CN112837542A - Method and device for counting traffic volume of highway section, storage medium and terminal - Google Patents

Method and device for counting traffic volume of highway section, storage medium and terminal Download PDF

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CN112837542A
CN112837542A CN202011626948.XA CN202011626948A CN112837542A CN 112837542 A CN112837542 A CN 112837542A CN 202011626948 A CN202011626948 A CN 202011626948A CN 112837542 A CN112837542 A CN 112837542A
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vehicle
section
passing
data acquisition
time
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CN112837542B (en
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杨珍珍
罗世伟
张华飞
郭胜敏
董萧
夏曙东
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Beijing Palmgo Information Technology Co ltd
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Beijing Palmgo Information Technology Co ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
    • 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
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • 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 invention discloses a method, a device, a storage medium and a terminal for counting the traffic volume of a highway section, wherein the method comprises the following steps: extracting track points of each vehicle passing through each data acquisition section on the high-speed road section to be counted; each data acquisition section is the position of each different type of data acquisition equipment arranged on the road section; preprocessing the track points of each vehicle passing through each data acquisition section to generate a track point set corresponding to each preprocessed vehicle; dividing the highway section to be counted into sub-section sets based on a preset distance, and setting a marking pile for each sub-section in the sub-section sets; calculating the passing time of each vehicle passing through each set marking pile based on the preprocessed track point set corresponding to each vehicle; and counting the traffic volume of each marking pile in a preset time period according to the set passing time of each vehicle passing through each marking pile. Therefore, by adopting the embodiment of the application, the statistical accuracy of the traffic volume of the highway section can be improved.

Description

Method and device for counting traffic volume of highway section, storage medium and terminal
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a method and a device for counting traffic volume of a highway section, a storage medium and a terminal.
Background
The traffic volume is an important index for monitoring and managing the operation of the road network. The data source has important influence on traffic volume estimation, and the commonly used data sources comprise floating car data, toll station data, gantry data, video data, card port data, microwave radar data and the like. In addition, as the provincial stations are cancelled in the national highway network, and simultaneously, the ETC portal system is arranged at the provincial toll station and the traffic diversion position, the ETC portal data can estimate the flow between the two portals, and the technical problem to be solved is how to fuse the data from different sources and estimate the traffic volume of the highway network.
In the prior art, the invention with the patent number of CN202010495061.5 provides a method for estimating section flow of a highway with multi-source data, which is characterized in that under the condition that traffic detection equipment on the section of the highway is sparsely distributed, the running state of a vehicle is restored by using historical charging data and vehicle detector data, and the section flow is estimated by analyzing the flow transfer relationship among road network toll stations; meanwhile, the RBF neural network model is used for further correction, and estimation of the section flow under the condition that the distribution of the detection equipment is sparse is realized.
The invention with the patent number of CNCN201510439696.2 provides a traffic flow obtaining method based on video, which comprises the steps of firstly drawing a virtual wire frame and obtaining a detection area; then extracting a motion foreground from each frame of detection area image, and extracting SURF (speeded up robust features) characteristic points in each connected area after erosion, expansion and connected area filtering; and finally, extracting the minimum circumscribed rectangle of each connected region, and if and only if a certain connected region of the current frame is not in the virtual wire frame and the SURF characteristic point of the connected region block is matched with the SURF characteristic point of a connected region block intersected with the virtual wire frame in the previous frame by more than 90%, determining that a vehicle passes through the virtual wire frame, thereby completing the acquisition of the traffic flow.
The invention with the patent number of CN201811551980.9 provides an urban road network flow estimation method based on taxi GPS data and gate data fusion, which comprises the following specific steps of (1) mapping taxi GPS track data; (2) extracting taxi speed characteristics; (3) road network feature statistics; (4) analyzing similarity among road sections; (5) establishing an estimation model; (6) selecting a flow estimation model; (7) and establishing a flow estimation model based on the support vector machine.
In the prior art, the considered data sources are limited, and the data of different sources such as toll station data, portal data, video data, checkpoint data, microwave radar data and the like cannot be fully utilized to estimate the traffic volume.
Therefore, how to find an effective method to fuse the data from different sources and estimate the traffic volume of the road network is an urgent problem to be solved.
Disclosure of Invention
The embodiment of the application provides a method and a device for counting traffic volume of a highway section, a storage medium and a terminal. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present application provides a statistical method for traffic volume of a highway segment, where the method includes:
extracting track points of each vehicle passing through each data acquisition section on the high-speed road section to be counted; each data acquisition section is the position of each different type of data acquisition equipment arranged on the road section;
preprocessing the track points of each vehicle passing through each data acquisition section to generate a track point set corresponding to each preprocessed vehicle;
dividing the highway section to be counted into sub-section sets based on a preset distance, and setting a marking pile for each sub-section in the sub-section sets;
calculating the passing time of each vehicle passing through each set marking pile based on the preprocessed track point set corresponding to each vehicle;
and counting the traffic volume of each marking pile in a preset time period according to the set passing time of each vehicle passing through each marking pile.
Optionally, the track points of each vehicle passing through each data acquisition section are preprocessed, and a track point set corresponding to each preprocessed vehicle is generated, including:
converting a plurality of parameters generated by each vehicle passing through the track point of each data acquisition section into a preset standard expression mode, and generating a converted track point set of each vehicle; wherein, each track point in the track point set of each converted vehicle corresponds to a plurality of converted parameters;
acquiring the passing time of each vehicle passing through each data acquisition section from a plurality of converted parameters corresponding to each track point in the converted track point set of each vehicle;
the track points in the converted track point set of each vehicle are sorted in an ascending order based on the passing time of each vehicle passing through each data acquisition section, and the sorted track point set of each vehicle is generated;
and performing abnormal data processing on the sequenced track point set of each vehicle to generate a track point set corresponding to each preprocessed vehicle.
Optionally, the track point set of each sequenced vehicle is subjected to abnormal data processing, and a track point set corresponding to each preprocessed vehicle is generated, including:
counting track points with the same position, the same license plate, the same driving direction and the snapshot time in accordance with a preset range from the sequenced track point set of each vehicle;
combining the counted track points with the same position, the same license plate, the same driving direction and the snapshot time in accordance with a preset range to generate a first track point set of each vehicle; and the number of the first and second groups,
removing the track points with the wrong section time from the first track point set of each vehicle to generate a second track point set of each vehicle; and
removing data with the section mileage pile number as a null value from the second track point set of each vehicle, and generating a third track point set of each vehicle; and
and when a plurality of track points in different time periods exist in each vehicle, performing track segmentation processing on the third track point set of each vehicle to generate a track point set corresponding to each preprocessed vehicle.
Optionally, calculating the passing time of each vehicle passing through each set marking pile based on the preprocessed track point set corresponding to each vehicle, includes:
when each vehicle has a plurality of track points on adjacent data acquisition sections and a plurality of parameters corresponding to the track points are complete, counting the number of marking piles between the adjacent data acquisition sections;
acquiring the passing time of each vehicle passing through the adjacent data acquisition section based on the preprocessed track point set corresponding to each vehicle;
calculating the passing time of each vehicle passing through the marked piles based on the number of the marked piles and the passing time;
the calculation formula of the number of the marked piles between adjacent data acquisition sections is as follows:
Figure BDA0002873267590000031
stakeiand stabei+1Mileage piles of the ith section and the (i + 1) th section are respectively represented,
Figure BDA0002873267590000032
the number of the marked piles between adjacent data acquisition sections is represented, and delta represents a preset distance;
wherein, the time calculation formula of each vehicle passing through the marked pile is as follows:
Figure BDA0002873267590000041
Figure BDA0002873267590000042
indicating the number of marker posts, Time, between adjacent data acquisition sectionsiShowing the time of passage of the vehicle through the i-th section, ti,i+1Representing the travel time between the ith section and the (i + 1) th section, wherein k represents the kth mark pile counted from the ith section;
optionally, calculating the passing time of each vehicle passing through each set marking pile based on the preprocessed track point set corresponding to each vehicle, includes:
when each vehicle has a track point on the adjacent data acquisition cross section, counting the number of the marker piles between the adjacent data acquisition cross sections;
judging whether other vehicles passing through at the same time interval exist or not;
if the vehicle passing time exists, acquiring the passing time of other vehicles passing through the adjacent data acquisition cross section based on the preprocessed track point set corresponding to each vehicle;
determining the time period between the passing moments of the other vehicles passing through the adjacent data acquisition sections as the passing time period of each vehicle on the adjacent data acquisition sections; alternatively, the first and second electrodes may be,
if not, acquiring a preset default historical passage time period;
determining preset default historical passing time as the passing time of each vehicle on the adjacent data acquisition cross section;
and calculating the passing time of each vehicle passing through each set marking pile based on the number of the marking piles between the adjacent data acquisition sections and the passing time of each vehicle on the adjacent data acquisition sections.
The time calculation formula of each mark pile set when each vehicle passes through other vehicles in the same time period is as follows:
Figure BDA0002873267590000043
wherein the content of the first and second substances,
Figure BDA0002873267590000044
the passing time of the vehicle which represents the single-point track passing through the ith section,
Figure BDA0002873267590000045
representing the transit time periods when other vehicles passing through the same time interval pass through the adjacent data acquisition sections,
Figure BDA0002873267590000046
the number of the mark piles between adjacent data acquisition sections is shown, and k represents the k mark pile counted from the ith section.
The time calculation formula of each mark pile set when each vehicle passes through other vehicles in the absence of the same time interval is as follows:
Figure BDA0002873267590000047
Figure BDA0002873267590000048
showing the passing time of the vehicle passing through the ith cross section,
Figure BDA0002873267590000049
indicating a preset default historical passage time period,
Figure BDA00028732675900000410
the number of the mark piles between adjacent data acquisition sections is shown, and k represents the k mark pile counted from the ith section.
In a second aspect, an embodiment of the present application provides an apparatus for statistics of traffic volume of a highway segment, where the apparatus includes:
the track point extraction module is used for extracting track points of each vehicle passing through each data acquisition section on the high-speed road section to be counted; each data acquisition section is the position of each different type of data acquisition equipment arranged on the road section;
the track point preprocessing module is used for preprocessing the track points of each vehicle passing through each data acquisition section to generate a track point set corresponding to each preprocessed vehicle;
the marking pile setting module is used for dividing the highway section to be counted into a sub-section set based on a preset distance and setting a marking pile for each sub-section in the sub-section set;
the passing time calculation module is used for calculating the passing time of each vehicle passing through each set marking pile based on the preprocessed track point set corresponding to each vehicle;
and the traffic volume counting module is used for counting the traffic volume of each marking pile in a preset time period according to the passing time of each vehicle passing through each set marking pile.
In a third aspect, embodiments of the present application provide a computer storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor and to perform the above-mentioned method steps.
In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the embodiment of the application, the traffic volume statistical device of the highway section firstly extracts the track points of each vehicle passing through each data acquisition section on the highway section to be statistically calculated, wherein each data acquisition section is the position of each different type of data acquisition equipment arranged on the road section, track points of each vehicle passing through each data acquisition section are preprocessed to generate a track point set corresponding to each preprocessed vehicle, then dividing the highway section to be counted into sub-section sets based on the preset distance, setting a mark pile for each sub-section in the sub-section sets, secondly, calculating the passing time of each vehicle passing through each set marking pile based on the preprocessed track point set corresponding to each vehicle, and finally counting the traffic volume of each marking pile in the preset time period according to the passing time of each vehicle passing through each set marking pile. According to the method and the device, the vehicle passing data are collected through the positions of the different types of data collection equipment arranged on the road sections to estimate the positions and the time of the vehicles passing the road sections, and finally the traffic volume of each set marking pile in the preset time period is calculated according to the positions and the time of the vehicles passing the road sections, so that the accuracy of the statistics of the traffic volume of the high-speed road sections is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic flow chart of a traffic volume statistical method for a highway section according to an embodiment of the present application;
FIG. 2 is a schematic block diagram of a traffic volume statistics process using highway sections according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of another method for traffic volume statistics on highway sections according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a traffic volume statistic device for highway sections according to an embodiment of the present application;
fig. 5 is a schematic diagram of a terminal according to an embodiment of the present application.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of systems and methods consistent with certain aspects of the invention, as detailed in the appended claims.
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
Up to now, the prior art considers limited data sources, and cannot fully utilize different source data such as toll station data, portal data, video data, card port data, microwave radar data and the like to estimate the traffic volume. Therefore, the present application provides a method, an apparatus, a storage medium and a terminal for traffic volume statistics on highway sections to solve the above-mentioned problems in the related art. According to the technical scheme, the position and the time of each vehicle passing through the road section are estimated by acquiring the vehicle passing data through the position of each different type of data acquisition equipment arranged on the road section, and finally the traffic volume of each set marking pile in the preset time period is calculated according to the position and the time of the vehicle passing through the road section, so that the statistical accuracy of the traffic volume of the high-speed road section is improved, and the detailed description is given by adopting an exemplary embodiment.
The statistical method for the traffic volume of the highway section provided by the embodiment of the application will be described in detail below with reference to fig. 1 to 3. The method may be implemented in dependence on a computer program, operable on a statistical device of highway section traffic based on von neumann architecture. The computer program may be integrated into the application or may run as a separate tool-like application.
Fig. 1 is a schematic flow chart of a traffic volume statistical method for a highway section according to an embodiment of the present disclosure. As shown in fig. 1, the method of the embodiment of the present application may include the following steps:
s101, extracting track points of each vehicle passing through each data acquisition section on the highway section to be counted; each data acquisition section is the position of each different type of data acquisition equipment arranged on the road section;
each data acquisition section is the position of each different type of data acquisition equipment arranged on the road section, such as toll station equipment, bayonet equipment, microwave radar equipment, video equipment, gantry equipment and other different types of data acquisition equipment.
Usually, before getting on the highway section of waiting to make statistics of every car and passing through every data acquisition sectional track point, still need set for unified standard data expression mode, because the data format that the data acquisition equipment of different grade type gathered is inconsistent, this application for convenience data processing, need set for the unified data expression mode of standard in advance, conveniently convert the data that different data acquisition equipment gathered, then determine the highway section of waiting to make statistics of according to geographic information's road network data again.
In one possible implementation, when the traffic volume of the highway is counted, the position expression modes of the data acquisition sections from different sources are unified firstly. The position expression modes of all the data acquisition sections are unified into the same set of standard, for example, the positions of the sections where the data acquisition equipment is located are determined by adopting road names, mileage stake numbers and road directions. The normalized vehicle track (track) includes a vehicle number (Carid), Time (Time), a position milepost number (stick), a road Direction (Direction), and a data Source (Source). Thus a unified standard format can be defined as: track ═ Carid, Time, Stake, Direction, Source.
Determining a highway section to be counted from a road network of a geographic information system, and then acquiring vehicle passing track points acquired by data acquisition equipment of different types on the highway section.
S102, preprocessing the track points of each vehicle passing through each data acquisition section, and generating a track point set corresponding to each preprocessed vehicle;
the preprocessing comprises format conversion, sorting and data cleaning operation on the collected data.
In one possible implementation manner, after obtaining the track points of each vehicle passing through each data acquisition cross section based on step S101, firstly converting a plurality of parameters generated by each vehicle passing through the track points of each data acquisition cross section into a preset standard expression manner, and generating a track point set of each converted vehicle; the method comprises the steps of converting track point sets of each vehicle into track point sets, acquiring the passing time of each vehicle passing through each data acquisition section from the converted parameters corresponding to the track points in the track point sets of each vehicle, sequencing the track points in the track point sets of each vehicle in an ascending order based on the passing time of each vehicle passing through each data acquisition section, generating the sequenced track point sets of each vehicle, and finally performing abnormal data processing on the sequenced track point sets of each vehicle to generate the preprocessed track point sets corresponding to each vehicle.
For example, after converting a plurality of parameters generated by each vehicle passing through the track point of each data acquisition cross section into a preset standard expression mode, the following steps are performed: and then sequencing a plurality of trajectors corresponding to each vehicle according to the sequence of the Time to generate a sequenced track point set of each vehicle, wherein each track point in the sequenced track point set can be expressed as:
Figure BDA0002873267590000081
where n denotes the number of sections a single vehicle passes, Time1≤Time2≤…≤Timen。
Further, abnormal data processing is performed on the sequenced trace point data, and the abnormal data processing method comprises the following steps: repeated data processing, section time error data processing, section unknown data processing and track mixing problem processing of the same vehicle in different time periods.
When the repeated data is processed, track points with the same position, the same license plate and the same driving direction and with the snapshot time in a preset range are counted from the sequenced track point set of each vehicle, then the counted track points with the same position, the same license plate and the same driving direction and with the snapshot time in the preset range are combined, and a first track point set of each vehicle is generated.
For example, data with the same position, license plate and direction and the same collection time or 1-2 seconds interval exist, the data are generated because the repeated data need to be removed due to the fact that the data are shot by the shooting equipment of other lanes in error, and therefore when the position is the same, the license plate is the same with the driving direction, the data with the shooting time interval smaller than 3 seconds only remain the first data.
And when the section time error data is processed, removing the section time error track points from the first track point set of each vehicle, and generating a second track point set of each vehicle.
For example, the time of the upstream position data is longer than the time of the downstream position data for each vehicle, and the actual traveling direction is not satisfied, so that the data having the upstream position time longer than the downstream position time needs to be removed. That is, when the Time isi≥Timei+1And (4) removing the data of the (i + 1) th section.
And when processing data with unknown sections, removing data with section mileage pile numbers as null values from the second track point set of each vehicle, and generating a third track point set of each vehicle.
For example, for each section data with unknown mileage stake marks, the specific position of the data cannot be determined, so that the section data with unknown mileage stake marks need to be directly removed. When Stake is usediAnd when the value is null, removing the data of the ith section.
When the problem of track mixing of the same vehicle in different time periods is solved, when a plurality of track points in different time periods exist in each vehicle, track segmentation processing is carried out on the third track point set of each vehicle, and a track point set corresponding to each preprocessed vehicle is generated.
It should be noted that there is no sequence between the steps of preprocessing the trace points of each vehicle passing through each data acquisition cross section.
For example, the average speed of the vehicle passing through two sections is calculated, and if the average speed is greater than a set threshold value epsilon, for example, epsilon 180km/h, the trajectories of the two sections are broken and divided into two trajectories.
And calculating the average speed of the vehicle between the two adjacent data acquisition sections according to the positions and the time of the two adjacent data acquisition sections. Vehicle Carid1The average velocity between the ith cross section and the (i + 1) th cross section is calculated as follows:
Figure BDA0002873267590000101
when v isi>When epsilon, the vehicle Carid1The broken track is as follows:
a first section of track:
Figure BDA0002873267590000102
and a second section of track:
Figure BDA0002873267590000103
s103, dividing the highway section to be counted into a sub-section set based on a preset distance, and setting a marking pile for each sub-section in the sub-section set;
in a possible implementation manner, the road to be counted is divided at intervals of 100 meters to obtain a plurality of sub-road sections, and the position of each sub-road section is represented by a hundred-meter pile, that is, the mark pile is a hundred-meter pile.
S104, calculating the passing time of each vehicle passing through each set marking pile based on the preprocessed track point set corresponding to each vehicle;
in a possible implementation mode, when the passing time of each vehicle passing through each set marking pile is calculated, when each vehicle has a plurality of track points on adjacent data acquisition sections and a plurality of parameters corresponding to the track points are complete, the number of the marking piles between the adjacent data acquisition sections is firstly counted, then the passing time of each vehicle passing through the adjacent data acquisition sections is obtained based on a track point set corresponding to each vehicle after preprocessing, and finally the passing time of each vehicle passing through the marking piles is calculated based on the number of the marking piles and the passing time.
In another possible implementation manner, when each vehicle has one track point on the adjacent data acquisition cross section, firstly counting the number of the marker piles between the adjacent data acquisition cross sections, then judging whether other vehicles passing through the adjacent data acquisition cross section exist, if so, acquiring the passing time of the other vehicles passing through the adjacent data acquisition cross section based on the track point set corresponding to each vehicle after preprocessing, and then determining the time period between the passing times of the other vehicles passing through the adjacent data acquisition cross section as the passing time period of each vehicle on the adjacent data acquisition cross section. Or if the current time does not exist, firstly acquiring a preset default historical passing time period, then determining the preset default historical passing time as the passing time of each vehicle on the adjacent data acquisition section, and finally calculating the passing time of each vehicle passing through each set marking pile based on the number of the marking piles between the adjacent data acquisition sections and the passing time of each vehicle on the adjacent data acquisition section.
If the vehicle has track points on two sections and the data field is complete, the time calculation method for the vehicle to pass through each marking pile is as follows:
firstly, counting the number of the marking piles between two adjacent sections. By using
Figure BDA0002873267590000111
The number of the marking piles between the ith cross section and the (i + 1) th cross section is represented, delta represents a preset distance,
Figure BDA0002873267590000112
the calculation formula is as follows:
Figure BDA0002873267590000113
wherein, StakeiAnd stabei+1Marker posts representing the ith and (i + 1) th sections, respectively, in units of: and (4) rice.
Then, the travel time of the vehicle passing through the two adjacent sections is calculated. Time of flight t between the ith cross section and the (i + 1) th cross sectioni,i+1The calculation formula is as follows:
ti,i+1＝Timei+1-Timei
wherein, TimeiAnd Timei+1Respectively representing the time for the vehicle to pass through the ith section and the (i + 1) th section.
Finally, the time for the vehicle to pass through each marker post between the ith cross section and the (i + 1) th cross section is calculated. By using
Figure BDA0002873267590000114
Representing the time for the vehicle to pass through the kth marker post between the ith cross section and the (i + 1) th cross section, then:
Figure BDA0002873267590000115
for example, if the vehicle has only one track point between two sections, i.e. a single point track, using
Figure BDA0002873267590000116
The time of the vehicle passing through the ith cross section is shown, and the time of the vehicle passing through the (i + 1) th cross section is unknown. At this time, the time for the vehicle to pass through the two sections cannot be calculated.
If other vehicles pass through the two sections in the period, the other vehicles pass through the two sections
Figure BDA0002873267590000117
Instead of the travel time of a single point trajectory. At this time, the vehicle on the single-point track passes through the time of the kth mark pile between the ith section and the (i + 1) th section
Figure BDA0002873267590000118
The calculation formula is as follows:
Figure BDA0002873267590000119
if no other vehicles pass through the time period, the default value is used
Figure BDA0002873267590000121
Such as historical averages instead of travel times for single point traces. At this time, the vehicle on the single-point track passes through the time of the kth mark pile between the ith section and the (i + 1) th section
Figure BDA0002873267590000122
The calculation formula is as follows:
Figure BDA0002873267590000123
and S105, counting the traffic volume of each marking pile in a preset time period according to the set passing time of each vehicle passing through each marking pile.
In one possible implementation manner, the marking piles are hundred-meter piles, and the distance between every two adjacent marking piles is 100 meters;
in a possible implementation manner, three fields of the time period, the marked pile and the road direction are combined into a key (key), and the value (value) of each key is counted, wherein the value of the key is the traffic volume of the marked pile in the specified direction specified by the time period specified by the table.
Fig. 2 is a block diagram of a traffic volume statistical process for a highway section according to an embodiment of the present application, in which a unified data format is first set for a plurality of data acquisition device positions on a highway (including toll station positions, gantry system facility positions, other data acquisition device positions, bayonet device positions, and microwave radar device positions), then trajectory data of each vehicle passing through the highway data acquisition device is extracted and converted according to the unified data format, the data converted into the standard format is sorted according to a time sequence, abnormal data processing (including repeated data processing, section time error data, section unknown data, and trajectory mixing problem processing of different time periods of the same vehicle) is performed on the sorted data, and then the highway section to be counted is divided into equally spaced segments according to a preset distance, the position of each segment is represented by a mark pile, the time of each vehicle passing each mark pile is calculated, and finally the number of vehicles passing each mark pile in the set time, namely the traffic volume of each mark pile is counted.
In the embodiment of the application, the traffic volume statistical device of the highway section firstly extracts the track points of each vehicle passing through each data acquisition section on the highway section to be statistically calculated, wherein each data acquisition section is the position of each different type of data acquisition equipment arranged on the road section, track points of each vehicle passing through each data acquisition section are preprocessed to generate a track point set corresponding to each preprocessed vehicle, then dividing the highway section to be counted into sub-section sets based on the preset distance, setting a mark pile for each sub-section in the sub-section sets, secondly, calculating the passing time of each vehicle passing through each set marking pile based on the preprocessed track point set corresponding to each vehicle, and finally counting the traffic volume of each marking pile in the preset time period according to the passing time of each vehicle passing through each set marking pile. According to the method and the device, the vehicle passing data are collected through the positions of the different types of data collection equipment arranged on the road sections to estimate the positions and the time of the vehicles passing the road sections, and finally the traffic volume of each set marking pile in the preset time period is calculated according to the positions and the time of the vehicles passing the road sections, so that the accuracy of the statistics of the traffic volume of the high-speed road sections is improved.
Fig. 3 is a schematic flow chart of a traffic volume statistical method for a highway section according to an embodiment of the present disclosure. The embodiment is exemplified by applying the statistical method of the traffic volume of the highway section to the user terminal. The method for counting the traffic volume of the highway section can comprise the following steps:
s201, extracting track points of each vehicle passing through each data acquisition section on the highway section to be counted; each data acquisition section is the position of each different type of data acquisition equipment arranged on the road section;
s202, converting a plurality of parameters generated by each vehicle passing through the track point of each data acquisition cross section into a preset standard expression mode, and generating a converted track point set of each vehicle; wherein, each track point in the track point set of each converted vehicle corresponds to a plurality of converted parameters;
s203, acquiring the passing time of each vehicle passing through each data acquisition section from a plurality of converted parameters corresponding to each track point in the converted track point set of each vehicle;
s204, performing ascending sequencing on all track points in the converted track point set of each vehicle based on the passing time of each vehicle passing through each data acquisition section, and generating a sequenced track point set of each vehicle;
s205, performing abnormal data processing on the sequenced track point set of each vehicle to generate a track point set corresponding to each preprocessed vehicle;
s206, dividing the highway section to be counted into a sub-section set based on a preset distance, and setting a marking pile for each sub-section in the sub-section set;
s207, counting the number of the marking piles between the adjacent data acquisition sections when each vehicle has a plurality of track points on the adjacent data acquisition sections and a plurality of parameters corresponding to the track points are complete;
s208, acquiring the passing time of each vehicle passing through the adjacent data acquisition section based on the preprocessed track point set corresponding to each vehicle;
s209, calculating the passing time of each vehicle passing through the marked piles based on the number of the marked piles and the passing time;
s210, counting the traffic volume of each marking pile in a preset time period according to the set passing time of each vehicle passing through each marking pile;
in this embodiment, the distance between two adjacent marking piles may be 1 to 100 meters; in a preferred embodiment, the marker post may be provided not on the road but on the road network structure diagram corresponding to the road.
In the embodiment of the application, the traffic volume statistical device of the highway section firstly extracts the track points of each vehicle passing through each data acquisition section on the highway section to be statistically calculated, wherein each data acquisition section is the position of each different type of data acquisition equipment arranged on the road section, track points of each vehicle passing through each data acquisition section are preprocessed to generate a track point set corresponding to each preprocessed vehicle, then dividing the highway section to be counted into sub-section sets based on the preset distance, setting a mark pile for each sub-section in the sub-section sets, secondly, calculating the passing time of each vehicle passing through each set marking pile based on the preprocessed track point set corresponding to each vehicle, and finally counting the traffic volume of each marking pile in the preset time period according to the passing time of each vehicle passing through each set marking pile. According to the method and the device, the vehicle passing data are collected through the positions of the different types of data collection equipment arranged on the road sections to estimate the positions and the time of the vehicles passing the road sections, and finally the traffic volume of each set marking pile in the preset time period is calculated according to the positions and the time of the vehicles passing the road sections, so that the accuracy of the statistics of the traffic volume of the high-speed road sections is improved.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.
Fig. 4 is a schematic structural diagram of a traffic volume statistic device for a highway section according to an exemplary embodiment of the invention. The device for counting the traffic volume of the highway section can be realized into all or part of the intelligent robot through software, hardware or a combination of the software and the hardware. The device 1 comprises a track point extraction module 10, a track point pretreatment module 20, a marking pile setting module 30, a transit time calculation module 40 and a traffic volume statistic module 50.
The track point extraction module 10 is used for extracting track points of each vehicle passing through each data acquisition section on the high-speed road section to be counted; each data acquisition section is the position of each different type of data acquisition equipment arranged on the road section;
the track point preprocessing module 20 is configured to preprocess the track points of each vehicle passing through each data acquisition cross section, and generate a track point set corresponding to each preprocessed vehicle;
the marking pile setting module 30 is used for dividing the highway section to be counted into a sub-road section set based on a preset distance and setting a marking pile for each sub-road section in the sub-road section set;
the passing time calculation module 40 is used for calculating the passing time of each vehicle passing through each set marking pile based on the preprocessed track point set corresponding to each vehicle;
and the traffic volume counting module 50 is used for counting the traffic volume of each marking pile in a preset time period according to the set passing time of each vehicle passing through each marking pile.
In a preferred embodiment, the marker post is a hundred meter post.
It should be noted that, when the device for counting traffic volume on a highway section provided in the foregoing embodiment executes the method for counting traffic volume on a highway section, the above-mentioned division of each functional module is merely used as an example, and in practical applications, the above-mentioned function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the above-mentioned functions. In addition, the traffic volume statistical device for the highway section and the traffic volume statistical method for the highway section provided by the embodiments belong to the same concept, and the implementation process is detailed in the method embodiments and is not repeated herein.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the embodiment of the application, the traffic volume statistical device of the highway section firstly extracts the track points of each vehicle passing through each data acquisition section on the highway section to be statistically calculated, wherein each data acquisition section is the position of each different type of data acquisition equipment arranged on the road section, track points of each vehicle passing through each data acquisition section are preprocessed to generate a track point set corresponding to each preprocessed vehicle, then dividing the highway section to be counted into sub-section sets based on the preset distance, setting a mark pile for each sub-section in the sub-section sets, secondly, calculating the passing time of each vehicle passing through each set marking pile based on the preprocessed track point set corresponding to each vehicle, and finally counting the traffic volume of each marking pile in the preset time period according to the passing time of each vehicle passing through each set marking pile. According to the method and the device, the vehicle passing data are collected through the positions of the different types of data collection equipment arranged on the road sections to estimate the positions and the time of the vehicles passing the road sections, and finally the traffic volume of each set marking pile in the preset time period is calculated according to the positions and the time of the vehicles passing the road sections, so that the accuracy of the statistics of the traffic volume of the high-speed road sections is improved.
The invention also provides a computer readable medium, on which program instructions are stored, which when executed by a processor implement the statistical method for traffic volume of highway sections provided by the above-mentioned method embodiments.
The present invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the highway section traffic volume statistical method of the various method embodiments described above.
Please refer to fig. 5, which provides a schematic structural diagram of a terminal according to an embodiment of the present application. As shown in fig. 5, terminal 1000 can include: at least one processor 1001, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002.
Wherein a communication bus 1002 is used to enable connective communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Processor 1001 may include one or more processing cores, among other things. The processor 1001, which is connected to various parts throughout the electronic device 1000 using various interfaces and lines, performs various functions of the electronic device 1000 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 1005 and calling data stored in the memory 1005. Alternatively, the processor 1001 may be implemented in at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 1001 may integrate one or more of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a modem, and the like. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the processor 1001, but may be implemented by a single chip.
The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may optionally be at least one memory device located remotely from the processor 1001. As shown in fig. 5, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a highway section traffic volume statistics application program.
In the terminal 1000 shown in fig. 5, the user interface 1003 is mainly used as an interface for providing input for a user, and acquiring data input by the user; and the processor 1001 may be configured to call the highway section traffic volume statistics application stored in the memory 1005, and specifically perform the following operations:
extracting track points of each vehicle passing through each data acquisition section on the high-speed road section to be counted; each data acquisition section is the position of each different type of data acquisition equipment arranged on the road section;
preprocessing the track points of each vehicle passing through each data acquisition section to generate a track point set corresponding to each preprocessed vehicle;
dividing the highway section to be counted into sub-section sets based on a preset distance, and setting a marking pile for each sub-section in the sub-section sets;
calculating the passing time of each vehicle passing through each set marking pile based on the preprocessed track point set corresponding to each vehicle;
and counting the traffic volume of each marking pile in a preset time period according to the set passing time of each vehicle passing through each marking pile.
In an embodiment, when the processor 1001 performs preprocessing on the trajectory points of each vehicle passing through each data acquisition cross section and generates a trajectory point set corresponding to each preprocessed vehicle, the following operations are specifically performed:
converting a plurality of parameters generated by each vehicle passing through the track point of each data acquisition section into a preset standard expression mode, and generating a converted track point set of each vehicle; wherein, each track point in the track point set of each converted vehicle corresponds to a plurality of converted parameters;
acquiring the passing time of each vehicle passing through each data acquisition section from a plurality of converted parameters corresponding to each track point in the converted track point set of each vehicle;
the track points in the converted track point set of each vehicle are sorted in an ascending order based on the passing time of each vehicle passing through each data acquisition section, and the sorted track point set of each vehicle is generated;
and performing abnormal data processing on the sequenced track point set of each vehicle to generate a track point set corresponding to each preprocessed vehicle.
In an embodiment, when performing abnormal data processing on the sorted track point set of each vehicle and generating a track point set corresponding to each pre-processed vehicle, the processor 1001 specifically performs the following operations:
counting track points with the same position, the same license plate, the same driving direction and the snapshot time in accordance with a preset range from the sequenced track point set of each vehicle;
combining the counted track points with the same position, the same license plate, the same driving direction and the snapshot time in accordance with a preset range to generate a first track point set of each vehicle; and the number of the first and second groups,
removing the track points with the wrong section time from the first track point set of each vehicle to generate a second track point set of each vehicle; and
removing data with the section mileage pile number as a null value from the second track point set of each vehicle, and generating a third track point set of each vehicle; and
and when a plurality of track points in different time periods exist in each vehicle, performing track segmentation processing on the third track point set of each vehicle to generate a track point set corresponding to each preprocessed vehicle.
In an embodiment, when the pre-processed trace point set corresponding to each vehicle calculates the transit time of each vehicle passing through each set marking pile, the processor 1001 specifically performs the following operations:
when each vehicle has a plurality of track points on adjacent data acquisition sections and a plurality of parameters corresponding to the track points are complete, counting the number of marking piles between the adjacent data acquisition sections;
acquiring the passing time of each vehicle passing through the adjacent data acquisition section based on the preprocessed track point set corresponding to each vehicle;
calculating the passing time of each vehicle passing through the marked piles based on the number of the marked piles and the passing time;
the calculation formula of the number of the marked piles between adjacent data acquisition sections is as follows:
Figure BDA0002873267590000181
Stakeiand stabei+1Mileage piles respectively representing the ith section and the (i + 1) th section;
wherein, the time calculation formula of each vehicle passing through the marked pile is as follows:
Figure BDA0002873267590000182
Figure BDA0002873267590000183
indicating the number of marker posts, Time, between adjacent data acquisition sectionsiShowing the time of passage of the vehicle through the i-th section, ti,i+1Represents the travel time between the ith cross section and the (i + 1) th cross section, and k represents the kth mark pile counted from the ith cross section.
In an embodiment, when the pre-processed trace point set corresponding to each vehicle calculates the transit time of each vehicle passing through each set marking pile, the processor 1001 specifically performs the following operations:
when each vehicle has a track point on the adjacent data acquisition cross section, counting the number of the marker piles between the adjacent data acquisition cross sections;
judging whether other vehicles passing through at the same time interval exist or not;
if the vehicle passing time exists, acquiring the passing time of other vehicles passing through the adjacent data acquisition cross section based on the preprocessed track point set corresponding to each vehicle;
determining the time period between the passing moments of the other vehicles passing through the adjacent data acquisition sections as the passing time period of each vehicle on the adjacent data acquisition sections; alternatively, the first and second electrodes may be,
if not, acquiring a preset default historical passage time period;
determining preset default historical passing time as the passing time of each vehicle on the adjacent data acquisition cross section;
and calculating the passing time of each vehicle passing through each set marking pile based on the number of the marking piles between the adjacent data acquisition sections and the passing time of each vehicle on the adjacent data acquisition sections.
In the embodiment of the application, the traffic volume statistical device of the highway section firstly extracts the track points of each vehicle passing through each data acquisition section on the highway section to be statistically calculated, wherein each data acquisition section is the position of each different type of data acquisition equipment arranged on the road section, track points of each vehicle passing through each data acquisition section are preprocessed to generate a track point set corresponding to each preprocessed vehicle, then dividing the highway section to be counted into sub-section sets based on the preset distance, setting a mark pile for each sub-section in the sub-section sets, secondly, calculating the passing time of each vehicle passing through each set marking pile based on the preprocessed track point set corresponding to each vehicle, and finally counting the traffic volume of each marking pile in the preset time period according to the passing time of each vehicle passing through each set marking pile. According to the method and the device, the vehicle passing data are collected through the positions of the different types of data collection equipment arranged on the road sections to estimate the positions and the time of the vehicles passing the road sections, and finally the traffic volume of each set marking pile in the preset time period is calculated according to the positions and the time of the vehicles passing the road sections, so that the accuracy of the statistics of the traffic volume of the high-speed road sections is improved.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer program instructions, and the program can be stored in a computer readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium of the program may be a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.

Claims (10)

1. A statistical method for traffic volume of a highway section is characterized by comprising the following steps:
extracting track points of each vehicle passing through each data acquisition section on the high-speed road section to be counted; each data acquisition section is the position of each different type of data acquisition equipment arranged on the road section;
preprocessing the track points of each vehicle passing through each data acquisition section to generate a track point set corresponding to each preprocessed vehicle;
dividing the highway section to be counted into a sub-section set based on a preset distance, and setting a marking pile for each sub-section in the sub-section set;
calculating the passing time of each vehicle passing through each set marking pile based on the preprocessed track point set corresponding to each vehicle;
and counting the traffic volume of each marking pile in a preset time period according to the passing time of each vehicle passing through each set marking pile.
2. The method according to claim 1, wherein the preprocessing the trace points of each vehicle passing through each data acquisition cross section to generate a set of preprocessed trace points corresponding to each vehicle comprises:
converting a plurality of parameters generated by each vehicle passing through the track point of each data acquisition section into a preset standard expression mode, and generating a converted track point set of each vehicle; wherein, each track point in the track point set of each vehicle after conversion corresponds to a plurality of converted parameters;
acquiring the passing time of each vehicle passing through each data acquisition section from a plurality of converted parameters corresponding to each track point in the converted track point set of each vehicle;
based on the passing time of each vehicle passing through each data acquisition section, performing ascending sequencing on each track point in the converted track point set of each vehicle to generate a sequenced track point set of each vehicle;
and performing abnormal data processing on the sequenced track point set of each vehicle to generate a track point set corresponding to each preprocessed vehicle.
3. The method according to claim 2, wherein the performing abnormal data processing on the sorted track point sets of each vehicle to generate a preprocessed track point set corresponding to each vehicle includes:
counting track points with the same position, the same license plate, the same driving direction and the capturing time in accordance with a preset range from the sequenced track point set of each vehicle;
combining the counted track points with the same position, the same license plate, the same driving direction and the snapshot time in accordance with a preset range to generate a first track point set of each vehicle; and the number of the first and second groups,
removing the track points with the wrong section time from the first track point set of each vehicle to generate a second track point set of each vehicle; and
removing data with the section mileage pile number as a null value from the second track point set of each vehicle to generate a third track point set of each vehicle; and
and when a plurality of track points in different time periods exist in each vehicle, carrying out track segmentation processing on the third track point set of each vehicle to generate a track point set corresponding to each preprocessed vehicle.
4. The method according to claim 1, wherein the calculating the transit time of each vehicle passing through each set marking pile based on the preprocessed set of track points corresponding to each vehicle comprises:
when track points exist on adjacent data acquisition sections of each vehicle and a plurality of parameters corresponding to the track points are complete, counting the number of marking piles between the adjacent data acquisition sections;
acquiring the passing time of each vehicle passing through the adjacent data acquisition section based on the preprocessed track point set corresponding to each vehicle;
calculating the passing time of each vehicle passing through the marking piles based on the number of the marking piles and the passing time;
the calculation formula of the number of the marked piles between the adjacent data acquisition sections is as follows:
Figure FDA0002873267580000021
Stakeiand stabei+1Respectively representing mile piles of the ith section and the (i + 1) th section, wherein delta represents a preset distance;
wherein, the time calculation formula of each vehicle passing through the marking pile is as follows:
Figure FDA0002873267580000022
Figure FDA0002873267580000023
indicating the number of marker posts, Time, between adjacent data acquisition sectionsiShowing the time of passage of the vehicle through the i-th section, ti，i+1Represents the travel time between the ith cross section and the (i + 1) th cross section, and k represents the kth mark pile counted from the ith cross section.
5. The method according to claim 1, wherein the calculating the transit time of each vehicle passing through each set marking pile based on the preprocessed set of track points corresponding to each vehicle comprises:
when each vehicle only has one track point on two adjacent data acquisition sections, counting the number of the marker piles between the adjacent data acquisition sections;
judging whether other vehicles passing through at the same time interval exist or not;
if yes, acquiring the passing time of other vehicles passing through the adjacent data acquisition cross section based on the preprocessed track point set corresponding to each vehicle;
determining the time period between the passing moments of the other vehicles passing through the adjacent data acquisition sections as the passing time period of each vehicle on the adjacent data acquisition sections; alternatively, the first and second electrodes may be,
if not, acquiring a preset default historical passage time period;
determining the preset default historical passing time as the passing time of each vehicle on the adjacent data acquisition cross section;
and calculating the passing time of each vehicle passing through each set marking pile based on the number of the marking piles between the adjacent data acquisition sections and the passing time of each vehicle on the adjacent data acquisition sections.
6. The method according to claim 5, wherein the time for each vehicle to pass each marker pile set when the same period of time exists through other vehicles is calculated by the formula:
Figure FDA0002873267580000031
wherein the content of the first and second substances,
Figure FDA0002873267580000032
the passing time of the vehicle which represents the single-point track passing through the ith section,
Figure FDA0002873267580000033
when it indicates the sameThe other vehicles passing by pass through the passage time section of the adjacent data acquisition section,
Figure FDA0002873267580000034
the number of the marked piles between adjacent data acquisition sections is represented, k represents the kth marked pile counted from the ith section, and delta represents a preset distance.
7. The method according to claim 5, wherein the time for each vehicle to pass each marker pile set when the same period of time does not pass other vehicles is calculated by the formula:
Figure FDA0002873267580000035
Figure FDA0002873267580000036
showing the passing time of the vehicle passing through the ith cross section,
Figure FDA0002873267580000037
indicating a preset default historical passage time period,
Figure FDA0002873267580000038
the number of the mark piles between adjacent data acquisition sections is shown, and k represents the k mark pile counted from the ith section.
8. A device for statistics of traffic volume on highway sections, the device comprising:
the track point extraction module is used for extracting track points of each vehicle passing through each data acquisition section on the high-speed road section to be counted; each data acquisition section is the position of each different type of data acquisition equipment arranged on the road section;
the track point preprocessing module is used for preprocessing the track points of each vehicle passing through each data acquisition section to generate a track point set corresponding to each preprocessed vehicle;
the marking pile setting module is used for dividing the highway section to be counted into a sub-section set based on a preset distance and setting a marking pile for each sub-section in the sub-section set;
a passing time calculation module, configured to calculate, based on the preprocessed trace point set corresponding to each vehicle, a passing time of each vehicle passing through each set marking pile;
and the traffic volume counting module is used for counting the traffic volume of each marking pile in a preset time period according to the passing time of each vehicle passing through each set marking pile.
9. A computer storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor and to perform the method steps according to any of claims 1-7.
10. A terminal, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1-7.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113674538A (en) * 2021-08-09 2021-11-19 南京美慧软件有限公司 Section flow monitoring system
CN114944083A (en) * 2022-05-13 2022-08-26 公安部交通管理科学研究所 Method for judging distance between running vehicle on expressway and front vehicle
CN115148020A (en) * 2022-06-13 2022-10-04 中国标准化研究院 Monitoring system and method based on traffic flow in unit time of expressway
CN115238024A (en) * 2022-09-26 2022-10-25 交通运输部科学研究院 Highway facility positioning method, device, electronic equipment and storage medium

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN204406671U (en) * 2015-02-13 2015-06-17 长安大学 A kind of device of the automatic monitoring and statistics volume of traffic
CN105006149A (en) * 2015-07-10 2015-10-28 信融源大数据科技(北京)有限公司 Traffic road condition estimation dynamic iteration method
CN106781455A (en) * 2016-11-28 2017-05-31 东南大学 A kind of region Expressway Information system based on cloud computing
CN108335482A (en) * 2017-01-20 2018-07-27 亚信蓝涛（江苏）数据科技有限公司 A kind of urban transportation Situation Awareness method and method for visualizing
CN111724595A (en) * 2020-06-23 2020-09-29 重庆大学 Highway section flow estimation method based on charging data

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN204406671U (en) * 2015-02-13 2015-06-17 长安大学 A kind of device of the automatic monitoring and statistics volume of traffic
CN105006149A (en) * 2015-07-10 2015-10-28 信融源大数据科技(北京)有限公司 Traffic road condition estimation dynamic iteration method
CN106781455A (en) * 2016-11-28 2017-05-31 东南大学 A kind of region Expressway Information system based on cloud computing
CN108335482A (en) * 2017-01-20 2018-07-27 亚信蓝涛（江苏）数据科技有限公司 A kind of urban transportation Situation Awareness method and method for visualizing
CN111724595A (en) * 2020-06-23 2020-09-29 重庆大学 Highway section flow estimation method based on charging data

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
李琦: "基于多源数据的交通状态监测与预测方法研究", 《中国博士学位论文全文数据库》 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113674538A (en) * 2021-08-09 2021-11-19 南京美慧软件有限公司 Section flow monitoring system
CN113674538B (en) * 2021-08-09 2023-04-18 南京领航交通科技有限公司 Section flow monitoring system
CN114944083A (en) * 2022-05-13 2022-08-26 公安部交通管理科学研究所 Method for judging distance between running vehicle on expressway and front vehicle
CN114944083B (en) * 2022-05-13 2023-03-24 公安部交通管理科学研究所 Method for judging distance between running vehicle on expressway and front vehicle
CN115148020A (en) * 2022-06-13 2022-10-04 中国标准化研究院 Monitoring system and method based on traffic flow in unit time of expressway
CN115238024A (en) * 2022-09-26 2022-10-25 交通运输部科学研究院 Highway facility positioning method, device, electronic equipment and storage medium
CN115238024B (en) * 2022-09-26 2022-12-20 交通运输部科学研究院 Highway facility positioning method and device, electronic equipment and storage medium

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