CN114549930A - Rapid road short-time vehicle head interval prediction method based on trajectory data - Google Patents

Rapid road short-time vehicle head interval prediction method based on trajectory data Download PDF

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CN114549930A
CN114549930A CN202210157748.7A CN202210157748A CN114549930A CN 114549930 A CN114549930 A CN 114549930A CN 202210157748 A CN202210157748 A CN 202210157748A CN 114549930 A CN114549930 A CN 114549930A
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张卫华
汪春
谢天舒
吴丛
朱文佳
李志斌
梁子君
胡恒
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Hefei University Of Technology Design Institute Group Co ltd
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Abstract

The invention discloses a method for predicting the short-time locomotive spacing of a fast way based on trajectory data, which belongs to the technical field of locomotive spacing prediction and comprises the following steps: the method comprises the following steps: acquiring vehicle track data of the express way and carrying out normalization processing; step two: converting the normalized vehicle i time sequence data into a polar coordinate system time sequence by using a coordinate change method; step three: carrying out trigonometric function transformation of angles and/or differences; step four: inputting a vehicle head distance two-dimensional image obtained by utilizing the GADF to a GAN-LSTM model, and outputting a vehicle head distance predicted value; the time sequence of the vehicle head distance is extracted based on the vehicle track data, and the GAF-GAN-LSTM model is utilized to predict the vehicle head distance between the express way vehicles, so that the position distribution characteristics of the vehicles on the express way in the future period are obtained, and support is provided for traffic state prediction, vehicle guidance and intelligent management and control of the express way.

Description

Rapid road short-time vehicle head interval prediction method based on trajectory data
Technical Field
The invention belongs to the technical field of vehicle head distance prediction, and particularly relates to a method for predicting a short-time vehicle head distance of a fast path based on track data.
Background
With the increase of the quantity of motor vehicles kept, the problem of expressway traffic jam is increasingly highlighted. In order to alleviate the problem of traffic jam of the express way, the future traffic flow running state of the express way needs to be predicted, so that a real-time traffic management scheme is formulated. The existing method for predicting short-term traffic flow of the express way mainly focuses on predicting macroscopic traffic flow parameters, such as: traffic flow, interval average speed, interval travel time, etc. With the popularization and application of radar monitoring technology and vehicle-mounted GPS equipment, the acquisition of microscopic vehicle track data becomes possible. The distance between the two car heads is used as an important parameter in the field of microscopic traffic flow, and reflects the position relation between the two front and rear cars. In addition, the development of the car networking technology provides technical support for real-time guidance of the vehicle. By predicting the distribution characteristic of the distance between the vehicle heads of the express way at the future time, the ramp networked vehicles are guided to merge into the main line by using the main line gap, so that the main line gap is utilized to the maximum extent, the influence of continuous merging of the ramp vehicles on the operation of the main line traffic flow is reduced, the fine management and control of the express way are realized, and the integral stable operation of the express way is ensured.
However, the existing short-time locomotive interval prediction method cannot fully mine the correlation between the historical locomotive interval sequence and the future locomotive interval sequence, and meanwhile, the prediction precision is limited. Therefore, the method combines the strong advantages of deep learning in the field of feature extraction and image prediction, converts one-dimensional headway distance time sequence data into a two-dimensional picture by using GAF, and extracts the correlation between the historical headway distance and the future headway distance based on LSTM to obtain the headway distance feature vector with stronger representation capability. And the strong advantages of the generated countermeasure network GAN in the field of image prediction are utilized to predict the distance between the car heads at the future moment.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides a method for predicting the short-time locomotive distance of the express way based on track data.
The purpose of the invention can be realized by the following technical scheme:
a method for predicting a short-time locomotive interval of a fast way based on track data comprises the following specific steps:
the method comprises the following steps: acquiring vehicle track data of the express way and carrying out normalization processing;
step two: converting the normalized vehicle i time sequence data into a polar coordinate system time sequence by using a coordinate change method;
step three: carrying out trigonometric function transformation of angles and/or differences;
step four: and inputting the two-dimensional image of the vehicle head space obtained by utilizing the transformation of the gram difference angular field GADF into the GAN-LSTM model, and outputting a predicted value of the vehicle head space.
Further, the method for performing normalization processing comprises:
the individual vehicles are labeled as i,obtaining the time sequence data of the head interval of a single vehicle i
Figure BDA0003513457910000021
Scaling the time sequence of the intervals between the car heads to [0, 1%]Or [ -1,1 [)]An interval; according to the formula:
Figure BDA0003513457910000022
Figure BDA0003513457910000023
in [0,1 ]]Or [ -1,1 [)]Normalizing the interval of (A); in the formula (I), the compound is shown in the specification,
Figure BDA0003513457910000024
in order to normalize the distance between the car heads,
Figure BDA0003513457910000025
for the raw headway data, min (d) and max (d) are the minimum and maximum values, respectively, in the vehicle i headway timing data set.
Further, the method for converting the normalized vehicle i time sequence data into the polar coordinate system time sequence comprises the following steps:
according to the formula
Figure BDA0003513457910000026
Carrying out transformation;
in the formula (I), the compound is shown in the specification,
Figure BDA0003513457910000027
is the polar angle corresponding to the distance between the heads of the vehicle i at the time t,
Figure BDA0003513457910000028
for the normalized headway distance of the vehicle i at the time t,
Figure BDA0003513457910000031
when the distance between the heads of the vehicles iAnd in the ordinal data set, r is the polar radius corresponding to the headway distance of the vehicle i at the time t, M is the total number of the headway distance timestamps of the vehicle i, and N is a timestamp set.
Further, the method of performing trigonometric function transformation of angles and/or differences comprises:
using a cos function of the two angle sums to obtain a gram sum angle field GASF, and using a sin function of the two angle differences to obtain a gram difference angle field GADF; the GASF and GADF calculation methods are as follows:
Figure BDA0003513457910000032
Figure BDA0003513457910000033
further, the method for establishing the GAN model comprises the following steps:
step SA 1: acquiring and generating a confrontation type network GAN, and setting initial parameters of a GAN model, wherein the initial parameters of the GAN model comprise: learning rate eta, batch processing quantity m and generator initial parameter thetag0And an initial parameter theta of the discriminatord0Number of generator iterations nGAnd the number of iterations n of the arbiter in each generator iterationD(ii) a Inputting the GAF converted image into a generation countermeasure network GAN;
step SA 2: randomly select m sample points { d } from the dataset1,d2,...,dm-randomly selecting m vectors z from the noisy data distribution1,z2,...,zm}；
Step SA 3: inputting Z into the generator to obtain m generated data d1,d2,...,dm}；
Step SA 4: updating the parameter θ of the discriminatordTo maximize
Figure BDA0003513457910000034
Step SA 5: judging whether the iteration number is more than nDWhen the number of iterations is greater than nDIf so, stopping iteration; the number of iterations is not more than nDIf so, continuing to step SA 1-step SA4 until an iteration stop condition is met;
step SA 6: again, m vectors z are randomly sampled from the noise distribution1,z2,...,zm}；
Step SA 7: updating the parameter θ of the generatordTo minimize
Figure BDA0003513457910000041
Step SA 8: judging whether the maximum iteration number n of the generator is reachedGWhen the maximum number of iterations n of the generator is reachedGIf so, stopping iterative training and outputting the trained GAN model; when the maximum iteration number n of the generator is not reachedGAnd if so, continuing to step SA 2-step SA7 until an iteration stop condition is met.
Further, in step SA 4;
Figure BDA0003513457910000042
Figure BDA0003513457910000043
further, in step SA 7:
Figure BDA0003513457910000044
Figure BDA0003513457910000045
further, the method for establishing the GAN-LSTM model comprises the following steps:
step SB 1: connecting a generator in the trained GAN model with the LSTM;
step SB 2: setting initial parameters of the LSTM modelThe number of the components comprises: learning rate eta, batch processing quantity m, and LSTM initial parameter thetaf0Maximum number of iterations n of LSTMfAnd vehicle i locomotive spacing time sequence data; inputting the GAF-converted image into the LSTM;
step SB 3: randomly selecting m vectors { v ] from real data1,ν2,...,νm}；
Step SB 4: updating parameter θ of LSTMf；
Figure BDA0003513457910000046
Figure BDA0003513457910000047
Step SB 5: judging whether the maximum iteration number n of the LSTM is reachedfWhen the maximum number of iterations n of LSTM is reachedfStopping iterative training and outputting the trained GAN-LSTM model; when the maximum number of iterations n of the LSTM is not reachedfAnd if so, continuing to step SB 2-step SB4 until the iteration stop condition is met.
Compared with the prior art, the invention has the beneficial effects that: by extracting a time sequence of the vehicle head distance based on vehicle track data and predicting the vehicle head distance between the vehicles on the express way by using a GAN-LSTM model, the position distribution characteristics of the vehicles on the express way in a future period are obtained, and support is provided for traffic state prediction, vehicle guidance and intelligent management and control of the express way.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of the present invention for vehicle headway distance prediction;
FIG. 2 is a schematic representation of the GADF of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
As shown in fig. 1 to 2, a method for predicting a short-time locomotive distance of a fast way based on trajectory data specifically includes:
the method comprises the following steps: acquiring vehicle track data of the express way and carrying out normalization processing;
acquiring vehicle track data of the express way by methods including but not limited to video acquisition, radar equipment acquisition, vehicle-mounted GPS or Beidou navigation equipment acquisition and the like, and acquiring corresponding vehicle track data by a corresponding vehicle track data acquisition method;
illustratively, a navigation map is acquired by collecting through a vehicle-mounted GPS or Beidou navigation equipment, and the position of a vehicle in the navigation map is acquired in real time according to the vehicle-mounted GPS or Beidou navigation equipment to acquire a vehicle track, so as to acquire time sequence data corresponding to the vehicle track.
The method for carrying out normalization processing comprises the following steps:
label individual vehicle as i; obtaining the time sequence data of the head interval of a single vehicle i
Figure BDA0003513457910000061
Scaling the time sequence of the intervals between the car heads to [0, 1%]Or [ -1,1 [)]An interval; according to the formula:
Figure BDA0003513457910000062
Figure BDA0003513457910000063
in [0,1 ]]Or [ -1,1 [)]Normalizing the interval of (A); in the formula (I), the compound is shown in the specification,
Figure BDA0003513457910000064
in order to normalize the distance between the car heads,
Figure BDA0003513457910000065
and (c) taking the original headway data, namely the headway value of the ith vehicle at the time t, wherein min (D) and max (D) are respectively the minimum value and the maximum value in the headway time sequence data set of the vehicle i.
Step two: converting the normalized vehicle i time sequence data into a polar coordinate system time sequence by using a coordinate change method;
the method for converting the normalized vehicle i time sequence data into the polar coordinate system time sequence comprises the following steps:
according to the formula
Figure BDA0003513457910000066
Carrying out transformation;
in the formula (I), the compound is shown in the specification,
Figure BDA0003513457910000067
is the polar angle corresponding to the distance between the heads of the vehicle i at the time t,
Figure BDA0003513457910000068
for the normalized headway distance of the vehicle i at the time t,
Figure BDA0003513457910000069
the method comprises the steps of collecting time sequence data of the vehicle head interval of a vehicle i, wherein r is a polar radius corresponding to the vehicle head interval of the vehicle i at the moment t, M is the total number of timestamps of the vehicle head interval of the vehicle i, and N is a timestamp collection.
Step three: carrying out trigonometric function transformation of angles and/or differences;
the method for performing trigonometric function transformation of angles and/or differences comprises:
using a cos function of the two angle sums to obtain a gram sum angle field GASF, and using a sin function of the two angle differences to obtain a gram difference angle field GADF; the calculation methods for GASF and GADF are as follows:
Figure BDA0003513457910000071
Figure BDA0003513457910000072
as shown in fig. 2, the present application selects a gram difference angular field GADF to convert the one-dimensional time series data of the headway distance of the vehicle into a two-dimensional image.
Step four: and inputting the two-dimensional image of the vehicle head space obtained by utilizing the transformation of the gram difference angular field GADF into the GAN-LSTM model, and outputting a predicted value of the vehicle head space.
The method for establishing the GAN model comprises the following steps:
step SA 1: acquiring and generating a confrontation type network GAN, and setting initial parameters of a GAN model, wherein the initial parameters of the GAN model comprise: learning rate eta, batch processing amount m, generator initial parameter thetag0And an initial parameter theta of a discriminatord0Number of generator iterations nGAnd the number of iterations n of the arbiter in each generator iterationD(ii) a Inputting the GAF converted image into a generation countermeasure network GAN; generating an antagonistic network GAN as an existing deep learning model;
step SA 2: randomly choosing m sample points { d } from the dataset1,d2,...,dm-randomly selecting m vectors z from the noisy data distribution1,z2,...,zm}; the noise data distribution comprises a Gaussian distribution mode, a normal distribution mode and other distribution modes;
step SA 3: inputting Z into the generator to obtain m generated data d1,d2,...,dm}; z is the vector in step SA 2;
step SA 4: updating the parameter θ of the discriminatordTo maximize
Figure BDA0003513457910000073
Wherein the content of the first and second substances,
Figure BDA0003513457910000074
is as follows;
Figure BDA0003513457910000081
Figure BDA0003513457910000082
step SA 5: judging whether the iteration number is more than nDWhen the number of iterations is greater than nDIf so, stopping iteration; the number of iterations is not more than nDIf so, continuing to step SA 1-step SA4 until an iteration stop condition is met;
step SA 6: again, m vectors z are randomly sampled from the noise distribution1,z2,...,zmThe vector does not need to be consistent with the vector in step SA 2;
step SA 7: updating the parameter θ of the generatordTo minimize
Figure BDA0003513457910000083
Figure BDA0003513457910000084
Figure BDA0003513457910000085
Step SA 8: judging whether the maximum iteration number n of the generator is reachedGWhen the maximum number of iterations n of the generator is reachedGIf so, stopping iterative training and outputting the trained GAN model; when the maximum iteration number n of the generator is not reachedGAnd if so, continuing to step SA 2-step SA7 until an iteration stop condition is met.
The method for establishing the GAN-LSTM model comprises the following steps:
step SB 1: connecting a generator in the trained GAN model with the LSTM;
step SB 2: setting initial parameters of the LSTM model, wherein the initial parameters of the LSTM model comprise: learning rate eta, batch processing quantity m, and LSTM initial parameter thetaf0Maximum number of iterations n of LSTMfAnd vehicle i locomotive spacing time sequence data; inputting the GAF-converted image into the LSTM;
step SB 3: randomly selecting m vectors { v ] from real data1,ν2,...,νm}；
Step SB 4: updating parameter θ of LSTMf；
Figure BDA0003513457910000091
Figure BDA0003513457910000092
Step SB 5: judging whether the maximum iteration number n of the LSTM is reachedfWhen the maximum number of iterations n of the LSTM is reachedfStopping iterative training and outputting the trained GAN-LSTM model; when the maximum number of iterations n of the LSTM is not reachedfAnd if so, continuing to step SB 2-step SB4 until the iteration stop condition is met.
The time sequence of the vehicle head distance is extracted based on the vehicle track data, and the GAF-GAN-LSTM model is utilized to predict the vehicle head distance between the express way vehicles, so that the position distribution characteristics of the vehicles on the express way in the future period are obtained, and support is provided for traffic state prediction, vehicle guidance and intelligent management and control of the express way.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
Working principle of the invention: acquiring vehicle track data of the express way and carrying out normalization processing; converting the normalized vehicle i time sequence data into a polar coordinate system time sequence by using a coordinate change method; using a cos function of the two angle sums to obtain a gram sum angle field GASF, and using a sin function of the two angle differences to obtain a gram difference angle field GADF; acquiring a generated countermeasure network GAN, setting initial parameters of a GAN model, and inputting an image converted by GAF into the generated countermeasure network GAN; randomly selecting m sample points from the data set, and randomly selecting m vectors from the noise data distribution; inputting Z into a generator to obtain m generated data; updating the parameter θ of the discriminatordTo maximize
Figure BDA0003513457910000093
Judging whether the iteration number is more than nDWhen the number of iterations is greater than nDIf so, stopping iteration; the number of iterations is not more than nDIf so, continuing to step SA 1-step SA4 until an iteration stop condition is met; randomly sampling m vectors from the noise distribution again; updating the parameter θ of the generatordTo minimize
Figure BDA0003513457910000094
Judging whether the maximum iteration number n of the generator is reachedGWhen the maximum number of iterations n of the generator is reachedGIf so, stopping iterative training and outputting the trained GAN model; when the maximum iteration number n of the generator is not reachedGIf so, continuing to step SA 2-step SA7 until an iteration stop condition is met;
connecting a generator in the trained GAN model with the LSTM; setting initial parameters of an LSTM model, and inputting the GAF converted image into the LSTM; randomly selecting m vectors from real data; updating parameter θ of LSTMf(ii) a Judging whether the maximum iteration number n of the LSTM is reachedfWhen the maximum number of iterations n of the LSTM is reachedfStopping iterative training and outputting the trained GAN-LSTM model; when the maximum number of iterations n of the LSTM is not reachedfIf so, continuing the step SB 2-the step SB4 until the iteration stop condition is met; will utilize gramAnd inputting the two-dimensional image of the vehicle head space obtained by converting the difference angle field GADF into the GAN-LSTM model, and outputting a predicted value of the vehicle head space.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.

Claims (8)

1. A method for predicting the short-time locomotive interval of a fast way based on track data is characterized by comprising the following steps:
the method comprises the following steps: acquiring vehicle track data of the express way and carrying out normalization processing;
step two: converting the normalized vehicle i time sequence data into a polar coordinate system time sequence by using a coordinate change method;
step three: carrying out trigonometric function transformation of angles and/or differences;
step four: and inputting the two-dimensional image of the vehicle head space obtained by utilizing the transformation of the gram difference angular field GADF into the GAN-LSTM model, and outputting a predicted value of the vehicle head space.
2. The method for predicting the short-time locomotive distance of the express way based on the trajectory data as claimed in claim 1, wherein the method for carrying out normalization processing comprises the following steps:
marking a single vehicle as i, and acquiring the time sequence data of the distance between the heads of the single vehicle i
Figure FDA0003513457900000011
Scaling the time sequence of the intervals between the car heads to [0, 1%]Or [ -1,1 [)]An interval; according to the formula:
Figure FDA0003513457900000012
Figure FDA0003513457900000013
in [0,1 ]]Or [ -1,1 [)]Normalizing the interval of (A); in the formula (I), the compound is shown in the specification,
Figure FDA0003513457900000014
in order to normalize the distance between the car heads,
Figure FDA0003513457900000015
for the raw headway data, min (d) and max (d) are the minimum and maximum values, respectively, in the vehicle i headway timing data set.
3. The method for predicting the short-term headway of the express way based on the track data as claimed in claim 1, wherein the method for converting the normalized vehicle i time sequence data into a polar coordinate system time sequence comprises the following steps:
according to the formula
Figure FDA0003513457900000016
Carrying out transformation;
in the formula (I), the compound is shown in the specification,
Figure FDA0003513457900000021
the polar angle corresponding to the distance between the heads of the vehicle i at the time t,
Figure FDA0003513457900000022
for the normalized headway distance of the vehicle i at the time t,
Figure FDA0003513457900000023
the method comprises the steps of collecting time sequence data of the vehicle head interval of a vehicle i, wherein r is a polar radius corresponding to the vehicle head interval of the vehicle i at the moment t, M is the total number of timestamps of the vehicle head interval of the vehicle i, and N is a timestamp collection.
4. The method for predicting the short-term headway of the express way based on the track data as claimed in claim 1, wherein the method for performing trigonometric function transformation on angles and/or differences comprises the following steps:
using a cos function of the two angle sums to obtain a gram sum angle field GASF, and using a sin function of the two angle differences to obtain a gram difference angle field GADF; the calculation methods for GASF and GADF are as follows:
Figure FDA0003513457900000024
Figure FDA0003513457900000025
5. the method for predicting the short-term headway of the express way based on the trajectory data as claimed in claim 1, wherein the method for establishing the GAN model comprises the following steps:
step SA 1: acquiring and generating a confrontation type network GAN, and setting initial parameters of a GAN model, wherein the initial parameters of the GAN model comprise: learning rate eta, batch processing amount m, generator initial parameter thetag0And an initial parameter theta of the discriminatord0Number of generator iterations nGAnd the number of iterations n of the discriminators in each generator iterationD(ii) a Inputting the GAF converted image into a generation countermeasure network GAN;
step SA 2: randomly choosing m sample points { d } from the dataset1,d2,...,dm-randomly selecting m vectors z from the noisy data distribution1,z2,...,zm}；
Step SA 3: inputting Z into the generator to obtain m generated data d1,d2,...,dm}；
Step SA 4: updating the parameter θ of the discriminatordTo maximize
Figure FDA0003513457900000026
Step SA 5: judging whether the iteration number is more than nDWhen the number of iterations is greater than nDIf so, stopping iteration; the number of iterations is not more than nDIf so, continuing to step SA 1-step SA4 until an iteration stop condition is met;
step SA 6: the m vectors z are again randomly sampled from the noise distribution1,z2,...,zm}；
Step SA 7: updating the parameter θ of the generatordTo minimize
Figure FDA0003513457900000031
Step SA 8: judging whether the maximum iteration number n of the generator is reachedGWhen the maximum number of iterations n of the generator is reachedGIf so, stopping iterative training and outputting the trained GAN model; when the maximum iteration number n of the generator is not reachedGAnd if so, continuing to step SA 2-step SA7 until an iteration stop condition is met.
6. The method for predicting the short-term headway of the express way based on the trajectory data as claimed in claim 5, wherein in the step SA 4;
Figure FDA0003513457900000032
Figure FDA0003513457900000033
7. the method for predicting the short-term headway of the express way based on the trajectory data as claimed in claim 5, wherein in step SA 7:
Figure FDA0003513457900000034
Figure FDA0003513457900000035
8. the method for predicting the short-time locomotive distance of the express way based on the trajectory data as claimed in claim 5, wherein the method for establishing the GAN-LSTM model comprises the following steps:
step SB 1: connecting a generator in the trained GAN model with the LSTM;
step SB 2: setting initial parameters of the LSTM model, wherein the initial parameters of the LSTM model comprise: learning rate eta, batch processing quantity m, and LSTM initial parameter thetaf0Maximum number of iterations n of LSTMfAnd vehicle i locomotive spacing time sequence data; inputting the GAF-converted image into the LSTM;
step SB 3: randomly selecting m vectors { v ] from real data1,ν2,...,νm}；
Step SB 4: updating parameter θ of LSTMf；
Figure FDA0003513457900000041
Figure FDA0003513457900000042
Step SB 5: judging whether the maximum iteration number n of the LSTM is reachedfWhen the maximum number of iterations n of the LSTM is reachedfStopping iterative training and outputting the trained GAN-LSTM model; when the maximum iteration number n of the LSTM is not reachedfAnd if so, continuing to step SB 2-step SB4 until the iteration stop condition is met.
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