CN117765740A - Method and device for identifying overtaking of vehicle - Google Patents
Method and device for identifying overtaking of vehicle Download PDFInfo
- Publication number
- CN117765740A CN117765740A CN202311853521.7A CN202311853521A CN117765740A CN 117765740 A CN117765740 A CN 117765740A CN 202311853521 A CN202311853521 A CN 202311853521A CN 117765740 A CN117765740 A CN 117765740A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- target
- target vehicle
- determining
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 37
- 230000011664 signaling Effects 0.000 claims abstract description 38
- 238000004364 calculation method Methods 0.000 claims abstract description 13
- 230000000903 blocking effect Effects 0.000 claims description 10
- 238000013507 mapping Methods 0.000 claims description 10
- 238000004422 calculation algorithm Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 8
- 238000004140 cleaning Methods 0.000 claims description 6
- 238000012216 screening Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 5
- 230000002159 abnormal effect Effects 0.000 claims description 4
- 230000009467 reduction Effects 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 2
- 238000004891 communication Methods 0.000 abstract description 3
- 238000007405 data analysis Methods 0.000 abstract description 2
- 230000008569 process Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 3
- 239000000969 carrier Substances 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Landscapes
- Traffic Control Systems (AREA)
Abstract
The application discloses a vehicle overguard identification method and device, which relate to the technical field of big data analysis and comprise the following steps: generating a time sequence of passing the gate of each target vehicle according to the gate data, and determining an effective path of each target vehicle based on the time sequence of passing the gate and the set information point; establishing a matching relation between a road network road and a base station coverage area according to the spatial positions of the road network and the base station, and determining a plurality of target users according to the signaling metadata and the matching relation; generating travel paths of all target users according to signaling metadata of a plurality of target users, and respectively calculating the similarity between each travel path and an effective path; and determining whether each target vehicle is overtime or not based on the similarity calculation result and a preset vehicle overtime condition. The method and the device match the on-road vehicle track data with the mobile phone signaling track data of the communication service operator to obtain the illegal suspected vehicle, and provide support for the floor check of the traffic police department.
Description
Technical Field
The application relates to the technical field of big data analysis, in particular to a method and a device for identifying overtaking of a vehicle.
Background
In order to effectively manage superbus vehicles, usually, traffic police manually checks key intersections in the peak period of passenger flow such as holidays, the method is very inconvenient, the situation of midway boarding is difficult to thoroughly check, and other methods for judging superbus vehicles are to add additional equipment such as cameras in the vehicles, so that the method is not strong in practicability, not all vehicles are provided with related equipment, on the one hand, drivers can easily cover the actual situation of superbus through operating equipment, and therefore, superbus has the characteristics of strong concealment and high organization, so that the difficulty of checking by means of traditional means is higher.
Disclosure of Invention
The identification method for the overtime of the vehicle aims at solving the problems that in the prior art, logistics management and electronic contract management are disjointed, the maintenance cost of an RFID logistics tracking system is high, the RFID cannot perform off-line identity authentication, and logistics records are easy to tamper or delete.
In order to achieve the above purpose, the present application adopts the following technical scheme:
the method for identifying the overtaking of the vehicle comprises the following steps:
generating a time sequence of passing the bayonet of each target vehicle according to the bayonet data, and determining an effective path of each target vehicle based on the time sequence of passing the bayonet and a set information point;
establishing a matching relation between a road network road and a base station coverage area according to the spatial positions of the road network and the base station, and determining a plurality of target users according to the signaling metadata and the matching relation;
generating travel paths of all target users according to the signaling metadata of the plurality of target users, and respectively calculating the similarity between each travel path and the effective path;
and determining whether each target vehicle is overtime or not based on the similarity calculation result and a preset vehicle overtime condition.
Preferably, the generating the time series of passing bayonets of the respective target vehicles according to the bayonet data includes:
acquiring bayonet data, and cleaning abnormal data and repeated record data in the bayonet data;
and screening target vehicle data from the cleaned data, and sequencing the time of each target vehicle passing through the gate according to the target vehicle data.
Preferably, the determining the effective path of each target vehicle based on the over-gate time series and the set information point includes:
mapping the bayonet equipment to a road network, and calculating the shortest driving distance between the bayonet equipment through a shortest path algorithm based on the road network topology;
respectively extracting adjacent passing time stamps and corresponding blocking positions of all target vehicles from all passing time sequences, and determining a first driving distance of each target vehicle according to the shortest driving distance and the blocking positions;
calculating a first time difference according to the time stamp, and calculating the running speed of each target vehicle according to the first running distance and the corresponding first time difference;
and determining the stay point of each target vehicle according to the bayonet position and the running speed, and determining the effective path of each target vehicle based on the stay point and the set information point.
Preferably, the determining the stop point of each target vehicle according to the bayonet position and the running speed includes:
when the positions of the bayonets are the same, judging whether each first time difference is larger than a first threshold value, and if so, judging that the bayonets are stay points;
and when the bayonet positions are not the same, judging whether each running speed is smaller than a second threshold value, and if so, determining that the current position is a stop point.
Preferably, before determining a number of target users according to the signaling metadata of a plurality of initial users and the matching relationship, the method further comprises:
and acquiring original signaling data of a plurality of initial users, and preprocessing the original signaling data, wherein the preprocessing comprises filtering and noise reduction to obtain signaling element data.
Preferably, the determining whether each of the target vehicles is overtime based on the similarity calculation result and a preset vehicle overtime condition includes:
and respectively judging whether each similarity calculation result is larger than a third threshold value, if so, taking the corresponding target user as the passenger of the corresponding target vehicle to obtain the passenger number of each target vehicle.
An apparatus for identifying a vehicle overtaking, comprising:
the generation module is used for generating an over-gate time sequence of each target vehicle according to gate data and determining an effective path of each target vehicle based on the over-gate time sequence and a set information point;
the matching module is used for establishing a matching relation between a road network road and a base station coverage area according to the spatial positions of the road network and the base station, and determining a plurality of target users according to the signaling metadata and the matching relation;
the calculation module is used for generating travel paths of all the target users according to the signaling metadata of the plurality of target users and calculating the similarity between each travel path and the effective path respectively;
and the judging module is used for determining whether each target vehicle is overtime or not based on the similarity calculation result and a preset vehicle overtime condition.
Preferably, the generating module includes:
the mapping unit is used for mapping the bayonet devices to the road network and calculating the shortest driving distance between the bayonet devices through a shortest path algorithm based on the road network topology;
the extraction unit is used for respectively extracting adjacent passing time stamps and corresponding blocking positions of all target vehicles from all passing time sequences, and determining a first driving distance of each target vehicle according to the shortest driving distance and the blocking positions;
a calculating unit, configured to calculate a first time difference according to the time stamp, and calculate a driving speed of each target vehicle according to the first driving distance and the corresponding first time difference;
and the determining unit is used for determining the stay point of each target vehicle according to the bayonet position and the running speed and determining the effective path of each target vehicle based on the stay point and the set information point.
An electronic device comprising a memory and a processor, the memory for storing one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement a method of identifying a vehicle overuse as claimed in any one of the preceding claims.
A computer readable storage medium storing a computer program which, when executed by a computer, causes the computer to implement a method of identifying a vehicle overage as claimed in any preceding claim.
The invention has the following beneficial effects:
the method adopts a multisource data fusion mode, integrates monitoring data, license plate data, mobile phone signaling data, POI data and road network data to establish a on-road minibus driving track database, matches on-road minibus track data with mobile phone signaling track data of a communication service operator to obtain a suspected illegal vehicle, provides support for the ground check of a traffic police department, and meanwhile combines the vehicle bayonet track with mobile phone signaling to obtain a result which is unique and accurate, and has strong popularization and high universality.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a first flowchart of a method of identifying a vehicle overage in accordance with the present application;
FIG. 2 is a second flowchart of a method of identifying a vehicle overage implemented in the present application;
FIG. 3 is a schematic diagram of a base station mapping to a road in the present application;
FIG. 4 is a schematic diagram of target user identification in the present application;
FIG. 5 is a schematic diagram of an identification device for vehicle overuse in accordance with the present application;
fig. 6 is a schematic diagram of an electronic device for implementing a method for identifying a vehicle overtime.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
The terms "first," "second," and the like in the claims and the description of the present application are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order, and it should be understood that the terms so used may be interchanged, if appropriate, merely to describe the manner in which objects of the same nature are distinguished in the embodiments of the present application when described, and furthermore, the terms "comprise" and "have" and any variations thereof are intended to cover a non-exclusive inclusion such that a process, method, system, article, or apparatus that comprises a list of elements is not necessarily limited to those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
A first embodiment of the present application provides a method for identifying a vehicle overtaking, as shown in fig. 1 and 2, including the following steps:
s110, generating a time sequence of passing the bayonet of each target vehicle according to the bayonet data, and determining an effective path of each target vehicle based on the time sequence of passing the bayonet and a set information point;
s120, establishing a matching relation between a road network road and a base station coverage area according to the spatial positions of the road network and the base station, and determining a plurality of target users according to the signaling metadata and the matching relation;
s130, generating travel paths of all target users according to the signaling metadata of the plurality of target users, and respectively calculating the similarity between each travel path and the effective path;
and S140, determining whether each target vehicle is overtime or not based on the similarity calculation result and a preset vehicle overtime condition.
Firstly, acquiring gate data, wherein the gate data is vehicle driving data collected through gate equipment installed on a road and mainly comprises information such as vehicle access time, license plate numbers, vehicle types, colors and the like, then carrying out pretreatment operations such as data cleaning, data screening, data sorting and the like on the acquired gate data to obtain target vehicle data, wherein the target vehicle is a vehicle taking people as a main purpose, in the embodiment, the target vehicle is a minibus, then determining a time sequence of each target vehicle passing gate according to the target vehicle data, wherein the time sequence not only records a time stamp of each target vehicle passing gate, but also comprises corresponding gate positions, and the driving speed of each target vehicle can be calculated through the adjacent gate passing time stamp and the corresponding gate positions of each target vehicle, when the positions of the bayonets recorded adjacently are identical, if the difference between the two time stamps is greater than a first threshold, in this embodiment, the first threshold is 1200s, the bayonets are determined to be the stop points corresponding to the target vehicles, if not identical, it is determined whether the running speed of the target vehicles is smaller than a second threshold, in this embodiment, the second threshold vl=max { Vp5,5}, the maximum value of 5% quantile Vp5 and 5km/h at the running speed is taken, wherein Vp5 (5% quantile) is a sign, the quantile is the order of the data obtained by multiplying the percentages by the number of the data after sorting according to the size of the data, then the data corresponding to the order is taken, if yes, the current position is determined to be the stop point, and the effective path of each target vehicle is determined in combination with the previously selected information point, i.e., POI point, the effective path refers to a travel path of a starting point and a stopping point of vehicle running in a POI set, for example, a starting point is in a labor market (POI point), an end point is a temporary recruited enterprise (POI point), the travel path is an effective path, and a stop point contained between the starting point and the end point is an intermediate point on the effective path.
Next, firstly, obtain the original signaling data of multiple initial users through the data interface opened by the operator, and preprocess them, in this embodiment, the preprocessing mainly includes filtering and noise reduction, namely, eliminating ping pong data and drift data in the original signaling data, then noise reduction is performed on the filtered data, thus obtaining signaling metadata, then, the space positions of the road network and the base station are analyzed by using the road network model, the matching relation between the coverage area of the base station and the road network is established accordingly, the road sections of the base station are switched according to the matching relation, and the surrounding base stations are selected in the base station database according to the coordinate information obtained by the map mark, in addition, the two sides of the road often have the intensive areas of factory buildings, schools and other people, in these intensive areas, the mobile communication operators can deploy a large number of indoor division sites, and these indoor division sites are generally weaker than the outdoor sites covering the road, the signal is not the main information covering the road, and the interference elimination processing should be performed, as shown in fig. 3, the road network in the outdoor base station coverage area is called an on-line area, the area outside the outdoor base station coverage area is called an off-line area, and according to the signaling metadata, namely, whether each initial user is an initial user can be judged as the signaling user is in the current, if the condition is satisfied in the embodiment, as shown in fig. 4: 1) X consecutive ones of the initial user signaling metadata belong to an online zone, where X is 3; 2) The distance that the initial user has travelled is greater than Y kilometers, where Y is 4KM; 3) The speed of the initial user running is greater than Z KM/H, where Z is 60KM/H, where it is to be noted that the values of X, Y and Z can be changed according to the actual situation, which is not limited, when the signaling metadata of a certain initial user satisfies the above condition, it is determined that the initial user is the target user, if the signaling metadata does not satisfy the above condition, it is continuously tracked, and once the signaling metadata of the subsequent user satisfies the above condition, it is also identified as the target user, since the traveling endpoint of each traveling of the resident performs a specific purpose activity, i.e. performs a long-time stay, the corresponding mobile phone position track forms a point cluster with a larger density, and a relatively sparse scattered point on the traveling way has a larger difference, and the traveling endpoint with a larger point space density is identified in the form of the point cluster by using a spatial clustering algorithm based on the density.
Finally, calculating the similarity between each travel path and the effective path by adopting a multi-line location distance (LIP) algorithm, which is not described In detail herein, and judging whether each target vehicle is overtime according to the actual situation by combining with a preset vehicle overtime condition, specifically, when the similarity between the travel path and the effective path is greater than a third threshold, in this embodiment, the third threshold is 85%, the corresponding target user is regarded as the passenger of the target vehicle, so as to obtain the number of passengers of each target vehicle, and then see whether the number of passengers of each target vehicle exceeds 10% of the number of nuclear carriers of the target vehicle, the number of nuclear carriers is determined by the vehicle type, and meanwhile, judging whether the overtime behavior of each target vehicle In a month is greater than 8 times, wherein, the figures such as 10% and 8 can be changed according to the actual situation, if a certain target vehicle meets the condition, the target vehicle is set as a suspected vehicle, and the suspected index is determined, in this embodiment, the suspected index is the average value of the similarity of the mobile phone signaling target user path is 100, and the corresponding effective path is taken as the effective path.
In some embodiments, generating the over-bayonet time series for each target vehicle from the bayonet data includes:
acquiring bayonet data, and cleaning abnormal data and repeated record data in the bayonet data;
and screening target vehicle data from the cleaned data, and sequencing the time of each target vehicle passing through the gate according to the target vehicle data.
In practice, due to weather reasons such as haze and rain, and equipment installation angle problems, the monitoring and identification system cannot achieve performance under ideal conditions, and needs to analyze actual quality of section data, specifically, data cleaning, data screening and data sorting are performed on bayonet data, wherein the data cleaning mainly aims at abnormal data and repeated record data in the bayonet data, the data screening mainly aims at non-manned vehicles such as trucks, namely, deleting data corresponding to all non-target vehicles in the bayonet data, and the data sorting mainly aims at the time of passing through the bayonet of a single vehicle, so that preparation is made for identifying the stop points of the single vehicle.
In some embodiments, determining the effective path of each of the target vehicles based on the over-bayonet time series and a set point of information includes:
mapping the bayonet equipment to a road network, and calculating the shortest driving distance between the bayonet equipment through a shortest path algorithm based on the road network topology;
respectively extracting adjacent passing time stamps and corresponding blocking positions of all target vehicles from all passing time sequences, and determining a first driving distance of each target vehicle according to the shortest driving distance and the blocking positions;
calculating a first time difference according to the time stamp, and calculating the running speed of each target vehicle according to the first running distance and the corresponding first time difference;
and determining the stay point of each target vehicle according to the bayonet position and the running speed, and determining the effective path of each target vehicle based on the stay point and the set information point.
The stay point identification is divided into two cases: 1) The last trip end point and the next trip start point are the same position, namely the trip start point and the trip stop point are the same position; 2) The last trip end point and the next trip start point are different positions, namely, trip start points are different positions.
The basic idea of the stay point judgment is to judge whether to stay between two track points according to the speed, and the specific judgment process is as follows:
mapping the bayonet equipment to a road network, and acquiring the shortest driving distance between the bayonet equipment through a shortest path algorithm based on the road network topology; and extracting the over-gate time stamp and the corresponding gate position recorded adjacently by the target vehicle from the over-gate time sequence, and calculating the first travel distance d and the time difference t of each target vehicle by combining the shortest travel distance among all gate devices to obtain the travel speed v=d/t. Since the fluctuation of the traffic flow in one day causes the travel time to change with time, a statistical time window is set, the travel time data in the same time window is considered to have stability, the time window of 0-6 points is set to 60 minutes, the time window of 6-24 points is set to 30 minutes in the present embodiment, and the travel speed lower limit vl=max { Vp5,5} is set, finally, for the case that the records are the same position before and after, if t >1200s, the gate is the stop point. If v < VL, the current position is a stop point, and the effective path of each target vehicle can be determined by combining the POI points which are the information points selected in advance.
According to the method, a driving track database of the target minibus is established by means of the video bayonet data, then the vehicle track data of the target minibus are matched with the mobile phone signaling track data of the communication service operator, so that the illegal suspected vehicle is determined, support can be provided for floor check of traffic police departments, meanwhile, the result obtained by combining and analyzing the vehicle bayonet track and the mobile phone signaling has uniqueness, and the data are more accurate.
The second embodiment of the present application provides an identification device for an overtaking vehicle, as shown in fig. 5, including the following modules:
the generating module 10 is configured to generate an over-gate time sequence of each target vehicle according to gate data, and determine an effective path of each target vehicle based on the over-gate time sequence and a set information point;
the matching module 20 is configured to establish a matching relationship between a road network road and a coverage area of the base station according to a spatial position of the road network and the base station, and determine a plurality of target users according to the signaling metadata and the matching relationship;
the calculating module 30 is configured to generate travel paths of each target user according to the signaling metadata of the plurality of target users, and calculate similarity between each travel path and the effective path;
a judging module 40, configured to determine whether each of the target vehicles is overtime based on the similarity calculation result and a preset vehicle overtime condition.
In some embodiments, the generating module 10 specifically includes:
a mapping unit 11, configured to map the bayonet devices onto a road network, and calculate a shortest driving distance between each bayonet device through a shortest path algorithm based on a road network topology;
an extracting unit 12, configured to extract, from each over-gate time sequence, an adjacent over-gate timestamp and a corresponding gate position of each target vehicle, and determine a first travel distance of each target vehicle according to the shortest travel distance and the gate position;
a calculating unit 13 for calculating a first time difference according to the time stamp, and calculating a running speed of each target vehicle according to the first running distance and the corresponding first time difference;
a determining unit 14 for determining a stop point of each target vehicle according to the stop position and the traveling speed, and determining an effective path of each target vehicle based on the stop point and the set information point.
As shown, the present application further provides an electronic device, including a memory 601 and a processor 602, where the memory 601 is configured to store one or more computer instructions, and the one or more computer instructions are executed by the processor 602 to implement a method for identifying a vehicle overtime.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the electronic device described above may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
The present application also provides a computer-readable storage medium storing a computer program which, when executed by a computer, implements a method for identifying a vehicle overtaking as described above.
By way of example, a computer program may be divided into one or more modules/units stored in the memory 601 and executed by the processor 602 and completed by the input interface 605 and the output interface 606 for data I/O interface transmission to complete the invention, and one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program in a computer device.
The computer device may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The computer device may include, but is not limited to, a memory 601, a processor 602, it will be appreciated by those skilled in the art that the present embodiment is merely an example of a computer device and is not limiting of a computer device, may include more or fewer components, or may combine certain components, or different components, e.g., a computer device may also include an input 607, a network access device, a bus, etc.
The processor 602 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors 602, digital signal processors 602 (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor 602 may be a microprocessor 602 or the processor 602 may be any conventional processor 602 or the like.
The memory 601 may be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. The memory 601 may also be an external storage device of the computer device, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, and further, the memory 601 may also include an internal storage unit of the computer device and an external storage device, the memory 601 may also be used to store computer programs and other programs and data required by the computer device, the memory 601 may also be used to temporarily store the programs and data in the output 608, and the aforementioned storage Media include a usb disk, a removable hard disk, a read-only memory ROM603, a random access memory RAM604, a disk or an optical disk and other various Media capable of storing program codes.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the present invention is not limited thereto, but any changes or substitutions within the technical scope of the present invention should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. The method for identifying the overtaking of the vehicle is characterized by comprising the following steps of
Generating a time sequence of passing the bayonet of each target vehicle according to the bayonet data, and determining an effective path of each target vehicle based on the time sequence of passing the bayonet and a set information point;
establishing a matching relation between a road network road and a base station coverage area according to the spatial positions of the road network and the base station, and determining a plurality of target users according to the signaling metadata and the matching relation;
generating travel paths of all target users according to the signaling metadata of the plurality of target users, and respectively calculating the similarity between each travel path and the effective path;
and determining whether each target vehicle is overtime or not based on the similarity calculation result and a preset vehicle overtime condition.
2. The method for identifying a vehicle overtaking situation according to claim 1, wherein the generating an overtaking slot time sequence of each target vehicle according to the slot data includes:
acquiring bayonet data, and cleaning abnormal data and repeated record data in the bayonet data;
and screening target vehicle data from the cleaned data, and sequencing the time of each target vehicle passing through the gate according to the target vehicle data.
3. The method according to claim 1, wherein the determining the effective path of each of the target vehicles based on the over-gate time series and the set information point includes:
mapping the bayonet equipment to a road network, and calculating the shortest driving distance between the bayonet equipment through a shortest path algorithm based on the road network topology;
respectively extracting adjacent passing time stamps and corresponding blocking positions of all target vehicles from all passing time sequences, and determining a first driving distance of each target vehicle according to the shortest driving distance and the blocking positions;
calculating a first time difference according to the time stamp, and calculating the running speed of each target vehicle according to the first running distance and the corresponding first time difference;
and determining the stay point of each target vehicle according to the bayonet position and the running speed, and determining the effective path of each target vehicle based on the stay point and the set information point.
4. A method of identifying a vehicle overrun according to claim 3, wherein said determining a dwell point for each target vehicle based on said bayonet location and speed of travel comprises:
when the positions of the bayonets are the same, judging whether each first time difference is larger than a first threshold value, and if so, judging that the bayonets are stay points;
and when the bayonet positions are not the same, judging whether each running speed is smaller than a second threshold value, and if so, determining that the current position is a stop point.
5. The method for identifying a vehicle overage according to claim 1, further comprising, before determining a plurality of target users from the signaling metadata of the plurality of initial users and the matching relationship:
and acquiring original signaling data of a plurality of initial users, and preprocessing the original signaling data, wherein the preprocessing comprises filtering and noise reduction to obtain signaling element data.
6. The method according to claim 1, wherein the determining whether each of the target vehicles is overtime based on the similarity calculation result and a preset vehicle overtime condition includes:
and respectively judging whether each similarity calculation result is larger than a third threshold value, if so, taking the corresponding target user as the passenger of the corresponding target vehicle to obtain the passenger number of each target vehicle.
7. A vehicle overguard recognition device, characterized by comprising:
the generation module is used for generating an over-gate time sequence of each target vehicle according to gate data and determining an effective path of each target vehicle based on the over-gate time sequence and a set information point;
the matching module is used for establishing a matching relation between a road network road and a base station coverage area according to the spatial positions of the road network and the base station, and determining a plurality of target users according to the signaling metadata and the matching relation;
the calculation module is used for generating travel paths of all the target users according to the signaling metadata of the plurality of target users and calculating the similarity between each travel path and the effective path respectively;
and the judging module is used for determining whether each target vehicle is overtime or not based on the similarity calculation result and a preset vehicle overtime condition.
8. The vehicle overguard recognition device of claim 7, wherein the generation module comprises:
the mapping unit is used for mapping the bayonet devices to the road network and calculating the shortest driving distance between the bayonet devices through a shortest path algorithm based on the road network topology;
the extraction unit is used for respectively extracting adjacent passing time stamps and corresponding blocking positions of all target vehicles from all passing time sequences, and determining a first driving distance of each target vehicle according to the shortest driving distance and the blocking positions;
a calculating unit, configured to calculate a first time difference according to the time stamp, and calculate a driving speed of each target vehicle according to the first driving distance and the corresponding first time difference;
and the determining unit is used for determining the stay point of each target vehicle according to the bayonet position and the running speed and determining the effective path of each target vehicle based on the stay point and the set information point.
9. An electronic device comprising a memory and a processor, the memory for storing one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement a method of identifying a vehicle overuse as claimed in any one of claims 1 to 6.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer, implements a method for identifying a vehicle overage according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311853521.7A CN117765740A (en) | 2023-12-29 | 2023-12-29 | Method and device for identifying overtaking of vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311853521.7A CN117765740A (en) | 2023-12-29 | 2023-12-29 | Method and device for identifying overtaking of vehicle |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117765740A true CN117765740A (en) | 2024-03-26 |
Family
ID=90323727
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311853521.7A Pending CN117765740A (en) | 2023-12-29 | 2023-12-29 | Method and device for identifying overtaking of vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117765740A (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106448160A (en) * | 2016-09-22 | 2017-02-22 | 江苏理工学院 | Target person tracking method combining vehicle running track and monitoring video data |
| CN108717790A (en) * | 2018-07-06 | 2018-10-30 | 广州市交通运输研究所 | A kind of vehicle driving analysis method based on bayonet license plate identification data |
| CN111757270A (en) * | 2020-06-19 | 2020-10-09 | 中国联合网络通信集团有限公司 | Method and device for identifying overmaning vehicle |
| CN111768619A (en) * | 2020-06-16 | 2020-10-13 | 苏州大学 | Express way vehicle OD point determining method based on checkpoint data |
| CN112309118A (en) * | 2020-11-03 | 2021-02-02 | 广州市交通规划研究院 | Vehicle trajectory calculation method based on space-time similarity |
| CN113870574A (en) * | 2021-09-17 | 2021-12-31 | 南京熊猫电子股份有限公司 | System and method for automatically detecting passenger car illegal passenger carrying early warning |
| CN114390459A (en) * | 2021-12-27 | 2022-04-22 | 安徽百诚慧通科技有限公司 | Method for identifying illegal and excessive person carrying of agricultural vehicle and storage medium |
| US20220219730A1 (en) * | 2022-03-31 | 2022-07-14 | Baidu Usa Llc | Method of determining vehicle travel trajectory, electronic device, storage medium, and vehicle |
| WO2022165614A1 (en) * | 2021-02-08 | 2022-08-11 | 浙江吉利控股集团有限公司 | Path construction method and apparatus, terminal, and storage medium |
| CN115862331A (en) * | 2022-11-30 | 2023-03-28 | 中山大学 | Vehicle travel track reconstruction method considering bayonet network topological structure |
| CN115877343A (en) * | 2023-02-02 | 2023-03-31 | 中电信数字城市科技有限公司 | Man-vehicle matching method and device based on radar target tracking and electronic equipment |
| WO2023051322A1 (en) * | 2021-09-29 | 2023-04-06 | 华为技术有限公司 | Travel management method, and related apparatus and system |
| CN116432933A (en) * | 2022-12-13 | 2023-07-14 | 东南大学 | Individual vehicle trip origin-destination discrimination method based on bayonet data |
-
2023
- 2023-12-29 CN CN202311853521.7A patent/CN117765740A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106448160A (en) * | 2016-09-22 | 2017-02-22 | 江苏理工学院 | Target person tracking method combining vehicle running track and monitoring video data |
| CN108717790A (en) * | 2018-07-06 | 2018-10-30 | 广州市交通运输研究所 | A kind of vehicle driving analysis method based on bayonet license plate identification data |
| CN111768619A (en) * | 2020-06-16 | 2020-10-13 | 苏州大学 | Express way vehicle OD point determining method based on checkpoint data |
| CN111757270A (en) * | 2020-06-19 | 2020-10-09 | 中国联合网络通信集团有限公司 | Method and device for identifying overmaning vehicle |
| CN112309118A (en) * | 2020-11-03 | 2021-02-02 | 广州市交通规划研究院 | Vehicle trajectory calculation method based on space-time similarity |
| WO2022165614A1 (en) * | 2021-02-08 | 2022-08-11 | 浙江吉利控股集团有限公司 | Path construction method and apparatus, terminal, and storage medium |
| CN113870574A (en) * | 2021-09-17 | 2021-12-31 | 南京熊猫电子股份有限公司 | System and method for automatically detecting passenger car illegal passenger carrying early warning |
| WO2023051322A1 (en) * | 2021-09-29 | 2023-04-06 | 华为技术有限公司 | Travel management method, and related apparatus and system |
| CN114390459A (en) * | 2021-12-27 | 2022-04-22 | 安徽百诚慧通科技有限公司 | Method for identifying illegal and excessive person carrying of agricultural vehicle and storage medium |
| US20220219730A1 (en) * | 2022-03-31 | 2022-07-14 | Baidu Usa Llc | Method of determining vehicle travel trajectory, electronic device, storage medium, and vehicle |
| CN115862331A (en) * | 2022-11-30 | 2023-03-28 | 中山大学 | Vehicle travel track reconstruction method considering bayonet network topological structure |
| CN116432933A (en) * | 2022-12-13 | 2023-07-14 | 东南大学 | Individual vehicle trip origin-destination discrimination method based on bayonet data |
| CN115877343A (en) * | 2023-02-02 | 2023-03-31 | 中电信数字城市科技有限公司 | Man-vehicle matching method and device based on radar target tracking and electronic equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106971552B (en) | Fake plate phenomenon detection method and system | |
| CN112447041B (en) | Method and device for identifying operation behavior of vehicle and computing equipment | |
| CN107645709B (en) | Method and device for determining personnel information | |
| CN109741482B (en) | Information sharing method and device | |
| CN105448103A (en) | Vehicle fake license plate detection method and system | |
| CN106022296A (en) | Fake plate vehicle detection method based on vehicle hot spot area probability aggregation | |
| CN105575118A (en) | Screening method of personnel without driving qualification | |
| CN110838232A (en) | Single vehicle OD (origin-destination) acquisition method based on vehicle-passing electric alarm data | |
| CN107329977B (en) | A kind of false-trademark vehicle postsearch screening method based on probability distribution | |
| CN109658693B (en) | Road condition information acquisition and processing method and device | |
| CN105448092A (en) | Analysis method and apparatus of associated vehicles | |
| CN114363842B (en) | Bus passenger departure station prediction method and device based on mobile phone signaling data | |
| CN103514745A (en) | Fake license plate vehicle recognition method based on intelligent transportation | |
| CN106297304A (en) | A kind of based on MapReduce towards the fake-licensed car recognition methods of extensive bayonet socket data | |
| CN112734219A (en) | Vehicle transportation driving behavior analysis method and system | |
| CN110852236A (en) | Target event determination method and device, storage medium and electronic device | |
| CN112562330A (en) | Method and device for evaluating road operation index, electronic equipment and storage medium | |
| US20180189588A1 (en) | Device for reading vehicle license plate number and method therefor | |
| CN113487877A (en) | Road vehicle illegal parking monitoring method | |
| CN114841712B (en) | Method and device for determining illegal operation state of network appointment vehicle tour and electronic equipment | |
| CN105070061A (en) | Evidence-obtaining inspection method and system for vehicle peccancy | |
| CN115273468B (en) | Traffic jam control strategy generation method and device | |
| CN117765740A (en) | Method and device for identifying overtaking of vehicle | |
| CN112182302B (en) | Subway taking route tracing method and device based on transfer management | |
| CN114463703A (en) | Shared bicycle parking management system and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |