceleration standard deviation
Difference of acceleration pole difference
The traffic characteristics include lane number W, traffic volume V. The traffic volume is obtained by calculating a road resistance function variable formula, wherein the formula is as follows:
wherein, t 0 Representing the length L of the link and the average value of the speed
T represents the link length L and the maximum speed v max A ratio; the constants a and beta are respectively 0.15 and 0.4 of default values; and c, replacing the actual situation of the road section and the historical data by a constant.
S32, constructing an input feature matrix M' i of the time slice i n，m ：
Wherein, the jth row and kth column element P j，k Represents the kth (k =1,2.... Times, m) original feature value in the jth (j =1,2.... Times, n) time interval.
And S33, normalizing the original characteristic matrix. The original characteristic value P is measured j，k Put into formula
Normalizing column by column to obtain an input feature matrix M' i of the time slice i n，m The calculation formula is as follows:
wherein the content of the first and second substances,
the average value of the kth feature element representing n time intervals in the time slice i, i.e. the original feature matrix Mi n，m Mean value of the k-th column; s is k Represents the standard deviation of the kth characteristic element of n time intervals in the time slice i, namely the original characteristic matrix Mi n，m Column k standard deviation; p' j，k For the original characteristic value P j，k A normalized numerical value representing a kth (k =1,2.... N) input feature value in a jth (j =1,2.... N) time interval; m' i n，m An input feature matrix representing a time slice i.
And S4, inputting the input characteristic matrix into a plurality of preset neural network models, and training and outputting the original traffic density estimation value of each neural network model for each time slice.
And inputting the characteristic matrix into a neural network model, and training and outputting the original traffic density estimation value of each time slice of each neural network model by combining a sliding time window principle. Wherein the neural network model comprises Long Short Term Memory (LSTM), extreme gradient boost (XGBOOST), multi-layer perceptron (MLP), random Forest (RF), and the like.
Further as an alternative embodiment, the input feature matrix M' i of the time slice i is sliced n，m Inputting the model of the p (p =1,2,3, 4) neural network, combining the sliding time window principle, and using the time sequenceSmoothness yields a more accurate estimate where the traffic density estimates for time slices i and i +1 obtained at the input of the feature matrix are trained and output. Discarding the estimate of time slice i +1, and taking the estimate d of time slice i p，i Is the raw estimate of the feature matrix. Wherein the neural network model comprises LSTM, XGBOST, MLP, RF and the like. As shown in fig. 3, an input/output process of the neural network model is shown by taking LSTM as an example.
And S5, correcting the estimation result of the traffic density of each time slice to obtain a corrected traffic density estimation value.
And correcting the estimation result of the traffic density of each time slice by using filtering to obtain a corrected traffic density estimation value so as to reduce the error between the estimation value and the actual value.
Further as an alternative embodiment, the original traffic density estimation d of the time slice i output by the p-th neural network model is modified by filtering p，i To obtain a corrected density estimation value d 'with a smaller error from the true value' p，t The calculation formula is as follows:
d′ p，i ＝f(d p，i-2 ，d p，i-1 ，d p，i )
wherein d is i-2 ，d i-1 ，d i Representing the raw traffic density estimates for time slices i-2, i-1, i, respectively;
as shown in fig. 4, the mapping function f is determined according to the concept of minimizing the difference between the estimated density value and the actual value in the training data set of the road segment.
And S6, fusing the estimation results of the plurality of neural network models to improve the accuracy of the traffic density estimation value of each time slice.
And fusing a plurality of neural network model estimation results by using a Bayesian algorithm to improve the accuracy of the traffic density estimation value of each time slice.
Further as an optional implementation way, a Bayesian algorithm is used to fuse a plurality of neural network model estimation results, and the calculation formula is as follows:
D i ＝g(d′ 1，i ，......，d′ q，i )
wherein, d' p，i Representing the corrected density estimation value of the time slice i obtained by applying the p-th neural network model; p =1,2.... Q; d i Represents the fused time slice i density estimate; the function g represents the operation of the Bayesian neural network, and the basic Bayesian formula is as follows:
wherein P (A) is prior probability and P (B) is evidence;
the mean square error and the accuracy of the estimated values obtained by each method are shown in table 2, and the estimated values obtained after Bayesian fusion have the highest accuracy; wherein the unit of mean square error is the square of the number of vehicles per kilometer, i.e. (pcu/km) 2 。
TABLE 2 mean square error and accuracy of estimates from each method
Fig. 5 shows a schematic diagram of the estimation result of the link density of the fuzhou data set according to the embodiment of the present invention.
In summary, compared with the prior art, the present embodiment has the following advantages and beneficial effects:
(1) The invention provides a new idea for estimating road section traffic density based on partial vehicle track data, and integrates various machine learning algorithms on the basis of fully mining the microscopic characteristics and traffic characteristics of floating car data to obtain a more accurate road section traffic density estimation value. The traffic information intelligent acquisition method designed by the invention is based on the combined drive of data and a model, has important value for traffic control and management, and can be widely applied to scenes such as ramp control, active control, congestion judgment and the like.
(2) The method fully excavates the data characteristics of the floating car, and improves the robustness of traffic density estimation and the adaptability to different quality data sets. When an input matrix is constructed, the characteristic values are divided into categories such as distance characteristics, motion characteristics, comparison characteristics, traffic characteristics and the like, characteristics such as coordinate point distances are extracted on a microscopic level, traffic characteristics such as road resistance functions are creatively introduced, and the connection between density estimation and traffic subject knowledge is strengthened.
(3) The invention adopts various methods to improve the estimation precision, not only introduces a sliding time window and filtering correction estimation values, but also fuses a plurality of neural network model estimation results by using a Bayesian algorithm. The method can reduce the error between the estimated value and the actual value and improve the accuracy of the traffic density estimation.
The embodiment further provides a device for estimating road section traffic density based on vehicle track data, including:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of fig. 1.
The device for estimating the road section traffic density based on the vehicle track data can execute the method for estimating the road section traffic density based on the vehicle track data provided by the embodiment of the method, can execute any combination of the implementation steps of the embodiment of the method, and has corresponding functions and beneficial effects of the method.
The embodiment of the application also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and executed by the processor to cause the computer device to perform the method illustrated in fig. 1.
The embodiment also provides a storage medium, which stores instructions or programs capable of executing the method for estimating the road section traffic density based on the vehicle track data, and when the instructions or the programs are run, the steps can be executed in any combination of the method embodiments, and the corresponding functions and the advantages of the method are achieved.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The functions may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following technologies, which are well known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the foregoing description of the specification, reference to the description of "one embodiment/example," "another embodiment/example," or "certain embodiments/examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A road section traffic density estimation method based on vehicle track data is characterized by comprising the following steps:
acquiring vehicle track data and preprocessing the vehicle track data;
obtaining an estimated time interval, and slicing the vehicle track data according to the estimated time interval;
extracting the characteristics of vehicle track data, constructing an original characteristic matrix of each time slice and carrying out normalization processing to obtain an input characteristic matrix;
inputting the input characteristic matrix into a plurality of preset neural network models, and training and outputting the original traffic density estimation value of each neural network model for each time slice;
correcting the estimation result of the traffic density of each time slice to obtain a corrected traffic density estimation value;
and fusing a plurality of neural network model estimation results to improve the accuracy of the traffic density estimation value of each time slice.
2. The method according to claim 1, wherein the vehicle trajectory data comprises vehicle number, recording time, relative coordinates, vehicle speed and acceleration;
the preprocessing the vehicle trajectory data includes:
acquiring a corresponding moment, an X coordinate and a Y coordinate of a first vehicle recorded by a data set as reference values;
and subtracting the recording time, the X coordinate and the Y coordinate in the rest vehicle track data from the reference value to obtain the preprocessed vehicle track data.
3. The method according to claim 1, wherein the slicing the vehicle trajectory data according to the estimation time interval comprises:
slicing the vehicle track data according to the estimated time interval, recording the serial number of each time slice as i, and further dividing the time slices into n time intervals;
the features include distance features, motion features, contrast features, and trafficCharacteristically, an input feature matrix M' i of time slices i is constructed in the following way n，m ：
Constructing an original feature matrix Mi of a time slice i n，m Comprises the following steps:
wherein, the jth row and kth column elements P j，k Denotes the jth (j =1, 2.), n) original feature values of a k (k =1,2.... Multidot.m) number of time intervals; extracting m characteristic values from each time interval j;
the original characteristic value P is measured j，k Normalizing to obtain an input feature matrix M' i of the time slice i n，m 。
4. The method as claimed in claim 3, wherein the input feature matrix M' i is a matrix of the input features n，m The expression of (c) is:
wherein the content of the first and second substances,
the average value of the kth feature element representing n time intervals in the time slice i, i.e. the original feature matrix Mi n，m Mean value of the k-th column; s k Representing the standard deviation of the kth feature element of n time intervals in a time slice i, i.e. the original feature matrix Mi n，m The kth column standard deviation; p' j，k For the original characteristic value P j，k A normalized numerical value representing a kth (k =1,2.... N) input feature value in a jth (j =1,2.... N) time interval; m' i n，m An input feature matrix representing a time slice i.
5. The method according to claim 1, wherein the extracting m feature values from each time interval j comprises:
the characteristic value of the distance characteristic comprises: average distance between adjacent vehicle coordinate points
Distance standard deviation s between adjacent vehicle coordinate points h Distance range R between coordinate points of adjacent vehicles h Mean square of distance between adjacent vehicle coordinate points
Distance squared standard deviation of adjacent vehicle coordinate points
Distance square pole difference between coordinate points of adjacent vehicles
The characteristic value of the motion characteristic comprises: average value of speed
Standard deviation of speed s v Extremely poor speed R v Mean value of acceleration
Standard deviation of acceleration s a Acceleration extreme difference R a ；
The characteristic value of the comparison characteristic comprises: difference between the j +1 th and j th time interval speed average value
Difference in standard deviation of velocity
Difference of speed difference
Difference between average values of acceleration
Difference of standard deviation of acceleration
Difference of acceleration pole difference
The characteristic value of the traffic characteristic comprises: lane number W, traffic volume V;
wherein, the calculation mode of the traffic volume V is as follows:
in the formula, t 0 Representing the length L of the link and the average value of the speed
T represents the link length L and the maximum speed v max A ratio;
a. β and c are both constants.
6. The method for estimating the road section traffic density based on the vehicle trajectory data according to claim 1, wherein the neural network model comprises a long-term and short-term memory neural network model, a polar gradient boosting neural network model, a multilayer perceptron neural network model and a random forest neural network model;
the method for inputting the input feature matrix into a plurality of preset neural network models and training and outputting the original traffic density estimation value of each neural network model for each time slice comprises the following steps:
input feature matrix M' i time sliced i n，m Inputting the data into a p-th neural network model, and combining a sliding time window principle to obtain a more accurate estimation value by utilizing the smoothness of a time sequence;
training and outputting, under the input of the input feature matrix, traffic density estimation values of the time slice i and the time slice i +1 are obtained;
the traffic density estimation value of the time slice i +1 is discarded, and the traffic density estimation value d of the time slice i is taken p，i As an estimate of the original traffic density of the input feature matrix.
7. The method as claimed in claim 6, wherein the step of correcting the estimation result of the traffic density at each time slice to obtain a corrected traffic density estimation value comprises:
correcting the original traffic density estimation value d of the time slice i output by the p-th neural network model by filtering p，t Obtaining a corrected density estimated value d 'having a smaller error from the actual value' p，t The calculation formula is as follows:
d′ p，i ＝f(d p，i-2 ，d p，i-1 ，d p，i )
wherein, d i-2 ，d i-1 ，d i Representing the raw traffic density estimates for time slices i-2, i-1, i, respectively; f () is a mapping function.
8. The method according to claim 1, wherein the fusing the neural network model estimation results comprises:
and fusing a plurality of neural network model estimation results by using a Bayesian algorithm, wherein the calculation formula is as follows:
D i ＝g(d′ 1，i ，......，d′ q，i )
wherein, d' p，i Represents corrected time slice i density estimates obtained using a p-th neural network model, p =1,2.... Q; d i Represents the fused time slice i density estimate; the function g represents the operation of a bayesian neural network.
9. A road section traffic density estimation device based on vehicle trajectory data, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of any one of claims 1-8.
10. A computer readable storage medium in which a program executable by a processor is stored, wherein the program executable by the processor is adapted to perform the method according to any one of claims 1 to 8 when executed by the processor.
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