CN114495490B - Traffic condition prediction method, device terminal, and storage medium - Google Patents
Traffic condition prediction method, device terminal, and storage medium Download PDFInfo
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Abstract
The embodiment of the application provides a traffic condition prediction method, a device terminal and a storage medium, wherein in the traffic condition prediction method, attribute analysis is performed on collected vehicle track data to extract target vehicle track data located in the same target area in the same target time period, and then a traffic predicted value in a future target time period is obtained according to the target vehicle track data in at least two previous target time periods of the current time point.
Description
[ technical field ] A method for producing a semiconductor device
The embodiment of the application relates to the technical field of image formation, in particular to a traffic condition prediction method, a device terminal and a storage medium.
[ background of the invention ]
With the vigorous development of the internet of things technology in China, as a specific application of the internet of things, the internet of vehicles becomes a new way for solving the hot problem of the traffic industry at present. With the increasing popularity of 5G, its characteristics of high speed, wide connectivity and low latency are highly compatible with the numerous scenarios of the internet of vehicles. Meanwhile, important data such as tracks, parameters, behaviors and performances generated on the basis of vehicles also show explosive growth, wherein the storage quantity of the data such as big data analysis logs and vehicle tracks reaches the TB level.
How to carry out real-time and efficient data preprocessing on TB-level data and then construct a prediction model to carry out timely prediction and alarm on road traffic conditions is always only a thought point for operators to endow social road traffic with energy by a technical means from the perspective of own data, but the TB-level data cannot be processed by only adopting a traditional single-machine big data analysis mode aiming at the requirements of huge data volume and instantaneity.
[ summary of the invention ]
The embodiment of the application provides a traffic condition prediction method, a device terminal and a storage medium, so as to realize the prediction.
In a first aspect, an embodiment of the present application provides a traffic condition prediction method applied to an electronic device terminal, where the method includes: obtaining vehicle track data, wherein the vehicle track data comprises time information and position information; extracting target vehicle track data which belong to the same target area in the same target time period from the vehicle track data according to the time information and the position information; and obtaining a traffic prediction value of the target area in a target time period in the future of the current time point according to the target vehicle track data in at least two target time periods before the current time point.
According to the traffic condition prediction method, the collected vehicle track data are subjected to attribute analysis, target vehicle track data located in the same target time period and located in the same target area are extracted, a traffic prediction value in a future target time period is obtained according to the target vehicle track data in at least two target time periods before the current time point, and compared with the prior art, the traffic prediction value in the future target time period of the target area can be obtained in time through the vehicle track data obtained through real-time analysis, and therefore the real-time performance of traffic condition prediction is improved.
In one embodiment, the extracting, according to the time information and the position information, target vehicle trajectory data that belong to a same target area in a same target time period from the vehicle trajectory data includes: extracting first vehicle track data located in the same target area from the vehicle track data according to the position information; and extracting the target vehicle track data in the same target time period from the first vehicle track data according to the time information.
In one embodiment, a road database is preset, where the road database includes a first road type and a second road type, and the extracting, according to the location information, first vehicle trajectory data located in a same target area in the vehicle trajectory data includes: judging the road type corresponding to the vehicle track data according to the road database; and when the road type corresponding to the vehicle track data is the first type of road, extracting the first vehicle track data which is not more than a first preset range from the central point of the target area in the vehicle track data.
In one embodiment, the extracting, according to the position information, first vehicle trajectory data located in the same target area in the vehicle trajectory data further includes: and when the road type corresponding to the vehicle track data is the second road, extracting the first vehicle track data which is not more than a second preset range from the central point of the target area in the vehicle track data.
In one embodiment, the method further comprises: and outputting the traffic condition identification of the target area in a future target time period of the current time point according to the traffic prediction value.
In one embodiment, the target vehicle trajectory data includes information that includes, in addition to the target area, one or a combination of: average vehicle speed, number of vehicles.
In a second aspect, an embodiment of the present application provides an electronic device terminal, where the terminal includes: the vehicle tracking system comprises a track data obtaining module, a tracking module and a tracking module, wherein the track data obtaining module is used for obtaining vehicle track data, and the vehicle track data comprises time information and position information; the target data extraction module is used for extracting target vehicle track data which belong to the same target area in the same target time period from the vehicle track data according to the time information and the position information; and the pre-estimation calculation module is used for obtaining a traffic pre-estimation value of the target area in a target time period in the future of the current time point according to the target vehicle track data in at least two target time periods before the current time point.
In one embodiment, the target data extraction module includes: the position information aggregation submodule is used for extracting first vehicle track data which are positioned in the same target area in the vehicle track data according to the position information; and the time information aggregation submodule is used for extracting the target vehicle track data in the same target time period from the first vehicle track data according to the time information.
In one embodiment, the electronic device terminal is preset with a road database, the road database includes a first road and a second road, and the location information aggregation sub-module includes: the road type judging subunit is used for judging the road type corresponding to the vehicle track data according to the road database; and the data extraction first subunit is configured to extract the first vehicle trajectory data, which is not more than a first preset range from the center point of the target area in the vehicle trajectory data, when the road type corresponding to the vehicle trajectory data is the first road type.
In one embodiment, the location information aggregation sub-module further includes: and the data extraction second subunit is used for extracting the first vehicle track data which is not more than a second preset range from the center point of the target area in the vehicle track data when the road type corresponding to the vehicle track data is the second road type.
In one embodiment, the terminal further includes: and the traffic sign output module is used for outputting a traffic condition sign of the target area in a future target time period of the current time point according to the traffic estimated value.
In a third aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer instructions are stored, and the instructions, when executed by a processor, implement the steps of the image processing method described above.
It should be understood that the second to third aspects of the embodiment of the present application are consistent with the technical solution of the first aspect of the embodiment of the present application, and beneficial effects obtained by each aspect and a corresponding possible implementation are similar, and are not described again.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present specification, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a traffic condition prediction method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a traffic condition prediction method according to another embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device terminal according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device terminal according to another embodiment of the present application.
[ detailed description ] A
For better understanding of the technical solutions in the present specification, the following detailed description of the embodiments of the present application is provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only a few embodiments of the present specification, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort belong to the protection scope of the present specification.
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the specification. As used in the examples of this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The traffic condition prediction method provided by the embodiment of the application can be executed by an electronic device terminal, and the electronic device terminal can be terminal devices such as a smart phone, a tablet computer, a PC (personal computer), a notebook computer and the like. In an alternative embodiment, the electronic device may have a service program installed thereon for executing the traffic situation prediction method.
Fig. 1 is a schematic flow chart of a traffic condition prediction method according to an embodiment of the present application, and as shown in the figure, the traffic condition prediction method includes:
step S101, vehicle track data is obtained, wherein the vehicle track data comprises time information and position information.
Optionally, the vehicle trajectory data includes specific vehicle information, time information, position information, and vehicle speed information, and the vehicle trajectory data obtained by the electronic device terminal is, for example, "14555141448 20201019, 1603092919, 107.31082, 34.388695, 0 component count and 298", and the vehicle trajectory data can be parsed according to a pre-stored data format, such as "14555141448" as the specific vehicle information, "20201019" as the date, "1603092919" as the collection time point, "107.31082" as the longitude, "34.388695" as the latitude, "0" as the speed, and "298" as the direction.
Step S102, extracting target vehicle track data which belong to the same target area in the same target time period from the vehicle track data according to the time information and the position information.
Optionally, the step S102 may specifically include:
and step S1021, extracting first vehicle track data positioned in the same target area in the vehicle track data according to the position information.
In one embodiment, the electronic device terminal is provided with a road database in advance, the road database comprises a first road and a second road, and the road type in which the vehicle trajectory data is specifically distributed can be obtained according to the position information in the vehicle trajectory data.
Alternatively, the road database may be road network code table data, in which names, types, area names, and area center positions of the roads are stored, where the road network code table data includes, for example: road id: s1, area id:01, area center point: 107.310822, u 34.388695.
Because the traffic requirements corresponding to different road types are different, when the traffic condition of a certain target area is measured, an appropriate area size needs to be selected for analysis and statistics according to the traffic attribute of the area, for example, the average speed of an urban road is low, and the traffic flow of each intersection is large, so that when whether certain vehicle track data is located in the area range of the urban road is measured, the area range is not suitable to be too large.
Optionally, an example formula for calculating the central point of a certain road area by using longitude and latitude information in the vehicle track data may be as follows:
D＝R*arccos(siny 1 siny 2 +cosy 1 cosy 2 cos(x 1 -x 2 ))
wherein R is the radius of the earth, the mean value is 6370km, and the track longitude and latitude of the vehicle are x respectively 1 And y 1 The longitude and latitude of the road area are x respectively 2 And y 2 。
In one embodiment, if the road type corresponding to the vehicle trajectory data is the first type of road, the first vehicle trajectory data within a first preset range from the center point of the target area in the vehicle trajectory data is extracted.
In one embodiment, if the road type corresponding to the vehicle trajectory data is the second type of road, the first vehicle trajectory data within a second preset range from the central point of the target area in the vehicle trajectory data is extracted.
Optionally, the first type of road and the second type of road may be an urban road type and an expressway type in the road network code table data, respectively, where a preset range corresponding to the urban road type may be 0-3 meters, and a preset range corresponding to the expressway type may be 0-5 meters.
It should be noted that the first road and the second road are only used for describing that the corresponding target area ranges under different road types are different in numerical value, and do not represent that only two types of roads are present in the road database.
Step S1022, extracting the target vehicle trajectory data in the same target time period from the first vehicle trajectory data according to the time information.
Optionally, the selection of the specific value of the target time period may be set by a person skilled in the art according to actual requirements, and the application is not particularly limited.
For example, if the target area is the traffic condition of the S1 highway 01 area in the future for 1 minute, the electronic device terminal may aggregate the vehicle trajectory data in the same minute, and for example, the electronic device terminal may process 15:00 to 15:01, the trajectory data of 1 minute is a target time period, then the total number and the total speed of the vehicles in 1 minute of the area are counted, and similarly, if the traffic condition of 5 minutes in the future in the area of the S1 road 01 is to be predicted, the following steps are processed 15:00 to 15:05 the 5 minute trajectory data is a target time period, and then the total number of vehicles and the total speed in 5 minutes in the area are counted.
For example, after the vehicle trajectory data in one target time period is processed, the electronic device terminal may continue to slide to the next target time period to process the data in the next target time period, and to ensure that the data processing is not repeated, the step size of the sliding should be consistent with the target time period, for example, after the data processing is finished 15:00 to 15:01, 1 minute, then the data would slide to 15:01 to 15:02 the target time period calculates the total number and speed of the 1 minute vehicles in the area, and similarly, 15:00 to 15:05 these 5 minutes trajectory data, then slide to 15:06 to 15: the target time period is 10 to calculate the total number and total speed of the 5 minute vehicles in the area.
Optionally, when vehicle trajectory data in a target time period is processed, the data in the target time period may be stored in a cache database, such as a redis database, to facilitate quick loading of subsequent data, where the stored data format may be "time: predicted regional vehicle total number — predicted regional vehicle total speed ", for example," 1501:20 v u 200", wherein 1501 is 15 points 01 points, 20 is the total number of vehicles in the area, and 200 is the sum of the speeds of the vehicles in the area.
Step S103, obtaining a traffic prediction value of the target area in a future target time period of the current time point according to the target vehicle track data in at least two target time periods before the current time point.
Alternatively, since the vehicle trajectory data is acquired in real time, the estimated value to be predicted for a future time period may be output based on data in the target time periods before the current time point.
Alternatively, the output of the predicted value may be performed by training a machine learning model.
Optionally, the model training process may include:
in step S1031, vehicle trajectory data in a certain target area within 120 minutes before a certain time point is provided, and the average vehicle speed and/or the number of vehicles in the vehicle trajectory data are extracted as target vehicle trajectory data, and it should be noted that, in order to reduce data fluctuation, if only the total speed in each target time period is stored in the vehicle trajectory data, the average speed in each target time period needs to be calculated in advance according to the total speed.
Step S1032, for the 120-minute data, extracting target vehicle trajectory data of the first minute and the second minute as a first feature vector, and extracting target vehicle trajectory data of the third minute as a first tag vector corresponding to the first feature vector; and extracting target vehicle track data of the second minute and the third minute as a second feature vector, extracting target vehicle track data of the fourth minute as a second label vector corresponding to the second feature vector, and so on, extracting all feature vectors and all corresponding label vectors in the 120 minutes.
For example, if a traffic prediction value of a target area is predicted for five minutes in the future at the current time point (15 00), the traffic prediction values of the time periods 15. At this time, the model used when performing prediction may be a model trained using target vehicle trajectory data for a period of 13 to 15. For example, the following can be used as the feature vector, the target vehicle trajectory data of the time period 13. In this case, the trained model may be trained using the data of the past 120 minutes, so that the traffic prediction value of the 15.
Step S1033, using the feature vectors as input values of the machine learning model, and using the label vectors corresponding to the feature vectors as output values of the machine learning model, and mapping and inputting the output values into the machine learning model one by one for training.
Optionally, a quasi-newton algorithm (L-BFGS) may be used for model training of the processed data in the specific training process, which is convenient to further improve the model operation efficiency in a limited memory.
Step S1034, after the model is trained, extracting the data which belongs to the target area in the vehicle track data, inputting the characteristic vector into the trained model after the data is extracted by the characteristic vector and the label vector in the step S1032, calculating an error value between an output value and the label vector, and storing the model with the error value smaller than a preset threshold value into a cache database, so that the road condition can be conveniently predicted in real time by rapidly loading the model subsequently.
According to the traffic condition prediction method, the collected vehicle track data are analyzed, the target vehicle track data located in the same target area in the same target time period are extracted, the prediction model is obtained through training according to the target vehicle track data in at least two target time periods before a certain time point, and the traffic predicted value of the current time point is obtained according to the prediction model.
Fig. 2 is a schematic flow chart of a traffic condition prediction method according to an embodiment of the present application, as shown in the figure, the method may further include:
and step S104, outputting a traffic condition identifier of the target area in a future target time period of the current time point according to the traffic prediction value.
Optionally, after obtaining a traffic prediction value in a future one of the target time periods at the current time point according to the machine learning model, hierarchical identification may be performed according to the tag vector output in step S1034, for example, the tag vector is divided into 6 levels, that is, the average vehicle speed/10 is an integer, the level 0 is the most congested state, and the level 5 is the most unobstructed state, for example: when the average vehicle speed is 8km/h,8/10=0, the rank is 0, the average vehicle speed is 25km/h,25/10=2, the rank is 2, and when the average vehicle speed is 50km/h or more than 50km/h, the rank is 5.
Alternatively, for direct display of the identification information in the road network database, the identification information may be color-identified, for example, with deep red at level 0, light red at level 1, deep yellow at level 2, light yellow at level 3, light green at level 4, and deep green at level 5.
It should be noted that the hierarchical identification of the tag vector in step S104 may be performed together when the tag vector is extracted in step 1032, and input into the model together with the tag vector for training, or may be performed separately after a specific numerical value is output.
According to the traffic condition prediction method, the acquired vehicle track data is subjected to attribute analysis, target vehicle track data located in the same target area in the same target time period is extracted, a traffic prediction value in a future target time period is obtained according to the target vehicle track data in at least two target time periods before the current time point, and a grading mark corresponding to the traffic prediction value is displayed in road network data, so that monitoring personnel can monitor the traffic running condition of the target area more intuitively, and the traffic early warning mark of each target area in the future time period can be observed in time, and therefore an emergency response is made.
Fig. 3 is a schematic structural diagram of an electronic device terminal according to an embodiment of the present application, and as shown in the drawing, the electronic device terminal 20 includes:
a trajectory data obtaining module 201, configured to obtain vehicle trajectory data, where the vehicle trajectory data includes time information and location information;
the target data extraction module 202 is configured to extract target vehicle trajectory data belonging to the same target area in the same target time period from the vehicle trajectory data according to the time information and the position information;
the estimation calculation module 203 is configured to obtain a traffic estimation value of the target area in a future target time period of the current time point according to the target vehicle trajectory data in at least two previous target time periods of the current time point.
In one embodiment, the target data extraction module 202 includes:
the position information aggregation submodule is used for extracting first vehicle track data which are positioned in the same target area in the vehicle track data according to the position information;
and the time information aggregation submodule is used for extracting the target vehicle track data in the same target time period from the first vehicle track data according to the time information.
In one embodiment, the electronic device terminal is preset with a road database, the road database includes a first type of road and a second type of road, and the location information aggregation sub-module includes:
the road type judging subunit is used for judging the road type corresponding to the vehicle track data according to the road database;
and the data extraction first subunit is configured to extract the first vehicle trajectory data, which is not more than a first preset range from the center point of the target area in the vehicle trajectory data, when the road type corresponding to the vehicle trajectory data is the first road type.
In one embodiment, the location information aggregation sub-module further includes:
and the data extraction second subunit is used for extracting the first vehicle track data which is not more than a second preset range from the center point of the target area in the vehicle track data when the road type corresponding to the vehicle track data is the second road type.
Fig. 4 is a schematic structural diagram of an electronic device terminal according to an embodiment of the present application, and as shown in the drawing, the electronic device terminal 20 further includes:
and the traffic sign output module 204 is configured to output a traffic condition sign of the target area in a future target time period of the current time point according to the traffic prediction value.
Embodiments of the present application also provide a computer-readable storage medium, on which computer instructions are stored, and the computer instructions, when executed by a processor, implement the steps of the above-mentioned traffic condition prediction method. The readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
In the description of embodiments of the invention, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means 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 specification. In this specification, the schematic representations of the terms used above are not necessarily intended to 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. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present description in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present description.
The word "if," as used herein, may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to a detection," depending on context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
It should be noted that, the terminal referred in the embodiments of the present application may include, but is not limited to, a Personal Computer (PC), a Personal Digital Assistant (PDA), a wireless handheld device, a tablet computer (tablet computer), a mobile phone, an MP3 player, an MP4 player, and the like.
In the several embodiments provided in this specification, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present description may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or in the form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer-readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods described in the embodiments of the present disclosure. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only a preferred embodiment of the present disclosure, and should not be taken as limiting the present disclosure, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
Claims (12)
1. A traffic condition prediction method is applied to an electronic equipment terminal, and comprises the following steps:
obtaining vehicle track data, wherein the vehicle track data comprises time information and position information;
extracting target vehicle track data which belong to the same target area in the same target time period from the vehicle track data according to the time information and the position information;
obtaining a traffic prediction value of the target area in a target time period in the future of the current time point according to the target vehicle track data in at least two target time periods before the current time point;
the electronic equipment terminal is provided with a road database in advance, the road database comprises a first type road and a second type road, and the target vehicle track data located in the same target area in the same target time period comprises:
and the distance between the vehicle track data and the center point of the target area of the first type of road does not exceed the vehicle track data within a first preset range in the same target time period, and the distance between the vehicle track data and the center point of the target area of the second type of road does not exceed the vehicle track data within a second preset range in the same target time period.
2. The method according to claim 1, wherein the extracting, according to the time information and the position information, target vehicle trajectory data belonging to a same target area in a same target time period from the vehicle trajectory data comprises:
extracting first vehicle track data located in the same target area from the vehicle track data according to the position information;
and extracting the target vehicle track data in the same target time period from the first vehicle track data according to the time information.
3. The method according to claim 2, wherein the extracting first vehicle trajectory data located in the same target area from the vehicle trajectory data according to the position information comprises:
judging the road type corresponding to the vehicle track data according to the road database;
and when the road type corresponding to the vehicle track data is the first type of road, extracting the first vehicle track data which is not more than a first preset range from the central point of the target area in the vehicle track data.
4. The method of claim 3, wherein the extracting first vehicle trajectory data located in the same target area from the vehicle trajectory data according to the position information further comprises:
and when the road type corresponding to the vehicle track data is the second road, extracting the first vehicle track data which is not more than a second preset range from the central point of the target area in the vehicle track data.
5. The method of claim 1, wherein the method further comprises:
and outputting the traffic condition identification of the target area in a future target time period of the current time point according to the traffic prediction value.
6. The method of any one of claims 1-5, wherein the target vehicle trajectory data includes information other than the target area including one or a combination of: average vehicle speed, number of vehicles.
7. An electronic device terminal, characterized in that the terminal comprises:
the vehicle tracking system comprises a track data obtaining module, a tracking module and a tracking module, wherein the track data obtaining module is used for obtaining vehicle track data, and the vehicle track data comprises time information and position information;
the target data extraction module is used for extracting target vehicle track data which belong to the same target area in the same target time period from the vehicle track data according to the time information and the position information;
the pre-estimation calculation module is used for obtaining a traffic pre-estimation value of the target area in a target time period in the future of the current time point according to the target vehicle track data in at least two target time periods before the current time point;
the electronic equipment terminal is provided with a road database in advance, the road database comprises a first type road and a second type road, and the target vehicle track data located in the same target area in the same target time period comprises:
and the distance between the vehicle track data and the center point of the target area of the first type of road does not exceed the vehicle track data in a first preset range within the same target time period, and the distance between the vehicle track data and the center point of the target area of the second type of road does not exceed the vehicle track data in a second preset range within the same target time period.
8. The terminal of claim 7, wherein the target data extraction module comprises:
the position information aggregation submodule is used for extracting first vehicle track data which are positioned in the same target area in the vehicle track data according to the position information;
and the time information aggregation submodule is used for extracting the target vehicle track data in the same target time period from the first vehicle track data according to the time information.
9. The terminal of claim 8, wherein the location information aggregation sub-module comprises:
the road type judging subunit is used for judging the road type corresponding to the vehicle track data according to the road database;
and the data extraction first subunit is configured to extract the first vehicle trajectory data, which is not more than a first preset range from the center point of the target area in the vehicle trajectory data, when the road type corresponding to the vehicle trajectory data is the first road type.
10. The terminal of claim 9, wherein the location information aggregation sub-module further comprises:
and the data extraction second subunit is used for extracting the first vehicle track data which is not more than a second preset range from the central point of the target area in the vehicle track data when the road type corresponding to the vehicle track data is the second road type.
11. The terminal of claim 8, wherein the terminal further comprises:
and the traffic sign output module is used for outputting a traffic condition sign of the target area in a future target time period of the current time point according to the traffic estimated value.
12. A computer-readable storage medium having stored thereon computer instructions, which when executed by a processor, perform the steps of the method of any one of claims 1 to 6.
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