CN113284337B - OD matrix calculation method and device based on vehicle track multidimensional data - Google Patents
OD matrix calculation method and device based on vehicle track multidimensional data Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
Embodiments of the present disclosure provide a method, apparatus, device, and computer-readable storage medium for OD matrix calculation based on multidimensional data of vehicle trajectories. Analyzing the multidimensional data of the vehicle track to obtain the track data of each vehicle; carrying out smoothing processing on the track data of each vehicle; matching the smoothed track data to a road network; identifying a stop point in the track data matched to the road network by a clustering method to obtain stop point data; and calculating an OD matrix based on the stop point data and displaying. In this way, the problems of inaccurate clustering information, lack of time information of clustering results, large error of OD analysis results and the like caused by clustering only depending on longitude and latitude information are solved.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of traffic planning and management, and more particularly, to an OD matrix calculation method, apparatus, device, and computer-readable storage medium based on multidimensional data of vehicle trajectories.
Background
In recent years, with the steady rise of traffic demand and the gradual increase of automobile holding capacity, the existing road network shows more defects and shortages, and higher requirements are put on the level of traffic management.
According to the requirements of the dynamic supervision and management method for road transport vehicles, heavy trucks and semi-tractors with the total mass of 12 tons or more entering the transport market are all installed and used with a Beidou satellite positioning device and are connected to a public platform for road cargo transport vehicles, and the network platform dynamically monitors the transport place, track and state by the aid of a network platform temporary road cargo transport management method issued by Ministry of transportation and State administration of tax and administration. The massive vehicle track data generated by the method can be stored in a big data platform which matures day by day, and OD matrixes in different areas are counted on a macroscopic level by combining a scientific extensible algorithm, so that people can know the current situation and trend of traffic more intuitively and accurately, traffic resources are dispatched and supported on a data level, and route planning is provided with a more scientific basis.
According to the needs of industrial planning and traffic management, a larger area can be divided into a plurality of sub-areas according to factors such as administrative areas, geographic conditions, social environments and the like, the number of trips occurring between any two sub-areas is counted to form a numerical matrix, namely a traffic OD matrix between the areas, the OD matrix reflects the space connection strength between different areas of a region and can also reflect the function division of the different areas, and valuable references are provided for traffic resource scheduling and city planning work of a governing department.
Traditional trip survey often relies on field survey or questionnaire survey, needs to consume a large amount of manpower, material resources and time, directly leads to the hysteresis quality of survey results, is difficult to avoid the ambiguity in expression in the survey process, cannot ensure the accuracy of memory, and can only carry out sampling survey, the accuracy of survey results is limited by a sampling method and a sampling range, so that the final survey results have low credibility and relatively limited representativeness.
Disclosure of Invention
According to an embodiment of the present disclosure, an OD matrix calculation scheme based on multidimensional data of a vehicle track is provided.
In a first aspect of the disclosure, a method of OD matrix calculation based on multidimensional data of vehicle trajectories is provided. The method comprises the following steps:
analyzing the multidimensional data of the vehicle track to obtain track data of each vehicle;
carrying out smoothing processing on the track data of each vehicle;
matching the smoothed track data to a road network;
identifying the staying points in the track data matched to the road network through a clustering method to obtain the staying point data;
and calculating an OD matrix based on the stop point data and displaying.
Further, the analyzing the vehicle trajectory multidimensional data to obtain trajectory data of each vehicle includes:
the vehicle track multi-dimensional data comprises license plate numbers, longitude and latitude, speed, direction and sampling time;
analyzing the multidimensional data of the vehicle track to obtain the license plate number, longitude and latitude, speed, direction and sampling time information of the vehicle;
and classifying the analyzed vehicle track multidimensional data based on the license plate number information of the vehicle to obtain track data of each vehicle.
Further, the matching the smoothed trajectory data to the road network includes:
respectively defining an observation probability matrix and a state transition matrix based on an ST _ Matching algorithm;
calculating a probability matrix of a designated route through the observation probability matrix and the state transition matrix;
and matching the smoothed track data to a road network through the probability matrix of the designated route.
Further, the identifying, by a clustering method, a stop point in the trajectory data matched to the road network to obtain stop point data includes:
and identifying the stop points in the track data matched to the road network by adopting a DBSCAN clustering algorithm based on track multi-dimensional data to obtain the stop point data.
Further, the identifying the stop point in the track data matched to the road network by using the DBSCAN clustering algorithm based on the track multidimensional data to obtain the stop point data includes:
setting a sample set and a field parameter based on the track data matched to the road network; the sample set includes longitude, latitude, direction and time of the vehicle when the vehicle is at the ith location; i is a positive integer greater than or equal to 1;
unifying units of longitude, latitude, direction and time information in the sample set, and updating the sample set;
processing the updated sample set based on the field parameters, and selecting a stop point meeting a preset condition to be added into an initialized core object set;
and sequencing the stop points in the core object set according to the time sequence to obtain stop point data.
Further, the processing the updated sample set based on the domain parameters, and the selecting a stop point meeting a preset condition and adding the stop point to the initialized core object set includes:
and processing the updated sample set through a four-dimensional Euclidean distance measurement mode based on the field parameters, and adding a stop point which accords with the four-dimensional Euclidean distance measurement mode into an initialized core object set.
Further, the step of calculating and displaying an OD matrix based on the stopping point data includes:
respectively converting the stopping point data of each vehicle into a sequence of passing areas of the vehicles;
generating an OD (origin-destination) of each vehicle according to the sequence of the passing areas of each vehicle;
and accumulating the OD of all the vehicles to obtain an OD matrix and displaying the OD matrix.
In a second aspect of the present disclosure, an OD matrix calculation device based on multidimensional data of a vehicle track is provided. The device comprises:
the analysis module is used for analyzing the vehicle track multidimensional data to obtain track data of each vehicle;
the processing module is used for carrying out smoothing processing on the track data of each vehicle;
the matching module is used for matching the smoothed track data to a road network;
the identification module is used for identifying the stop points in the track data matched to the road network by a clustering method to obtain stop point data;
and the calculating module is used for calculating and displaying an OD matrix based on the stop point data.
In a third aspect of the disclosure, an electronic device is provided. The electronic device includes: a memory having a computer program stored thereon and a processor implementing the method as described above when executing the program.
In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, performs the method according to the first aspect of the present disclosure.
According to the OD matrix calculation method based on the vehicle track multidimensional data, the track data of each vehicle are obtained by analyzing the vehicle track multidimensional data; carrying out smoothing processing on the track data of each vehicle; matching the smoothed track data to a road network; identifying a stop point in the track data matched to the road network by a clustering method to obtain stop point data; and calculating and displaying an OD matrix based on the stop point data, and solving the problems of inaccurate clustering information, short time information of clustering results, large OD analysis result error and the like caused by clustering only by depending on longitude and latitude information, so that the OD matrix can be automatically generated at low cost and high frequency, and the spatial connection strength among different regions in a certain time period is reflected.
It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters denote like or similar elements, and wherein:
FIG. 1 shows a flow chart of a method of OD matrix computation based on multidimensional data of vehicle trajectories, according to an embodiment of the disclosure;
FIG. 2 illustrates a block diagram of a computation flow of Kalman trajectory filtering in accordance with an embodiment of the present disclosure;
FIG. 3 shows a schematic diagram of the ST-Matching algorithm according to an embodiment of the disclosure;
FIG. 4 shows a schematic diagram of OD matrix calculations according to an embodiment of the disclosure;
FIG. 5 shows a block diagram of an OD matrix calculation device based on multidimensional data of vehicle trajectories, according to an embodiment of the disclosure;
FIG. 6 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions in the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without inventive step, are intended to be within the scope of the present disclosure.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
FIG. 1 shows a flowchart of a method 100 for OD matrix computation based on multidimensional data of vehicle trajectories, according to an embodiment of the disclosure. The method 100 comprises:
and S110, analyzing the vehicle track multidimensional data to obtain track data of each vehicle.
In some embodiments, the vehicle trajectory multi-dimensional data includes license plate number, longitude and latitude, speed and direction information.
In some embodiments, the vehicle trajectory multidimensional data may be obtained from a large data platform (e.g., a road freight vehicle common platform, etc.).
In some embodiments, before/after/while the vehicle track multidimensional data is analyzed, the vehicle track multidimensional data may be cleaned to remove data outside a research range, that is, data within the research range such as license plate number, longitude and latitude, speed and direction are retained.
In some embodiments, the vehicle track multidimensional data is analyzed, information such as license plate numbers, longitude and latitude, speed, direction and sampling time in the vehicle track multidimensional data is extracted, and the analyzed vehicle track multidimensional data is classified based on the license plate number information of the vehicle to obtain track data of each vehicle.
Expressing the trajectory data of each vehicle as a time series traj = (p) in which the position information is chronologically formed 1 ,p 2 ,…,p n )；
Wherein p is i ＝(lng i ,lat i ,speed i ,angle i ,time i )；
The long i Represents the longitude of the vehicle at the ith position;
the lat i Representing the latitude of the vehicle at the ith location;
the speed i Representing the speed of the vehicle at the ith position;
the angle i Indicating the direction of the vehicle when in the ith position;
the time i Indicating the time when the vehicle was at the ith position; and i is a positive integer greater than or equal to 1.
The time for sampling the trajectory data is generally set to half a minute, and if the time difference between two adjacent trajectory points is large, the trajectory is broken here to generate a plurality of temporally continuous trajectories.
And S120, smoothing the track data of each vehicle.
In some embodiments, the trajectory data of each vehicle may be smoothed by filtering. For example, kalman filtering, complementary filtering (poor interference rejection), etc.
As shown in fig. 2, in the present disclosure, kalman filtering (strong interference rejection) is used to smooth the trajectory data of each vehicle.
In particular, the amount of the solvent to be used,
the message model of the vehicle is set as follows:
x t ＝Ax t-1 +w
wherein, the x t Is the state vector at time t;
said x t-1 Is a state vector at the time of t-1;
a is a state transition matrix;
w is a driving noise vector;
further, a covariance matrix is defined that forces the noise:
Q t ＝E{WW T }
the observation model of the vehicle is set as follows:
z t ＝Hx t +V
wherein, z is t Is an observation vector at the t moment;
h is an observation matrix;
the V is a measurement noise vector;
further, a covariance matrix of the measurement noise is defined:
r t ＝E{VV T }
setting an estimation model of the vehicle as follows:
wherein, K is t Is a Kalman gain matrix;
the above-mentioned
For predictive estimation, the observation z representing the time at which t is obtained is indicated t Previously made with respect to x t (ii) is estimated;
in summary, the kalman filter recursion equation can be found as follows:
wherein, the
To estimate an error covariance matrix.
In some embodiments, the trajectory data for each vehicle is smoothed by a kalman filtering recursion equation as described above.
And S130, matching the track data after the smoothing processing to a road network.
In some embodiments, vehicle trajectory data may be matched to the road network by the ST _ Matching algorithm.
The ST _ Matching algorithm combines time characteristics and space characteristics, defines an observation probability matrix and a state transition matrix respectively, and the probability matrix of the specified route is obtained by multiplying the observation probability matrix and the state transition matrix.
In particular, the amount of the solvent to be used,
the probability matrix is:
F＝F s *F t
wherein F is the probability of a given route;
said F s Calculating the observation probability matrix;
said F t Is the result of the state transition matrix calculation.
Further, the formula of the observation probability matrix is as follows:
wherein σ is the standard deviation, typically set to 50 meters;
and d is the distance between the observation point and the candidate point, and generally, the larger the distance is, the smaller the observation probability is.
Further, the formula of the state transition matrix is as follows:
wherein d is t→t+1 The Euclidean distance between adjacent observation points is taken as the reference point;
said w t→t+1 The length of the shortest path between candidate points respectively corresponding to adjacent observation points;
as shown in fig. 3, for each track point in the vehicle track data, a set of candidate road segments and candidate points are retrieved, and then a candidate graph is constructed according to the candidate road segments and the candidate points, where a node in the graph is a candidate point set of each track point, and an edge is a shortest path set between any two adjacent candidate points, and a path with a highest score is found in the candidate graph by combining the observation probability and the transition probability. And matching the path with the highest score to a road network.
And S140, identifying the stop points in the track data matched to the road network by a clustering method to obtain stop point data.
In some embodiments, a DBSCAN clustering algorithm based on track multidimensional data may be adopted to identify the stop points in the track data matched to the road network, so as to obtain stop point data. Namely, a DBSCAN clustering algorithm based on track multi-dimensional data is adopted to identify the parking points in the track data matched to the road network, and longitude and latitude, time and direction information are adopted in distance measurement, so that the vehicle parking points in different time periods are judged.
Specifically, a sample set and neighborhood parameters are first set (input).
Wherein the sample set is (p) 1 ,p 2 ,…,p n )；p i ＝(lng i ,lat i ,θ i ,time i ) (ii) a (refer to trajectory data of each vehicle in step S110);
the domain parameter is (epsilon, minPts);
wherein epsilon is a neighborhood distance threshold of a certain sample;
the MinPts is a threshold value of the number of samples in a neighborhood with a distance epsilon of a certain sample.
Then, a distance measurement mode is defined as an Euclidean distance based on longitude and latitude information, direction information and time information. Since the units of the latitude and longitude, time, direction, and other information are not consistent and are not convenient to compare, in the present disclosure, the time and direction information may be converted (unified unit) first, and the conversion result may be updated to the sample set.
In particular, the amount of the solvent to be used,
the time conversion formula is:
t new ＝t old /10000
wherein, t is old The number of seconds corresponding to the acquisition time of the track points;
said t is new Is the result after time conversion.
The direction conversion formula is:
θ new ＝θ old /180
wherein, the theta old The direction (in degrees) corresponding to a track point;
theta is a value of new As a result of the conversion.
Further, the core object set is initialized to be an empty set, and the number of clustering clusters is 0. Distance measurement based on four-dimensional Euclidean distances, i.e.
Find sample p j Epsilon-neighborhood subsample set N ε (x j ) If the number of sub-sample set samples satisfies | N ε (x j )|>MinPts, then the sample x j And adding the core object set. J =1,2, \8230;, n.
And further, sequencing the track points in the core object set according to the time sequence to obtain the stop point data.
It should be noted that, in this step, spatial information and attribute information of the trajectory data are fully mined, and problems of inaccurate clustering information, lack of time information in clustering results, large error in OD analysis results, and the like caused by clustering only depending on latitude and longitude information in the prior art are solved.
And S150, calculating an OD matrix based on the dwell point data and displaying.
OD matrix: the OD matrix is an acronym for a source-destination matrix, a point is actually a traffic divided area, and the data in the matrix is the traffic flow from area a to area B, i.e. the data is used to indicate the degree of congestion on a route from one place to another.
In some embodiments, the parking points and the sub-regions of each vehicle are subjected to spatial analysis, the region where the parking point is located is judged, the sequence of the parking points of each vehicle is converted into the sequence of the sub-regions which each vehicle passes through successively, the OD of each vehicle is generated according to the sequence of the region which each vehicle passes through, the ODs of all vehicles are accumulated to obtain an OD matrix, and the OD matrix is displayed, referring to fig. 4.
According to the embodiment of the disclosure, the following technical effects are achieved:
the OD matrix between different sub-areas in one area can be rapidly and accurately calculated, the hidden value in the track data is fully mined, the data on different sides are mutually verified, the analysis result is more reliable, the spatial contact strength in different sub-areas is visually reflected, the main activity range and the travel rule of each vehicle are identified, and scientific basis is provided for reasonable configuration of public resources and optimal adjustment of spatial structures.
Meanwhile, the embodiment of the disclosure can fully mine the spatial information and the attribute information of the data (not only the longitude and latitude information, but also the speed, the direction and other information are used), and solves the problems of inaccurate clustering information, lack of time information of a clustering result, large error of an OD analysis result and the like caused by clustering only by the longitude and latitude information in the prior art.
It is noted that while for simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art will appreciate that the embodiments described in the specification are exemplary embodiments and that acts and modules are not necessarily required for the disclosure.
The above is a description of embodiments of the method, and the embodiments of the apparatus are further described below.
Fig. 5 shows a block diagram of an OD matrix calculation device 500 based on multidimensional data of vehicle trajectories, according to an embodiment of the disclosure. As shown in fig. 5, the apparatus 500 includes:
the analysis module 510 is configured to analyze the vehicle trajectory multidimensional data to obtain trajectory data of each vehicle;
a processing module 520, configured to perform smoothing processing on the trajectory data of each vehicle;
a matching module 530, configured to match the smoothed trajectory data to a road network;
the identification module 540 is configured to identify a stop point in the track data matched to the road network by using a clustering method, so as to obtain stop point data;
and a calculating module 550, configured to calculate an OD matrix based on the stop point data and display the OD matrix.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and details are not described herein again.
FIG. 6 illustrates a schematic block diagram of an electronic device 600 that may be used to implement embodiments of the present disclosure. As shown, device 600 includes a Central Processing Unit (CPU) 601 that can perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM) 602 or loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the device 600 can also be stored. The CPU601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processing unit 601 performs the various methods and processes described above, such as the method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into RAM 603 and executed by CPU601, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, CPU601 may be configured to perform method 100 in any other suitable manner (e.g., by way of firmware).
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), and the like.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions/acts specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (7)
1. An OD matrix calculation method based on vehicle track multidimensional data is characterized by comprising the following steps:
analyzing the multidimensional data of the vehicle track to obtain the track data of each vehicle, wherein the track data comprises the following steps:
the vehicle track multi-dimensional data comprises license plate numbers, longitude and latitude, speed, direction and sampling time;
analyzing the vehicle track multidimensional data to obtain license plate number, longitude and latitude, speed, direction and sampling time information of the vehicle;
classifying the analyzed vehicle track multidimensional data based on the license plate number information of the vehicle to obtain track data of each vehicle;
carrying out smoothing processing on the track data of each vehicle;
matching the smoothed track data to a road network;
identifying a stop point in the track data matched to the road network by a DBSCAN clustering method based on track multidimensional data to obtain stop point data;
calculating and displaying an OD matrix based on the stop point data;
the identifying the stop points in the track data matched to the road network by the DBSCAN clustering method based on the track multidimensional data to obtain the stop point data comprises the following steps: setting a sample set and a field parameter based on the track data matched to the road network; the sample set includes longitude, latitude, direction and time of the vehicle when the vehicle is at the ith location; i is a positive integer greater than or equal to 1;
unifying units of longitude, latitude, direction and time information in the sample set, and updating the sample set;
processing the updated sample set based on the field parameters, and selecting a stop point meeting a preset condition to be added into an initialized core object set;
and sequencing the stop points in the core object set according to the time sequence to obtain stop point data.
2. The method of claim 1, wherein the matching the smoothed trajectory data onto a road network comprises:
respectively defining an observation probability matrix and a state transition matrix based on an ST _ Matching algorithm;
calculating a probability matrix of the designated route according to the observation probability matrix and the state transition matrix;
and matching the smoothed track data to a road network through the probability matrix of the designated route.
3. The method of claim 1, wherein the processing the updated sample set based on the domain parameters, and wherein the selecting a stop point meeting a preset condition to be added to the initialized core object set comprises:
and processing the updated sample set through a four-dimensional Euclidean distance measurement mode based on the field parameters, and adding a stop point which accords with the four-dimensional Euclidean distance measurement mode into an initialized core object set.
4. The method of claim 3, wherein calculating and displaying an OD matrix based on the dwell point data comprises:
respectively converting the stopping point data of each vehicle into a sequence of passing areas of the vehicles;
generating an OD (origin-destination) of each vehicle according to the sequence of the passing areas of each vehicle;
and accumulating the OD of all the vehicles to obtain an OD matrix and displaying the OD matrix.
5. An OD matrix calculation apparatus based on multidimensional data of a vehicle track, comprising:
the analysis module is used for analyzing the vehicle track multidimensional data to obtain the track data of each vehicle, and comprises:
the vehicle track multi-dimensional data comprises license plate numbers, longitude and latitude, speed, direction and sampling time;
analyzing the multidimensional data of the vehicle track to obtain the license plate number, longitude and latitude, speed, direction and sampling time information of the vehicle;
classifying the analyzed vehicle track multidimensional data based on the license plate number information of the vehicle to obtain track data of each vehicle;
the processing module is used for carrying out smoothing processing on the track data of each vehicle;
the matching module is used for matching the track data after the smoothing processing to a road network;
the identification module is used for identifying the staying points in the track data matched to the road network through a DBSCAN clustering method based on the track multidimensional data to obtain the staying point data, and comprises the following steps:
identifying a stopping point in the track data matched to the road network by adopting a clustering algorithm to obtain stopping point data; setting a sample set and a field parameter based on the track data matched to the road network; the sample set includes longitude, latitude, direction and time of the vehicle when the vehicle is at the ith position; i is a positive integer greater than or equal to 1;
unifying units of longitude, latitude, direction and time information in the sample set, and updating the sample set;
processing the updated sample set based on the field parameters, and selecting a stop point meeting a preset condition to be added into an initialized core object set;
sorting the stop points in the core object set according to a time sequence to obtain stop point data;
and the calculating module is used for calculating and displaying an OD matrix based on the stop point data.
6. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, wherein the processor, when executing the program, implements the method of any of claims 1-4.
7. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method of any one of claims 1 to 4.
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