CN113393666A - Traffic simulation method, device, equipment and medium for commercial vehicle - Google Patents
Traffic simulation method, device, equipment and medium for commercial vehicle Download PDFInfo
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Abstract
The invention discloses a traffic simulation method, a device, equipment and a medium for commercial vehicles, wherein the method comprises the following steps: acquiring GPS data; performing road network matching according to the GPS data to determine a target road network, or adding a new road section in the original road network according to the GPS data; according to the GPS data and the target road network, carrying out data analysis on the traffic track of the commercial vehicle, and determining the OD path of the commercial vehicle; calculating the number of operating vehicles in each time period on different OD paths; calculating the occupation ratio of the operating vehicles on different road sections in each time period; calculating the speed of the commercial vehicle on each road section; and determining a simulation data file according to the number of the commercial vehicles, the percentage of the commercial vehicles and the vehicle speed. The invention improves the efficiency of traffic simulation of the commercial vehicles, can improve the accuracy of simulation results, and can be widely applied to the technical field of intelligent traffic.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a traffic simulation method, a device, equipment and a medium for a commercial vehicle.
Background
The traffic simulation is carried out, and firstly, the real vehicle running environment is simulated as much as possible, and the establishment of a simulation road network, the setting of simulation parameters and the like are related. The most basic traffic simulation building method at present is to build a simulation road network in simulation software, set simulation parameters, build a simulation environment, and finally perform simulation.
At present, some related automatic simulation road network establishing methods exist, but most of the methods rely on an electronic road network map, and a simulation road network model which can be identified by simulation software is converted on the basis of the existing electronic road network map, but the methods are only simulation conversion of the road network model, and parameter information related to vehicles is basically obtained by manually counting on a certain road section, then manually setting in the simulation software and finally simulating.
The traffic simulation defects of the existing commercial vehicles are also prominent, namely, an electronic map is firstly used, and then a simulation road network model is manually or automatically established; and then, carrying out statistics of vehicles, measurement of vehicle speed, estimation of vehicle OD and the like on different road sections, carrying out statistical analysis on the collected data, giving parameters such as vehicle speed, vehicle OD and vehicle type proportion which are relatively fit for reality, and carrying out simulation operation. The method is time-consuming and labor-consuming, and is easy to make mistakes, which brings problems to the subsequent setting of the simulation environment and further influences the simulation result.
Disclosure of Invention
In view of this, embodiments of the present invention provide an efficient traffic simulation method, apparatus, device and medium for operating vehicles, so as to improve the accuracy of a traffic simulation result.
One aspect of the present invention provides a traffic simulation method for a commercial vehicle, including:
acquiring GPS data;
performing road network matching according to the GPS data to determine a target road network, or adding a new road section in the original road network according to the GPS data;
according to the GPS data and the target road network, carrying out data analysis on the traffic track of the commercial vehicle, and determining the OD path of the commercial vehicle;
calculating the number of operating vehicles in each time period on different OD paths;
calculating the number of operating vehicles in each time period on different road sections;
calculating the occupation ratio of the operating vehicles on different road sections in each time period;
calculating the speed of the commercial vehicle on each road section;
and determining a simulation data file according to the number of the commercial vehicles, the percentage of the commercial vehicles and the vehicle speed.
Optionally, the method further comprises a step of GPS data preprocessing, which comprises:
according to a recording format in the GPS data storage process, carrying out first cleaning treatment on the GPS data;
according to the data type in the GPS data storage process, carrying out second cleaning treatment on the GPS data;
carrying out third cleaning treatment on the GPS data according to a data format in the GPS data storage process;
performing fourth cleaning processing on the GPS data according to the data range in the GPS data storage process;
performing fifth cleaning treatment on the GPS data according to the time range in the GPS data storage process;
carrying out sixth cleaning treatment on the GPS data according to the space range in the GPS data storage process;
and obtaining the GPS data meeting the corresponding requirements according to the results of the first cleaning treatment, the second cleaning treatment, the third cleaning treatment, the fourth cleaning treatment, the fifth cleaning treatment and the sixth cleaning treatment.
Optionally, the determining a target road network by performing road network matching according to the GPS data includes:
calculating a minimum distance set from each GPS point in the GPS data to a nearby road section;
sequencing all the distance values in the minimum distance set according to the sequence from small to large to obtain a target distance set;
and traversing the target distance set, determining a target road section according to a preset azimuth matching condition, and determining a target road network according to the target road section.
Optionally, the adding of the road segments in the original road network according to the GPS data includes at least one of:
determining a newly added road section according to the GPS data;
when the two ends of the newly added road section are both intersection nodes of the original road network, updating the newly added road section to the topological connection of the original road network by adopting a first updating mode;
when one end of the newly added road section is an intersection node of the original road network and the other end of the newly added road section is not the intersection node of the original road network, updating the newly added road section to the topological connection of the original road network by adopting a second updating mode;
and when the two ends of the newly added road section are not the intersection nodes of the original road network, updating the newly added road section to the topological connection of the original road network by adopting a third updating mode.
Optionally, the determining the OD path of the commercial vehicle by performing data analysis on the traffic track of the commercial vehicle according to the GPS data and the target road network includes:
classifying the GPS data according to the vehicle identification of the commercial vehicles, sequencing the classified data according to the time sequence to obtain time sequence data, and determining the passing GPS data of each commercial vehicle in a preset time range according to the time sequence data;
respectively determining the OD point of each commercial vehicle according to the preset OD point judgment condition for the time sequence data of each commercial vehicle;
splitting a running route of each commercial vehicle into a plurality of OD routes according to the OD point of each commercial vehicle;
and calculating the number of the operating vehicles passing by each OD path in different time periods.
Optionally, the calculating the percentage of operating vehicles on different road sections in each time period includes:
determining the types of various operating vehicles on different driving paths;
and respectively calculating the occupation ratio of the service vehicles of different types on each running path.
Another aspect of an embodiment of the present invention provides a traffic simulation apparatus for a commercial vehicle, including:
the first module is used for acquiring GPS data;
the second module is used for carrying out road network matching according to the GPS data to determine a target road network, or adding a new road section in the original road network according to the GPS data;
the third module is used for carrying out data analysis on the traffic track of the commercial vehicle according to the GPS data and the target road network and determining the OD path of the commercial vehicle;
the fourth module is used for calculating the number of the operating vehicles in each time period on different OD paths;
the fifth module is used for calculating the number of the operating vehicles in each time period on different road sections;
the sixth module is used for calculating the occupation ratio of the operating vehicles on different road sections in each time period;
the seventh module is used for calculating the speed of the commercial vehicle on each road section;
and the eighth module is used for determining a simulation data file according to the number of the commercial vehicles, the percentage of the commercial vehicles and the vehicle speed.
Optionally, the third module includes:
the first unit is used for calculating a minimum distance set from each GPS point in the GPS data to a nearby road section;
the second unit is used for sequencing all the distance values in the minimum distance set according to the sequence from small to large to obtain a target distance set;
and the third unit is used for traversing the target distance set, determining a target road section according to a preset azimuth matching condition and determining a target road network according to the target road section.
Another aspect of the embodiments of the present invention provides an electronic device, including a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method described above.
Another aspect of the embodiments of the present invention provides a computer-readable storage medium storing a program, the program being executed by a processor to implement the method described above.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and the computer instructions executed by the processor cause the computer device to perform the foregoing method.
Embodiments of the invention acquire GPS data; performing road network matching according to the GPS data to determine a target road network, or adding a new road section in the original road network according to the GPS data; according to the GPS data and the target road network, carrying out data analysis on the traffic track of the commercial vehicle, and determining the OD path of the commercial vehicle; calculating the number of operating vehicles in each time period on different OD paths; calculating the occupation ratio of the operating vehicles on different road sections in each time period; calculating the speed of the commercial vehicle on each road section; and determining a simulation data file according to the number of the commercial vehicles, the percentage of the commercial vehicles and the vehicle speed. The invention improves the efficiency of traffic simulation of commercial vehicles and can improve the accuracy of simulation results.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of the overall implementation steps provided in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
First, explanation is made for specific words that may appear in the embodiments of the present invention:
operating the vehicle: approved by a competent authority to participate in the operation of the vehicle.
GPS track: the vehicle track formed on the time sequence by the position point of the GPS (Global Positioning System) and the vehicle running related information is continuously returned in the running process of the vehicle.
OD: o (origin) represents the starting location of the vehicle, D (destination) represents the destination of the vehicle, and OD is the path taken by the vehicle from the starting location to the destination.
Road speed: the speed of different vehicles is different on a certain road section, but the speed of the same type of vehicle is basically consistent, so the speed of the road section is generally the weighted average of the average speeds of different types of vehicles.
Vehicle type: different types can be divided according to the different divisions of vehicle and army, and in this patent, the commercial vehicle mainly divide into: coach cars, taxies, heavy goods vehicles, passenger regular buses, school buses, bulk material transport vehicles, hazardous article transport vehicles and travel package cars.
Vehicle proportion: the proportion of the number of vehicles of different vehicle types passing on a certain road segment within a certain time period.
In view of the problems in the prior art, an aspect of the present invention provides a traffic simulation method for a commercial vehicle, as shown in fig. 1, including:
acquiring GPS data;
performing road network matching according to the GPS data to determine a target road network, or adding a new road section in the original road network according to the GPS data, thereby realizing the updating of the original road network;
according to the GPS data and the target road network, carrying out data analysis on the traffic track of the commercial vehicle, and determining the OD path of the commercial vehicle;
calculating the number of operating vehicles in each time period on different OD paths;
calculating the number of operating vehicles in each time period on different road sections;
calculating the occupation ratio of the operating vehicles on different road sections in each time period;
calculating the speed of the commercial vehicle on each road section;
and determining a simulation data file according to the number of the commercial vehicles, the percentage of the commercial vehicles and the vehicle speed.
Optionally, the method further comprises a step of GPS data preprocessing, which comprises:
according to a recording format in the GPS data storage process, carrying out first cleaning treatment on the GPS data;
according to the data type in the GPS data storage process, carrying out second cleaning treatment on the GPS data;
carrying out third cleaning treatment on the GPS data according to a data format in the GPS data storage process;
performing fourth cleaning processing on the GPS data according to the data range in the GPS data storage process;
performing fifth cleaning treatment on the GPS data according to the time range in the GPS data storage process;
carrying out sixth cleaning treatment on the GPS data according to the space range in the GPS data storage process;
and obtaining the GPS data meeting the corresponding requirements according to the results of the first cleaning treatment, the second cleaning treatment, the third cleaning treatment, the fourth cleaning treatment, the fifth cleaning treatment and the sixth cleaning treatment.
Optionally, the determining a target road network by performing road network matching according to the GPS data includes:
calculating a minimum distance set from each GPS point in the GPS data to a nearby road section;
sequencing all the distance values in the minimum distance set according to the sequence from small to large to obtain a target distance set;
and traversing the target distance set, determining a target road section according to a preset azimuth matching condition, and determining a target road network according to the target road section.
Optionally, the adding of the road segments in the original road network according to the GPS data includes at least one of:
determining a newly added road section according to the GPS data;
when the two ends of the newly added road section are both intersection nodes of the original road network, updating the newly added road section to the topological connection of the original road network by adopting a first updating mode;
when one end of the newly added road section is an intersection node of the original road network and the other end of the newly added road section is not the intersection node of the original road network, updating the newly added road section to the topological connection of the original road network by adopting a second updating mode;
and when the two ends of the newly added road section are not the intersection nodes of the original road network, updating the newly added road section to the topological connection of the original road network by adopting a third updating mode.
Optionally, the determining the OD path of the commercial vehicle by performing data analysis on the traffic track of the commercial vehicle according to the GPS data and the target road network includes:
classifying the GPS data according to the vehicle identification of the commercial vehicles, sequencing the classified data according to the time sequence to obtain time sequence data, and determining the passing GPS data of each commercial vehicle in a preset time range according to the time sequence data;
respectively determining the OD point of each commercial vehicle according to the preset OD point judgment condition for the time sequence data of each commercial vehicle;
splitting a running route of each commercial vehicle into a plurality of OD routes according to the OD point of each commercial vehicle;
and calculating the number of the operating vehicles passing by each OD path in different time periods.
Optionally, the calculating the percentage of operating vehicles on different road sections in each time period includes:
determining the types of various operating vehicles on different driving paths;
and respectively calculating the occupation ratio of the service vehicles of different types on each running path.
Another aspect of an embodiment of the present invention provides a traffic simulation apparatus for a commercial vehicle, including:
the first module is used for acquiring GPS data;
the second module is used for carrying out road network matching according to the GPS data to determine a target road network, or adding a new road section in the original road network according to the GPS data;
the third module is used for carrying out data analysis on the traffic track of the commercial vehicle according to the GPS data and the target road network and determining the OD path of the commercial vehicle;
the fourth module is used for calculating the number of the operating vehicles in each time period on different OD paths;
the fifth module is used for calculating the number of the operating vehicles in each time period on different road sections;
the sixth module is used for calculating the occupation ratio of the operating vehicles on different road sections in each time period;
the seventh module is used for calculating the speed of the commercial vehicle on each road section;
and the eighth module is used for determining a simulation data file according to the number of the commercial vehicles, the percentage of the commercial vehicles and the vehicle speed.
Optionally, the third module includes:
the first unit is used for calculating a minimum distance set from each GPS point in the GPS data to a nearby road section;
the second unit is used for sequencing all the distance values in the minimum distance set according to the sequence from small to large to obtain a target distance set;
and the third unit is used for traversing the target distance set, determining a target road section according to a preset azimuth matching condition and determining a target road network according to the target road section.
Another aspect of the embodiments of the present invention provides an electronic device, including a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method described above.
Another aspect of the embodiments of the present invention provides a computer-readable storage medium storing a program, the program being executed by a processor to implement the method described above.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and the computer instructions executed by the processor cause the computer device to perform the foregoing method.
The following describes in detail a specific implementation process of the method for simulating traffic of commercial vehicles, and the implementation method specifically includes the following eight parts:
(1) raw GPS data
The GPS data is cleaned according to different bases, which mainly comprises the following steps:
a) recording format
b) Data type
c) Data format
d) Data range
e) Time range of data
f) Spatial extent of data
The original GPS data is cleaned for 7 times according to the above 7 types, dirty data which do not meet the above requirements are cleaned, no problem in data format and quality is ensured in the subsequent analysis and processing process of the data, and the abnormity of the analysis result caused by abnormal data is reduced as much as possible.
(2) GPS data network matching
a) Calculating GPS point GPSiTo nearby road section { Ri1，Ri2，……RinMinimum distance of { L } Li1，Li2，……Lin}。
b) For { Li1，Li2，……LinSorting from small to large { L }imin1，Limin2，……Liminn}。
c) Traverse { Limin1，Limin2，……LiminnAnd simultaneously calculating the road section R with the nearest distanceimin1Of the direction of travel of the vehicleimin1AZ and GPSiReturned azimuth angle AiAbsolute value of difference | Rimin1AZ–AiIf | is less than AZ (where AZ is a threshold of the same angle, which may be set).
d) If less than AZ, the GPS can be usediPoint matching to road segment Rimin1The above.
e) If greater than AZ, then the traversal continues until less than AZ is satisfied.
f) If traverse the minimum distance set, | Rimin1AZ–AiIf | is always greater than AZ, it can be determined that the matching fails.
(3) Possible road network generation
a) If a region does not have a road network, but there is a regular distribution of GPS spatial data within the region, then the GPS data will likely indicate that it is a new road, but the existing road network has not yet been updated in time.
b) Assume that the newly added road segment is Rnewi；
c) If R isnewiThe two ends are exactly the intersection nodes of the original road network, and R is directly addednewiRoad sections are updated simultaneously, and road network topological connection is only needed;
d) if R isnewiOnly one end is not the intersection node of the original road network (assuming that the starting point is not the intersection node of the original road network), and the road section R is addednewiIn the existing road network, the topological connection relation, R, of the existing road network is updated simultaneouslynewiThe road number connected with the starting end is RnewisIs changed into Rnewis-1And Rnewis-2Two stages, updating road network topological connection;
e) if R isnewiThe two ends are not the intersection nodes of the original road network, and the R of the road section is increasednewiIn the existing road network, the topological connection relation, R, of the existing road network is updated simultaneouslynewiThe road serial number connected with the two ends is the original RnewisOr RnewieIs changed into Rnewis-1、Rnewis-2And Rnewie-1、Rnewie-2Fourthly, the topological connection of the road network is displayed;
(4) trajectory data analysis
a) And classifying the cleaned data according to the vehicle ID for the GPS data, and sequencing the classified data according to a time sequence, wherein the sequenced data is the GPS record data of the passing position of a certain vehicle within a certain time range.
b) Analyzing the time sequence data of a certain vehicle, and if the time difference T is recorded before and afterod(the threshold value of the OD interval can be set artificially) for more than hours, and the period of time is between 6:00 and 22:00, the data position before the period of time is confirmed to be D point, and the data after the period of time is confirmed to be O point.
c) According to the method of b), a commercial vehicle V can be transportediIs divided into a number of OD paths. ViOD1{R11T11，R12T12，……R1nT1n}；ViOD2{R21T21，R22T22，……R2nT2n}……ViODn{Rn1Tn1，Rn2Tn2，……RnnTnn}。
d) According to the method of c), the OD paths of all the commercial vehicles can be found.
e) For a certain ODiCan count up MikFor a certain period of time TikInternally walk through this ODiA path.
(5) OD and vehicle number extraction
a) For all commercial vehicles V, and for each commercial vehicle ViPassed ODi { V }iOD1，ViOD2，……ViODniAnd (5) carrying out statistical sorting (from large to small) on the number of vehicles by taking the OD as an index.
b) On the path of ODi, the number of vehicles in different time periods (granularity of 5 minutes) is Mi1，Mi2，……Mi288。
(6) Calculating the vehicle type ratio
a) In the k-th time period, in the road section RjThe jkm commercial vehicles pass through the upper common commercial vehicle, and the jkm commercial vehicles can be refined according to the types of the commercial vehicles.
b) Assume that Type1 Type commercial vehicle is in the kth time period, at RjThe number of vehicles running on the road section is jkmt1, and the Type2 Type commercial vehicle runs in the k time periodjThe number of vehicles traveling on the road section is jkmt2, and so on, and the type of Typen commercial vehicle is in the k-th time period, in RjThe number of vehicles running on the road section is jkmtn.
c) Satisfies the following conditions: jkmt1+ jkmt2+ … … + jkmtn jkm
d) Thus, a Type1 commercial vehicle is operating in the kth time period at RjThe proportion on the road section is as follows: jkmt 1/jkm; type2 commercial vehicle at time k, at time RjThe proportion on the road section is as follows: jkmt 2/jkm; by analogy with Typen-typeThe commercial vehicle is in the k time period, in RjThe proportion on the road section is as follows: jkmtn/jkm.
(7) Calculating the speed of a vehicle over a road segment
a)ODi{Ri1，Ri2，……RinIs a block consisting of road segments { R }i1，Ri2，……RinFormed sequentially and connected in sequence.
b) In the k-th time period, in the road section RjThe upper operation vehicle jkm vehicles pass through, and each vehicle has an own OD path.
c) Suppose ViIs the k-th time period, in the road section RjThe operating vehicle running on the vehicle is any one of jkm vehicles, and the vehicle ViHaving its own OD path ViODjk，ViODjk{Rik1，Rik2，…Rj-1，Rj，Rj+1…Rikn}。
d) During the k-th time period, ViThe road section that the vehicle passes through is: { Rm，…，Rj-1，Rj，Rj+1…，Rn}. I.e. at time (k-1) × 5, ViThe vehicle is in RmOn a road section; at time k x 5ViThe vehicle is in RnOn a road segment.
e) During the k-th time period, ViThe GPS sequence returned by the vehicle is: GPSVik{GPSik1，GPSik2，……GPSikn}。
f)GPSVikSequence of RjDivided into p segments.
g) According to two adjacent GPSjAnd GPSiDistance L between pointsijAnd the time difference T between GPS point locationsijCalculating the velocity V between the two pointsij，Vij＝Lij/Tij。
h) Then ViVehicle at kth time period, at RjThe average speed over the road segment is: vjk＝Rj/(Tj1+Tj2+……+Tjp)。
i) From h) the individual speeds of all the commercial vehicles can be determinedFurther, it is possible to find the degree that each category of commercial vehicles (assuming that Vi belongs to type 1) is in R at the k-th time zonejAverage speed over a road section: vtype＝(Vj1k+Vj2k+……+Vjjkmt1)/jkmt1。
(8) Generating a simulation data file
a) Road network simulation data generation
i. Road section label: < link >, information of the link section is written into the tag;
a lane: < lane > </lane >, writing information of the lane into the tag;
lane connectors:
<link>
<fromLinkEndPt></fromLinkEndPt>
<toLinkEndPt></toLinkEndPt>
</link>
writing lane connector information into the tag;
lane markings:
<pavementMarking></pavementMarking>
writing information of lane dividing lines into the label;
b) vehicle route (OD)
<vehicleRoutingDecisionStatic></vehicleRoutingDecisionStatic>
Writing vehicle OD path information into the tag;
c) the vehicle comprises the following components:
<vehicleComposition></vehicleComposition>
writing a vehicle composition to the tag;
d) speed:
<desSpeedDecision></desSpeedDecision>
the speed information of the commercial vehicle is written into the tag.
It can be understood that the line traffic simulation of the commercial vehicles can truly reflect the running conditions of the commercial vehicles, so that the daily running of the commercial vehicles is conveniently restored, and an effective method is provided for monitoring and managing the commercial vehicles. Performing traffic simulation on commercial vehicle lines, wherein OD lines of the commercial vehicles need to be collected; travel speeds on different road segments; a vehicle type; and the proportion of different types of vehicles, etc.
Compared with the prior art, the method has the following remarkable advantages:
1. according to the method and the device, possible newly added road network updating is carried out according to the GPS track matching analysis result, the situation of road network data can be guaranteed, and the simulation is more practical due to timeliness;
2. the data source of the method is obtained by analyzing and calculating the big data of the GPS, and the data is large in quantity and wide in source;
3. the method can determine the specific OD track of a single vehicle; classifying the commercial vehicles and determining the proportion of each type; the running speeds of different types of vehicles can be calculated; the speed of the road section can be calculated according to the speeds of different types of vehicles; the method and the device have the advantages that the operation behavior of the commercial vehicle is more carefully depicted and restored, so that the simulation is more fit with the actual operation environment;
4. the method and the device mainly aim at obtaining parameters such as vehicle road network establishment, vehicle OD, vehicle road speed, vehicle type proportion and the like, and can automatically establish simulation parameters such as simulation road network, vehicle OD, road speed, vehicle type proportion and the like according to GPS information returned by a commercial vehicle and gate information on a road, so that the traffic simulation environment of the commercial vehicle can be established quickly and efficiently, and the traffic simulation of the commercial vehicle can be carried out according to any space and time.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A traffic simulation method for a commercial vehicle, comprising:
acquiring GPS data;
performing road network matching according to the GPS data to determine a target road network, or adding a new road section in the original road network according to the GPS data;
according to the GPS data and the target road network, carrying out data analysis on the traffic track of the commercial vehicle, and determining the OD path of the commercial vehicle;
calculating the number of operating vehicles in each time period on different OD paths;
calculating the number of operating vehicles in each time period on different road sections;
calculating the occupation ratio of the operating vehicles on different road sections in each time period;
calculating the speed of the commercial vehicle on each road section;
and determining a simulation data file according to the number of the commercial vehicles, the percentage of the commercial vehicles and the vehicle speed.
2. The traffic simulation method of a commercial vehicle of claim 1, further comprising a step of GPS data preprocessing, the step comprising:
according to a recording format in the GPS data storage process, carrying out first cleaning treatment on the GPS data;
according to the data type in the GPS data storage process, carrying out second cleaning treatment on the GPS data;
carrying out third cleaning treatment on the GPS data according to a data format in the GPS data storage process;
performing fourth cleaning processing on the GPS data according to the data range in the GPS data storage process;
performing fifth cleaning treatment on the GPS data according to the time range in the GPS data storage process;
carrying out sixth cleaning treatment on the GPS data according to the space range in the GPS data storage process;
and obtaining the GPS data meeting the corresponding requirements according to the results of the first cleaning treatment, the second cleaning treatment, the third cleaning treatment, the fourth cleaning treatment, the fifth cleaning treatment and the sixth cleaning treatment.
3. The traffic simulation method of an operating vehicle according to claim 1, wherein the determining a target road network by road network matching according to the GPS data comprises:
calculating a minimum distance set from each GPS point in the GPS data to a nearby road section;
sequencing all the distance values in the minimum distance set according to the sequence from small to large to obtain a target distance set;
and traversing the target distance set, determining a target road section according to a preset azimuth matching condition, and determining a target road network according to the target road section.
4. The traffic simulation method of an operating vehicle of claim 1, wherein the adding of new road segments in the original road network according to the GPS data comprises at least one of:
determining a newly added road section according to the GPS data;
when the two ends of the newly added road section are both intersection nodes of the original road network, updating the newly added road section to the topological connection of the original road network by adopting a first updating mode;
when one end of the newly added road section is an intersection node of the original road network and the other end of the newly added road section is not the intersection node of the original road network, updating the newly added road section to the topological connection of the original road network by adopting a second updating mode;
and when the two ends of the newly added road section are not the intersection nodes of the original road network, updating the newly added road section to the topological connection of the original road network by adopting a third updating mode.
5. The traffic simulation method of claim 1, wherein the determining the OD path of the commercial vehicle by performing data analysis on a traffic track of the commercial vehicle according to the GPS data and a target road network comprises:
classifying the GPS data according to the vehicle identification of the commercial vehicles, sequencing the classified data according to the time sequence to obtain time sequence data, and determining the passing GPS data of each commercial vehicle in a preset time range according to the time sequence data;
respectively determining the OD point of each commercial vehicle according to the preset OD point judgment condition for the time sequence data of each commercial vehicle;
splitting a running route of each commercial vehicle into a plurality of OD routes according to the OD point of each commercial vehicle;
and calculating the number of the operating vehicles passing by each OD path in different time periods.
6. The traffic simulation method of a commercial vehicle according to claim 1, wherein the calculating the percentage of commercial vehicles on different road segments in each time slot comprises:
determining the types of various operating vehicles on different driving paths;
and respectively calculating the occupation ratio of the service vehicles of different types on each running path.
7. A traffic simulation apparatus for a commercial vehicle, comprising:
the first module is used for acquiring GPS data;
the second module is used for carrying out road network matching according to the GPS data to determine a target road network, or adding a new road section in the original road network according to the GPS data;
the third module is used for carrying out data analysis on the traffic track of the commercial vehicle according to the GPS data and the target road network and determining the OD path of the commercial vehicle;
the fourth module is used for calculating the number of the operating vehicles in each time period on different OD paths;
the fifth module is used for calculating the number of the operating vehicles in each time period on different road sections;
the sixth module is used for calculating the occupation ratio of the operating vehicles on different road sections in each time period;
the seventh module is used for calculating the speed of the commercial vehicle on each road section;
and the eighth module is used for determining a simulation data file according to the number of the commercial vehicles, the percentage of the commercial vehicles and the vehicle speed.
8. The traffic simulation method of a commercial vehicle according to claim 7, wherein the third module comprises:
the first unit is used for calculating a minimum distance set from each GPS point in the GPS data to a nearby road section;
the second unit is used for sequencing all the distance values in the minimum distance set according to the sequence from small to large to obtain a target distance set;
and the third unit is used for traversing the target distance set, determining a target road section according to a preset azimuth matching condition and determining a target road network according to the target road section.
9. An electronic device comprising a processor and a memory;
the memory is used for storing programs;
the processor executing the program realizes the method of claims 1-6.
10. A computer-readable storage medium, characterized in that the storage medium stores a program, which is executed by a processor to implement the method according to claims 1-6.
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Application publication date: 20210914 |