CN117975718A - Intelligent traffic accurate and dynamic implementation method and related equipment - Google Patents

Abstract

The embodiment of the application provides a method for realizing accurate and dynamic intelligent traffic and related equipment, wherein the method comprises the following steps: acquiring path information and vehicle information; inputting the path information into a preset traffic specification model, and mapping the vehicle information in the traffic specification model to obtain vehicle dynamic data of the vehicle information relative to the path information; and determining effective state data in the dynamic data of the vehicle according to a preset judging rule, and calculating to obtain predicted data of the vehicle reaching the target request position on the target path according to the effective state data. The technical scheme of the embodiment of the application can improve the prediction precision of traffic dynamic information.

Description

Intelligent traffic accurate and dynamic implementation method and related equipment

Technical Field

The application relates to the technical field of intelligent traffic, in particular to a method and related equipment for realizing accurate and dynamic intelligent traffic.

Background

Along with the rapid development of science and technology, in people's daily life, the demand to public transportation is increasingly greater, and reasonable planning travel route and travel time can improve people's trip comfort level.

In the related art, there is a traffic prediction method, by inputting a start point and a target point, and selecting a traffic route between the start point and the target point, positioning coordinates of an operation vehicle corresponding to the traffic route are obtained, time and distance of the vehicle reaching the start point are calculated based on the traffic route, the positioning coordinates and the start point, and time and distance of the vehicle reaching the target point are calculated based on the traffic route, the positioning coordinates and the target point.

However, in the actual use process of the traffic prediction method, the road conditions of different road sections are often different, and the interference factors are more, so that the prediction error is easy to be larger.

Disclosure of Invention

In order to solve the technical problems, the embodiment of the application provides a method for realizing intelligent traffic accurate and dynamic and related equipment.

According to an aspect of the embodiment of the application, there is provided a method for realizing intelligent traffic accurate and dynamic, comprising: acquiring path information and vehicle information; inputting the path information into a preset traffic specification model, and mapping the vehicle information in the traffic specification model to obtain vehicle dynamic data of the vehicle information relative to the path information; and determining effective state data in the dynamic data of the vehicle according to a preset judging rule, and calculating to obtain predicted data of the vehicle reaching the target request position on the target path according to the effective state data.

In one embodiment of the present application, the determining valid state data in the vehicle dynamic data according to a preset determination rule includes: acquiring a current positioning point of a vehicle in the vehicle dynamic data and a historical positioning point of the vehicle before a preset time interval; according to the current positioning point and the historical positioning point, calculating to obtain the displacement distance of the vehicle in the time interval; and judging whether the displacement distance is larger than a preset drift distance, if so, screening out the current positioning point to be used as the effective state data.

In one embodiment of the present application, the determining valid state data in the vehicle dynamic data according to a preset determination rule includes: acquiring a current running route and a current vehicle speed of a vehicle in the vehicle dynamic data; splitting the current driving route into a plurality of sub-routes according to different road conditions on the current driving route, wherein each sub-route is matched with different speed thresholds; and judging whether the current vehicle speed is smaller than or equal to a speed threshold value corresponding to the sub-route, and if so, screening out the current positioning point to be used as the effective state data.

In one embodiment of the present application, before acquiring the current positioning point of the vehicle in the vehicle dynamic data, the method further includes: acquiring a current running route and a current positioning coordinate of a vehicle in the vehicle dynamic data; and projecting the current positioning coordinate on the current running route according to the current running route and the current positioning coordinate, and determining the corresponding projection position as the current positioning point.

In one embodiment of the application, the method further comprises: acquiring a plurality of preset site positions on the current driving route, and setting a plurality of track points close to the site, wherein the track points are arranged along the current driving route and distributed on two sides of the site; and acquiring the positions of a plurality of track points, calculating to obtain the track point with the shortest distance from the current positioning coordinate, taking the track point as a projection position, and determining the corresponding projection position as the current positioning point near the site position.

In one embodiment of the present application, before acquiring the positions of the plurality of track points, the method further includes: judging whether the distance between a plurality of track points is larger than a preset interpolation distance or not; if yes, the number of the track points is supplemented until the intervals among all the track points meet the interpolation interval.

In one embodiment of the application, the method further comprises: determining the index number change rule of the vehicle on the current running route according to the index numbers preset by the site positions; and determining the final position of the current driving route according to the index number change rule.

According to an aspect of the embodiment of the present application, there is provided a smart traffic accurate and dynamic implementation system, including: an acquisition unit configured to acquire path information and vehicle information; the mapping unit is configured to input the path information into a preset traffic specification model, and map the vehicle information in the traffic specification model to obtain vehicle dynamic data of the vehicle information relative to the path information; and the prediction unit is configured to determine effective state data in the dynamic data of the vehicle according to a preset judging rule, and calculate and obtain predicted data of the vehicle reaching a target request position on a target path according to the effective state data.

According to an aspect of the embodiments of the present application, there is provided a computer-readable storage medium having stored thereon computer-readable instructions, which when executed by a processor of a computer, cause the computer to perform the implementation method of intelligent traffic precision dynamics as described in the above embodiments.

According to an aspect of an embodiment of the present application, there is provided an electronic apparatus including: one or more processors; and the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the electronic equipment realizes the intelligent traffic accurate dynamic realization method in the embodiment.

According to the technical scheme, vehicle dynamic data are determined based on the vehicle information and the path information, effective state data in the vehicle dynamic data are screened out, and the target request position on the target path is predicted according to the effective state data, so that the accuracy of the predicted data is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:

FIG. 1 is a schematic diagram of an architecture of an implementation environment to which the present application relates.

FIG. 2 is a flow chart illustrating a method of implementing intelligent traffic precision dynamics in accordance with an exemplary embodiment of the present application.

Fig. 3 is a flowchart of step S120 in an example embodiment of the embodiment shown in fig. 2.

Fig. 4 is a flowchart of step S120 in another example embodiment of the embodiment shown in fig. 2.

Fig. 5 is a flowchart in an example embodiment prior to step S200 in the embodiment shown in fig. 3.

Fig. 6 is an exemplary diagram of a projection position in step S410 in the embodiment shown in fig. 5.

FIG. 7 is a flow chart illustrating a site vicinity projection process according to an exemplary embodiment of the present application.

Fig. 8 is an exemplary diagram of a projection position in step S510 in the embodiment shown in fig. 7.

Fig. 9 is a flowchart illustrating determination of a travel route end point according to an exemplary embodiment of the present application.

FIG. 10 is a block diagram of an implementation system of intelligent traffic precision dynamics, as illustrated in an exemplary embodiment of the present application.

Fig. 11 is a schematic structural view of an electronic device according to an exemplary embodiment of the present application.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the application may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the application.

The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.

It should be noted that: references herein to "a plurality" means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., a and/or B may represent: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.

FIG. 1 is a schematic diagram of an exemplary intelligent traffic precision dynamic implementation system. As shown in fig. 1, the implementation system of intelligent traffic precision and dynamics includes a server 100, a plurality of mobile terminals 200, and a plurality of data processing units 300 disposed at the server, including an acquisition unit, a mapping unit, and a prediction unit. The obtaining unit, the mapping unit and the predicting unit of the server may include, but are not limited to, accessing data, storing data, reading data, calculating data and outputting data.

It should be understood that fig. 1 is only a schematic architecture diagram of an exemplary smart traffic precision dynamic implementation system, and is not meant to limit the architecture of the smart traffic precision dynamic implementation system. In a practical application scenario, the implementation system of intelligent traffic precision dynamics may include components different from the architecture shown in fig. 1, such as more or fewer components than the architecture shown in fig. 1, which is not limited herein.

The technical scheme of the embodiment of the application provides a method for realizing intelligent traffic accurate and dynamic, and particularly relates to a method shown in fig. 2. The method can be executed by the server in the intelligent traffic accurate and dynamic implementation system, and of course, the method can also be executed by other devices in the intelligent traffic accurate and dynamic implementation system, and the method is not limited. The method at least comprises the steps S100 to S120, and the detailed description is as follows:

In step S100, path information and vehicle information are acquired.

The route information may be a route preset in advance, for example, a bus of a certain shift is set to run on a fixed route, and for example, a bus corresponding to the shift is set to run on a fixed route one during a peak hours of the shift, that is, a certain period of the morning, and is set to run on another fixed route two after a period corresponding to the peak hours of the shift. Meanwhile, the path information processing comprises a line and also comprises information such as a station, a line station list, a line track and the like on the corresponding line. The route information may be fixed or may be set in real time. For vehicle information, GPS (Global Positioning System) anchor points, line numbers, train numbers, and the like of the vehicle are included.

Based on the acquired path information and the vehicle information, further, in step S110, the path information is input into a preset traffic specification model, and the vehicle information is mapped in the traffic specification model, so as to obtain vehicle dynamic data of the vehicle information relative to the path information.

Specifically, the traffic specification model may be established based on IPIS (IntelligentPassengerInformationSystem) intelligent passenger information system, and the path information is input into a preset traffic specification model, that is, all the path information contained in all the traffic routes are unified, static data corresponding to the path information are integrated through the traffic specification model, and the integrated data are fed back to a plurality of mobile terminals through a server, so that the data are conveniently represented in a map form. For example, each traffic route is input into a traffic specification model according to the data form of bus route, station, route station list and route track, and the content specifically fed back to the mobile terminal can be that the corresponding route is displayed by a colored line, and the station, the driving track road condition information and the like on the route are represented by a preset symbol or pattern. Of course, only exemplary illustrations are provided herein, and no limitation is placed on the specific data content and data format of the path information.

And meanwhile, mapping the vehicle information in the traffic standard model to obtain vehicle dynamic data of the vehicle information relative to the path information. For example, the vehicle positioning information obtained through the GPS, such as longitude and latitude coordinates, is input into the traffic specification model, and the specific feedback to the mobile terminal is represented in real time at the corresponding map position through a certain preset symbol or pattern, and the traffic specification model itself has the input path information, so that the dynamic information of the vehicle on the corresponding path can be determined. Wherein the location point of the vehicle can be represented by latitude and longitude location coordinates P (x, y), and for all the location points on the travel route, each location point on the travel route is represented by the set l= { P0, P1, P2, P3,.

Further, in step S120, valid state data in the vehicle dynamic data is determined according to a preset determination rule, and predicted data of the vehicle reaching the target request position on the target path is calculated according to the valid state data.

Specifically, the determination of the valid state data is described by way of example, in which GPS vehicle positioning information acquired in real time is expressed as latitude and longitude coordinates at a plurality of points in time among vehicle information, and the offset distance with respect to the vehicle travel route can be calculated from the latitude and longitude coordinates, and when the determination rule is set such that the offset distance is greater than a certain threshold value, the determination rule is set as invalid positioning information. Of course, the setting of the determination rule may be one or a plurality of. In the case of a plurality of determination rules, the determination may be performed simultaneously or one by one.

And acquiring the positioning coordinates of the vehicle in the effective state data and the positioning coordinates of the passenger position when the predicted data of the target request position is calculated on the target path based on the effective state data obtained by screening, and calculating the distance and time of the vehicle reaching the passenger position and the distance and time of the vehicle reaching the target request position according to the target path.

According to the embodiment, the effective state data in the vehicle dynamic data are screened out based on the determined vehicle dynamic data, and the target request position on the target path is predicted according to the effective state data, so that the accuracy of the predicted data is improved.

In some embodiments of the present application, in step S120, for determining valid status data in the vehicle dynamic data according to a preset determination rule, as shown in fig. 3, at least steps S200 to S220 are included, which is described in detail below:

in step S200, a current anchor point of the vehicle in the vehicle dynamic data and a history anchor point of the vehicle before a preset time interval are acquired.

Specifically, while the current anchor point of the vehicle is acquired through the GPS, a historical GPS anchor point before a period of time, for example, a vehicle anchor point before one second is read.

After the current positioning point and the historical positioning point of the vehicle are obtained, in step S210, the displacement distance of the vehicle in the time interval is calculated according to the current positioning point and the historical positioning point.

By taking the above time interval as one second for the exemplary explanation, the travel distance of the vehicle in one second can be calculated by acquiring the current anchor point of the vehicle and the history anchor point before one second.

After the displacement distance of the vehicle in the time interval is obtained, in step S220, it is determined whether the displacement distance is greater than a preset drift distance, and if so, the current positioning point is screened out to be used as the valid state data.

In particular, by way of example, a bus is used to limit speed within 60 km/h when driving in urban areas, and the bus is generally compliant with the corresponding speed limit, so that the speed is generally between 25 km/h and 50 km/h when driving in urban areas, but the speed can be up to 80 km/h when driving in suburban areas, if the bus is a high-speed bus, the speed can reach 100 km/h, and the speed can reach 120 km/h, the highest speed of the bus can be basically determined, that is, 34 m cannot be driven in one second. If the GPS locating point of the vehicle drifts more than 34 meters within one second, invalid locating data of the current locating point can be considered, the current locating point needs to be screened out, and the current locating point can be relocated. It should be noted that, for the preset time interval, it may be one second, or may be other time, which is only illustrated here as an example.

According to the embodiment, the locating point larger than the maximum driving distance is screened out based on the maximum driving distance of the vehicle in the time interval, so that the reliability of the GPS locating point of the vehicle is improved, and the accuracy of prediction data is further improved.

In order to further improve the reliability of the valid state data, the valid state data in the vehicle dynamic data is determined according to a preset determination rule, as shown in fig. 4, at least steps S300 to S320 are further included, and the following details are described:

in step S300, the current travel route of the vehicle and the current vehicle speed in the vehicle dynamics data are acquired.

In this embodiment, the plurality of driving routes are preset in advance according to the path information, and the determination of the current driving route may be determined according to a plurality of positioning points of the historical driving process of the vehicle, or may be determined through other dynamic information of the vehicle, for example, a bus stop record of the route. The real-time speed of the vehicle is acquired through a speed sensor preset on the vehicle, and the corresponding speed is uploaded to the server through communication preset on the vehicle.

Based on the current driving route of the vehicle, in step S310, the current driving route is split into a plurality of sub-routes according to different road conditions on the current driving route, each sub-route matching a different speed threshold.

Specifically, because there may be a difference in road conditions of each road section on one driving route, for example, in a road section belonging to an urban area on one route, a part of road sections have multiple road widths, another part of road sections have fewer roads and narrower roads, and the driving speeds of vehicles corresponding to the road sections are limited, for example, the roads near the office building are congested during peak travel periods, and the vehicles are rare during off-peak travel periods, and the driving speeds of vehicles corresponding to the road sections are also different during corresponding time periods; therefore, there may be corresponding differences in the running speeds of the vehicles in different road segments or different times of the same road segment. By further describing the above examples of different road conditions, the current driving route is split into a plurality of sub-routes according to the different road conditions, and then different speed thresholds are matched for each sub-route.

It should be noted that, for the same sub-route, the speed threshold may be fixed, or may be set according to different time periods, for example, the current driving route includes a first road segment, a second road segment, and a third road segment, where the first road segment corresponds to a speed threshold of 20 km/h, the second road segment corresponds to a speed threshold of 40 km/h, and the third road segment corresponds to a speed threshold of 60 km/h. Further describing the above example, the speed threshold value of the first segment is 20 km/h, the speed threshold value of the second segment is 40 km/h, the speed threshold value of the third segment is 60 km/h, the speed threshold value of the first segment is 20 km/h, the speed threshold value of the second segment is 60 km/h, and the speed threshold value of the third segment is 30 km/h. Of course, for a specific speed threshold, the above examples are only for convenience in describing the setting manner of the speed threshold according to the actual road condition.

Based on the speed thresholds set by different road segments of the vehicle, in step S320, it is determined whether the current vehicle speed is equal to or less than the speed threshold on the corresponding sub-route, and if yes, the current positioning point is screened out to be used as the valid state data.

Specifically, the real-time speed collected on different sub-routes is compared with the speed threshold value set by the corresponding road section, if the real-time speed is larger than the speed threshold value, the fact that the vehicle is likely not to run on the corresponding road section is indicated, the GPS locating point corresponding to the vehicle is screened out again, and effective state data are selected.

Through the implementation mode, the effective state data in the GPS locating points of the vehicle are screened out by combining the speed threshold values in different driving road sections, so that the reliability of the GPS locating points of the vehicle is improved, and the accuracy of the predicted data is further improved.

In some embodiments of the present application, to further improve the reliability of the GPS positioning point of the vehicle, before the current positioning point of the vehicle in the vehicle dynamic data is obtained, as shown in fig. 5, at least steps S400 to S410 are further included, which are described in detail below:

In step S400, a current travel route and current positioning coordinates of the vehicle in the vehicle dynamic data are acquired.

The current driving route is a static route where the vehicle actually drives, and the current positioning coordinates can be longitude and latitude coordinate points directly obtained through a GPS positioning module of the vehicle.

In step S410, the current positioning coordinates are projected on the current travel route according to the current travel route and the current positioning coordinates, and the corresponding projected positions are determined as the current positioning points.

Specifically, as shown in fig. 6, the current positioning coordinate is projected on the current driving route, a road section within a preset distance of the position of the vehicle on the current driving route can be selected, a perpendicular line to the road section is made based on the current positioning coordinate of the vehicle, and the intersection point of the perpendicular line is used as the projection of the current positioning coordinate.

It should be noted that, because the positioning points acquired by the GPS often have a certain deviation, the positioning points directly acquired by the GPS may not fall on the current driving route of the vehicle. But for points where the location point of the vehicle has fallen on the current travel route, the current location coordinates may be considered to be a projection of itself. By determination of a specific projection, it may be determined not by a perpendicular, for example, by a diagonal intersection of a preset inclination angle.

In some embodiments of the present application, in order to more accurately obtain the dynamic data of the vehicle, especially the dynamic data of the vehicle entering and exiting the station, as shown in fig. 7, at least steps S500 to S510 are further included, which is described in detail as follows:

In step S500, a plurality of preset site positions on the current driving route are obtained, and a plurality of track points are set near the site, wherein the track points are set along the current driving route and distributed on two sides of the site.

The method comprises the steps of establishing a relation between a plurality of preset track points and a vehicle running state, so that whether the vehicle enters or exits is conveniently determined, for example, for a bus, a plurality of stations are often arranged on a known running route of the bus, so that passengers in different areas go out, meanwhile, as passengers on and off the station need a certain time, if the prediction is needed according to the running state of the vehicle, the entering and exiting state of the bus is accurately determined, and the time needs to be calculated.

Based on the determined track points, in step S510, the positions of the track points are acquired, the track point with the shortest distance from the current positioning coordinate is calculated, the track point is taken as the projection position, and the corresponding projection position is determined as the current positioning point near the site position.

Specifically, after the positioning coordinates of the vehicle are obtained, the distances between the positioning coordinates and each track point on the current running route are calculated, the track point corresponding to the shortest distance is selected as a projection position, and the position is used as the position corresponding to the vehicle on the current running route.

In order to further improve the use effect of the track points, before the positions of the track points are acquired, judging whether the distance between the track points is larger than a preset interpolation distance or not; if yes, the number of the track points is supplemented until the intervals among all the track points meet the interpolation interval.

In the exemplary explanation, as shown in fig. 8, the trajectory point indicated as Pi is closest to the vehicle positioning coordinate, and in this case, the trajectory point indicated as Pi may be taken as the projection position of the vehicle, and further, by way of example, if there is an error in correspondence of the trajectory points within 15 meters, the error can be controlled within 15 meters, and meanwhile, the error in positioning by the GPS is generally about 15 meters, so that the reliability in taking the trajectory point as the projection position can be further improved. Of course, it should be noted that the specific difference distance is not necessarily 15 meters, but may be other values, and the adaptive setting is performed in consideration of the calculation efficiency of all the track points.

In particular, for a travel route with a station at a corner, the track point density near the station corresponding to the corner is set to be greater than the track point density of other stations, so as to improve the accuracy of vehicle positioning. Meanwhile, in order to reduce the calculation pressure, the track points where the traveling is completed, that is, the track points through which the vehicle has passed, are eliminated.

In addition, track points may be set throughout the travel route, track points within a preset distance threshold are taken as in-station track points along the travel route, track points outside the preset distance threshold are taken as out-of-station track points, for example, the distance threshold is 30 meters, track points with a distance of 30 meters or less are taken as in-station track points with a distance of 30 meters or less, and track points with a distance of more than 30 meters are taken as out-of-station track points.

Through the embodiment, the plurality of track points preset near the station are used as the GPS positioning coordinates of the vehicle to be corresponding to the projection points, so that the positioning points of the vehicle near the station can be conveniently determined, and the positioning accuracy of the vehicle is improved.

In some embodiments of the present application, in order to accurately determine the form direction of the vehicle and the end position of the form route, as shown in fig. 9, at least steps S600 to S610 are further included, and the following details are described:

In step S600, a law of change of index numbers of the vehicle on the current driving route is determined according to the index numbers preset at the plurality of station positions.

For example, 5 stations are provided on the current driving route, each station corresponds to an index number, for example, the stations are sequentially arranged according to the numerical values 1 to 5, if one direction of the driving route is set to be the positive direction, and the corresponding station sequence is 1-2-3-4-5, and conversely, the station sequence for the reverse direction is 5-4-3-2-1. If the change rule of the vehicle running index number is 1-2-3, it can be determined that the vehicle is running in the positive direction.

Further, in step S610, the end position of the current travel route is determined according to the index number change rule.

For example, the change rule of the running index number of the vehicle is 1-2-3-2, and the change rule of the index number can be used for obtaining that the vehicle turns around after passing through the station 3, so that the end point of the current running route is determined as the station 3, and the relevant dynamic data of the vehicle in the next running route is recalculated.

It should be noted that, for the end point judgment of the current running route, it may also be determined according to specific positioning point information, for example, the change rule of the positioning point is corresponding to the change rule of the index number, when the positioning point is separated from the preset distance threshold of the current running route, the end point of the current running route is determined, and after the vehicle turns around, the preset distance threshold or time threshold of running on the current running route is determined. Further, by way of example, on a bus travel route of a certain shift, the number of travel passengers on a part of the road section is relatively large, and the number of travel passengers on a part of the road section is relatively small, so that there may be a case where a part of the bus travels only on a part of the road section.

Through the embodiment, the bus running state with incomplete running route can be mapped on the preset running route, so that the calculation of the predicted data is facilitated.

The following describes embodiments of the system of the present application that may be used to implement the smart traffic accurate and dynamic implementation method of the above embodiments of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method for implementing intelligent traffic precision and dynamics.

Fig. 10 shows a block diagram of an implementation system 700 of intelligent traffic precision dynamics according to one embodiment of the application.

Referring to fig. 10, a smart traffic precision dynamic implementation system 700 according to one embodiment of the present application includes: an acquisition unit 710 configured to acquire path information and vehicle information; the mapping unit 720 is configured to input the path information into a preset traffic specification model, and map the vehicle information in the traffic specification model to obtain vehicle dynamic data of the vehicle information relative to the path information; and a prediction unit 730 configured to determine valid state data in the vehicle dynamic data according to a preset determination rule, and calculate, according to the valid state data, predicted data of the vehicle reaching the target request position on the target path.

In some embodiments of the present application, based on the foregoing scheme, the prediction unit 730 is further configured to: determining effective state data in the vehicle dynamic data according to a preset judging rule, wherein the effective state data comprises the following steps: acquiring a current positioning point of a vehicle in the dynamic data of the vehicle and a historical positioning point of the vehicle before a preset time interval; according to the current positioning point and the historical positioning point, calculating to obtain the displacement distance of the vehicle in the time interval; and judging whether the displacement distance is larger than a preset drift distance, and if so, screening out a current positioning point to be used as effective state data.

In some embodiments of the present application, based on the foregoing scheme, the prediction unit 730 is further configured to: determining effective state data in the vehicle dynamic data according to a preset judging rule, and further comprising: acquiring a current running route and a current vehicle speed of a vehicle in the vehicle dynamic data; splitting the current driving route into a plurality of sub-routes according to different road conditions on the current driving route, wherein each sub-route is matched with different speed thresholds; and judging whether the current vehicle speed is less than or equal to a speed threshold value on the corresponding sub-route, and if so, screening out a current positioning point to be used as effective state data.

In some embodiments of the present application, based on the foregoing scheme, the prediction unit 730 is further configured to: before the current positioning point of the vehicle in the dynamic data of the vehicle is acquired, the method further comprises the following steps: acquiring a current driving route and a current positioning coordinate of a vehicle in vehicle dynamic data; and projecting the current positioning coordinate on the current running route according to the current running route and the current positioning coordinate, and determining the corresponding projection position as the current positioning point.

In some embodiments of the present application, based on the foregoing scheme, the prediction unit 730 is further configured to: acquiring a plurality of preset site positions on a current driving route, and setting a plurality of track points close to a site, wherein the track points are arranged along the current driving route and distributed on two sides of the site; and acquiring the positions of a plurality of track points, calculating to obtain the track point with the shortest distance from the current positioning coordinate, taking the track point as a projection position, and determining the corresponding projection position as the current positioning point near the site position.

In some embodiments of the present application, based on the foregoing scheme, the prediction unit 730 is further configured to: before acquiring the positions of the plurality of track points, the method further comprises: judging whether the distance between a plurality of track points is larger than a preset interpolation distance or not; if yes, the number of the track points is supplemented until the intervals among all the track points meet the interpolation interval.

In some embodiments of the present application, based on the foregoing scheme, the prediction unit 730 is further configured to: determining the index number change rule of the vehicle on the current running route according to index numbers preset at a plurality of station positions; and determining the final position of the current driving route according to the index number change rule.

It should be noted that, the implementation system 700 of the intelligent transportation precise dynamic state provided in the above embodiment and the implementation method of the intelligent transportation precise dynamic state provided in the above embodiment belong to the same concept, wherein the specific manner of executing the operations by each module and unit has been described in detail in the method embodiment, and will not be described herein.

The embodiment of the application also provides electronic equipment, which comprises a processor and a memory, wherein the memory is stored with computer readable instructions, and the computer readable instructions realize the intelligent traffic accurate dynamic realization method when being executed by the processor.

Fig. 11 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the application.

It should be noted that, the computer system 800 of the electronic device shown in fig. 11 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.

As shown in fig. 10, the computer system 800 includes a central processing unit (Central Processing Unit, CPU) 801 that can perform various appropriate actions and processes, such as performing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 802 or a program loaded from a storage section 808 into a random access Memory (Random Access Memory, RAM) 803. In the RAM 803, various programs and data required for system operation are also stored. The CPU 801, ROM 802, and RAM 803 are connected to each other by a bus 804. An Input/Output (I/O) interface 805 is also connected to bus 804.

The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, mouse, etc.; an output portion 807 including a Cathode Ray Tube (CRT), a Liquid crystal display (Liquid CRYSTAL DISPLAY, LCD), and a speaker, etc.; a storage section 808 including a hard disk or the like; and a communication section 809 including a network interface card such as a LAN (Local Area Network ) card, modem, or the like. The communication section 809 performs communication processing via a network such as the internet. The drive 810 is also connected to the I/O interface 805 as needed. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as needed so that a computer program read out therefrom is mounted into the storage section 808 as needed.

In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section 809, and/or installed from the removable media 811. When executed by a Central Processing Unit (CPU) 801, performs the various functions defined in the system of the present application.

It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having 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 (Erasable Programmable Read Only Memory, EPROM), a flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. 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 involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.

As another aspect, the present application also provides a computer-readable storage medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer-readable storage medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the methods described in the above embodiments.

It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.

From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present application.

Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains.

It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (10)

1. The method for realizing intelligent traffic accurate and dynamic is characterized by comprising the following steps:

Acquiring path information and vehicle information;

inputting the path information into a preset traffic specification model, and mapping the vehicle information in the traffic specification model to obtain vehicle dynamic data of the vehicle information relative to the path information;

And determining effective state data in the dynamic data of the vehicle according to a preset judging rule, and calculating to obtain predicted data of the vehicle reaching the target request position on the target path according to the effective state data.

2. The method according to claim 1, wherein said determining valid status data in the vehicle dynamics data according to a preset decision rule comprises:

Acquiring a current positioning point of a vehicle in the vehicle dynamic data and a historical positioning point of the vehicle before a preset time interval;

according to the current positioning point and the historical positioning point, calculating to obtain the displacement distance of the vehicle in the time interval;

And judging whether the displacement distance is larger than a preset drift distance, if so, screening out the current positioning point to be used as the effective state data.

3. The method according to claim 2, wherein said determining valid status data in said vehicle dynamics data according to a preset decision rule further comprises:

acquiring a current running route and a current vehicle speed of a vehicle in the vehicle dynamic data;

Splitting the current driving route into a plurality of sub-routes according to different road conditions on the current driving route, wherein each sub-route is matched with different speed thresholds;

and judging whether the current vehicle speed is smaller than or equal to a speed threshold value corresponding to the sub-route, and if so, screening out the current positioning point to be used as the effective state data.

4. The method of claim 2, further comprising, prior to acquiring the current location point of the vehicle in the vehicle dynamics data:

acquiring a current running route and a current positioning coordinate of a vehicle in the vehicle dynamic data;

and projecting the current positioning coordinate on the current running route according to the current running route and the current positioning coordinate, and determining the corresponding projection position as the current positioning point.

5. The method according to claim 4, wherein the method further comprises:

Acquiring a plurality of preset site positions on the current driving route, and setting a plurality of track points close to the site, wherein the track points are arranged along the current driving route and distributed on two sides of the site;

And acquiring the positions of a plurality of track points, calculating to obtain the track point with the shortest distance from the current positioning coordinate, taking the track point as a projection position, and determining the corresponding projection position as the current positioning point near the site position.

6. The method of claim 5, wherein prior to acquiring the locations of the plurality of trace points, the method further comprises:

judging whether the distance between a plurality of track points is larger than a preset interpolation distance or not;

if yes, the number of the track points is supplemented until the intervals among all the track points meet the interpolation interval.

7. The method of claim 5, wherein the method further comprises:

Determining the index number change rule of the vehicle on the current running route according to the index numbers preset by the site positions;

And determining the final position of the current driving route according to the index number change rule.

8. The utility model provides an accurate dynamic realization system of wisdom traffic which characterized in that includes:

an acquisition unit configured to acquire path information and vehicle information;

The mapping unit is configured to input the path information into a preset traffic specification model, and map the vehicle information in the traffic specification model to obtain vehicle dynamic data of the vehicle information relative to the path information;

And the prediction unit is configured to determine effective state data in the dynamic data of the vehicle according to a preset judging rule, and calculate and obtain predicted data of the vehicle reaching a target request position on a target path according to the effective state data.

9. A computer readable storage medium having stored thereon computer readable instructions which, when executed by a processor of a computer, cause the computer to perform the method of implementing intelligent traffic precision dynamics according to any one of claims 1-7.

10. An electronic device, comprising:

one or more processors;

storage means for storing one or more programs which, when executed by the one or more processors, cause the electronic device to implement the method of intelligent traffic accurate and dynamic implementation of any of claims 1 to 7.