CN113034904B - ETC data-based traffic state estimation method and device - Google Patents

ETC data-based traffic state estimation method and device Download PDF

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CN113034904B
CN113034904B CN202110247250.5A CN202110247250A CN113034904B CN 113034904 B CN113034904 B CN 113034904B CN 202110247250 A CN202110247250 A CN 202110247250A CN 113034904 B CN113034904 B CN 113034904B
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kalman filter
traffic
highway network
matrix
value
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CN113034904A (en
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李斌
郭宇奇
李宏海
牛树云
常征
侯德藻
高剑
李茜瑶
朱丽丽
车晓琳
黄烨然
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Research Institute of Highway Ministry of Transport
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation

Abstract

The invention relates to a traffic state estimation method and a device based on ETC data, wherein the method comprises the following steps: dividing a highway network into a plurality of cells according to the positions of an entrance ramp, an exit ramp, an ETC portal position and the number of lanes; based on the divided cells, establishing a macroscopic traffic flow model of the highway network by taking the traffic flow density as a traffic state variable; acquiring target cells with ETC gantries at an entrance and an exit, and calculating the actual vehicle density value of the target cells; respectively constructing a state observer and a Kalman filter according to a macroscopic traffic flow model, calculating a vehicle density estimation value of each cell, and simultaneously calculating a pre-estimation value of the Kalman filter; performing fusion calculation on the vehicle density estimated value and the pre-estimated value of the Kalman filter to obtain a fusion pre-estimated value; and correcting the fusion pre-estimated value of the Kalman filter by combining the measured value of the traffic sensor to obtain a new estimated value so as to determine the traffic state of the highway network. Therefore, the estimation precision of the traffic state of the highway is improved.

Description

ETC data-based traffic state estimation method and device
Technical Field
The present disclosure relates to the field of intelligent transportation system technologies, and in particular, to a traffic state estimation method and apparatus based on ETC data.
Background
The traditional traffic state estimation generally adopts a single type of state estimator such as a state observer, a Kalman filter and the like, and the two types of estimators and more than two types of estimators are rarely designed in a fusion mode. Although a single type of estimator can solve the state estimation problem, each type of estimator has its own inevitable disadvantages, resulting in a limited estimation accuracy.
Disclosure of Invention
In order to overcome the problems in the related art, the present disclosure provides a traffic state estimation method and device based on ETC data, so as to further improve the estimation accuracy of the traffic state on the basis of not increasing the design difficulty.
According to a first aspect of the embodiments of the present disclosure, a traffic state estimation method based on ETC data is provided
Dividing a highway network into a plurality of cells according to the positions of an entrance ramp, an exit ramp, an ETC portal position and the number of lanes;
based on the divided cells, establishing a macroscopic traffic flow model of the highway network by taking the traffic flow density as a traffic state variable;
acquiring target cells with ETC gantries at an entrance and an exit, and calculating the actual vehicle density value of the target cells according to the measured value of a sensor of the ETC gantry;
respectively constructing a state observer and a Kalman filter according to the macroscopic traffic flow model of the highway network, calculating the vehicle density estimation value of each cell based on the state observer, and deducing the pre-estimation value of the Kalman filter based on the macroscopic traffic flow model;
performing fusion calculation on the vehicle density estimated value obtained by the state observer and the pre-estimated value of the Kalman filter to obtain a fusion pre-estimated value of the Kalman filter;
and correcting the fusion pre-estimated value of the Kalman filter by combining the measured value of the traffic sensor to obtain a new estimated value as the final traffic state of the expressway network.
In one embodiment, preferably, the macroscopic traffic flow model of the highway network includes:
Figure BDA0002964530340000021
wherein x represents a traffic flow density vector of the highway network, and x ∈ RnN represents the number of divided cells; u represents the control input of the highway network system, and u belongs to RpP represents the number of entrance ramps; y represents the traffic parameters obtained by the traffic sensors in the highway network, and y belongs to RmM represents the number of road segments in the highway network where traffic sensors are arranged; a represents a highway network system matrix; b represents an input matrix of the highway network system; c represents a matrix corresponding to the traffic sensors arranged in the highway network, and is called an output matrix; f represents a constant matrix; ω (k) represents system noise, t represents time, ω (k) to N (0, Q); v (k) represents measurement noise v (k) N (0, R).
In one embodiment, preferably, the state observer is represented by:
Figure BDA0002964530340000022
wherein the content of the first and second substances,
Figure BDA0002964530340000023
representing a vehicle density estimate of the cell; l represents a gain matrix of the state observer, and A represents a highway network system matrix; b represents an input matrix of the highway network system; c represents an output matrix corresponding to the traffic sensors arranged in the highway network, and F represents a constant matrix.
In one embodiment, the pre-estimated value of the kalman filter is preferably calculated using the following formula:
Figure BDA0002964530340000024
wherein the content of the first and second substances,
Figure BDA0002964530340000031
representing a pre-estimated value of the Kalman filter, wherein A represents a highway network system matrix; b represents an input matrix of the highway network system; f denotes a constant matrix.
In one embodiment, preferably, performing a fusion calculation on the vehicle density estimation value and a pre-estimation value of a kalman filter to obtain a fusion estimation value of the kalman filter includes:
determining pairs of parameter combinations (lambda)1，λ2) So that the following equation holds:
Figure BDA0002964530340000032
wherein the content of the first and second substances,
Figure BDA0002964530340000033
represents a combination of parameters (lambda)1，λ2) The corresponding fused estimate of the value of the estimated,
Figure BDA0002964530340000034
represents a vehicle density estimated value of the unit cell,
Figure BDA0002964530340000035
a pre-estimated value (λ) representing the Kalman filter1，λ2) Representing a combination of parameters;
calculating the standard deviation of the fusion pre-estimation value corresponding to each pair of parameter combinations, the vehicle density estimation value of the cell and the pre-estimation value of the Kalman filter by adopting the following calculation formula:
Figure BDA0002964530340000036
selecting the corresponding (lambda) when the standard deviation is minimum from all the standard deviations1，λ2) And determining the parameter combination as an optimal parameter combination, and determining a fusion pre-estimation value corresponding to the optimal parameter combination as a fusion pre-estimation value of the Kalman filter.
In one embodiment, the first and second electrodes are, preferably,
the method for determining the traffic state of the highway network by correcting the fusion pre-estimated value of the Kalman filter according to the measured value of the traffic sensor and obtaining a new estimated value comprises the following steps:
respectively calculating a priori error covariance matrix and a gain matrix of the Kalman filter;
the prior error covariance matrix P-and the gain matrix G are respectively calculated by adopting the following formulas:
Figure BDA0002964530340000037
G(t+1)＝p-(t)CT[CP-(t)CT+R]-1
wherein, P-A prior error covariance matrix is represented,
Figure BDA0002964530340000038
representing a posterior error covariance, A representing a highway network system matrix, G representing a gain matrix, and C representing an output matrix corresponding to a traffic sensor arranged in the highway network;
updating a posterior state estimation value according to the corrected fusion pre-estimation value of the Kalman filter, the prior error covariance matrix and the gain matrix to obtain a final traffic state estimation value;
Figure BDA0002964530340000041
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002964530340000042
representing the new pre-estimated value of the kalman filter, G representing the gain matrix,
Figure BDA0002964530340000043
representing the pre-estimated value, y, of the Carniemann filteriAnd C represents an output matrix corresponding to the traffic sensors arranged in the highway network, and F represents a constant matrix.
And determining the traffic state of the highway network according to the final traffic state estimation value.
In one embodiment, preferably, acquiring target cells with ETC gantries at the entrance and the exit, and calculating an actual vehicle density value of the target cells according to ETC gantry sensor measurement values comprises:
the method comprises the following steps of obtaining target cells with ETC gantries at an entrance and an exit, respectively calculating the actual vehicle density value of the target cells by adopting the following calculation formula through the vehicle flow in a sampling period collected by ETC at the entrance and the exit:
Figure BDA0002964530340000044
wherein x isiDenotes the actual vehicle density value of the target cell i, T denotes the sampling period, LiDenotes the length of the target cell i, qi-inRepresenting the amount of traffic entering the target cell i, q, over a sampling periodi-outThe traffic flow out of the target cell i in one sampling period is shown.
According to a second aspect of the embodiments of the present disclosure, there is provided a traffic state estimation device based on ETC data, the device including:
the division module is used for dividing the highway network into a plurality of cells according to the entrance and exit ramp positions, the ETC portal frame position and the lane number change position;
the modeling module is used for establishing a macroscopic traffic flow model of the highway network by taking the traffic flow density as a traffic state variable based on the divided cells;
the first calculation module is used for acquiring target cells with ETC gantries at the entrance and the exit and calculating the actual vehicle density value of the target cells according to the measured value of a sensor of the ETC gantry;
the second calculation module is used for respectively constructing a state observer and a Kalman filter according to the macroscopic traffic flow model of the highway network, calculating the vehicle density estimation value of each cell based on the state observer, and deducing the pre-estimation value of the Kalman filter based on the macroscopic traffic flow model;
the third calculation module is used for performing fusion calculation on the vehicle density estimated value obtained by the state observer and the pre-estimated value of the Kalman filter to obtain a fusion pre-estimated value of the Kalman filter;
the fourth calculation module is used for correcting the fusion pre-estimation value of the Kalman filter by combining the measurement value of the traffic sensor to obtain the final Kalman filter estimation value;
and the determining module is used for determining the traffic state of the highway network according to the corrected estimated value of the Kalman filter.
According to a third aspect of the embodiments of the present disclosure, there is provided a traffic state estimation device based on ETC data, characterized by comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
dividing a highway network into a plurality of cells according to the positions of an entrance ramp, an exit ramp, the ETC portal position and the change position of the number of lanes;
based on the divided cells, establishing a macroscopic traffic flow model of the highway network by taking the traffic flow density as a traffic state variable;
acquiring target cells with ETC gantries at an entrance and an exit, and calculating the actual vehicle density value of the target cells according to the measured value of a sensor of the ETC gantry;
respectively constructing a state observer and a Kalman filter according to the macroscopic traffic flow model of the highway network, calculating the vehicle density estimated value of each cell based on the state observer, and deducing the pre-estimated value of the Kalman filter based on the macroscopic traffic flow model;
performing fusion calculation on a vehicle density estimated value obtained by a state observer and a pre-estimated value of a Kalman filter to obtain a fusion pre-estimated value of the Kalman filter;
and correcting the fusion pre-estimated value of the Kalman filter by combining the measured value of the traffic sensor to obtain a new estimated value as the final traffic state of the expressway network.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method of any one of the first aspects.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
in the embodiment of the invention, the state observer and the Kalman filter are subjected to algorithm fusion to obtain the fusion estimation value of the Kalman filter, and the traffic state of the highway network is estimated by utilizing the fusion estimation value, so that the estimation precision of the traffic state is further improved on the basis of not increasing the design difficulty.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flowchart illustrating a traffic state estimation method based on ETC data according to an exemplary embodiment.
FIG. 2 is a schematic diagram illustrating highway ETC portal layout and cellular partitioning, according to an exemplary embodiment.
Fig. 3 is a flowchart illustrating a step S106 in a traffic state estimation method based on ETC data according to an exemplary embodiment.
Fig. 4 is a block diagram illustrating a traffic state estimation device based on ETC data according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The invention provides a traffic state estimation method and device based on ETC data. Both the state observer and the Kalman filter are estimators that estimate the state of the system based on a state space model of the system. From a control theory perspective, the greatest difference between the system model and the state estimator is that: (1) the estimation and prediction of the system state by using the system model is equivalent to open-loop control, and because no feedback error is used for correction, the estimation and prediction errors are large and cannot reflect the real system state. (2) When the state estimator is used for state estimation, the result is corrected by adopting the real-time error feedback of the measured value of the sensor, so that the feedback control of the system is equivalent, the obtained result error is relatively small, and the state of the system can be truly reflected. Therefore, if the measured values of a part of the system can be collected by the sensor, the estimation of the system state is generally performed by using a state estimator. However, the design principle of each type of state estimator is different, and the accuracy of the obtained result is also different.
As a recursive estimator, as long as a Kalman filter acquires an estimated value of a state at the last moment of the system and an observed value acquired by a traffic sensor at the current moment, the estimated value of the current state can be calculated, the Kalman filter mainly carries out recursion in a sequence of 'prediction/pre-estimation-actual measurement-correction', and random interference is eliminated according to a measured value of the system to obtain the state of the system; in the 'prediction/pre-estimation' link, an estimation value of the last moment of the system is mainly used as an initial value, and the prediction/estimation value of the current moment is obtained through system model deduction. For the state observer, the design of the state observer is first done by taking the difference between the measured output and the actual measured output of the observer as feedback; then, in each step of prediction/estimation link, the estimation value of the observer is differed with the model deduction value to obtain an error system; and finally, obtaining a gain matrix of the observer by solving a feasible solution of the stability of the error system, and further calculating to obtain a state estimation value. Since the estimation problem is converted into the stability problem of the system, the estimation is completed through the gradual stability of the system, and the estimation precision is to be improved but is much higher than that obtained by directly utilizing a model to deduce. In view of the advantages and disadvantages of the two estimators, the advantages of the two estimators can be combined, and in the 'prediction/pre-estimation' (also called a priori state estimation) link of the Kalman filter, the estimation value of the state observer and the pre-estimation value of the Kalman filter are adopted at the same time, and then the value obtained by algorithm fusion of the two results is used as the pre-estimation value of the Kalman filter, so that the precision of 'prediction/pre-estimation' is improved. Meanwhile, along with the gradual popularization of the ETC, the ETC becomes a traffic sensing system with higher real-time performance and accuracy and wider coverage, and the acquired data can provide support for monitoring the traffic state. Therefore, the traffic data acquired by the ETC portal system of the highway is taken as measurement output, and a new mixed state estimator is designed to solve the traffic state estimation and prediction problems of the highway by carrying out algorithm fusion on the two estimators of the state observer and the Kalman filter.
Fig. 1 is a flowchart illustrating a traffic state estimation method based on ETC data according to an exemplary embodiment, as shown in fig. 1, the method including:
step S101, dividing a highway network into a plurality of cells according to the entrance and exit ramp position, the ETC portal frame position and the lane number change position;
as shown in fig. 2, 1 is an ETC portal; 2, a video acquisition device for acquiring traffic flow on the road; the road test unit on the ETC portal is a vehicle-mounted unit in the ETC system, and is a vehicle identity recognition device, information such as the speed and the traffic flow of a vehicle can be acquired through the vehicle identity recognition device, and for the divided road sections, the road side unit of the portal system can acquire the traffic flow of entering and flowing out of the cellular in the sampling period; and 4, a roadside calculation unit which can calculate the vehicle density of the cellular by the traffic flow entering and exiting the cellular.
Specifically, the highway network is divided into a plurality of road sections according to the positions of the entrance and exit ramps, the positions of ETC gantries, the number of lanes and the like, each road section is called a cell, and the divided cells are sequentially marked with serial numbers, so that the arrangement positions of the sensors can be conveniently marked. The place with the entrance ramp is used as the boundary of two adjacent cells, the ETC portal is also used as the boundary of two adjacent cells, and the position with the changed lane number is also used as the boundary of two adjacent cells.
Step S102, based on the divided cells, establishing a macroscopic traffic flow model of the highway network by taking the traffic flow density as a traffic state variable;
in one embodiment, preferably, the macroscopic traffic flow model of the highway network includes:
Figure BDA0002964530340000091
wherein x represents a traffic flow density vector of the highway network, and x ∈ RnN represents the number of divided unit cells; u represents the control input of the highway network system, and u belongs to RpP represents the number of entrance ramps; y represents the traffic parameters obtained by the traffic sensors in the highway network, and y belongs to RmM represents the number of road segments in the highway network on which traffic sensors are arranged; a represents a highway network system matrix; b represents an input matrix of the highway network system; c represents a matrix corresponding to the traffic sensors arranged in the highway network, and is called an output matrix; f represents a constant matrix; ω (t) represents system noise, ω (t) to N (0, Q); v (t) represents measurement noise v (t) N (0, R).
Step S103, acquiring target cells with ETC gantries at the entrance and the exit, and calculating the actual vehicle density value of the target cells according to the measured value of a sensor of the ETC gantry;
obtain the access & exit and all have the target cell of ETC portal to calculate according to ETC portal sensor measurement value the actual vehicle density value of target cell includes:
the method comprises the following steps of obtaining target cells with ETC gantries at an entrance and an exit, respectively calculating the actual vehicle density value of the target cells by adopting the following calculation formula through the vehicle flow in a sampling period collected by ETC at the entrance and the exit:
Figure BDA0002964530340000092
wherein x isiRepresenting the actual vehicle density value of the target cell i, T representing the sampling period, LiDenotes the length of the target cell i, qi-inRepresenting the amount of traffic entering the target cell i, q, over a sampling periodi-outThe traffic flow out of the target cell i in one sampling period is shown.
And calculating the vehicle density value of each cell through the formula, so that the vehicle density values of all road sections where the ETC device is arranged can be obtained, and the output matrix C is determined. The calculated vehicle density can be regarded as the actual measured value of the cell i, so the corresponding element in the corresponding output matrix is 1, otherwise it is 0.
Step S104, respectively constructing a state observer and a Kalman filter according to the macroscopic traffic flow model of the highway network, calculating the vehicle density estimation value of each cell based on the state observer, and deducing the pre-estimation value of the Kalman filter based on the macroscopic traffic flow model;
in one embodiment, preferably, the state observer is represented by:
Figure BDA0002964530340000101
wherein the content of the first and second substances,
Figure BDA0002964530340000102
representing a vehicle density estimate of the cell; l represents a gain matrix of the state observer, and A represents a highway network system matrix; b represents an input matrix of the highway network system; c represents an output matrix corresponding to the traffic sensors arranged in the highway network, and F represents a constant matrix.
In one embodiment, the pre-estimated value of the kalman filter is preferably calculated using the following formula:
Figure BDA0002964530340000103
wherein the content of the first and second substances,
Figure BDA0002964530340000104
representing a pre-estimated value of the Kalman filter, wherein A represents a highway network system matrix; b represents an input matrix of the highway network system; f denotes a constant matrix.
Step S105, carrying out fusion calculation on the vehicle density estimated value obtained by the state observer and the pre-estimated value of the Kalman filter to obtain a fusion pre-estimated value of the Kalman filter;
in one embodiment, preferably, performing a fusion calculation on the vehicle density estimation value obtained by the state observer and a pre-estimation value of a kalman filter to obtain a fusion pre-estimation value of the kalman filter includes:
determining pairs of parameter combinations (lambda)1，λ2) So that the following equation holds:
Figure BDA0002964530340000105
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002964530340000106
represents a combination of parameters (lambda)1，λ2) The corresponding fused estimate of the value of the estimated,
Figure BDA0002964530340000107
represents a vehicle density estimated value of the unit cell,
Figure BDA0002964530340000108
a pre-estimated value (λ) representing the Kalman filter1，λ2) Representing a combination of parameters;
calculating the standard deviation of the fusion estimation value corresponding to each pair of parameter combination, the vehicle density estimation value of the cell and the pre-estimation value of the Kalman filter respectively by adopting the following calculation formula:
Figure BDA0002964530340000111
selecting the corresponding (lambda) when the standard deviation is minimum from all the standard deviations1，λ2) And determining the parameter combination as an optimal parameter combination, and determining a fusion estimation value corresponding to the optimal parameter combination as a fusion estimation value of the Kalman filter.
And step S106, correcting the fusion pre-estimated value of the Kalman filter by combining the measured value of the traffic sensor to obtain a new estimated value as the final traffic state of the highway network.
In the embodiment, traffic data acquired by an ETC portal system of the highway is taken as measurement output, and a new mixed state estimator is designed to solve the problems of traffic state estimation and prediction of the highway by carrying out algorithm fusion on a state observer and a Kalman filter.
Fig. 3 is a flowchart illustrating a step S106 in a traffic state estimation method based on ETC data according to an exemplary embodiment.
As shown in fig. 3, in one embodiment, preferably, the step S106 includes:
step S301, respectively calculating a prior error covariance matrix and a gain matrix of the Kalman filter;
wherein, the prior error covariance matrix P is calculated by the following calculation formula-And a gain matrix G:
Figure BDA0002964530340000112
G(t+1)＝P-(t)CT[CP-(t)CT+R]-1
step S302, updating a posterior state estimation value according to the fusion pre-estimation value corrected by the Kalman filter, the prior error covariance matrix and the gain matrix to obtain a final traffic state estimation value;
final traffic state estimate
Figure BDA0002964530340000113
The formula is adopted to calculate the following formula:
Figure BDA0002964530340000114
and step S303, determining the traffic state of the highway network according to the final traffic state estimation value.
Fig. 4 is a block diagram illustrating a traffic state estimation device based on ETC data according to an exemplary embodiment.
As shown in fig. 4, according to a second aspect of the embodiments of the present disclosure, there is provided a traffic state estimation device based on ETC data, the device including:
the dividing module 41 is used for dividing the highway network into a plurality of cells according to the entrance and exit ramp position, the ETC portal position and the lane number change position;
the modeling module 42 is used for establishing a macroscopic traffic flow model of the highway network by taking the traffic flow density as a traffic state variable based on the divided cells;
the first calculating module 43 is used for acquiring target cells with ETC portals at the entrances and exits, and calculating the actual vehicle density value of the target cells according to the measured value of an ETC portal sensor;
the second calculation module 44 is configured to respectively construct a state observer and a kalman filter according to the macroscopic traffic flow model of the highway network, calculate a vehicle density estimation value of each cell based on the state observer, and deduce a pre-estimation value of the kalman filter based on the macroscopic traffic flow model;
the third calculation module 45 is configured to perform fusion calculation on the vehicle density estimation value obtained by the state observer and the pre-estimation value of the kalman filter to obtain a fusion pre-estimation value of the kalman filter;
a fourth calculation module 46, configured to correct the fusion pre-estimation value of the kalman filter by combining the measurement value of the traffic sensor, to obtain a final kalman filter estimation value;
and a determining module 47, configured to determine a traffic state of the highway network according to the corrected estimated value of the kalman filter.
According to a third aspect of the embodiments of the present disclosure, there is provided a traffic state estimation device based on ETC data, characterized by comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
dividing a highway network into a plurality of cells according to the positions of an entrance ramp, an exit ramp, the ETC portal position and the change position of the number of lanes;
based on the divided cells, establishing a macroscopic traffic flow model of the highway network by taking the traffic flow density as a traffic state variable;
acquiring target cells with ETC gantries at an entrance and an exit, and calculating the actual vehicle density value of the target cells according to the measured value of a sensor of the ETC gantry;
respectively constructing a state observer and a Kalman filter according to the macroscopic traffic flow model of the highway network, calculating the vehicle density estimation value of each cell based on the state observer, and deducing the pre-estimation value of the Kalman filter based on the macroscopic traffic flow model;
performing fusion calculation on the vehicle density estimated value obtained by the state observer and the pre-estimated value of the Kalman filter to obtain a fusion pre-estimated value of the Kalman filter;
and correcting the fusion pre-estimated value of the Kalman filter by combining the measured value of the traffic sensor to obtain a new estimated value as the final traffic state of the expressway network.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method of any one of the first aspects.
It is further understood that the use of "a plurality" in this disclosure means two or more, and other terms are analogous. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. The singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It will be further understood that the terms "first," "second," and the like are used to describe various information and that such information should not be limited by these terms. These terms are only used to distinguish one type of information from another and do not denote a particular order or importance. Indeed, the terms "first," "second," and the like are fully interchangeable. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure.
It is further to be understood that while operations are depicted in the drawings in a particular order, this is not to be understood as requiring that such operations be performed in the particular order shown or in serial order, or that all illustrated operations be performed, to achieve desirable results. In certain environments, multitasking and parallel processing may be advantageous.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims (5)

1. A traffic state estimation method based on ETC data, characterized by comprising:
dividing a highway network into a plurality of cells according to the positions of an entrance ramp, an exit ramp, an ETC portal position and the number of lanes;
based on the divided cells, establishing a macroscopic traffic flow model of the highway network by taking the traffic flow density as a traffic state variable;
acquiring target cells with ETC gantries at an entrance and an exit, and calculating the actual vehicle density value of the target cells according to the measured value of a sensor of the ETC gantry;
respectively constructing a state observer and a Kalman filter according to the macroscopic traffic flow model of the highway network, calculating the vehicle density estimation value of each cell based on the state observer, and deducing the pre-estimation value of the Kalman filter based on the macroscopic traffic flow model;
performing fusion calculation on a vehicle density estimated value obtained by a state observer and a pre-estimated value of a Kalman filter to obtain a fusion pre-estimated value of the Kalman filter;
correcting the fusion pre-estimated value of the Kalman filter by combining the measured value of the traffic sensor to obtain a new estimated value as the final traffic state of the expressway network;
the macroscopic traffic flow model of the highway network comprises:
Figure FDF0000016774750000011
wherein x represents a traffic flow density vector of the highway network, and x ∈ RnN represents the number of divided cells; u represents the control input of the highway network system, and u belongs to RpP represents the number of entrance ramps; y represents the traffic parameters obtained by the traffic sensors in the highway network, and y belongs to RmM represents the number of road segments in the highway network on which traffic sensors are arranged; a represents a highway network system matrix; b represents an input matrix of the highway network system; c represents a matrix corresponding to the traffic sensors arranged in the highway network, and is called an output matrix; f represents a constant matrix; ω (k) represents system noise, t represents time, ω (k) to N (0, Q); v (k) represents measurement noise v (k) N (0, R);
the state observer is represented as:
Figure FDF0000016774750000021
wherein the content of the first and second substances,
Figure FDF0000016774750000022
representing a vehicle density estimate of the cell; l represents a gain matrix of the state observer, and A represents a highway network system matrix; b represents an input matrix of the highway network system; c represents an output matrix corresponding to the traffic sensors arranged in the highway network, and F represents a constant matrix;
estimating the vehicle density
Figure FDF0000016774750000023
And the pre-estimated value of the Kalman filter
Figure FDF0000016774750000024
Performing fusion calculation to obtain a fusion pre-estimation value of the Kalman filter, including:
determining pairs of parameter combinations (lambda)1，λ2) So that the following equation holds:
Figure FDF0000016774750000025
wherein the content of the first and second substances,
Figure FDF0000016774750000026
represents a combination of parameters (lambda)1，λ2) The corresponding kalman filter fuses the pre-estimated values,
Figure FDF0000016774750000027
represents a vehicle density estimated value of the unit cell,
Figure FDF0000016774750000028
a pre-estimated value (λ) representing the Kalman filter1，λ2) Representing a combination of parameters;
calculating the standard deviation of the fusion pre-estimation value corresponding to each pair of parameter combination, the vehicle density estimation value of the cell and the pre-estimation value of the Kalman filter respectively by adopting the following formula:
Figure FDF0000016774750000029
selecting the corresponding (lambda) when the standard deviation is minimum from all the standard deviations1，λ2) Determining the optimal parameter combination as a parameter combination, and determining a fusion pre-estimation value corresponding to the optimal parameter combination as a fusion pre-estimation value of the Kalman filter;
the method for determining the traffic state of the highway network by correcting the fusion pre-estimated value of the Kalman filter according to the measured value of the traffic sensor and obtaining a new estimated value comprises the following steps:
respectively calculating a prior error covariance matrix and a gain matrix of the Kalman filter;
updating a posterior state estimation value according to the corrected fusion pre-estimation value of the Kalman filter, the prior error covariance matrix and the gain matrix to obtain a final traffic state estimation value;
final traffic state estimate
Figure FDF00000167747500000210
The formula is adopted to calculate the following formula:
Figure FDF0000016774750000031
wherein G represents a gain matrix;
determining the traffic state of the expressway network according to the final traffic state estimation value;
calculating a pre-estimated value of the Kalman filter using the following formula:
Figure FDF0000016774750000032
wherein the content of the first and second substances,
Figure FDF0000016774750000033
representing a pre-estimated value of the Kalman filter, wherein A represents a highway network system matrix; b represents an input matrix of the highway network system; f denotes a constant matrix.
2. The method according to claim 1, wherein obtaining target cells having an ETC portal at each doorway and calculating actual vehicle density values of the target cells from ETC portal sensor measurements comprises:
the method comprises the following steps of obtaining target cells with ETC gantries at an entrance and an exit, respectively calculating the actual vehicle density value of the target cells by adopting the following calculation formula through the vehicle flow in a sampling period collected by ETC at the entrance and the exit:
Figure FDF0000016774750000034
wherein x isiRepresenting the actual vehicle density value of the target cell i, T representing the sampling period, LiDenotes the length of the target cell i, qi-inRepresenting the amount of traffic entering the target cell i, q, over a sampling periodi-outThe traffic flow out of the target cell i in one sampling period is shown.
3. A traffic state estimation device based on ETC data, characterized by comprising:
the division module is used for dividing the highway network into a plurality of cells according to the entrance and exit ramp positions, the ETC portal frame position and the lane number change position;
the modeling module is used for establishing a macroscopic traffic flow model of the highway network by taking the traffic flow density as a traffic state variable based on the divided cells;
the first calculation module is used for acquiring target cells with ETC portals at the entrances and exits and calculating the actual vehicle density value of the target cells according to the measured value of an ETC portal sensor;
the second calculation module is used for respectively constructing a state observer and a Kalman filter according to the macroscopic traffic flow model of the highway network, calculating the vehicle density estimation value of each cell based on the state observer, and deducing the pre-estimation value of the Kalman filter based on the macroscopic traffic flow model;
the third calculation module is used for performing fusion calculation on the vehicle density estimated value obtained by the state observer and the pre-estimated value of the Kalman filter to obtain a fusion pre-estimated value of the Kalman filter;
the fourth calculation module is used for correcting the fusion pre-estimation value of the Kalman filter by combining the measurement value of the traffic sensor to obtain the final Kalman filter estimation value;
the determining module is used for determining the traffic state of the highway network according to the corrected estimated value of the Kalman filter;
the macroscopic traffic flow model of the highway network comprises:
Figure FDF0000016774750000041
wherein x represents a traffic flow density vector of the highway network, and x ∈ RnN represents the number of divided cells; u represents the control input of the highway network system, and u belongs to RpP represents the number of entrance ramps; y represents the traffic parameters obtained by the traffic sensors in the highway network, and y belongs to RmM represents the number of road segments in the highway network on which traffic sensors are arranged; a represents a highway network system matrix; b represents an input matrix of the highway network system; c represents a matrix corresponding to the traffic sensors arranged in the highway network, and is called an output matrix; f represents a constant matrix; ω (k) represents system noise, t represents time, ω (k) to N (0, Q); v (k) represents measurement noise v (k) N (0, R);
the state observer is represented as:
Figure FDF0000016774750000042
wherein the content of the first and second substances,
Figure FDF0000016774750000043
representing a vehicle density estimate of the cell; l represents a gain matrix of the state observer, and A represents a highway network system matrix; b represents an input matrix of the highway network system; c represents an output matrix corresponding to the traffic sensors arranged in the highway network, and F represents a constant matrix;
estimating the vehicle density
Figure FDF0000016774750000044
And the pre-estimated value of the Kalman filter
Figure FDF0000016774750000045
Performing fusion calculation to obtain a fusion pre-estimation value of the Kalman filter, including:
determining pairs of parameter combinations (lambda)1，λ2) So that the following equation holds:
Figure FDF0000016774750000051
wherein, the first and the second end of the pipe are connected with each other,
Figure FDF0000016774750000052
represents a combination of parameters (lambda)1，λ2) The corresponding kalman filter fuses the pre-estimated values,
Figure FDF0000016774750000053
represents a vehicle density estimated value of the unit cell,
Figure FDF0000016774750000054
a pre-estimated value (λ) representing the Kalman filter1，λ2) Representing a combination of parameters;
calculating the standard deviation of the fusion pre-estimation value corresponding to each pair of parameter combination, the vehicle density estimation value of the cell and the pre-estimation value of the Kalman filter respectively by adopting the following formula:
Figure FDF0000016774750000055
selecting the corresponding (lambda) when the standard deviation is minimum from all the standard deviations1，λ2) Determining the parameter combination as the optimal parameter combination, and determining the fusion pre-estimation value corresponding to the optimal parameter combination as the Karl FischerFusion pre-estimation value of the Mandarin filter;
the method for determining the traffic state of the highway network by correcting the fusion pre-estimated value of the Kalman filter according to the measured value of the traffic sensor and obtaining a new estimated value comprises the following steps:
respectively calculating a priori error covariance matrix and a gain matrix of the Kalman filter;
updating a posterior state estimation value according to the corrected fusion pre-estimation value of the Kalman filter, the prior error covariance matrix and the gain matrix to obtain a final traffic state estimation value;
final traffic state estimate
Figure FDF0000016774750000056
The formula is adopted to calculate the following formula:
Figure FDF0000016774750000057
wherein G represents a gain matrix;
determining the traffic state of the highway network according to the final traffic state estimation value;
calculating a pre-estimated value of the Kalman filter using the following formula:
Figure FDF0000016774750000058
wherein the content of the first and second substances,
Figure FDF0000016774750000059
representing a pre-estimated value of the Kalman filter, wherein A represents a highway network system matrix; b represents an input matrix of the highway network system; f denotes a constant matrix.
4. A traffic state estimation device based on ETC data, characterized by comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
dividing a highway network into a plurality of cells according to the positions of an entrance ramp, an exit ramp, an ETC portal position and the number of lanes;
based on the divided cells, taking the traffic flow density as a traffic state variable, establishing a macroscopic traffic flow model of the highway network;
acquiring target cells with ETC gantries at an entrance and an exit, and calculating the actual vehicle density value of the target cells according to the measured value of a sensor of the ETC gantry;
respectively constructing a state observer and a Kalman filter according to the macroscopic traffic flow model of the highway network, calculating the vehicle density estimation value of each cell based on the state observer, and deducing the pre-estimation value of the Kalman filter based on the macroscopic traffic flow model;
performing fusion calculation on a vehicle density estimated value obtained by a state observer and a pre-estimated value of a Kalman filter to obtain a fusion pre-estimated value of the Kalman filter;
correcting the fusion pre-estimation value of the Kalman filter by combining the measurement value of the traffic sensor to obtain a new estimation value as the final traffic state of the expressway network;
the macroscopic traffic flow model of the highway network comprises:
Figure FDF0000016774750000061
wherein x represents a traffic flow density vector of the highway network, and x ∈ RnN represents the number of divided cells; u represents the control input of the highway network system, and u belongs to RpP represents the number of entrance ramps; y represents the traffic parameters obtained by the traffic sensors in the highway network, and y belongs to RmM represents the number of road segments in the highway network on which traffic sensors are arranged; a represents a highway network system matrix; b represents an expresswayAn input matrix of the net system; c represents a matrix corresponding to the traffic sensors arranged in the highway network, and is called an output matrix; f represents a constant matrix; ω (k) represents system noise, t represents time, ω (k) to N (0, Q); v (k) represents measurement noise v (k) N (0, R);
the state observer is represented as:
Figure FDF0000016774750000071
wherein the content of the first and second substances,
Figure FDF0000016774750000072
representing a vehicle density estimate of the cell; l represents a gain matrix of the state observer, and A represents a highway network system matrix; b represents an input matrix of the highway network system; c represents an output matrix corresponding to the traffic sensors arranged in the highway network, and F represents a constant matrix;
estimating the vehicle density
Figure FDF0000016774750000073
And the pre-estimated value of the Kalman filter
Figure FDF0000016774750000074
Performing fusion calculation to obtain a fusion pre-estimation value of the Kalman filter, including:
determining pairs of parameter combinations (lambda)1，λ2) So that the following equation holds:
Figure FDF0000016774750000075
wherein the content of the first and second substances,
Figure FDF0000016774750000076
represents a combination of parameters (lambda)1，λ2) The corresponding kalman filter fuses the pre-estimated values,
Figure FDF0000016774750000077
represents a vehicle density estimated value of the unit cell,
Figure FDF0000016774750000078
a pre-estimated value (λ) representing the Kalman filter1，λ2) Representing a combination of parameters;
calculating the standard deviation of the fusion pre-estimation value corresponding to each pair of parameter combination, the vehicle density estimation value of the cell and the pre-estimation value of the Kalman filter respectively by adopting the following formula:
Figure FDF0000016774750000079
selecting the corresponding (lambda) when the standard deviation is minimum from all the standard deviations1，λ2) Determining a parameter combination as an optimal parameter combination, and determining a fusion pre-estimation value corresponding to the optimal parameter combination as a fusion pre-estimation value of the Kalman filter;
the method for correcting the fusion pre-estimation value of the Kalman filter by combining the measurement value of the traffic sensor to obtain a new estimation value to determine the traffic state of a highway network comprises the following steps:
respectively calculating a prior error covariance matrix and a gain matrix of the Kalman filter;
updating a posterior state estimation value according to the corrected fusion pre-estimation value of the Kalman filter, the prior error covariance matrix and the gain matrix to obtain a final traffic state estimation value;
final traffic state estimate
Figure FDF00000167747500000710
The formula is calculated by adopting the following formula:
Figure FDF0000016774750000081
wherein G represents a gain matrix;
determining the traffic state of the expressway network according to the final traffic state estimation value;
calculating a pre-estimated value of the Kalman filter using the following formula:
Figure FDF0000016774750000082
wherein the content of the first and second substances,
Figure FDF0000016774750000083
representing a pre-estimated value of the Kalman filter, wherein A represents a highway network system matrix; b represents an input matrix of the highway network system; f denotes a constant matrix.
5. A computer-readable storage medium having stored thereon computer instructions, which when executed by a processor, perform the steps of the method of claim 1 or 2.
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