CN111739299B - Sparse-track vehicle queuing length determination method, device, equipment and medium - Google Patents
Sparse-track vehicle queuing length determination method, device, equipment and medium Download PDFInfo
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- CN111739299B CN111739299B CN202010695697.4A CN202010695697A CN111739299B CN 111739299 B CN111739299 B CN 111739299B CN 202010695697 A CN202010695697 A CN 202010695697A CN 111739299 B CN111739299 B CN 111739299B
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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
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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
- G08G1/0125—Traffic data processing
Abstract
The application relates to the technical field of artificial intelligence and data processing, and provides a method, a device, equipment and a medium for determining vehicle queuing length of a sparse track. The method can be used for calculating the longitudinal relative driving distance by combining coordinate data and Link data, a vertical mapping mode is adopted, the transverse drift error of an estimation point in the intersection during low-speed driving can be eliminated to the maximum extent, non-queuing vehicles are used for correcting the queuing length to obtain the corrected length, the error between the estimated queuing length and the real queuing length is eliminated, and the vehicle queuing length of the missing period is filled through Kalman filtering to realize the automatic determination of the vehicle queuing length, and the determination of the vehicle queuing length is more accurate. In addition, the invention can also be applied to intelligent traffic, thereby promoting the construction of intelligent cities. The invention also relates to blockchain technology, and the vehicle queuing length can be stored in blockchain nodes.
Description
Technical Field
The invention relates to the technical field of artificial intelligence and data processing, in particular to a method, a device, equipment and a medium for determining vehicle queuing length of a sparse track.
Background
With the increasing level of urban road traffic informatization and the advancing of intelligent traffic, various novel traffic detection devices and technologies are gradually applied to the operation monitoring and control of traffic systems, including automatic vehicle Identification devices (such as a gate, an electric police, an RFID (Radio Frequency Identification, etc.), a vehicle navigation GPS (Global Positioning System), and the like. The novel traffic data sources can provide IDs of partial vehicles and running track information thereof, and provide new data support and opportunities for control evaluation and optimization of signal control intersections of a wide area road network.
In recent years, scholars at home and abroad have been working on estimating a queuing length using vehicle trajectory data. From the premise hypothesis and the method means of the existing queuing length estimation model, the related invention achievements can be divided into two main categories: firstly, a queuing length or delay estimation model established by using a traffic wave theory is utilized on the premise of approximately simplifying or completely ignoring uncertainty factors. And secondly, on the premise of considering partial uncertainty factors in the traffic system, establishing an obtained queuing length or delay estimation model by using a probability theory method. The expected value of the queue length is calculated assuming that the vehicles arrive in a distribution, while knowing the last floating vehicle position, floating vehicle ratio, etc. The above methods all have certain disadvantages.
Firstly, the distribution of the queued vehicles in time and space can be obtained by a traffic wave-based queuing length estimation method, although the traffic evolution trend of the signalized intersection can be better reflected, the method has higher requirements on track data, and the following assumptions are also needed: (1) the signal parameters and vehicle arrival are known, requiring that at least two vehicles in line be captured in a cycle. Thus, in the absence period where two queued vehicles are not captured, an estimate of the queue length cannot be achieved. (2) The distance from the position of the last vehicle in line to the stop line is taken as the queuing length, and the assumption has larger error under the condition of sparse vehicle track; or the distance from the intersection point of the collective wave and the evanescent wave to the stop line is taken as the queuing length, and the assumption has large errors under the conditions of low saturation and uneven traffic arrival.
Secondly, although the queuing length estimation method based on the probability theory method embodies the random queuing process, uncertain factors of a traffic system cannot be comprehensively analyzed, and the method also comprises the assumptions that at least one queuing vehicle needs to be captured in one period, the arrival type of the vehicle is known, and the like.
In addition, the two types of methods are mainly based on simulation data or low-frequency floating car data for analysis and demonstration, large-scale commercial vehicle track data cannot be used for on-site verification, and the estimation of the queuing length is not accurate enough.
Disclosure of Invention
In view of the foregoing, there is a need to provide a method, an apparatus, a device, and a medium for determining a vehicle queue length with a sparse trajectory, which can fill up a vehicle queue length in a missing period through kalman filtering, so as to achieve automatic determination of the vehicle queue length, and determine the vehicle queue length more accurately.
A vehicle queuing length determination method of a sparse track comprises the following steps:
connecting a designated platform, and acquiring vehicle track data from the designated platform;
acquiring coordinate data and Link data from the vehicle track data;
mapping each coordinate point in the coordinate data to a corresponding Link in the Link data, and calculating the accumulated sum of the distances from each mapped coordinate point to the starting point of the first Link in the Link data;
acquiring a time stamp of the vehicle track data, and constructing a vehicle space-time track graph based on the time stamp and the accumulated sum;
in response to a command for determining the vehicle queuing length of a target period with a sparse track, acquiring aggregate wave data and evanescent wave data of a previous period from the vehicle spatiotemporal track diagram;
calculating a first distance from the intersection point of the cluster wave and the evanescent wave to a stop line in the previous period according to the cluster wave data and the evanescent wave data;
obtaining the non-queued vehicles in the previous period, and calculating each second distance between each non-queued vehicle and the parking line;
selecting the shortest distance of the first distance and the second distance as a correction length;
and carrying out deletion filling on the corrected length based on an extended Kalman filtering algorithm to obtain the vehicle queuing length of the target period.
According to a preferred embodiment of the present invention, the mapping each coordinate point in the coordinate data to a corresponding Link in the Link data includes:
acquiring Link IDs corresponding to the coordinate points from the coordinate data;
determining a Link corresponding to each coordinate point according to the Link ID;
and vertically mapping each coordinate point to the Link corresponding to each coordinate point.
According to a preferred embodiment of the present invention, the calculating a first distance from the intersection point of the cluster wave and the evanescent wave to the stop line in the previous period according to the cluster wave data and the evanescent wave data comprises:
determining at least one section of aggregate waves from the aggregate wave data based on vehicle kinematics;
determining the slope and intercept of each section of the at least one section of the aggregated wave;
obtaining the slope and intercept of the evanescent wave from the evanescent wave data;
and calculating the first distance according to the slope and the intercept of the aggregate wave of each section and the slope and the intercept of the evanescent wave.
According to a preferred embodiment of the present invention, said calculating the first distance according to the slope and intercept of the aggregate wave of each segment and the slope and intercept of the evanescent wave comprises:
determining the distance between each section of aggregate wave and the parking line;
acquiring an aggregation wave with the shortest distance as a target aggregation wave;
acquiring a target slope and a target intercept of the target aggregate wave;
calculating a first difference between the intercept of the evanescent wave and the target intercept, and calculating a second difference between the target slope and the slope of the evanescent wave;
calculating a quotient of the first difference and the second difference and calculating a product of the quotient and a slope of the evanescent wave;
determining a longitudinal distance of the stop line;
calculating a third difference of the longitudinal distance and the product;
calculating a sum of the third difference and an intercept of the evanescent wave as the first distance.
According to a preferred embodiment of the present invention, the missing filling of the correction length based on the extended kalman filter algorithm to obtain the vehicle queue length of the target period includes:
determining a state transition matrix acting on the correction length;
calculating a priori predicted value of the target period according to the state transformation matrix and the correction length;
determining a first covariance acting on the correction length;
determining a second covariance acting on the prior predicted value based on the first covariance;
calculating a Kalman gain of the target period according to the second covariance;
and calculating the vehicle queuing length according to the Kalman gain, the prior predicted value and the correction length.
According to the preferred embodiment of the present invention, the prior predicted value of the target period is calculated according to the state transformation matrix and the correction length by using the following formula:
wherein the content of the first and second substances,
representing the a priori predicted value or values,Arepresenting the state-transformation matrix in question,
representing the correction length;
determining a second covariance acting on the a priori predicted values from the first covariance using the following equation:
wherein the content of the first and second substances,
representing the second covariance as a function of the second covariance,
representing the first covariance as a function of the first covariance,
representing a transpose of the state transition matrix,
a covariance matrix representing noise of the state process;
calculating a Kalman gain for the target period from the second covariance using the following equation:
wherein the content of the first and second substances,
indicating the cardThe gain of the Raman spectrum is obtained by the Raman spectrum,
a matrix of coefficients is represented by a matrix of coefficients,
represents a transpose of the matrix of coefficients,
a covariance matrix representing noise in the measurement process.
According to a preferred embodiment of the present invention, the vehicle queue length is calculated according to the kalman gain, the a priori predicted value, and the correction length using the following formula:
wherein the content of the first and second substances,
indicating the vehicle queue length.
A sparse trajectory vehicle queue length determination apparatus, comprising:
the acquisition unit is used for connecting a specified platform and acquiring vehicle track data from the specified platform;
the acquisition unit is further used for acquiring coordinate data and Link data from the vehicle track data;
the calculating unit is used for mapping each coordinate point in the coordinate data to a corresponding Link in the Link data and calculating the accumulated sum of the distances from each mapped coordinate point to the starting point of the first Link in the Link data;
the building unit is used for acquiring a time stamp of the vehicle track data and building a vehicle space-time track graph based on the time stamp and the accumulated sum;
the acquiring unit is further used for responding to a determination instruction of the vehicle queuing length of a target period with a sparse track, and acquiring aggregate wave data and evanescent wave data of a previous period from the vehicle space-time track diagram;
the calculation unit is further used for calculating a first distance from the intersection point of the cluster wave and the evanescent wave to a stop line in the previous period according to the cluster wave data and the evanescent wave data;
the calculation unit is further configured to acquire non-queued vehicles in the previous period, and calculate each second distance between each non-queued vehicle and the parking line;
a selection unit configured to select a shortest distance of the first distance and the second distance as a correction length;
and the filling unit is used for carrying out deletion filling on the correction length based on an extended Kalman filtering algorithm to obtain the vehicle queuing length of the target period.
An electronic device, the electronic device comprising:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement the sparse trajectory vehicle queue length determination method.
A computer-readable storage medium having at least one instruction stored therein, the at least one instruction being executable by a processor in an electronic device to implement a vehicle queue length determination method for the sparse trajectory.
According to the technical scheme, the modified length can be subjected to missing filling based on an extended Kalman filtering algorithm to obtain the vehicle queuing length of the target period, so that the vehicle queuing length of the missing period can be filled through Kalman filtering, the vehicle queuing length can be automatically determined, and the vehicle queuing length can be more accurately determined.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of the sparse trajectory vehicle queue length determination method of the present invention.
FIG. 2 is a functional block diagram of a preferred embodiment of the sparse trajectory vehicle queue length determining apparatus of the present invention.
Fig. 3 is a schematic structural diagram of an electronic device according to a preferred embodiment of the method for determining the vehicle queue length based on the sparse trajectory of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a flow chart of a vehicle queue length determining method according to a preferred embodiment of the sparse trajectory of the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.
The method for determining the vehicle queue length with the sparse track is applied to one or more electronic devices, wherein the electronic devices are devices capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and hardware of the electronic devices includes but is not limited to a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device and the like.
The electronic device may be any electronic product capable of performing human-computer interaction with a user, for example, a Personal computer, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a game machine, an interactive Internet Protocol Television (IPTV), an intelligent wearable device, and the like.
The electronic device may also include a network device and/or a user device. The network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of hosts or network servers.
The Network where the electronic device is located includes, but is not limited to, the internet, a wide area Network, a metropolitan area Network, a local area Network, a Virtual Private Network (VPN), and the like.
And S10, connecting the appointed platform, and acquiring vehicle track data from the appointed platform.
In at least one embodiment of the invention, the designated platform may include various types of traffic management platforms having various types of traffic data stored thereon.
To ensure the security and privacy of the data, the vehicle trajectory data may be deployed on a blockchain.
And S11, acquiring coordinate data and Link data from the vehicle track data.
The Link data is based on a road network topological structure, road intersections are taken as nodes, a road network is disassembled into nodes and Link sets, and the links are directed paths among different nodes.
For example: the coordinate data may be in the form of a table, see table 1.
TABLE 1 coordinate data sheet
| Vehicle ID | Time stamp | Longitude (G) | Latitude | Link ID | Distance of passage | Linear velocity |
| e93e2532c87e0789e | 1480288802 | 120.40033 | 36.086 | 66320951 | 16 | 3.2 |
| e93e2532c87e0789e | 1480288805 | 120.4005 | 36.08579 | 66320951 | 44 | 9.3 |
| e93e2532c87e0789e | 1480288808 | 120.40055 | 36.08573 | 66320951 | 52 | 2.7 |
| e93e2532c87e0789e | 1480288811 | 120.4006 | 36.08567 | 66320951 | 61 | 2.7 |
| e93e2532c87e0789e | 1480288814 | 120.40064 | 36.08558 | 66320941 | 4 | 3.5 |
Further, the link data may also be in the form of a table, see table 2.
TABLE 2 Link data sheet
S12, each coordinate point in the coordinate data is mapped to a corresponding Link in the Link data, and the accumulated sum of the distances from each mapped coordinate point to the starting point of the first Link in the Link data is calculated.
In at least one embodiment of the present invention, the mapping each coordinate point in the coordinate data to a corresponding Link in the Link data includes:
acquiring Link IDs corresponding to the coordinate points from the coordinate data;
determining a Link corresponding to each coordinate point according to the Link ID;
and vertically mapping each coordinate point to the Link corresponding to each coordinate point.
It can be understood that, because the coordinate point of the vehicle is prone to drift and large errors when the vehicle runs at a low speed in the intersection range, if the relative running distance is directly calculated through the coordinate point, the large errors are generated, and the estimation effect of the vehicle queuing length is affected. For this reason, the present embodiment performs calculation of the longitudinal relative travel distance by combining the coordinate data and the Link data, calculates the relative travel distance by the Link data (i.e., calculates the cumulative sum of the distances from each mapped coordinate point to the first Link start point in the Link data), and adopts a vertical mapping manner, so as to eliminate the lateral drift error of the estimated point in the intersection during low-speed travel to the greatest extent.
And S13, acquiring the time stamp of the vehicle track data, and constructing a vehicle space-time track graph based on the time stamp and the accumulated sum.
In at least one embodiment of the invention, the vehicle spatiotemporal trajectory graph reflects the relation of time and distance, the vehicle spatiotemporal trajectory graph can provide basic data for subsequent schemes, and the graph is constructed mainly for facilitating the storage of data, so the invention does not limit the way of constructing the vehicle spatiotemporal trajectory graph.
S14, in response to a command for determining the vehicle queue length of a target period with a sparse track, acquiring aggregate wave data and evanescent wave data of a previous period from the vehicle space-time track diagram.
In view of the stable characteristic of the traffic flow in a short time, the embodiment acquires the aggregate wave data and the evanescent wave data of the previous period to determine the vehicle queuing length of the target period, so as to correct the queuing length of the target period in a sparse state by combining historical estimated queuing length data, thereby improving the accuracy.
And S15, calculating a first distance from the intersection point of the aggregation wave and the evanescent wave to the stop line in the previous period according to the aggregation wave data and the evanescent wave data.
In the embodiment, based on the traffic wave theory, the queuing up and dissipating process of the vehicles caused by the periodic change of the signal lamps can be described in detail. When the red light is turned on, the arriving vehicles are decelerated under the influence of the red light, the vehicles are decelerated and queued close to the stop line, and the queued vehicles are gradually propagated upstream to form the aggregation wave; when the red light is finished and the green light is turned on, the queued vehicles drive away from the intersection at a saturated flow rate, and are gradually dissipated in a queue, so that the dissipated waves are transmitted upstream. And the evanescent wave velocity is greater than the aggregate wave, so that after a period of time, the vehicles are queued to a position furthest from the stop line, and thereafter completely dissipate.
In at least one embodiment of the present invention, said calculating a first distance from an intersection point of the aggregate wave and the evanescent wave to a stop line in the previous period according to the aggregate wave data and the evanescent wave data comprises:
determining at least one section of aggregate waves from the aggregate wave data based on vehicle kinematics;
determining the slope and intercept of each section of the at least one section of the aggregated wave;
obtaining the slope and intercept of the evanescent wave from the evanescent wave data;
and calculating the first distance according to the slope and the intercept of the aggregate wave of each section and the slope and the intercept of the evanescent wave.
Through the above embodiment, the first distance from the intersection point of the aggregate wave and the evanescent wave to the stop line is calculated for subsequent estimation of the maximum queuing length.
Specifically, the calculating the first distance according to the slope and intercept of the aggregate wave and the slope and intercept of the evanescent wave of each segment includes:
determining the distance between each section of aggregate wave and the parking line;
acquiring an aggregation wave with the shortest distance as a target aggregation wave;
acquiring a target slope and a target intercept of the target aggregate wave;
calculating a first difference between the intercept of the evanescent wave and the target intercept, and calculating a second difference between the target slope and the slope of the evanescent wave;
calculating a quotient of the first difference and the second difference and calculating a product of the quotient and a slope of the evanescent wave;
determining a longitudinal distance of the stop line;
calculating a third difference of the longitudinal distance and the product;
calculating a sum of the third difference and an intercept of the evanescent wave as the first distance.
With the above embodiment, the first distance can be calculated based on the aggregate wave and the evanescent wave formed by the queued vehicles.
S16, obtaining the non-queued vehicles in the previous period, and calculating each second distance between each non-queued vehicle and the parking line.
It will be appreciated that the first distance is calculated from the collected information of the vehicles in line, but in the case of sparse trajectories with low saturation or large traffic arrival fluctuations, the above approach will estimate some of the non-vehicles in line as in line, i.e. the estimated queue length is always greater than or equal to the true queue length, and therefore, the correction will be performed subsequently on the basis of each second distance.
And S17, selecting the shortest distance from the first distance and the second distance as a correction length.
Through the embodiment, the queuing length can be corrected by using the non-queuing vehicles, so that the corrected length is obtained, and the error between the estimated queuing length and the real queuing length is eliminated.
And S18, performing missing filling on the corrected length based on an extended Kalman filtering algorithm to obtain the vehicle queuing length of the target period.
In at least one embodiment of the present invention, the missing filling of the correction length based on the extended kalman filter algorithm, and obtaining the vehicle queue length of the target period includes:
determining a state transition matrix acting on the correction length;
calculating a priori predicted value of the target period according to the state transformation matrix and the correction length;
determining a first covariance acting on the correction length;
determining a second covariance acting on the prior predicted value based on the first covariance;
calculating a Kalman gain of the target period according to the second covariance;
and calculating the vehicle queuing length according to the Kalman gain, the prior predicted value and the correction length.
It can be understood that if the number of the vehicles in the queue sampled in one period is less than or equal to 1 (i.e. the target period with the sparse track where the missing period occurs), a missing value is generated in the calculation process, and the determination of the vehicle queue length is inaccurate.
By the implementation mode, the vehicle queue length of the missing period is filled by Kalman filtering in combination with historical estimated queue length data. The basic idea of Kalman filtering is recursive unbiased minimum mean square error estimation, which estimates the current optimal value according to the previous estimation value and the latest observation data, estimates by using a state equation and a recursive method, and can process smooth data and data with larger fluctuation compared with the prior art in which multiple interpolation, EM algorithm and the like are adopted for missing value filling, and the Kalman filtering has the characteristics of unbiased, linear, real-time and the like. The Kalman filtering is a closed correction and feedback process, and accurate calculation of the vehicle queue length of a target period is realized by calculating a predicted value and correcting the predicted value.
In at least one embodiment of the present invention, the a priori predicted value of the target period is calculated according to the state transformation matrix and the correction length by using the following formula:
wherein the content of the first and second substances,
representing the a priori predicted value or values,Arepresenting the state-transformation matrix in question,
representing the correction length;
determining a second covariance acting on the a priori predicted values from the first covariance using the following equation:
wherein the content of the first and second substances,
representing the second covariance as a function of the second covariance,
representing the first covariance as a function of the first covariance,
representing a transpose of the state transition matrix,
a covariance matrix representing noise of the state process;
calculating a Kalman gain for the target period from the second covariance using the following equation:
wherein the content of the first and second substances,
representing the value of the Kalman gain (in terms of,
a matrix of coefficients is represented by a matrix of coefficients,
represents a transpose of the matrix of coefficients,
a covariance matrix representing noise in the measurement process.
Further, calculating the vehicle queue length according to the Kalman gain, the priori predicted value and the correction length by adopting the following formula:
wherein the content of the first and second substances,
indicating the vehicle queue length.
By the aid of the embodiment, the vehicle queuing length of the missing period can be filled through extended Kalman filtering, so that the vehicle queuing length can be determined automatically, and the vehicle queuing length can be determined more accurately.
According to the technical scheme, the vehicle track data acquisition device can be connected with a designated platform, acquire the vehicle track data from the designated platform, acquire coordinate data and Link data from the vehicle track data, map each coordinate point in the coordinate data to the corresponding Link in the Link data, calculate the accumulated sum of the distances from each mapped coordinate point to the first Link starting point in the Link data by combining the coordinate data and the Link data, eliminate the transverse drift error of low-speed driving of an estimated point in an intersection range to the maximum extent, acquire the timestamp of the vehicle track data, construct a vehicle space-time track graph based on the timestamp and the accumulated sum, respond to a command for determining the vehicle queue length of a target period with a sparse track, acquiring aggregated wave data and dissipated wave data of a previous period from the vehicle space-time trajectory diagram, calculating a first distance from an intersection point of the aggregated wave and the dissipated wave in the previous period to a stop line according to the aggregated wave data and the dissipated wave data, acquiring non-queued vehicles in the previous period, calculating each second distance between each non-queued vehicle and the stop line, selecting the shortest distance between the first distance and the second distance as a correction length, correcting the queuing length by using the non-queued vehicles to obtain the correction length so as to eliminate an error between the estimated queuing length and the real queuing length, missing the correction length based on an extended Kalman filtering algorithm to obtain the vehicle queuing length of the target period so as to fill the vehicle queuing length of the missing period through filling filtering, the automatic determination of the vehicle queuing length is realized, and the determination of the vehicle queuing length is more accurate.
Fig. 2 is a functional block diagram of a preferred embodiment of the sparse trajectory vehicle queue length determining apparatus of the present invention. The vehicle queue length determining device 11 of the sparse track comprises an acquisition unit 110, a calculation unit 111, a construction unit 112, a selection unit 113 and a padding unit 114. The module/unit referred to in the present invention refers to a series of computer program segments that can be executed by the processor 13 and that can perform a fixed function, and that are stored in the memory 12. In the present embodiment, the functions of the modules/units will be described in detail in the following embodiments.
The acquisition unit 110 is connected to a specified platform, and acquires vehicle trajectory data from the specified platform.
In at least one embodiment of the invention, the designated platform may include various types of traffic management platforms having various types of traffic data stored thereon.
To ensure the security and privacy of the data, the vehicle trajectory data may be deployed on a blockchain.
The acquisition unit 110 acquires coordinate data and Link data from the vehicle trajectory data.
The Link data is based on a road network topological structure, road intersections are taken as nodes, a road network is disassembled into nodes and Link sets, and the links are directed paths among different nodes.
For example: the coordinate data may be in the form of a table, see table 1.
TABLE 1 coordinate data sheet
| Vehicle ID | Time stamp | Longitude (G) | Latitude | Link ID | Distance of passage | Linear velocity |
| e93e2532c87e0789e | 1480288802 | 120.40033 | 36.086 | 66320951 | 16 | 3.2 |
| e93e2532c87e0789e | 1480288805 | 120.4005 | 36.08579 | 66320951 | 44 | 9.3 |
| e93e2532c87e0789e | 1480288808 | 120.40055 | 36.08573 | 66320951 | 52 | 2.7 |
| e93e2532c87e0789e | 1480288811 | 120.4006 | 36.08567 | 66320951 | 61 | 2.7 |
| e93e2532c87e0789e | 1480288814 | 120.40064 | 36.08558 | 66320941 | 4 | 3.5 |
Further, the link data may also be in the form of a table, see table 2.
TABLE 2 Link data sheet
The calculation unit 111 maps each coordinate point in the coordinate data to a corresponding Link in the Link data, and calculates the cumulative sum of distances from each mapped coordinate point to the starting point of the first Link in the Link data.
In at least one embodiment of the present invention, the calculating unit 111 mapping each coordinate point in the coordinate data to a corresponding Link in the Link data includes:
acquiring Link IDs corresponding to the coordinate points from the coordinate data;
determining a Link corresponding to each coordinate point according to the Link ID;
and vertically mapping each coordinate point to the Link corresponding to each coordinate point.
It can be understood that, because the coordinate point of the vehicle is prone to drift and large errors when the vehicle runs at a low speed in the intersection range, if the relative running distance is directly calculated through the coordinate point, the large errors are generated, and the estimation effect of the vehicle queuing length is affected. For this reason, the present embodiment performs calculation of the longitudinal relative travel distance by combining the coordinate data and the Link data, calculates the relative travel distance by the Link data (i.e., calculates the cumulative sum of the distances from each mapped coordinate point to the first Link start point in the Link data), and adopts a vertical mapping manner, so as to eliminate the lateral drift error of the estimated point in the intersection during low-speed travel to the greatest extent.
The construction unit 112 obtains a time stamp of the vehicle trajectory data, and constructs a vehicle spatiotemporal trajectory map based on the time stamp and the accumulated sum.
In at least one embodiment of the invention, the vehicle spatiotemporal trajectory graph reflects the relation of time and distance, the vehicle spatiotemporal trajectory graph can provide basic data for subsequent schemes, and the graph is constructed mainly for facilitating the storage of data, so the invention does not limit the way of constructing the vehicle spatiotemporal trajectory graph.
The obtaining unit 110 obtains aggregate wave data and evanescent wave data of a previous period from the vehicle spatiotemporal trajectory diagram in response to a determination instruction of a vehicle queue length of a target period with a sparse trajectory.
In view of the stable characteristic of the traffic flow in a short time, the embodiment acquires the aggregate wave data and the evanescent wave data of the previous period to determine the vehicle queuing length of the target period, so as to correct the queuing length of the target period in a sparse state by combining historical estimated queuing length data, thereby improving the accuracy.
The calculating unit 111 calculates a first distance from the intersection point of the cluster wave and the evanescent wave to the stop line in the previous period according to the cluster wave data and the evanescent wave data.
In the embodiment, based on the traffic wave theory, the queuing up and dissipating process of the vehicles caused by the periodic change of the signal lamps can be described in detail. When the red light is turned on, the arriving vehicles are decelerated under the influence of the red light, the vehicles are decelerated and queued close to the stop line, and the queued vehicles are gradually propagated upstream to form the aggregation wave; when the red light is finished and the green light is turned on, the queued vehicles drive away from the intersection at a saturated flow rate, and are gradually dissipated in a queue, so that the dissipated waves are transmitted upstream. And the evanescent wave velocity is greater than the aggregate wave, so that after a period of time, the vehicles are queued to a position furthest from the stop line, and thereafter completely dissipate.
In at least one embodiment of the present invention, the calculating unit 111 calculates a first distance from the intersection point of the cluster wave and the evanescent wave to the stop line in the previous period according to the cluster wave data and the evanescent wave data includes:
determining at least one section of aggregate waves from the aggregate wave data based on vehicle kinematics;
determining the slope and intercept of each section of the at least one section of the aggregated wave;
obtaining the slope and intercept of the evanescent wave from the evanescent wave data;
and calculating the first distance according to the slope and the intercept of the aggregate wave of each section and the slope and the intercept of the evanescent wave.
Through the above embodiment, the first distance from the intersection point of the aggregate wave and the evanescent wave to the stop line is calculated for subsequent estimation of the maximum queuing length.
Specifically, the calculating unit 111 calculates the first distance according to the slope and intercept of the aggregate wave of each segment and the slope and intercept of the evanescent wave comprises:
determining the distance between each section of aggregate wave and the parking line;
acquiring an aggregation wave with the shortest distance as a target aggregation wave;
acquiring a target slope and a target intercept of the target aggregate wave;
calculating a first difference between the intercept of the evanescent wave and the target intercept, and calculating a second difference between the target slope and the slope of the evanescent wave;
calculating a quotient of the first difference and the second difference and calculating a product of the quotient and a slope of the evanescent wave;
determining a longitudinal distance of the stop line;
calculating a third difference of the longitudinal distance and the product;
calculating a sum of the third difference and an intercept of the evanescent wave as the first distance.
With the above embodiment, the first distance can be calculated based on the aggregate wave and the evanescent wave formed by the queued vehicles.
The calculation unit 111 acquires the non-queued vehicles in the previous cycle, and calculates each second distance between each non-queued vehicle and the parking line.
It will be appreciated that the first distance is calculated from the collected information of the vehicles in line, but in the case of sparse trajectories with low saturation or large traffic arrival fluctuations, the above approach will estimate some of the non-vehicles in line as in line, i.e. the estimated queue length is always greater than or equal to the true queue length, and therefore, the correction will be performed subsequently on the basis of each second distance.
The selection unit 113 selects the shortest distance of the first distance and the second distance as the correction length. Through the embodiment, the queuing length can be corrected by using the non-queuing vehicles, so that the corrected length is obtained, and the error between the estimated queuing length and the real queuing length is eliminated.
The filling unit 114 fills the missing of the correction length based on the extended kalman filter algorithm to obtain the vehicle queue length of the target period.
In at least one embodiment of the present invention, the padding unit 114 performs missing padding on the correction length based on an extended kalman filter algorithm, and obtaining the vehicle queue length of the target period includes:
determining a state transition matrix acting on the correction length;
calculating a priori predicted value of the target period according to the state transformation matrix and the correction length;
determining a first covariance acting on the correction length;
determining a second covariance acting on the prior predicted value based on the first covariance;
calculating a Kalman gain of the target period according to the second covariance;
and calculating the vehicle queuing length according to the Kalman gain, the prior predicted value and the correction length.
It can be understood that if the number of the vehicles in the queue sampled in one period is less than or equal to 1 (i.e. the target period with the sparse track where the missing period occurs), a missing value is generated in the calculation process, and the determination of the vehicle queue length is inaccurate.
By the implementation mode, the vehicle queue length of the missing period is filled by Kalman filtering in combination with historical estimated queue length data. The basic idea of Kalman filtering is recursive unbiased minimum mean square error estimation, which estimates the current optimal value according to the previous estimation value and the latest observation data, estimates by using a state equation and a recursive method, and can process smooth data and data with larger fluctuation compared with the prior art in which multiple interpolation, EM algorithm and the like are adopted for missing value filling, and the Kalman filtering has the characteristics of unbiased, linear, real-time and the like. The Kalman filtering is a closed correction and feedback process, and accurate calculation of the vehicle queue length of a target period is realized by calculating a predicted value and correcting the predicted value.
In at least one embodiment of the present invention, the padding unit 114 calculates the a priori prediction value of the target period according to the state transformation matrix and the correction length by using the following formula:
wherein the content of the first and second substances,
representing the a priori predicted value or values,Arepresenting the state-transformation matrix in question,
representing the correction length;
determining a second covariance acting on the a priori predicted values from the first covariance using the following equation:
wherein the content of the first and second substances,
representing the second covariance as a function of the second covariance,
representing the first covariance as a function of the first covariance,
representing a transpose of the state transition matrix,
a covariance matrix representing noise of the state process;
the padding unit 114 calculates the kalman gain of the target period from the second covariance using the following equation:
wherein the content of the first and second substances,
representing the value of the Kalman gain (in terms of,
a matrix of coefficients is represented by a matrix of coefficients,
represents a transpose of the matrix of coefficients,
a covariance matrix representing noise in the measurement process.
Further, the shim unit 114 calculates the vehicle queue length according to the kalman gain, the a priori predicted value, and the revised length by using the following formula:
wherein the content of the first and second substances,
indicating the vehicle queue length.
By the aid of the embodiment, the vehicle queuing length of the missing period can be filled through extended Kalman filtering, so that the vehicle queuing length can be determined automatically, and the vehicle queuing length can be determined more accurately.
According to the technical scheme, the vehicle track data acquisition device can be connected with a designated platform, acquire the vehicle track data from the designated platform, acquire coordinate data and Link data from the vehicle track data, map each coordinate point in the coordinate data to the corresponding Link in the Link data, calculate the accumulated sum of the distances from each mapped coordinate point to the first Link starting point in the Link data by combining the coordinate data and the Link data, eliminate the transverse drift error of low-speed driving of an estimated point in an intersection range to the maximum extent, acquire the timestamp of the vehicle track data, construct a vehicle space-time track graph based on the timestamp and the accumulated sum, respond to a command for determining the vehicle queue length of a target period with a sparse track, acquiring aggregated wave data and dissipated wave data of a previous period from the vehicle space-time trajectory diagram, calculating a first distance from an intersection point of the aggregated wave and the dissipated wave in the previous period to a stop line according to the aggregated wave data and the dissipated wave data, acquiring non-queued vehicles in the previous period, calculating each second distance between each non-queued vehicle and the stop line, selecting the shortest distance between the first distance and the second distance as a correction length, correcting the queuing length by using the non-queued vehicles to obtain the correction length so as to eliminate an error between the estimated queuing length and the real queuing length, missing the correction length based on an extended Kalman filtering algorithm to obtain the vehicle queuing length of the target period so as to fill the vehicle queuing length of the missing period through filling filtering, the automatic determination of the vehicle queuing length is realized, and the determination of the vehicle queuing length is more accurate.
Fig. 3 is a schematic structural diagram of an electronic device according to a preferred embodiment of the method for determining the queuing length of a vehicle with a sparse track according to the present invention.
The electronic device 1 may comprise a memory 12, a processor 13 and a bus, and may further comprise a computer program, such as a sparse trajectory vehicle queue length determination program, stored in the memory 12 and executable on the processor 13.
It will be understood by those skilled in the art that the schematic diagram is merely an example of the electronic device 1, and does not constitute a limitation to the electronic device 1, the electronic device 1 may have a bus-type structure or a star-type structure, the electronic device 1 may further include more or less hardware or software than those shown in the figures, or different component arrangements, for example, the electronic device 1 may further include an input and output device, a network access device, and the like.
It should be noted that the electronic device 1 is only an example, and other existing or future electronic products, such as those that can be adapted to the present invention, should also be included in the scope of the present invention, and are included herein by reference.
The memory 12 includes at least one type of readable storage medium, which includes flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 12 may in some embodiments be an internal storage unit of the electronic device 1, for example a removable hard disk of the electronic device 1. The memory 12 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device 1. Further, the memory 12 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 12 may be used not only to store application software installed in the electronic apparatus 1 and various types of data such as codes of a vehicle queue length determination program of a sparse trajectory and the like, but also to temporarily store data that has been output or is to be output.
The processor 13 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 13 is a Control Unit (Control Unit) of the electronic device 1, connects various components of the whole electronic device 1 by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (for example, a vehicle queue length determining program that executes a sparse trace, and the like) stored in the memory 12 and calling data stored in the memory 12.
The processor 13 executes an operating system of the electronic device 1 and various installed application programs. The processor 13 executes the application program to implement the steps in the above-described embodiments of the sparse trajectory vehicle queue length determination method, such as the steps shown in fig. 1.
Illustratively, the computer program may be divided into one or more modules/units, which are stored in the memory 12 and executed by the processor 13 to accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into an acquisition unit 110, a calculation unit 111, a construction unit 112, a selection unit 113, a shim unit 114.
Alternatively, the processor 13, when executing the computer program, implements the functions of the modules/units in the above device embodiments, for example:
connecting a designated platform, and acquiring vehicle track data from the designated platform;
acquiring coordinate data and Link data from the vehicle track data;
mapping each coordinate point in the coordinate data to a corresponding Link in the Link data, and calculating the accumulated sum of the distances from each mapped coordinate point to the starting point of the first Link in the Link data;
acquiring a time stamp of the vehicle track data, and constructing a vehicle space-time track graph based on the time stamp and the accumulated sum;
in response to a command for determining the vehicle queuing length of a target period with a sparse track, acquiring aggregate wave data and evanescent wave data of a previous period from the vehicle spatiotemporal track diagram;
calculating a first distance from the intersection point of the cluster wave and the evanescent wave to a stop line in the previous period according to the cluster wave data and the evanescent wave data;
obtaining the non-queued vehicles in the previous period, and calculating each second distance between each non-queued vehicle and the parking line;
selecting the shortest distance of the first distance and the second distance as a correction length;
and carrying out deletion filling on the corrected length based on an extended Kalman filtering algorithm to obtain the vehicle queuing length of the target period.
The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a computer device, or a network device) or a processor (processor) to execute part of the sparse trajectory vehicle queue length determining method according to the embodiments of the present invention.
The integrated modules/units of the electronic device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented.
Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
Further, the computer-usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one arrow is shown in FIG. 3, but this does not indicate only one bus or one type of bus. The bus is arranged to enable connection communication between the memory 12 and at least one processor 13 or the like.
Although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 13 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
Fig. 3 only shows the electronic device 1 with components 12-13, and it will be understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.
With reference to fig. 1, the memory 12 of the electronic device 1 stores a plurality of instructions to implement a sparse trajectory vehicle queue length determination method, and the processor 13 executes the plurality of instructions to implement:
connecting a designated platform, and acquiring vehicle track data from the designated platform;
acquiring coordinate data and Link data from the vehicle track data;
mapping each coordinate point in the coordinate data to a corresponding Link in the Link data, and calculating the accumulated sum of the distances from each mapped coordinate point to the starting point of the first Link in the Link data;
acquiring a time stamp of the vehicle track data, and constructing a vehicle space-time track graph based on the time stamp and the accumulated sum;
in response to a command for determining the vehicle queuing length of a target period with a sparse track, acquiring aggregate wave data and evanescent wave data of a previous period from the vehicle spatiotemporal track diagram;
calculating a first distance from the intersection point of the cluster wave and the evanescent wave to a stop line in the previous period according to the cluster wave data and the evanescent wave data;
obtaining the non-queued vehicles in the previous period, and calculating each second distance between each non-queued vehicle and the parking line;
selecting the shortest distance of the first distance and the second distance as a correction length;
and carrying out deletion filling on the corrected length based on an extended Kalman filtering algorithm to obtain the vehicle queuing length of the target period.
Specifically, the processor 13 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the instruction, which is not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A vehicle queuing length determination method of a sparse track is characterized by comprising the following steps:
connecting a designated platform, and acquiring vehicle track data from the designated platform;
acquiring coordinate data and Link data from the vehicle track data;
mapping each coordinate point in the coordinate data to a corresponding Link in the Link data, and calculating the accumulated sum of the distances from each mapped coordinate point to the starting point of the first Link in the Link data;
acquiring a time stamp of the vehicle track data, and constructing a vehicle space-time track graph based on the time stamp and the accumulated sum;
in response to a command for determining the vehicle queuing length of a target period with a sparse track, acquiring aggregate wave data and evanescent wave data of a previous period from the vehicle spatiotemporal track diagram;
calculating a first distance from the intersection point of the cluster wave and the evanescent wave to a stop line in the previous period according to the cluster wave data and the evanescent wave data;
obtaining the non-queued vehicles in the previous period, and calculating each second distance between each non-queued vehicle and the parking line;
selecting the shortest distance of the first distance and the second distance as a correction length of the vehicle queue length of the target period with the sparse track;
and carrying out deletion filling on the corrected length based on an extended Kalman filtering algorithm to obtain the vehicle queuing length of the target period.
2. The sparse trajectory vehicle queue length determination method of claim 1, wherein said mapping each coordinate point in the coordinate data to a corresponding Link in the Link data comprises:
acquiring Link IDs corresponding to the coordinate points from the coordinate data;
determining a Link corresponding to each coordinate point according to the Link ID;
and vertically mapping each coordinate point to the Link corresponding to each coordinate point.
3. The method of determining vehicle queue length for a sparse track of claim 1, wherein said calculating a first distance from an intersection of an aggregate wave and an evanescent wave to a stop line in said previous cycle from said aggregate wave data and said evanescent wave data comprises:
determining at least one section of aggregate waves from the aggregate wave data based on vehicle kinematics;
determining the slope and intercept of each section of the at least one section of the aggregated wave;
obtaining the slope and intercept of the evanescent wave from the evanescent wave data;
and calculating the first distance according to the slope and the intercept of the aggregate wave of each section and the slope and the intercept of the evanescent wave.
4. The method of claim 3, wherein said calculating said first distance from a slope and intercept of a set wave and a slope and intercept of said evanescent wave for each segment comprises:
determining the distance between each section of aggregate wave and the parking line;
acquiring an aggregation wave with the shortest distance as a target aggregation wave;
acquiring a target slope and a target intercept of the target aggregate wave;
calculating a first difference between the intercept of the evanescent wave and the target intercept, and calculating a second difference between the target slope and the slope of the evanescent wave;
calculating a quotient of the first difference and the second difference and calculating a product of the quotient and a slope of the evanescent wave;
determining the distance from the intersection point of the target aggregate wave and the evanescent wave to the stop line as the longitudinal distance;
calculating a third difference of the longitudinal distance and the product;
calculating a sum of the third difference and an intercept of the evanescent wave as the first distance.
5. The method for determining the vehicle queue length of the sparse track according to claim 1, wherein the missing filling of the correction length based on the extended kalman filter algorithm is performed, and the obtaining of the vehicle queue length of the target period comprises:
determining a state transition matrix acting on the correction length;
calculating a priori predicted value of the target period according to the state transformation matrix and the correction length;
determining a first covariance acting on the correction length;
determining a second covariance acting on the prior predicted value based on the first covariance;
calculating a Kalman gain of the target period according to the second covariance;
and calculating the vehicle queuing length according to the Kalman gain, the prior predicted value and the correction length.
6. The sparse track vehicle queuing length determination method of claim 5 wherein said a priori predicted value of said target period is calculated from said state transformation matrix and said correction length using the following formula:
wherein the content of the first and second substances,
representing the a priori prediction value, A representing the state transition matrix,
representing the correction length;
determining a second covariance acting on the a priori predicted values from the first covariance using the following equation:
p(J|J-1)＝Ap(J-1|J-1)A′+ω
wherein p (J | J-1) represents the second covariance, p (J-1| J-1) represents the first covariance, A' represents the transpose of the state transition matrix, and ω represents the covariance matrix of the noise of the state process;
calculating a Kalman gain for the target period from the second covariance using the following equation:
wherein kg (j) represents the kalman gain, H represents a coefficient matrix, and H' represents a transpose of the coefficient matrix, representing a covariance matrix of noise in a measurement process.
7. The sparse trajectory vehicle queue length determination method of claim 5, wherein the vehicle queue length is calculated from the Kalman gain, the a priori predicted value and the revised length using the following equation:
wherein the content of the first and second substances,
indicating the vehicle queue length.
8. A sparse trajectory vehicle queue length determination apparatus, comprising:
the acquisition unit is used for connecting a specified platform and acquiring vehicle track data from the specified platform;
the acquisition unit is further used for acquiring coordinate data and Link data from the vehicle track data;
the calculating unit is used for mapping each coordinate point in the coordinate data to a corresponding Link in the Link data and calculating the accumulated sum of the distances from each mapped coordinate point to the starting point of the first Link in the Link data;
the building unit is used for acquiring a time stamp of the vehicle track data and building a vehicle space-time track graph based on the time stamp and the accumulated sum;
the acquiring unit is further used for responding to a determination instruction of the vehicle queuing length of a target period with a sparse track, and acquiring aggregate wave data and evanescent wave data of a previous period from the vehicle space-time track diagram;
the calculation unit is further used for calculating a first distance from the intersection point of the cluster wave and the evanescent wave to a stop line in the previous period according to the cluster wave data and the evanescent wave data;
the calculation unit is further configured to acquire non-queued vehicles in the previous period, and calculate each second distance between each non-queued vehicle and the parking line;
a selection unit configured to select a shortest distance of the first distance and the second distance as a correction length of a vehicle queue length of the target period having the sparse trajectory;
and the filling unit is used for carrying out deletion filling on the correction length based on an extended Kalman filtering algorithm to obtain the vehicle queuing length of the target period.
9. An electronic device, characterized in that the electronic device comprises:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement the sparse trajectory vehicle queue length determination method of any one of claims 1 to 7.
10. A computer-readable storage medium characterized by: the computer readable storage medium has stored therein at least one instruction that is executable by a processor in an electronic device to implement the sparse track vehicle queue length determination method of any one of claims 1 to 7.
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| CN113012430A (en) * | 2021-02-23 | 2021-06-22 | 西南交通大学 | Vehicle queuing length detection method, device, equipment and readable storage medium |
| CN113032425A (en) * | 2021-03-04 | 2021-06-25 | 武汉理工大学 | Intersection queuing length estimation method and device |
| CN113256961B (en) * | 2021-06-25 | 2022-05-24 | 上海交通大学 | Crossing autonomous vehicle scheduling and control method based on vehicle formation |
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