CN117334051B - Highway vehicle track reconstruction method and system - Google Patents
Highway vehicle track reconstruction method and system Download PDFInfo
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
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Abstract
The invention provides a method for reconstructing a track of a highway vehicle, which comprises the following steps: inputting fixed detection data and network connection data, and estimating the space position of a vehicle in a road section and the traffic speed of running under time; the fixed detection data carries out candidate track calculation on the detected non-network-connected vehicles, and the network-connected vehicle track is taken as a reference; the invention fully fuses sparse network vehicle-connected data and fixed detector data, considers different characteristics of traffic flow in traffic flow blocking and free flow states, complements a space-time velocity matrix by a self-adaptive smoothing method, takes the space-time velocity matrix generated by a macroscopic model as constraint, combines the vehicle track generated by a following model, and realizes full-sample high-resolution track reconstruction of robustness of different data scenes.
Description
Technical Field
The invention relates to a track reconstruction method and system for a highway vehicle, in particular to the technical field of track reconstruction.
Background
The vehicle track reconstruction means that the vehicle running track is obtained through processing and analyzing vehicle running data, the vehicle track data is basic data of road traffic design, optimization, control and management work in each stage, and for vehicles running at high speed, the track of the vehicle can be obtained through recording the track of the vehicle through GPS, vehicle-mounted radar and video equipment; because the vehicle is blocked, unobstructed and detained on the expressway and the motion parameters of the vehicle track are not consistent, track reconstruction is needed before track data is used, the existing method based on the traffic macro-micro model is poor in macro-level consideration, only the characteristics of blocking flow propagation are applied to traffic speed estimation, but in practice, the propagation of traffic flow is influenced by blocking flow and free flow characteristics at the same time, only the characteristics of blocking flow are considered to have a certain negative effect on the vehicle track reconstruction precision, most of high-resolution vehicle track reconstruction methods depend on the traffic micro model, only a single data source, namely network vehicle connection data, is considered by the method based on the traffic micro model, the data of the existing fixed detector are ignored, and the reconstruction precision is required to be further improved.
Disclosure of Invention
The invention aims to: an object is to propose a method for reconstructing the trajectory of a highway vehicle, so as to solve the above-mentioned problems existing in the prior art;
The technical scheme is as follows: a highway vehicle track reconstruction method, comprising:
Step 1, inputting fixed detection data and network connection data, and estimating the space position of a vehicle in a road section and the traffic speed of running under time;
step 2, fixed detection data carries out candidate track calculation on the detected non-network-connected vehicles, and the network-connected vehicle track is taken as a reference;
And step 3, carrying out weighting method fusion on the non-networked vehicles to obtain two candidate tracks, and obtaining the reconstruction track of each vehicle.
In a further embodiment, the step 1, the fixed detector data includes a detection record unique identification, a vehicle location speed, and a vehicle elapsed time;
the unique identification of the detection record is used for carrying out detection record on vehicles running in the road section;
The vehicle location speed is used for setting the fixed point position of the detected and recorded vehicle;
the vehicle elapsed time; calculating a time stamp of the vehicle passing through the detector according to the fixed point position;
the network connection data comprise a vehicle unique identifier, a time stamp, vehicle coordinates and vehicle speed;
the unique identification of the vehicle is used for tracking and marking the network-connected vehicle;
time stamp, time of network connection passing through unique identification position;
And directly uploading the vehicle coordinates and the vehicle speed of the internet-connected vehicle according to the tracking mark and the time stamp.
In a further embodiment, estimating the space position of the vehicle in the road section and the speed of running under time by a self-adaptive smoothing method on the basis of the fixed detection data and the network connection data, complementing a space-time speed matrix, and performing inclined setting on a smoothing core in the self-adaptive smoothing method according to the characteristics of traffic jam flow and free flow; the expression is as follows:
Where x i、ti、vi (i=1,., n) are the known location, time, and traffic speed under the corresponding space-time, respectively; (x, t) represents a position in space-time; phi represents the smooth core of the image, Representing the normalization factor; v free denotes the free flow threshold; v cong denotes the blocking flow threshold; c free represents the speed of disturbance propagation in the traffic free stream; c cong represents the speed of disturbance propagation in traffic congestion.
In a further embodiment, parameters of the smoothing kernel and normalization factor are calculated from the slope setting of the smoothing kernel as follows:
Where x i、ti、vi (i=1,., n) are the known location, time, and traffic speed under the corresponding space-time, respectively; (x, t) represents a position in space-time; phi represents the smooth core of the image, Representing the normalization factor; sigma represents the smoothed width in the spatial coordinates; τ represents the smoothed width in time coordinates.
In a further embodiment, the free flow and the blocked flow characteristics are weighted based on a weight W, the specific expression is as follows:
wherein V free represents the free flow threshold; v cong denotes the blocking flow threshold; v thr denotes a threshold between free flow and blocked flow; deltaV represents the transition width between free and choked flow.
In a further embodiment, the traffic speed for the space-time under estimation based on weights W, V free and V cong is expressed as follows:
Vrefer(x,t)=w(x,t)Vcong(x,t)+[1-w(x,t)]Vfree(x,t)
Wherein V refer represents traffic speed; v free denotes the free flow threshold; v cong denotes the blocking flow threshold; w represents the shock velocity; (x, t) represents a position in space-time.
In a further embodiment, step 2 is performed according to a reconstruction interval between the upstream network-connected vehicle track Y i and the downstream network-connected vehicle track Y i+1 adjacent to each other in the network-connected vehicle track set YN represents the number of non-networked vehicle tracks that need to be reconstructed within this interval.
According to the following model, the track of the rear vehicle n is set to be consistent with that of the front vehicle n-1, and the track has time lag tau n and space lag delta n, and the specific calculation mode is as follows:
Where w represents shock velocity and k j represents blocking density;
For each reconstruction interval Based on a following model, each non-networked vehicle in the vehicle is respectively referenced by an upstream networked vehicle track Y i and a downstream networked vehicle track Y i+1 to generate an nth non-networked vehicle of tracks to be reconstructed in two adjacent networked vehicle tracks of non-networked vehicle N (n=1, 2, N), and candidate tracks/>, which are generated according to the upstream networked vehicle tracks, are generatedAnd an nth non-networked vehicle of the tracks to be reconstructed in the two adjacent networked vehicle tracks, and generating a candidate track according to the downstream networked vehicle trackThe expression is as follows:
(when n=1,/> )
(When n=n,/>)
Where τ represents the time interval; delta represents the spatial separation; t represents the position in space-time.
In a further embodiment, the step 3 uses the traffic speed calculated in the step 1 as a constraint to solve the candidate track weight of each non-internet-connected vehicleAnd/>The specific expression is as follows:
Wherein T represents the time interval of uploading the internet-connected vehicle data; The speed of the vehicle represented by the reconstructed track of the non-internet-connected vehicle n at the time t is represented; /(I) Representing that the space-time velocity matrix estimated in step S1 is at (t,/>) Traffic speed values at space-time locations; /(I)The position of the vehicle represented by the reconstruction track of the non-internet-connected vehicle n at the time t is represented; The position of a vehicle represented by a candidate track generated by the non-internet-connected vehicle n according to an upstream internet-connected vehicle track at the time t is represented; representing the position of a vehicle represented by a candidate track generated by the non-internet-connected vehicle n according to the downstream internet-connected vehicle track at the time t; Candidate track/>, representing non-networked vehicle n Weights of (2); s.t. represents constraint conditions; /(I)The position of a vehicle represented by a candidate track generated by the non-internet-connected vehicle n according to an upstream internet-connected vehicle track at the time t is represented;
fusing candidate tracks of the non-internet-connected vehicle n according to the weight calculation result And/>Outputting the vehicle track reconstruction result/>
Wherein,Representing a reconstruction track of the non-internet-connected vehicle n; /(I)The position of the vehicle represented by the candidate track generated by the non-internet-connected vehicle n according to the upstream internet-connected vehicle track at the time t is represented.
The beneficial effects are that: the invention provides a method and a system for reconstructing a vehicle track of a highway, which fully fuses sparse network vehicle connection data and fixed detector data, considers different characteristics of traffic flow in traffic flow blocking and free flow states, complements a space-time velocity matrix by a self-adaptive smoothing method, takes the space-time velocity matrix generated by a macroscopic model as constraint and combines the vehicle track generated by a following model, so that full-sample high-resolution track reconstruction of robustness of different data scenes is realized, in the macroscopic traffic model, the characteristics of free flow and blocking flow propagation are considered at the same time, the method and the system are applied to traffic speed estimation, speed estimation structural errors caused by only considering blocking are reduced, and a reliable scheme is provided for robust high-resolution track reconstruction.
Drawings
FIG. 1 is a schematic flow chart of the treatment method of the present invention.
Fig. 2 is a schematic diagram of a flow of fixed detection data and internet protocol vehicles according to the present invention.
Detailed Description
The applicant believes that the existing method only uses the characteristic of blocking flow propagation in traffic speed estimation, but in practice, the propagation of traffic flow is influenced by both the blocking flow and free flow characteristics, and only considering the characteristic of blocking flow can have a certain negative effect on vehicle track reconstruction accuracy, so that it is necessary to reduce the structural error of speed estimation caused by blocking.
In order to solve the problems in the prior art, the invention realizes the aim of simultaneously considering the characteristics of free flow and blocking flow propagation by a method for reconstructing the track of the highway vehicle, and applies the method to traffic speed estimation to reduce the structural error of speed estimation caused by only considering blocking,
The present invention will be described in more detail with reference to the following examples and the accompanying drawings.
In the application, we propose a method for reconstructing the track of a highway vehicle, comprising the following steps:
step 1, inputting fixed detection data and network connection data, and estimating the space position of a vehicle in a road section and the traffic speed of running under time; the fixed detector data includes a detection record unique identifier, a vehicle location speed, and a vehicle elapsed time;
the unique identification of the detection record is used for carrying out detection record on vehicles running in the road section;
The vehicle location speed is used for setting the fixed point position of the detected and recorded vehicle;
the vehicle elapsed time; calculating a time stamp of the vehicle passing through the detector according to the fixed point position;
the network connection data comprise a vehicle unique identifier, a time stamp, vehicle coordinates and vehicle speed;
the unique identification of the vehicle is used for tracking and marking the network-connected vehicle;
time stamp, time of network connection passing through unique identification position;
And directly uploading the vehicle coordinates and the vehicle speed of the internet-connected vehicle according to the tracking mark and the time stamp.
Estimating the space position of a vehicle in a road section and the running speed under time by a self-adaptive smoothing method on the basis of the fixed detection data and the network vehicle connection data, complementing a space-time speed matrix, and obliquely setting a smoothing core in the self-adaptive smoothing method according to the characteristics of traffic jam flow and free flow; the expression is as follows:
Where x i、ti、vi (i=1,., n) are the known location, time, and traffic speed under the corresponding space-time, respectively; (x, t) represents a position in space-time; phi represents the smooth core of the image, Representing the normalization factor; v free denotes the free flow threshold; v cong denotes the blocking flow threshold; c free represents the speed of disturbance propagation in the traffic free stream; c cong represents the speed of disturbance propagation in traffic congestion.
Parameters of the smoothing kernel and the normalization factor are calculated according to the inclination setting of the smoothing kernel, and the expression is as follows:
Where x i、ti、vi (i=1,., n) are the known location, time, and traffic speed under the corresponding space-time, respectively; (x, t) represents a position in space-time; phi represents the smooth core of the image, Representing the normalization factor; sigma represents the smoothed width in the spatial coordinates; τ represents the smoothed width in time coordinates.
The free flow and the blocked flow characteristics are weighted based on the weight W, and the specific expression is as follows:
wherein V free represents the free flow threshold; v cong denotes the blocking flow threshold; v thr denotes a threshold between free flow and blocked flow; deltaV represents the transition width between free and choked flow.
The traffic speed in time and space under estimation based on the weights W, V free and V cong is expressed as follows:
Vrefer(x,t)=w(x,t)Vcong(x,t)+[1-w(x,t)]Vfree(x,t)
Wherein V refer represents traffic speed; v free denotes the free flow threshold; v cong denotes the blocking flow threshold; w represents the shock velocity; (x, t) represents a position in space-time.
Step 2, fixed detection data carries out candidate track calculation on the detected non-network-connected vehicles, and the network-connected vehicle track is taken as a reference; according to the reconstruction interval between the adjacent upstream network-connected vehicle track Y i and downstream network-connected vehicle track Y i+1 in the network-connected vehicle track set YN represents the number of non-networked vehicle tracks that need to be reconstructed within this interval.
According to the following model, the track of the rear vehicle n is set to be consistent with that of the front vehicle n-1, and the track has time lag tau n and space lag delta n, and the specific calculation mode is as follows:
Where w represents shock velocity and k j represents blocking density;
For each reconstruction interval Based on a following model, each non-networked vehicle in the vehicle is respectively referenced by an upstream networked vehicle track Y i and a downstream networked vehicle track Y i+1 to generate an nth non-networked vehicle of tracks to be reconstructed in two adjacent networked vehicle tracks of non-networked vehicle N (n=1, 2, N), and candidate tracks/>, which are generated according to the upstream networked vehicle tracks, are generatedAnd an nth non-networked vehicle of the tracks to be reconstructed in the two adjacent networked vehicle tracks, and generating a candidate track according to the downstream networked vehicle trackThe expression is as follows:
(when n=1,/> )
(When n=n,/>)
Where τ represents the time interval; delta represents the spatial separation; t represents the position in space-time.
Step 3, carrying out weighting method fusion on the non-network vehicles to obtain two candidate tracks, obtaining a reconstruction track of each vehicle, using the traffic speed calculated in the step 1 as constraint, and solving the candidate track weight of each non-network vehicleAnd/>The specific expression is as follows:
Wherein T represents the time interval of uploading the internet-connected vehicle data; The speed of the vehicle represented by the reconstructed track of the non-internet-connected vehicle n at the time t is represented; /(I) Representing that the space-time velocity matrix estimated in step S1 is at (t,/>) Traffic speed values at space-time locations; /(I)The position of the vehicle represented by the reconstruction track of the non-internet-connected vehicle n at the time t is represented; The position of a vehicle represented by a candidate track generated by the non-internet-connected vehicle n according to an upstream internet-connected vehicle track at the time t is represented; representing the position of a vehicle represented by a candidate track generated by the non-internet-connected vehicle n according to the downstream internet-connected vehicle track at the time t; Candidate track/>, representing non-networked vehicle n Weights of (2); s.t. represents constraint conditions; /(I)The position of a vehicle represented by a candidate track generated by the non-internet-connected vehicle n according to an upstream internet-connected vehicle track at the time t is represented;
fusing candidate tracks of the non-internet-connected vehicle n according to the weight calculation result And/>Outputting the vehicle track reconstruction result/>
Wherein,Representing a reconstruction track of the non-internet-connected vehicle n; /(I)The position of the vehicle represented by the candidate track generated by the non-internet-connected vehicle n according to the upstream internet-connected vehicle track at the time t is represented.
The invention is compared with the vehicle track reconstruction in the existing mode, and the following data are obtained:
Wherein MAPE represents the mean absolute percentage error; RMSE represents root mean square error; MAE represents the mean absolute error; compared with the prior art, the track reconstruction method provided by the invention has higher precision when solving the problem of vehicle track reconstruction under extremely low network vehicle connection permeability, realizes 44.6% improvement under 5% permeability and improves the reconstruction effect under 10% and 15% permeability, so that the track reconstruction method has certain robustness for scenes with scarce network vehicle connection data, can be compatible with vehicle track reconstruction requirements under different data scenes, and has important significance in application of real scenes.
As described above, although the present invention has been shown and described with reference to certain preferred embodiments, it is not to be construed as limiting the invention itself. 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 (5)
1. A method for reconstructing a track of a highway vehicle, comprising:
Step 1, inputting fixed detection data and network connection data, and estimating the space position of a vehicle in a road section and the traffic speed of running under time; estimating the space position of a vehicle in a road section and the running speed under time by a self-adaptive smoothing method on the basis of the fixed detection data and the network vehicle connection data, complementing a space-time speed matrix, and obliquely setting a smoothing core in the self-adaptive smoothing method according to the characteristics of traffic jam flow and free flow; the expression is as follows:
where x i、ti、vi, i=1,..n is the known location, time and traffic speed under the corresponding space-time, respectively; (x, t) represents a position in space-time; phi represents the smooth core of the image, Representing the normalization factor; v free denotes the free flow threshold; v cong denotes the blocking flow threshold; c free represents the speed of disturbance propagation in the traffic free stream; c cong represents the speed of disturbance propagation in traffic congestion;
Step 2, fixed detection data carries out candidate track calculation on the detected non-network-connected vehicles, and the network-connected vehicle track is taken as a reference; according to the reconstruction interval between the adjacent upstream network-connected vehicle track Y i and downstream network-connected vehicle track Y i+1 in the network-connected vehicle track set Y N represents the number of non-network vehicle tracks to be reconstructed in the interval;
According to the following model, the track of the rear vehicle n is set to be consistent with that of the front vehicle n-1, and the track has time lag tau n and space lag delta n, and the specific calculation mode is as follows:
Where w represents shock velocity and k j represents blocking density;
For each reconstruction interval Each non-internet-connected vehicle in the network-connected vehicle is based on a following model, and an upstream internet-connected vehicle track Y i and a downstream internet-connected vehicle track Y i+1 are used as references respectively to generate an nth non-internet-connected vehicle of tracks to be reconstructed in two adjacent internet-connected vehicle tracks of the non-internet-connected vehicle N, wherein n=1, 2, and N is a candidate track/>, which is generated according to the upstream internet-connected vehicle trackAnd an nth non-networked vehicle of the tracks to be reconstructed in the two adjacent networked vehicle tracks, and generating a candidate track according to the downstream networked vehicle trackThe expression is as follows:
(when n=1,/> )
(When n=n,/>)
Where τ represents the time interval; delta represents the spatial separation; t represents a position in time and space;
Step 3, carrying out weighting method fusion on the non-networked vehicles to obtain two candidate tracks, and obtaining a reconstruction track of each vehicle; taking the traffic speed calculated in the step 1 as constraint, and solving the candidate track weight of each non-network-connected vehicle And/>The specific expression is as follows:
Wherein T represents the time interval of uploading the internet-connected vehicle data; The speed of the vehicle represented by the reconstructed track of the non-internet-connected vehicle n at the time t is represented; /(I) Representing that the space-time velocity matrix estimated in step S1 is at (t,/>) Traffic speed values at space-time locations; /(I)The position of the vehicle represented by the reconstruction track of the non-internet-connected vehicle n at the time t is represented; /(I)The position of a vehicle represented by a candidate track generated by the non-internet-connected vehicle n according to an upstream internet-connected vehicle track at the time t is represented; /(I)Representing the position of a vehicle represented by a candidate track generated by the non-internet-connected vehicle n according to the downstream internet-connected vehicle track at the time t; /(I)Candidate track/>, representing non-networked vehicle nWeights of (2); s.t. represents constraint conditions; /(I)The position of a vehicle represented by a candidate track generated by the non-internet-connected vehicle n according to an upstream internet-connected vehicle track at the time t is represented;
fusing candidate tracks of the non-internet-connected vehicle n according to the weight calculation result And/>Outputting a vehicle track reconstruction result
Wherein,Representing a reconstruction track of the non-internet-connected vehicle n; /(I)The position of the vehicle represented by the candidate track generated by the non-internet-connected vehicle n according to the upstream internet-connected vehicle track at the time t is represented.
2. The method for reconstructing a track of a highway vehicle according to claim 1, wherein said fixed detection data comprises a detection record unique identifier, a vehicle location speed and a vehicle elapsed time in step 1;
the unique identification of the detection record is used for carrying out detection record on vehicles running in the road section;
The vehicle location speed is used for setting the fixed point position of the detected and recorded vehicle;
Calculating the time stamp of the vehicle passing detector according to the vehicle passing time and the fixed point position;
the network connection data comprise a vehicle unique identifier, a time stamp, vehicle coordinates and vehicle speed;
the unique identification of the vehicle is used for tracking and marking the network-connected vehicle;
time stamp, time of network connection passing through unique identification position;
And directly uploading the vehicle coordinates and the vehicle speed of the internet-connected vehicle according to the tracking mark and the time stamp.
3. The method for reconstructing a track of a highway vehicle according to claim 1, wherein parameters of the smoothing kernel and the normalization factor are calculated according to an inclination setting of the smoothing kernel, expressed as follows:
where x i、ti、vi, i=1,..n is the known location, time and traffic speed under the corresponding space-time, respectively; (x, t) represents a position in space-time; phi represents the smooth core of the image, Representing the normalization factor; sigma represents the smoothed width in the spatial coordinates; τ represents the smoothed width in time coordinates.
4. The method for reconstructing the track of a highway vehicle according to claim 1, wherein the characteristics of free flow and blocked flow are weighted based on a weight W, and the specific expression is as follows:
wherein V free represents the free flow threshold; v cong denotes the blocking flow threshold; v thr denotes a threshold between free flow and blocked flow; deltaV represents the transition width between free and choked flow.
5. The method for reconstructing a track of a highway vehicle according to claim 1, wherein the traffic speed in space-time under estimation based on weights W, V free and V cong is expressed as follows:
Vrefer(x,t)=w(x,t)Vcong(x,t)+[1-w(x,t)]Vfree(x,t)
Wherein V refer represents traffic speed; v free denotes the free flow threshold; v cong denotes the blocking flow threshold; w represents the shock velocity; (x, t) represents a position in space-time.
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