CN116913073A - Road congestion prediction method, device, equipment and computer storage medium - Google Patents
Road congestion prediction method, device, equipment and computer storage medium Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 75
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/012—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from other sources than vehicle or roadside beacons, e.g. mobile networks
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract
The application discloses a road congestion prediction method, a device, equipment and a computer storage medium, comprising the following steps: acquiring signaling data of a base station; ordering the base station position information to obtain time sequence data of the user equipment; determining a user movement direction according to the base station position information; deleting data deviating from the target motion direction in the time sequence data by a vectorization screening method according to the motion direction of the user to obtain second communication data; matching with the matching information of the base station and the waypoints according to the position information of the rest base stations in the second communication data; calculating the average speed of the road section passing through the same road section in the same time period; determining a road congestion index according to the speed when the road is unblocked and the average speed of the road; and determining the road section as a congestion road section according to the road section congestion index, the behavior information of the user and the bearing information of the road section. According to the embodiment of the application, before traffic jam occurs, the jam condition is more accurately predicted, and the travel experience of travelers is practically improved.
Description
Technical Field
The application belongs to cloud computing big data edge computing, and particularly relates to a road congestion prediction method.
Background
Road traffic is responsible for the accessibility of passenger flows and logistics in various areas and the connection circulation of traffic between cities. Along with the rapid development of economy and the continuous improvement of the living standard of people, more and more people prefer to drive motor vehicles to go out, so that the traffic flow on the road is increased, accidents are frequent, and the frequency of traffic jam occurrence is higher and higher.
Therefore, before traffic congestion occurs, the congestion situation needs to be predicted more accurately, and the travel experience of the traveler is improved practically.
Disclosure of Invention
The embodiment of the application provides a road congestion prediction method, a device, equipment and a computer storage medium, which can more accurately predict congestion conditions before traffic congestion occurs and practically improve travel experience of travelers.
In one aspect, an embodiment of the present application provides a method for predicting road congestion, where the method includes:
acquiring signaling data of a base station in a preset time period, wherein the signaling data comprises first communication data of a user equipment in an area where the base station is located, and the first communication data comprises communication time and base station position information;
ordering the base station position information according to the communication time to obtain time sequence data of the user equipment;
determining a user movement direction according to the base station position information in the time sequence data;
deleting data deviating from the target motion direction in the time sequence data by a vectorization screening method according to the motion direction of the user to obtain second communication data;
matching with the matching information of the base station and the waypoints according to the position information of the rest base stations in the second communication data to obtain the path information of the user equipment;
calculating the average speed of the road section passing through the same road section in the same time period according to the path information;
determining a road congestion index according to the speed when the road is unblocked and the average speed of the road;
determining a congestion prediction index according to the road section congestion index, the behavior information of the user and the bearing information of the road section;
and when the congestion prediction index is larger than a preset threshold value, determining the road section as a congestion road section.
In one possible embodiment, deleting data deviating from the target motion direction in the time series data according to the motion direction of the user by using a vectorization screening method to obtain second communication data, including:
acquiring a starting point base station position of an area where a starting point of a user is located, an ending point base station position of an area where an ending point is located and a base station position of a passing area in a movement process;
determining a vector from the starting point base station position to the end point base station position, wherein an included angle between the vector and the vector from the starting point base station position to the base station position passing through in the motion process is formed, and the vector comprises a motion direction;
when the included angle is larger than a preset threshold value, determining the moving direction of the user deviated from the target;
deleting data deviating from the target movement direction in the time series data according to the movement direction of the user to obtain second communication data.
In one possible implementation, the target motion direction includes a direction from an area where the user starts to an area where the user ends.
In a possible implementation embodiment, before ordering the base station location information according to the communication time to obtain the time sequence data of the user equipment, the method further includes:
and eliminating repeated data in the signaling data.
In one possible implementation, determining the link congestion index from the speed at the time of smoothness and the average speed of the link includes:
determining a traffic index according to the speed when the road is unblocked and the average speed of the road section;
and selecting the traffic index meeting the preset condition as the congestion index.
In one possible implementation, after determining the link congestion index according to the speed at the time of smoothness and the average speed of the link, the method further includes:
and counting the congestion road sections according to the congestion index to obtain a congestion road section list.
In another aspect, an embodiment of the present application provides a road congestion prediction apparatus, including:
the acquisition module is used for acquiring signaling data of the base station in a preset time period, wherein the signaling data comprises first communication data of the user equipment in an area where the base station is located, and the first communication data comprises communication time and base station position information;
the sequencing module is used for sequencing the position information of the base station according to the communication time to obtain time sequence data of the user equipment;
the determining module is used for determining the movement direction of the user according to the base station position information in the time sequence data;
the deleting module is used for deleting the data deviating from the target motion direction in the time sequence data according to the motion direction of the user by a vectorization screening method to obtain second communication data;
the matching module is used for matching with the matching information of the base station and the waypoints according to the position information of the rest base stations in the second communication data to obtain the path information of the user equipment;
the calculation module is used for calculating the average speed of the road section passing through the same road section in the same time period according to the path information;
the determining module is also used for determining the road congestion index according to the speed during smooth traffic and the average speed of the road;
the determining module is also used for determining a congestion prediction index according to the road section congestion index, the behavior information of the user and the bearing information of the road section;
the determining module is further configured to determine that the road section is a congested road section when the congestion prediction index is greater than a preset threshold value.
In still another aspect, an embodiment of the present application provides a road congestion prediction apparatus, including:
a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements the method for predicting road congestion according to any one of the above.
In yet another aspect, embodiments of the present application provide a computer storage medium,
the computer readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement the road congestion prediction method of any of the above.
According to the road congestion prediction method, the device, the equipment and the computer storage medium, the base station positions are sequenced according to the communication time according to the acquired signaling data to obtain time sequence data of the user equipment, the user movement direction is determined according to the base station positions in the time sequence data, the data deviating from the target movement direction is deleted according to the user movement direction to obtain second communication data, the rest base station information in the second communication data is matched with the matching information of the base station and the road points to obtain path information of the user equipment, the average speed of a road section passing through the same road section in the same time period is calculated according to the path information, the road section congestion index is determined according to the speed and the average speed of the road section when the road is unblocked, the congestion prediction index is determined according to the road section congestion index, the behavior information of the user and the bearing information of the road section, and the congestion prediction situation is predicted according to the road section.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present application, the drawings that are needed to be used in the embodiments of the present application will be briefly described, and it is possible for a person skilled in the art to obtain other drawings according to these drawings without inventive effort.
Fig. 1 is a flow chart of a road congestion prediction method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a road congestion prediction apparatus according to another embodiment of the present application;
fig. 3 is a schematic structural diagram of a road congestion prediction apparatus according to still another embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail below with reference to the accompanying drawings and the detailed embodiments. It should be understood that the particular embodiments described herein are meant to be illustrative of the application only and not limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the application by showing examples of the application.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
Road traffic is responsible for the accessibility of passenger flows and logistics in various areas and the connection circulation of traffic between cities. Along with the rapid development of economy and the continuous improvement of the living standard of people, more and more people prefer to drive motor vehicles to go out, so that the traffic flow on the road is increased, accidents are frequent, and the frequency of traffic jam occurrence is higher and higher. Therefore, before traffic congestion occurs, the congestion situation needs to be predicted more accurately, and the travel experience of the traveler is improved practically.
In order to solve the problems in the prior art, the embodiment of the application provides a road congestion prediction method, a device, equipment and a computer storage medium. The following first describes a road congestion prediction method provided by the embodiment of the present application.
Fig. 1 is a schematic flow chart of a road congestion prediction method according to an embodiment of the present application. As shown in figure 1 of the drawings,
as shown in fig. 1, the road congestion prediction method provided by the embodiment of the application includes the following steps:
s101, acquiring signaling data of a base station in a preset time period, wherein the signaling data comprises first communication data of a user equipment in an area where the base station is located, and the first communication data comprises communication time and base station position information.
The preset time period may be a historical holiday, the signaling data of the base station may include first communication data of the user equipment in an area where the base station is located, the first communication data may include call data, change data, short message data, internet surfing behavior, communication time and base station location information, the call data may include a voice calling party and a voice called party, the change data includes cell switching and location updating, the short message data includes sending and receiving short messages, the base station location information may include an international mobile identification code, a number of a sector area corresponding to the base station, a city ID, a city name, a county name, a country number, a country name, a residence time, a scene name, a route number and a route name, and the communication time may include a year, a month and a day.
In some embodiments, the base station collects holiday signaling data, and selects seven ticket types and selected fields from the signaling data as shown in the following table:
according to the seven-big phone bill types at different moments and the selected fields, and according to the movement behaviors (stay time length) of the user, the data are collated to obtain first communication data, wherein the first communication data can be call data, change data, short message data, internet surfing behaviors, communication time and base station position information in a period of time.
S102, sequencing the position information of the base stations according to the communication time to obtain time sequence data of the user equipment.
The base station position information is ordered by communication time, the number of the sector area corresponding to the base station is changed along with the change of the communication time, and the base station position information sequence data is generated while the time sequence data is ordered by the communication time.
As an example, the base station location information is ordered according to the sequence of communication time, wherein the base station location information is base station location information of a period of time or a time point in different time periods, and the base station location information comprises the following information:
it should be noted that, as time varies, data is correspondingly changed, the data is ordered according to the communication time, and the obtained ordered base station position information is time sequence data (including the communication time).
S103, determining the movement direction of the user according to the base station position information in the time sequence data.
And determining the movement direction of the user according to the base station position information in the time sequence data, wherein when the base station position changes, the movement direction of the user can be determined by the change of the user relative to the front base station and the rear base station, and the vector from the front base station to the rear base station can represent the movement direction of the user.
As an example, when the user is in a motion state, in different time periods, whether the position of the base station is changed is determined based on the time of the user motion and the base station position corresponding to each time point, specifically, in time sequence, the motion direction of the user is represented based on the vector from the previous base station position to the next base station position, and the motion direction of the user is represented based on the direction of the vector identification.
And S104, deleting the data deviating from the target motion direction in the time series data by a vectorization screening method according to the user motion direction to obtain second communication data.
According to the movement direction of the user, whether the user moves in the direction deviating from the destination or not can be determined, and data deviating from the target movement direction in the time series data are deleted through a vectorization screening method, so that second communication data are obtained. The target movement direction may be the direction of the destination the user wants to reach. The vectorization screening method screens out some data meeting preset conditions according to the magnitude of the angle between vectors. The second communication data may include communication time and base station position information data of all users moving toward the destination.
And S105, matching with the matching information of the base station and the waypoints according to the position information of the rest base stations in the second communication data to obtain the path information of the user equipment.
The road information is obtained by disclosing map data, and can comprise frequently congested road point start points, road point end points, the level of the road where the road section is located, the length of the road section, the name of the road where the road section is located, whether the road where the road section is located is a double-lane, road section numbers, road point longitudes and road point dimensions, the road is digitized into road points according to the road information to be stored for identifying and using the digitized road points and the road section data, and the road points are connected in series to form the road. And matching the base station with the waypoint to obtain the matching information of the base station and the waypoint, and obtaining the position information of the rest base stations in the second communication data, and matching the position information with the matching information of the base station and the waypoint to obtain the path information of the user equipment.
In some embodiments, a base station waypoint matching table is constructed, and the matching information of the base station waypoints can be obtained from the base station waypoint matching table.
As an example, a base station waypoint matching table is constructed based on the shortest distance principle of the KDtree algorithm, and specifically includes the following steps: 1. first a leaf node is found inside the kd-tree containing the target point. 2. Taking the target point as the center of a circle and the distance from the target point to the leaf node sample example as the radius, a hypersphere is obtained, and the nearest neighbor point is necessarily inside the hypersphere. 3. And then returning to the parent node of the leaf node, checking whether the hyper-cuboid contained in another child node is intersected with the hyper-sphere, if so, searching whether the child node has a more near neighbor, if so, updating the nearest neighbor, and updating the hyper-sphere. 4. And directly returning to the parent node of the parent node, and continuing searching the nearest neighbor in another sub-tree. When the root node is traced back, the algorithm is ended, the nearest neighbor node stored at the moment is the nearest road point which is closest to the base station in the sector coverage area of the final nearest neighbor searching base station, the nearest neighbor node is used as the position of the base station sector mapped on the road, the one-to-one correspondence information table of each base station sector and the nearest road point is stored, and when the nearest road point of the base station sector is needed in the follow-up process, the base station road point matching table is constructed.
S106, calculating the average speed of the road section passing through the same road section in the same time period according to the path information.
And screening users passing through the same road section in the same time period, calculating the speed of each user according to the path information of the user equipment, further obtaining the average traffic flow speed of the road section in the time period, namely the road section speed, and obtaining the average speed of the road section by taking the average value of all the speeds passing through the road section.
As an example, there are a plurality of users on the same road section, the speeds of each user are different, the movement speed of each user with respect to the road surface is calculated, and the average speed of the road section is obtained by averaging the calculated plurality of movement speeds.
And S107, determining the road congestion index according to the speed during smooth traffic and the average speed of the road.
And acquiring complete data in a week of the holiday, selecting partial data as the speed when the road is unblocked according to a preset condition, and setting the congestion index according to the speed when the road is unblocked and the average speed of the road section.
As an example, taking a week of complete data of a non-holiday, all speeds of all road sections passing through the road are ordered from small to large, and two-thirds of the speeds are taken as speeds when the road is unblocked.
S108, determining a congestion prediction index according to the road section congestion index, the behavior information of the user and the bearing information of the road section.
The behavior information of the user may include that the bearer information of the road section is predetermined and may be obtained in advance. The user behavior information may include information that may be user occupancy distribution characteristics, traffic start and stop data, commuter travel characteristics, holiday travel rules, movement speed information, and the like. And obtaining the existing personnel density information of the target road section according to the congestion index and the user behavior information, and predicting the congestion condition at the next moment according to the ratio of the personnel density information to the road section bearing information as a congestion prediction index.
In some embodiments, partial users are screened out according to user information such as user age, sex and the like, continuous one period is analyzed according to average working time [09:00,18:00] of cities and sleeping time [00:00,8:00] of night, users with accumulated days longer than two hours and larger than working days in the same grid are screened out, the signaling data and the base station position are combined, resident location of the signaling data and the base station position is realized, whether the users are resident population of the places is judged, and on the basis of distinguishing resident users and short-term resident users, the corresponding relation between traffic partitions and base stations and traffic partitions determined in advance is utilized to further analyze and identify night residence places and daytime working place distribution of each user, so that the job location distribution characteristics of the users are obtained.
In some embodiments, aiming at the urban travel peak phenomenon, holiday travel characteristics, on the basis of distinguishing night living areas and daytime work place distribution, the commuting track of the peak in the morning and evening is tracked by identifying the commuting population, and the commuting travel characteristics of different dates, such as commuting departure and arrival time-varying conditions, the distribution of commuting time and distance and the like, are obtained.
In some embodiments, the holiday trip rule and the moving speed data of the user are obtained through signaling data such as holiday base station dotting, talking, surfing the internet and the like.
In some embodiments, the travel track of the user can be restored by combining with enough support of the switching sequence of the mobility management entity, so as to obtain the travel speed of the user and judge the traffic means adopted by the user.
In some embodiments, statistics is performed on the starting place and the target place of the user holiday travel in the grid based on space dimensions and the like, so that the user holiday travel traffic starting and stopping point OD data is obtained.
S109, when the congestion prediction index is larger than a preset threshold value, determining the road section as a congestion road section,
when the congestion prediction index is larger than a preset threshold value, the target road section is already congested or is about to be congested, and the congestion condition of the road at the next moment is predicted according to the congestion prediction index.
As an example, the congestion prediction index predicts the definition of the next time congestion situation as follows: the congestion prediction index is smaller than 1.2, which indicates smoothness, and is larger than 1.2 and smaller than or equal to 1.5, which indicates impending congestion; the congestion prediction index is greater than 1.5, indicating that congestion has occurred.
Therefore, before traffic jam occurs, the jam condition can be predicted more accurately, and the travel experience of travelers is improved practically.
Based on this, in some embodiments, before the above S102, it may further include:
and eliminating repeated data in the signaling data.
In some embodiments, culling may be performed for some duplicate data.
In some embodiments, the mobile phone can reject data generated by communication failure in the communication process, false switching data between two pieces of signaling data under the same base station in a short time, and some false data and drift data generated when instantaneous mobile acquisitions are too large.
Thus, the interference of abnormal data can be discharged, and the predicted result is more accurate.
Based on this, in some embodiments, the S104 may specifically include:
acquiring a starting point base station position of an area where a starting point of a user is located, an ending point base station position of an area where an ending point is located and a base station position of a passing area in a movement process;
determining a vector from the starting point base station position to the end point base station position, wherein an included angle between the vector and the vector from the starting point base station position to the base station position passing through in the motion process is formed, and the vector comprises a motion direction;
when the included angle is larger than a preset threshold value, determining the moving direction of the user deviated from the target;
deleting data deviating from the target movement direction in the time series data according to the movement direction of the user to obtain second communication data.
As an example, a starting base station position a of an area where a user starting point is located, an ending base station position D of an area where an ending point is located, and a base station position X of a passing area during movement are obtained, and a user passes through a plurality of intermediate base stations during movement, so that a plurality of base station positions X exist.
Calculating the included angle between the vector AD from the starting base station position to the ending base station position and the vector AX from each starting base station position to the base station position passing through in the moving process, when the included angle is smaller than 135 degrees, considering that the user does not deviate from the target moving direction, and when the included angle is larger than or equal to 135 degrees, considering that the user deviates from the target moving direction, deleting the data deviating from the target moving direction according to the moving direction of the user, and obtaining second communication data.
Therefore, the static user staying at one position for a long time can be identified, the user is a static user around the road with high probability, for example, the resident area along the road is not a participant in vehicle traffic on the road, the traffic participant which is temporarily appeared on the road and is not normally travelling along the road can be removed, the noise data of the part can be removed, the problems that the monitored expressway is partially overlapped with other roads according to the historical track judgment of a sample, the data of the monitored expressway is cleaned, the history data is not leaked, and the like are solved, so that the noise data exists in the sampled data.
Based on this, in some embodiments, the target movement direction may include a direction from an area where the user starts to an area where the user ends.
In this way we can clearly know the direction of the destination the user wants to reach.
Based on this, in some embodiments, the S107 may specifically include:
determining a traffic index according to the speed when the road is unblocked and the average speed of the road section;
and selecting the traffic index meeting the preset condition as the congestion index.
As an example, the traffic index is determined according to the ratio of the speed at the time of smooth traffic and the average speed of the road section, the traffic indexes of the same road section in the same time period in a week are sorted from small to large, two-thirds of the traffic indexes are taken as congestion indexes, and the severity of congestion can be defined according to the congestion indexes as follows: the congestion index is smaller than 1.2, which indicates smoothness, and the congestion index is larger than 1.2 and smaller than or equal to 1.5, which indicates slight congestion; the congestion index is greater than 1.5 and less than or equal to 1.8, indicating that there is moderate congestion, and the congestion index is greater than 1.8, indicating severe congestion.
Thus, the congestion state can be more intuitively judged according to the congestion index.
Based on this, in some embodiments, after S107 above, the method further comprises:
and counting the congestion road sections according to the congestion index to obtain a congestion road section list.
In some embodiments, different road segments can be dyed according to the congestion index, and the user can know the congestion condition of different road segments at the current time according to different colors.
In some embodiments, a request for opening the congestion interface from the user may also be received, and one sub-page in the main page of the navigation software may be opened, where the user may learn about the congestion of the road through the sub-page.
Therefore, the congestion road section list can be displayed on the navigation software, and the user can know the road congestion condition at the current time according to the congestion road section list.
Based on the road congestion prediction method provided by the embodiment, correspondingly, the application further provides a specific implementation mode of the road congestion prediction device. Please refer to the following examples.
Referring first to fig. 2, a road congestion prediction apparatus 200 provided in an embodiment of the present application includes:
the obtaining module 210 is configured to obtain signaling data of the base station in a preset time period, where the signaling data includes first communication data of the user equipment in an area where the base station is located, and the first communication data includes communication time and base station location information;
a ranking module 220, configured to rank the base station location information according to the communication time, so as to obtain time sequence data of the user equipment;
a determining module 230, configured to determine a user movement direction according to the base station location information in the time sequence data;
the deleting module 240 is configured to delete data deviating from the target motion direction in the time series data according to the user motion direction by using a vectorization screening method, so as to obtain second communication data;
a matching module 250, configured to match the remaining base station location information in the second communication data with matching information of the base station and the waypoint to obtain path information of the user equipment;
a calculating module 260, configured to calculate an average speed of a road segment passing through the same road segment in the same time period according to the path information;
the determining module 230 is further configured to determine a link congestion index according to the speed at the time of smoothness and the average speed of the link;
the determining module 230 is further configured to determine a congestion prediction index according to the congestion index of the road segment, the behavior information of the user, and the bearer information of the road segment;
the determining module 230 is further configured to determine that the road segment is a congested road segment when the congestion prediction index is greater than a preset threshold.
Based on this, in some embodiments, the deletion module 240 includes:
the acquisition unit is used for acquiring the starting point base station position of the area where the starting point of the user is located, the end point base station position of the area where the end point is located and the base station position of the passing area in the movement process;
the determining unit is used for determining the vector from the starting point base station position to the end point base station position, and the included angle between the vector and the vector from the starting point base station position to the base station position passing through in the moving process, wherein the vector comprises a moving direction;
the determining unit is further used for determining that the user deviates from the target movement direction when the included angle is larger than a preset threshold value;
and the deleting unit is used for deleting the data deviating from the target movement direction in the time sequence data according to the movement direction of the user to obtain second communication data.
Based on this, in some embodiments, the apparatus 200 further comprises a culling module:
for rejecting repeated data in signaling data.
Based on this, in some embodiments, the determination module 230 includes:
the determining unit is also used for determining the traffic index according to the speed during smooth traffic and the average speed of the road section;
and the selecting unit is used for selecting the traffic index meeting the preset condition as the congestion index.
Based on this, in some embodiments, the apparatus 200 further comprises a statistics module:
and the method is used for counting the congestion road sections according to the congestion index to obtain a congestion road section list.
The modules of the road congestion prediction apparatus provided in the embodiment of the present application can implement the functions of each step of the road congestion prediction method provided in fig. 1, and achieve the corresponding technical effects thereof, and for brevity description, will not be repeated here.
Fig. 3 shows a schematic hardware structure of a road congestion prediction apparatus according to an embodiment of the present application.
The road congestion prediction device may comprise a processor 301 and a memory 302 storing computer program instructions.
In particular, the processor 301 may include a central processing unit (Central Processing Unit, CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured as one or more integrated circuits implementing embodiments of the present application.
Memory 302 may include mass storage for data or instructions. By way of example, and not limitation, memory 302 may comprise a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. Memory 302 may include removable or non-removable (or fixed) media, where appropriate. Memory 302 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 302 is a non-volatile solid-state memory.
The Memory may include Read Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk storage media devices, optical storage media devices, flash Memory devices, electrical, optical, or other physical/tangible Memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors) it is operable to perform the operations described with reference to methods in accordance with aspects of the present disclosure.
The processor 301 implements any one of the road congestion prediction methods of the above embodiments by reading and executing computer program instructions stored in the memory 302.
In one example, the road congestion prediction device may also include a communication interface 303 and a bus 310. As shown in fig. 3, the processor 301, the memory 302, and the communication interface 303 are connected to each other by a bus 310 and perform communication with each other.
The communication interface 303 is mainly used to implement communication between each module, device, unit and/or apparatus in the embodiment of the present application.
Bus 310 includes hardware, software, or both, that couple components of the road congestion prediction device to each other. By way of example, and not limitation, the buses may include an accelerated graphics port (Accelerated Graphics Port, AGP) or other graphics Bus, an enhanced industry standard architecture (Extended Industry Standard Architecture, EISA) Bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an industry standard architecture (Industry Standard Architecture, ISA) Bus, an Infiniband interconnect, a low pin count (Linear Predictive Coding, LPC) Bus, a memory Bus, a micro channel architecture (MicroChannel Architecture, MCa) Bus, a peripheral component interconnect (Peripheral Component Interconnect, PCI) Bus, a PCI-Express (Peripheral Component Interconnect-X, PCI-X) Bus, a serial advanced technology attachment (Serial Advanced Technology Attachment, SATA) Bus, a video electronics standards association Local Bus (VLB) Bus, or other suitable Bus, or a combination of two or more of these. Bus 310 may include one or more buses, where appropriate. Although embodiments of the application have been described and illustrated with respect to a particular bus, the application contemplates any suitable bus or interconnect.
The road congestion prediction device may execute the road congestion prediction method in the embodiment of the present application based on the signaling data, thereby implementing the road congestion prediction method and apparatus described in connection with fig. 1 and 2.
In addition, in combination with the road congestion prediction method in the above embodiment, the embodiment of the present application may be implemented by providing a computer storage medium. The computer storage medium has stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the road congestion prediction methods of the above embodiments.
The application also provides a computer program product, wherein the instructions in the computer program product are executed by a processor of electronic equipment, so that the electronic equipment executes various processes for realizing any one of the road congestion prediction method embodiments.
It should be understood that the application is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after appreciating the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor Memory devices, read-Only Memory (ROM), flash Memory, erasable Read-Only Memory (Erasable Read Only Memory, EROM), floppy disks, compact discs (Compact Disc Read-Only Memory, CD-ROM), optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations 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, 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, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present application, and they should be included in the scope of the present application.
Claims (10)
1. A method of predicting road congestion, comprising:
acquiring signaling data of a base station in a preset time period, wherein the signaling data comprises first communication data of user equipment in an area where the base station is located, and the first communication data comprises communication time and base station position information;
ordering the base station position information according to the communication time to obtain time sequence data of user equipment;
determining a user movement direction according to the base station position information in the time sequence data;
deleting data deviating from the target movement direction in the time sequence data by a vectorization screening method according to the movement direction of the user to obtain second communication data;
matching with the matching information of the base station and the waypoints according to the position information of the rest base stations in the second communication data to obtain the path information of the user equipment;
calculating the average speed of the road section passing through the same road section in the same time period according to the path information;
determining a road section congestion index according to the speed when the road section is smooth and the average speed of the road section;
determining a congestion prediction index according to the road section congestion index, the behavior information of the user and the bearing information of the road section;
and when the congestion prediction index is larger than a preset threshold value, determining the road section as a congestion road section.
2. The method for predicting road congestion according to claim 1, wherein deleting data deviating from the target moving direction in the time-series data by a vectorization screening method according to the moving direction of the user to obtain the second communication data comprises:
acquiring a starting point base station position of an area where a starting point of a user is located, an ending point base station position of an area where an ending point is located and a base station position of a passing area in a movement process;
determining a vector from the starting point base station position to the end point base station position, wherein an included angle between the vector from the starting point base station position to the base station position passing in the motion process is formed, and the vector comprises a motion direction;
when the included angle is larger than a preset threshold value, determining that the user deviates from the target movement direction;
deleting the data deviating from the target movement direction in the time sequence data according to the movement direction of the user to obtain second communication data.
3. The road congestion prediction method according to claim 1 or 2, wherein the target movement direction includes a direction from an area where a user start point is located to an area where an end point is located.
4. The method according to claim 1, wherein before ordering the base station location information according to the communication time to obtain time-series data of the user equipment, the method further comprises:
and eliminating repeated data in the signaling data.
5. The method according to claim 1, wherein the determining the link congestion index from the speed at the time of smoothness and the average speed of the link comprises:
determining a traffic index according to the speed when the road section is unblocked and the average speed of the road section;
and selecting the traffic index meeting the preset condition as the congestion index.
6. The road congestion prediction method according to claim 1, wherein after determining a road congestion index from a speed at the time of smoothness and the average speed of the road segments, the method further comprises:
and counting the congestion road sections according to the congestion index to obtain a congestion road section list.
7. A road congestion prediction apparatus, the apparatus comprising:
the acquisition module is used for acquiring signaling data of the base station in a preset time period, wherein the signaling data comprises first communication data of the user equipment in an area where the base station is located, and the first communication data comprises communication time and base station position information;
the ordering module is used for ordering the base station position information according to the communication time to obtain time sequence data of the user equipment;
the determining module is used for determining the movement direction of the user according to the base station position information in the time sequence data;
the deleting module is used for deleting the data deviating from the target movement direction in the time sequence data according to the movement direction of the user by a vectorization screening method to obtain second communication data;
the matching module is used for matching with the matching information of the base station and the waypoints according to the position information of the rest base stations in the second communication data to obtain the path information of the user equipment;
the calculation module is used for calculating the average speed of the road section passing through the same road section in the same time period according to the path information;
the determining module is further used for determining a road section congestion index according to the speed during smooth traffic and the average speed of the road section;
the determining module is further configured to determine a congestion prediction index according to the congestion index of the road section, behavior information of the user, and load information of the road section;
and the determining module is further used for determining that the road section is a congestion road section when the congestion prediction index is greater than a preset threshold value.
8. A road congestion prediction apparatus, the apparatus comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements the method for predicting road congestion as claimed in any one of claims 1-6.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon computer program instructions, which when executed by a processor, implement a road congestion prediction method according to any of claims 1-6.
10. A computer program product, characterized in that instructions in the computer program product, when executed by a processor of an electronic device, enable the electronic device to perform the road congestion prediction method according to any of claims 1-6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117671965A (en) * | 2024-02-02 | 2024-03-08 | 北京大也智慧数据科技服务有限公司 | Data processing method, device, equipment and storage medium |
| CN117809460A (en) * | 2024-03-01 | 2024-04-02 | 电子科技大学 | Intelligent traffic regulation and control method and system |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117671965A (en) * | 2024-02-02 | 2024-03-08 | 北京大也智慧数据科技服务有限公司 | Data processing method, device, equipment and storage medium |
| CN117671965B (en) * | 2024-02-02 | 2024-06-18 | 北京大也智慧数据科技服务有限公司 | Method, device, equipment and storage medium for traffic diversion based on signaling data |
| CN117809460A (en) * | 2024-03-01 | 2024-04-02 | 电子科技大学 | Intelligent traffic regulation and control method and system |
| CN117809460B (en) * | 2024-03-01 | 2024-05-14 | 电子科技大学 | Intelligent traffic regulation and control method and system |
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