CN110708664B - Traffic flow sensing method and device, computer storage medium and electronic equipment - Google Patents
Traffic flow sensing method and device, computer storage medium and electronic equipment Download PDFInfo
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- CN110708664B CN110708664B CN201910965105.3A CN201910965105A CN110708664B CN 110708664 B CN110708664 B CN 110708664B CN 201910965105 A CN201910965105 A CN 201910965105A CN 110708664 B CN110708664 B CN 110708664B
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Abstract
A traffic flow perception method, a traffic flow perception device, a computer storage medium and an electronic device comprise: determining a road to be perceived and a plurality of base stations near the road; each base station comprises signaling data for communicating with communication numbers of mobile equipment in the coverage range of the base stations; determining time points of each mobile device accessing and leaving each base station according to the signaling data in the plurality of base stations; calculating the movement parameters of each mobile device in the coverage range of each base station according to the coverage range of the base station and the time points of each mobile device accessing and leaving the same base station; and calculating the traffic flow condition of the road in the coverage area of the base station according to the movement parameters of each mobile device in the coverage area of each base station. By adopting the scheme in the application, data analysis and mining are carried out by acquiring and integrating the data of operators, so that the road traffic flow condition is sensed with the least cost, and the road sensing precision is greatly improved due to the segmented processing of the road.
Description
Technical Field
The present application relates to big data processing technology in the field of computers and communications, and in particular, to a traffic flow sensing method and apparatus, a computer storage medium, and an electronic device.
Background
With the development of society and the improvement of living standard of people, more and more private cars enter every family, but the traffic pressure therewith also increases day by day, so that pedestrians can know the road conditions of the driving road section in time or know the pressure of traffic flow in a jurisdiction area conveniently, and the traffic flow of the road needs to be sensed.
The existing traffic perception technology mainly captures the traffic flow condition of a certain or certain fixed place area in real time through a camera or realizes the traffic flow perception through a GPS technology so as to perceive the traffic flow of a certain road or a certain area.
Problems existing in the prior art:
the manner of capturing traffic flow via cameras requires the installation of several cameras per road or area where traffic flow needs to be perceived, which is very costly and, although it can be done in more dense areas (e.g., inside cities), it is difficult to do so in areas that are wide and expansive like suburbs or highways. The existing GPS technology is used in suburbs or expressways, road traffic prediction can be achieved, but only individual vehicle prediction can be conducted, the whole road state cannot be perceived and predicted macroscopically, meanwhile, the existing GPS technology needs to upload data to a GPS satellite, the safety of the data and the stability of a system cannot be guaranteed, and perception cannot be achieved without a GPS transmitting device.
Disclosure of Invention
The embodiment of the application provides a traffic flow perception method and device, a computer storage medium and electronic equipment, the application is independently implemented in a system, has the characteristics of reliability, stability, accuracy and universe, and can accurately complete the macroscopic perception of the whole road and complete the prediction by utilizing traditional data through locally building a model so as to solve the technical problem.
According to a first aspect of embodiments of the present application, there is provided a traffic flow perception method, including the steps of:
determining a road to be perceived and a plurality of base stations near the road; each base station comprises signaling data for communicating with communication numbers of mobile equipment in the coverage range of a plurality of base stations;
determining time points of each mobile device accessing and leaving each base station according to the signaling data in the plurality of base stations;
calculating the movement parameters of each mobile device in the coverage range of each base station according to the coverage range of the base station and the time points of each mobile device accessing and leaving the same base station;
and calculating the road traffic flow condition in the coverage range of each base station according to the movement parameters of each mobile device in the coverage range of each base station so as to determine the traffic flow condition of the road.
According to a second aspect of embodiments of the present application, there is provided a traffic flow perception device, including:
the device comprises a first determination module, a second determination module and a control module, wherein the first determination module is used for determining a road to be perceived;
a second determination module for determining a plurality of base stations in the vicinity of the road; each base station comprises signaling data for communicating with communication numbers of mobile equipment in the coverage range of a plurality of base stations;
a third determining module, configured to determine, according to the signaling data in the plurality of base stations, a time point when each mobile device accesses and leaves each base station;
the first calculation module is used for calculating the movement parameters of each mobile device in the coverage range of each base station according to the coverage range of the base station and the time points of each mobile device accessing and leaving the same base station;
and the second calculation module is used for calculating the road traffic flow condition in each base station coverage range according to the mobile parameters of each mobile device in each base station coverage range so as to determine the traffic flow condition of the road.
According to a third aspect of embodiments of the present application, there is provided a computer storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the traffic flow awareness method as described above.
According to a fourth aspect of embodiments herein, there is provided an electronic device comprising one or more processors, and memory for storing one or more programs; the one or more programs, when executed by the one or more processors, implement a traffic flow awareness method as described above.
By adopting the traffic flow perception method and device, the computer storage medium and the electronic equipment provided by the embodiment of the application, data analysis and mining are carried out by acquiring and integrating data of operators, so that the road traffic flow condition can be perceived most comprehensively in real time with the least cost, the most stable data, the most accurate model and the most reliable system; in addition, the road is subjected to segmentation processing according to the coverage range of the base station, so that the road perception precision is greatly improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flow chart diagram illustrating an implementation of a traffic flow perception method according to a first embodiment of the present application;
fig. 2 is a schematic structural diagram illustrating a traffic flow sensing device according to a second embodiment of the present application;
FIG. 3 is a schematic structural diagram of an electronic device in a fourth embodiment of the present application;
FIG. 4 is a schematic diagram of a framework of a road traffic flow perception prediction system in the fifth embodiment of the application;
fig. 5 is a schematic diagram illustrating a road traffic flow perception process in the fifth embodiment of the present application;
FIG. 6 is a schematic diagram illustrating the location of a road base station in the fifth embodiment of the present application;
fig. 7 shows a schematic diagram of distance calculation of a base station in the fifth embodiment of the present application.
Detailed Description
The scheme in the embodiment of the application can be implemented by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following further detailed description of the exemplary embodiments of the present application with reference to the accompanying drawings makes it clear that the described embodiments are only a part of the embodiments of the present application, and are not exhaustive of all embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Example one
Fig. 1 shows a flow chart of implementation of a traffic flow perception method in an embodiment of the present application.
As shown in the figure, the traffic flow perception method includes:
103, calculating the movement parameters of each mobile device in the coverage area of each base station according to the coverage area of the base station and the time points of each mobile device accessing and leaving the same base station;
and 104, calculating the road traffic flow condition in the coverage area of each base station according to the movement parameters of each mobile device in the coverage area of each base station, and further determining the traffic flow condition of the road.
In specific implementation, the road to be perceived may be a certain traffic section in a city, may be a city-surrounding expressway, or a city-crossing expressway, and the like, and the road to be perceived may be determined based on the name, identification, number, and the like of the road.
A plurality of base stations are typically located near a road so that vehicles or pedestrians traveling on the road can communicate, which is used by embodiments of the present application to correlate traffic flow awareness predictions with base station data. Specifically, each base station may record, in real time, signaling data for communication between the base station and communication numbers of a plurality of mobile devices within the coverage area of the base station.
Specifically, the mobile device may be a smart phone, a smart wearable device, or a vehicle-mounted terminal, and the communication number may be a number used for communication on these devices, for example, a mobile phone number.
The signaling data recorded by the base station includes time information of each mobile device accessing and leaving the base station, and in the embodiment of the application, the movement parameter of each mobile device in the coverage area of the base station can be calculated according to the coverage area of the base station and the time information, and finally, the traffic flow condition of the road or the road section is calculated according to the movement parameters of a plurality of mobile devices.
Specifically, the movement parameter may be a movement speed, a movement time, or the like.
By adopting the traffic flow perception method provided by the embodiment of the application, data analysis and mining are carried out by acquiring and integrating data of operators, so that the road traffic flow condition is perceived in real time at the least cost; in addition, because the road is processed in sections according to the coverage range of the base station, the whole road is composed of different base station coverage ranges (the coverage range of each base station is different from hundreds of meters to kilometers), and the traffic flow condition of the whole road is sensed according to the traffic flow condition of the base station coverage range of each section on the road, so that the accuracy of road sensing is greatly improved.
In one embodiment, the determining the road to be perceived comprises:
determining an identification of a road to be perceived;
determining the trend data of the road to be perceived according to the identification of the road; the trend data is a set of coordinate values of a plurality of position points.
In specific implementation, assuming that the identifier of the road to be perceived is 102, the trend data of the road to be perceived can be determined as { a (east longitude: 116 ° 23 '17', and north latitude: 39 ° 54 '27', B (east longitude: 116 ° 23 '18', and north latitude: 39 ° 54 '27'), } M (east longitude: 116 ° 23 '58', and north latitude: 39 ° 54 '49') }, and coordinate values of a plurality of position points of the road are included in the set.
In specific implementation, the corresponding relationship between the identifier of the road to be perceived and the heading data of the road may be stored in the database in advance, or may be obtained from other external data sources, which is not limited in this application.
In one embodiment, the determining a plurality of base stations in the vicinity of the road includes:
acquiring position information of a plurality of base stations in a preset area;
matching the road to be perceived with the position information of the base stations to obtain a plurality of base stations matched with the position of the road to be perceived;
and determining the base stations matched with the position of the road to be perceived as base stations nearby the road.
In specific implementation, the preset area may be an area or a range formed by a position where the road to be perceived passes, for example: the road to be sensed is a jingshi highway, and the preset area may refer to an area or range formed by the highway from beijing to shenyang.
In order to determine which base stations are closer to the road to be perceived or meet a preset distance threshold, the embodiment of the application may match the road to be perceived with all base stations in the area, so as to obtain a matched base station as a base station near the road.
In one embodiment, the road to be perceived is a first set of a plurality of continuous location points, and the location information of the plurality of base stations is a second set of a plurality of location points;
the matching the road to be perceived with the position information of the base stations to obtain a plurality of base stations matched with the position of the road to be perceived comprises:
for each element in the second set, respectively performing distance calculation with each element in the first set, and determining the element in the first set with the minimum distance to the element in the second set;
obtaining a third set according to the element with the minimum element distance from the first set to the second set, wherein the third set comprises the position information of each base station and the distance value from the base station to the road position point closest to the base station;
and determining the base station corresponding to the element with the distance value smaller than the preset distance threshold value in the third set as the base station matched with the road position to be perceived.
Assuming that the first set of the road to be perceived is J1, the second set of the base stations is J2, the set J1 includes coordinates of a plurality of location points on the road, the set J2 includes coordinates of location points of a plurality of base stations, for each element in the set J2 (i.e., the location point of each base station), distance calculation is performed with each element in the set J1 (each location point on the road) respectively, the location point coordinate on the road closest to each base station is determined, and further, the base station with the distance value smaller than a preset distance threshold is determined as the base station matched with the road.
That is, in the embodiment of the present application, a closest position point from each base station to a road is determined, a distance from each base station to the closest position point is determined, and a base station whose distance is smaller than a preset distance threshold is used as a base station near the road.
In one embodiment, the calculating the movement parameters of each mobile device within the coverage area of each base station according to the coverage area of the base station and the time points of each mobile device accessing and leaving the same base station includes:
determining location information of a second base station and location information of a third base station adjacent to the first base station;
determining the coverage range of the first base station according to the distance between the first base station and the second base station and the distance between the first base station and the third base station;
and calculating the movement parameters of the mobile equipment in the coverage range of the first base station according to the coverage range of the first base station and the time points of the mobile equipment accessing and leaving the first base station.
In specific implementation, in the embodiment of the present application, the coverage area of a base station located in the middle of three base stations may be calculated according to the position relationship between three adjacent base stations, and then, the moving time or the moving speed of the mobile device in the coverage area of the base station located in the middle is calculated by combining the time points when the mobile device accesses and leaves the base station located in the middle.
Specifically, the positional relationship of the three base stations may be arranged in sequence according to the extending direction of the road, and the base station at the middle position of the three base stations may refer to the base station at the middle position of the three base stations arranged in sequence according to the extending direction of the road, that is, a second base station accessed by any vehicle on the road after leaving one base station for the first time.
In one embodiment, the determining the coverage area of the first base station according to the distance between the first base station and the second base station and the distance between the first base station and the third base station includes:
determining a first distance between the first base station and the second base station and a second distance between the first base station and the third base station;
summing the first distance and the second distance to obtain a third distance;
and dividing the third distance by 2 to obtain the coverage area of the first base station.
In specific implementation, assuming that the distance between the first base station and the second base station is 10 kilometers, and the distance between the first base station and the third base station is 20 kilometers, in this embodiment of the present application, the coverage area of the first base station is calculated to be (10 kilometers +20 kilometers)/2 ═ 15 kilometers in an approximate rough manner.
The coverage area of the base station in the embodiment of the present application may refer to a coverage area of the base station on a road, that is, a range in which a vehicle can access the base station when traveling on the road.
In one embodiment, the calculating the movement parameters of each mobile device within the coverage area of each base station according to the coverage area of the base station and the time points of each mobile device accessing and leaving the same base station includes:
determining transceiving power of a second base station and transceiving power of a third base station which are adjacent to the first base station;
determining the coverage area of the first base station according to the ratio and the distance between the transceiving power of the first base station and the transceiving power of a second base station and the ratio and the distance between the transceiving power of the first base station and the transceiving power of a third base station;
and calculating the movement parameters of the mobile equipment in the coverage range of the first base station according to the coverage range of the first base station and the time points of the mobile equipment accessing and leaving the first base station.
In specific implementation, the transceiving power of the first base station is assumed to be 3, the transceiving power of the second base station is assumed to be 1, the transceiving power of the third base station is assumed to be 7, the transceiving power ratio between the first base station and the second base station is assumed to be 3:1, and the transceiving power ratio between the first base station and the third base station is assumed to be 3: 7; meanwhile, the distance between the first base station and the second base station is assumed to be 10 kilometers, and the distance between the first base station and the third base station is assumed to be 20 kilometers; thereby determining the coverage area of the first base station to be 10 × (3/(3+1)) +20 × (3/(3+7)) -13.5 km.
It is assumed that the signaling data of the first base station shows or records a time point when the mobile device accesses the first base station and a time point when the mobile device leaves the first base station, and a difference value between the two time points is a travel time of the mobile device within a coverage area of the first base station.
According to the coverage range of the first base station on the road and the driving time, the moving speed of the mobile device in the coverage range of the first base station can be calculated.
In one embodiment, the determining the coverage area of the first base station according to the ratio between the transceiving power of the first base station and the transceiving power of the second base station and the ratio between the transceiving power of the first base station and the transceiving power of the third base station includes:
determining a first ratio between the transceiving power of the first base station and the transceiving power of a second base station, and a second ratio between the transceiving power of the first base station and the transceiving power of a third base station;
determining a first coverage range of the first base station close to the second base station side and a second coverage range of the first base station close to the third base station side according to the relative proportion of the first ratio and the second ratio, the distance between the first base station and the second base station and the distance between the first base station and the third base station;
and summing the first coverage range and the second coverage range to obtain the coverage range of the first base station.
In specific implementation, assuming that the transceiving power of the first base station is 3, the transceiving power of the second base station is 1, the transceiving power of the third base station is 7, the first ratio of the transceiving power between the first base station and the second base station is 3:1, and the second ratio of the transceiving power between the first base station and the third base station is 3:7, the coverage area of the first base station on the side close to the second base station is calculated as AB base station distance (3/(3+1)), and the coverage area of the first base station on the side close to the third base station is calculated as AC base station distance (3/(3+7)), and the coverage areas are added to obtain the coverage area of the first base station.
In one embodiment, the method further comprises:
acquiring position information of the mobile equipment on the road to be perceived;
predicting the mobile parameters of the mobile equipment in the coverage range of a base station according to the position information of the mobile equipment on the road to be perceived and a pre-established base station data model;
and predicting the traffic flow condition of the road to be perceived according to the movement parameters of a plurality of mobile devices in the coverage range of the base station.
In specific implementation, the embodiment of the application can also predict the moving speed or moving time of the mobile device according to the real-time position of the mobile device and a pre-established data model, so as to predict the traffic flow condition of the road.
The pre-established data model may be trained from signaling data of a plurality of base stations and a number/number of vehicle samples.
In one embodiment, the base station data model building process includes:
respectively calculating theoretical movement parameters of the mobile equipment sample passing through the coverage range of the base station under different preset vehicle speeds according to the coverage range of any base station for any mobile equipment sample;
and training according to the theoretical movement parameters and the actual movement parameters of the mobile equipment sample in the coverage range of the base station to obtain a base station data model.
In specific implementation, for any one mobile device sample, the theoretical moving time or moving speed of the mobile device sample passing through the coverage range of the base station at different vehicle speeds can be respectively calculated according to the coverage range of each base station, then the calculated theoretical moving time or moving speed is compared with the actual moving time or moving speed, further the value of the model data parameter is adjusted, and the base station data model can be finally obtained through continuous adjustment training.
Specifically, the different vehicle speeds may be determined according to actual needs or road speeds specified by traffic, for example, the upper speed limit value on each lane on the expressway may be respectively used as the vehicle speed during model training.
Example two
Based on the same inventive concept, the embodiment of the application provides a traffic flow sensing device, the principle of the device for solving the technical problem is similar to that of a traffic flow sensing method, and repeated parts are not repeated.
Fig. 2 shows a schematic structural diagram of a traffic flow sensing device in the second embodiment of the present application.
As shown in the figure, the traffic flow perception device includes:
a first determination module 201, configured to determine a road to be perceived;
a second determining module 202, configured to determine a plurality of base stations near the road; each base station comprises signaling data for communicating with communication numbers of mobile equipment in the coverage range of a plurality of base stations;
a third determining module 203, configured to determine, according to the signaling data in the plurality of base stations, time points at which each mobile device accesses and leaves each base station;
a first calculating module 204, configured to calculate a moving parameter of each mobile device within a coverage area of each base station according to the coverage area of the base station and a time point when each mobile device accesses and leaves the same base station;
the second calculating module 205 is configured to calculate, according to the movement parameter of each mobile device in the coverage area of each base station, a traffic flow condition of a road in the coverage area of each base station, and further determine the traffic flow condition of the road.
By adopting the traffic flow sensing device provided by the embodiment of the application, data analysis and mining are carried out by acquiring and integrating data of operators, so that the road traffic flow condition is sensed in real time at the least cost; in addition, the road is subjected to segmentation processing according to the coverage range of the base station, so that the road perception precision is greatly improved.
In one embodiment, the first determining module includes:
an identification determination unit for determining an identification of a road to be perceived;
the trend determining unit is used for determining trend data of the road to be perceived according to the identification of the road; the trend data is a set of coordinate values of a plurality of position points.
In one embodiment, the second determining module includes:
a base station position acquisition unit, configured to acquire position information of a plurality of base stations in a preset area;
the matching unit is used for matching the road to be perceived with the position information of the base stations to obtain a plurality of base stations matched with the position of the road to be perceived;
and the base station determining unit is used for determining the base stations matched with the position of the road to be perceived as the base stations nearby the road.
In one embodiment, the road to be perceived is a first set of a plurality of continuous location points, and the location information of the plurality of base stations is a second set of a plurality of location points;
the matching unit includes:
a first processing subunit, configured to perform distance calculation on each element in the second set and each element in the first set, and determine an element in the first set that has a minimum distance to the element in the second set;
a second processing subunit, configured to obtain a third set according to an element in the first set whose element distance to the element in the second set is the smallest, where the third set includes location information of each base station and a distance value from the base station to a road location point closest to the base station;
and the matching subunit is used for determining the base station corresponding to the element with the distance value smaller than the preset distance threshold value in the third set as the base station matched with the road position to be perceived.
In one embodiment, the first computing module includes:
a base station position determination unit for determining position information of a second base station and position information of a third base station adjacent to the first base station;
a first coverage determining unit, configured to determine a coverage of the first base station according to a distance between the first base station and a second base station and a distance between the first base station and a third base station;
and the first parameter calculation unit is used for calculating the mobile parameters of the mobile equipment in the coverage area of the first base station according to the coverage area of the first base station and the time points of the mobile equipment accessing and leaving the first base station.
In one embodiment, the coverage determination unit includes:
a first distance determining subunit, configured to determine a first distance between the first base station and the second base station, and a second distance between the first base station and the third base station;
a second distance determining subunit, configured to sum the first distance and the second distance to obtain a third distance;
and a coverage determining subunit, configured to divide the third distance by 2 to obtain a coverage of the first base station.
In one embodiment, the first computing module includes:
a power determining unit for determining a transceiving power of a second base station and a transceiving power of a third base station adjacent to the first base station;
a second coverage area determining unit, configured to determine a coverage area of the first base station according to a ratio and a distance between the transceiving power of the first base station and the transceiving power of the second base station, and a ratio and a distance between the transceiving power of the first base station and the transceiving power of a third base station;
and the second parameter calculating unit is used for calculating the mobile parameters of the mobile equipment in the coverage range of the first base station according to the coverage range of the first base station and the time points of the mobile equipment accessing and leaving the first base station.
In one embodiment, the second coverage determining unit includes:
a ratio determining subunit, configured to determine a first ratio between the transceiving power of the first base station and the transceiving power of the second base station, and a second ratio between the transceiving power of the first base station and the transceiving power of a third base station;
a first range determining subunit, configured to determine, according to relative proportions of the first ratio and the second ratio, a distance between the first base station and the second base station, and a distance between the first base station and the third base station, a first coverage range of the first base station on a side close to the second base station, and a second coverage range of the first base station on a side close to the third base station;
and a second range determining subunit, configured to sum the first coverage range and the second coverage range to obtain a coverage range of the first base station.
In one embodiment, the apparatus further comprises:
the device position acquisition module is used for acquiring the position information of the mobile device on the road to be perceived;
the mobile parameter prediction module is used for predicting the mobile parameters of the mobile equipment in the coverage range of the base station according to the position information of the mobile equipment on the road to be perceived and a pre-established base station data model;
and the traffic flow prediction module is used for predicting the traffic flow condition of the road to be perceived according to the mobile parameters of the mobile devices in the coverage range of the base station.
In one embodiment, the device further comprises a model building module, configured to calculate, for any mobile device sample, theoretical movement parameters of the mobile device sample passing through the coverage area of the base station at different preset vehicle speeds according to the coverage area of any base station; and training according to the theoretical movement parameters and the actual movement parameters of the mobile equipment sample in the coverage range of the base station to obtain a base station data model.
EXAMPLE III
Based on the same inventive concept, embodiments of the present application further provide a computer storage medium, which is described below.
The computer storage medium has a computer program stored thereon, and the computer program, when executed by a processor, implements the steps of the traffic flow perception method according to an embodiment.
By adopting the computer storage medium provided by the embodiment of the application, data analysis and mining are carried out by acquiring and integrating data of operators, so that the road traffic flow condition is sensed in real time at the least cost; in addition, the road is subjected to segmentation processing according to the coverage range of the base station, so that the road perception precision is greatly improved.
Example four
Based on the same inventive concept, the embodiment of the present application further provides an electronic device, which is described below.
Fig. 3 shows a schematic structural diagram of an electronic device in the fourth embodiment of the present application.
As shown, the electronic device includes memory 301 for storing one or more programs, and one or more processors 302; the one or more programs, when executed by the one or more processors, implement a traffic flow awareness method as described in embodiment one.
By adopting the electronic equipment provided by the embodiment of the application, data analysis and mining are carried out by acquiring and integrating data of operators, so that the road traffic flow condition is sensed in real time at the least cost; in addition, the road is subjected to segmentation processing according to the coverage range of the base station, so that the road perception precision is greatly improved.
EXAMPLE five
In order to facilitate the implementation of the present application, the embodiments of the present application are described with a specific example of traffic flow perception on a certain highway.
Fig. 4 shows a frame schematic diagram of a road traffic flow perception prediction system in the fifth embodiment of the present application.
As shown in the drawings, the traffic flow perception prediction system provided by the embodiment of the present application may include a middleware, a foreground system and a background system, wherein,
the middleware is used for acquiring data of an operator in an interface mode or a data import mode, and butting the acquired data with a foreground system and a background system respectively to complete data interaction, and specifically comprises the following steps: the method comprises the following steps of interfacing with an operator system, importing service data, interfacing with a background, interfacing with a foreground and the like.
The background system is used for realizing data analysis and processing and comprises: establishing a model, analyzing service logic and flow, and sensing and predicting road traffic flow conditions through mobile phone signaling data of an operator, which specifically comprises the following steps: and data analysis, wherein the data analysis specifically comprises system modeling, business analysis, data mining and the like.
The foreground system is used for realizing the interface presentation of the foreground interface and comprises: the mobile application APP provides a unified large screen of regional traffic flow conditions for users, and provides real-time traffic flow query or perception prediction for public pedestrians.
Suppose that the highway to be sensed is the Jingha highway G1 with the total length of 1200 km
According to the road identifier of the kyaha highway G1, the trend data of the highway can be determined, that is, the coordinate set of several position points of the highway can be determined.
The base station information in the area may be determined according to the range area from beijing to harabine, and the base station information may specifically include numbers or identifiers of a plurality of base stations and location information (which may be latitude and longitude information) of each base station.
And matching the trend data of the Kyoto-Ha highway G1 with the position information of a plurality of base stations in the area, and determining that the base stations near the highway (or part of the highway section) comprise the base station A, B, C, D, E, F, G, H.
Fig. 5 is a schematic diagram illustrating a road traffic flow perception process in the fifth embodiment of the present application.
As shown in the figure, the concrete steps include the following steps:
The mobile phone signaling data may be specifically shown in the following table:
| mobile phone number | Base station ID | Point in time |
| 13100000001 | A | 05:30 |
| 13100000003 | A | 07:00 |
| 13100000006 | E | 05:40 |
| 13100000005 | B | 05:50 |
| 13100000003 | B | 07:50 |
| 13100000002 | B | 05:30 |
| 13100000007 | H | 05:40 |
| 13100000009 | B | 05:40 |
| 13100000008 | C | 06:00 |
| 13100000004 | D | 06:00 |
| 13100000003 | C | 09:50 |
| ... | ... | ... |
Assuming that vehicles a and b exist on the highway section in the time period of 05: 30-06: 00, assuming that each vehicle only comprises one mobile phone number, which corresponds to 13100000001 and 13100000002, respectively, the base station accessed by each vehicle at different moments has corresponding change according to the movement of the vehicle position, and sample data is as follows:
| mobile phone number | Base station ID | Point in time |
| 13100000001 | A | 05:30 |
| 13100000001 | B | 05:40 |
| 13100000001 | B | 06:40 |
| 13100000001 | C | 08:00 |
| 13100000002 | B | 05:30 |
| 13100000002 | B | 05:40 |
| 13100000002 | B | 05:40 |
| 13100000002 | C | 08:30 |
13100000001 at time point 05:40 for base station B and 08:00 for base station C;
13100000001 visit base station B at time point 05:30 and visit base station C at time point 08: 30.
And step 504, determining the speed according to the distance of the base station and the time points of two times of accessing different base stations.
Specifically, the distance between two base stations is determined by analyzing the IDs of the two base stations. And meanwhile, determining the time difference of two times of recording according to the recording time point, and further determining the running speed of the mobile phone number.
Fig. 6 shows a schematic location diagram of a road base station in the fifth embodiment of the present application.
As shown in the figure, assuming that the distance between the base station a and the base station B is 10 km, the distance between the base station B and the base station C is 20km, the power of the base station A, B is 1 and 3 respectively, and when the vehicle of the user, which receives the signal of the base station a is 0.1 and the signal of the base station B is greater than 0.1, it is determined that the vehicle is leaving the base station a and visiting the base station B (that is, the position where the current vehicle is obtained is the m1 position point); 13100000001 when the vehicle of the user receives the signal of the base station B as 0.2 and the signal of the base station C is greater than 0.2, it is determined that the vehicle is leaving the base station B and visiting the base station C (i.e. the current vehicle is acquired as the m2 position point), and the distance between the m1 and the m2 position points is the coverage of the base station B.
13100000001 the user's vehicle leaves base station A (m1 location point) at a time point of 05:40, leaves base station B (m2 location point) at a time point of 08:30, and has a time difference of 170 minutes.
The calculation speed according to the coverage area and the time difference of the base station B is as follows: m1m2 distance/(170 fen 60)
And 505, acquiring the running speed of a large number of people in a certain area.
The running speeds of a large number of samples are determined by analyzing user samples and calculating the speeds between base stations of different users.
And step 506, determining the road traffic flow condition in the area through the running speeds of a large number of samples.
And step 507, transmitting the real-time road traffic flow information to the middleware.
In step 509, different users use the application according to the application scenario (for example, viewing and emergency command through a unified large screen).
The traffic flow prediction process is described in detail below.
Firstly, a system model is established in the embodiment of the application, and the specific process is as follows:
suppose that the base station A, B, C has three base stations with vertical distances of L1, L2 and L3 meters to the road, and three points where the three base stations vertically fall on the road are A ', B ' and C ', respectively.
Fig. 7 shows a schematic diagram of distance calculation of a base station in the fifth embodiment of the present application.
As shown in the figure, it is assumed that the current automobile is at a certain position m in the road, and the vertical distances from the three base stations are respectively the distances from the position points m to a ', B' and C 'are respectively L1', L2 'and L3' (the vertical distance from the base station to the road is negligible compared with the width of the road).
Then: the distances from m to the three base stations are respectively:
similarly, the distance values of LB and LC are calculated.
According to the real-time change of the position point of m, the distance values of LA, LB and LC also change in real time.
In the embodiment of the application, the vehicle mobile phone is preferentially accessed to the base station a when the vehicle mobile phone is close to the base station a, and similarly, the vehicle mobile phone is preferentially accessed to the base station B when the vehicle mobile phone is close to the base station B.
Then, a specific position m1 (specific position of the access base station B) away from the base station A and a specific position m2 (specific position of the access base station C) away from the base station B are calculated through a computer, and the distance between the two positions (namely the coverage of the base station B) can be obtained through the position difference between the m1 and the m2 base stations. Specifically, the coverage area of the base station B may be determined by calculating according to the power method, or by summing a value obtained by dividing the distance between the base station a and the base station B by 2 and a value obtained by dividing the distance between the base station B and the base station C by 2.
Next, assuming that the default vehicle speeds are 80KM/s, 100KM/s, and 120KM/s, it is possible to calculate the approximate time that the vehicle has elapsed within the coverage of the base station B.
Because the road is divided into different sections according to the coverage range of the base station, each section is responsible for one base station, and each section can calculate the approximate passing theoretical time according to the running speed of the vehicle. And finally, the whole model is built as follows:
| running speed of different base stations | A(ms) | B(ms) | C(ms) | D(ms) | E(ms) |
| 80KM/s | 4 | 5 | 3 | 2 | 6 |
| 100KM/s | 3.2 | 4 | 2.4 | 1.6 | 4.8 |
| 120KM/s | 2.67 | 3.33 | 2 | 1.33 | 4 |
The above table shows A, B, C, D, E theoretical travel times at different travel speeds within the coverage area of the base station, for example: the theoretical travel time within the coverage area of the base station A is 4ms, 3.2ms and 2.67ms, and the theoretical travel time within the coverage area of the base station B is 5ms, 4ms, 3.33ms and the like.
The time elapsed in the range of different base stations on the road is determined to be compared with the calculated theoretical time through statistical analysis of the interval time between the two different base stations of a large number of vehicles. And determining the final reasonable model parameter value to obtain the final system model.
Then, statistical analysis is carried out on the mobile phone signals of different vehicles, and if the experience time of the mobile phone signals in some nearby base stations is continuously increased, the situation that congestion occurs is indicated.
If there is some small difference between the time that the mobile phone signal passes through some base stations and the time that the model is established, there is a possibility that the weather condition is not good at that time, or the road traffic condition is not good, but there is no congestion.
The real-time perception and prediction of the road traffic flow are completed through the whole model.
The embodiment of the application provides a brand-new road traffic flow perception and prediction method, provides real-time road traffic flow conditions in a region (wide range), facilitates governments to cope with emergencies and make timely decisions, greatly reduces the cost of traffic flow perception and prediction, and greatly improves the perception and prediction precision of road traffic by carrying out segmentation processing on roads; in addition, according to historical traffic flow data statistics, data support can be provided for future urban road planning.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (9)
1. A traffic flow perception method is characterized by comprising the following steps:
determining a road to be perceived and a plurality of base stations near the road; each base station comprises signaling data for communicating with communication numbers of mobile equipment in the coverage range of a plurality of base stations;
determining time points of each mobile device accessing and leaving each base station according to the signaling data in the plurality of base stations;
calculating the movement parameters of each mobile device in the coverage range of each base station according to the coverage range of the base station and the time points of each mobile device accessing and leaving the same base station;
calculating to obtain the road traffic flow condition in the coverage area of each base station according to the mobile parameters of each mobile device in the coverage area of each base station so as to determine the traffic flow condition of the road;
the calculating the moving parameters of each mobile device in the coverage area of each base station according to the coverage area of the base station and the time points of each mobile device accessing and leaving the same base station comprises the following steps:
determining transceiving power of a second base station and transceiving power of a third base station which are adjacent to the first base station;
determining the coverage area of the first base station according to the ratio and the distance between the transceiving power of the first base station and the transceiving power of a second base station and the ratio and the distance between the transceiving power of the first base station and the transceiving power of a third base station;
and calculating the movement parameters of the mobile equipment in the coverage range of the first base station according to the coverage range of the first base station and the time points of the mobile equipment accessing and leaving the first base station.
2. The method of claim 1, wherein determining a plurality of base stations in the vicinity of the roadway comprises:
acquiring position information of a plurality of base stations in a preset area;
matching the road to be perceived with the position information of the base stations to obtain a plurality of base stations matched with the position of the road to be perceived;
and determining the base stations matched with the position of the road to be perceived as base stations nearby the road.
3. The method according to claim 2, wherein the road to be perceived is a first set of a plurality of consecutive location points, and the location information of the plurality of base stations is a second set of a plurality of location points;
the matching the road to be perceived with the position information of the base stations to obtain a plurality of base stations matched with the position of the road to be perceived comprises:
for each element in the second set, respectively performing distance calculation with each element in the first set, and determining the element in the first set with the minimum distance to the element in the second set;
obtaining a third set according to the element with the minimum element distance from the first set to the second set, wherein the third set comprises the position information of each base station and the distance value from the base station to the road position point closest to the base station;
and determining the base station corresponding to the element with the distance value smaller than the preset distance threshold value in the third set as the base station matched with the road position to be perceived.
4. The method of claim 1, wherein calculating the movement parameters of each mobile device within the coverage of each base station according to the coverage of the base station and the time points of each mobile device accessing and leaving the same base station comprises:
determining location information of a second base station and location information of a third base station adjacent to the first base station;
determining the coverage range of the first base station according to the distance between the first base station and the second base station and the distance between the first base station and the third base station;
and calculating the movement parameters of the mobile equipment in the coverage range of the first base station according to the coverage range of the first base station and the time points of the mobile equipment accessing and leaving the first base station.
5. The method of claim 1, further comprising:
acquiring position information of the mobile equipment on the road to be perceived;
predicting the mobile parameters of the mobile equipment in the coverage range of a base station according to the position information of the mobile equipment on the road to be perceived and a pre-established base station data model;
and predicting the traffic flow condition of the road to be perceived according to the movement parameters of a plurality of mobile devices in the coverage range of the base station.
6. The method of claim 5, wherein the base station data model building process comprises:
respectively calculating theoretical movement parameters of the mobile equipment sample passing through the coverage range of the base station under different preset vehicle speeds according to the coverage range of any base station for any mobile equipment sample;
and training according to the theoretical movement parameters and the actual movement parameters of the mobile equipment sample in the coverage range of the base station to obtain a base station data model.
7. A traffic flow perception device, comprising:
the device comprises a first determination module, a second determination module and a control module, wherein the first determination module is used for determining a road to be perceived;
a second determination module for determining a plurality of base stations in the vicinity of the road; each base station comprises signaling data for communicating with communication numbers of mobile equipment in the coverage range of a plurality of base stations;
a third determining module, configured to determine, according to the signaling data in the plurality of base stations, a time point when each mobile device accesses and leaves each base station;
the first calculation module is used for calculating the movement parameters of each mobile device in the coverage range of each base station according to the coverage range of the base station and the time points of each mobile device accessing and leaving the same base station;
the second calculation module is used for calculating the traffic flow condition in each base station coverage range according to the mobile parameters of each mobile device in each base station coverage range so as to sense the traffic flow condition of the road;
the first computing module, comprising:
a power determining unit for determining a transceiving power of a second base station and a transceiving power of a third base station adjacent to the first base station;
a second coverage area determining unit, configured to determine a coverage area of the first base station according to a ratio and a distance between the transceiving power of the first base station and the transceiving power of the second base station, and a ratio and a distance between the transceiving power of the first base station and the transceiving power of a third base station;
and the second parameter calculating unit is used for calculating the mobile parameters of the mobile equipment in the coverage range of the first base station according to the coverage range of the first base station and the time points of the mobile equipment accessing and leaving the first base station.
8. A computer storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, carries out the steps of the traffic flow perception method according to any one of claims 1 to 6.
9. An electronic device comprising one or more processors, and memory for storing one or more programs; the one or more programs, when executed by the one or more processors, implement the traffic flow awareness method of any of claims 1 to 6.
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