CN117746640A - Road traffic flow rolling prediction method, system, terminal and medium - Google Patents
Road traffic flow rolling prediction method, system, terminal and medium Download PDFInfo
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Abstract
The invention discloses a road traffic flow rolling prediction method, a system, a terminal and a medium, which relate to the technical field of intelligent traffic and have the technical scheme that: according to the invention, global analysis is carried out on the return situation of the vehicle traveling in all holiday events, return probability matrixes of different traveling mileage ranges and different stay time ranges are generated under the condition of considering traveling mileage, and the input quantity of the corresponding target entrance station to the vehicle with the target point at the target time is analyzed in a rolling way according to the traffic flow data of the target exit station which is driven out at different times, so that the traffic flows of different times can be accurately predicted, and reliable reference data can be provided for making a return plan in advance for people.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a road traffic flow rolling prediction method, a system, a terminal and a medium.
Background
With the popularization of household automobiles, more and more people choose to drive or ride automobiles to travel through traffic roads, and particularly during holidays, traffic accidents are extremely easy to be caused by higher traffic flow, so that traffic roads are congested. Under the condition of traffic road congestion, the vehicle continuously enters the traffic road to cause congestion aggravation, and the prediction of traffic flow of the traffic road can provide reference data for the travel of a driver to a certain extent, so that the self-control flow is realized.
At present, a traffic flow prediction method of a traffic road is mainly used for predicting according to historical data, and traffic flow data conforming to the current development trend is obtained by matching through mining the change trend characteristics of the traffic flow, so that the traffic flow prediction of the traffic road is realized, for example, a traffic flow prediction method based on a neural network model and a traffic flow prediction method based on similarity matching are realized. However, the above-mentioned traffic flow prediction methods are all considered from the perspective of overall traffic flow, and the influencing factors to be considered include various factors such as holiday duration, traffic flow of departure routes, climate conditions, and traffic accidents, so that the method is generally only suitable for short-term traffic flow prediction, and in the long-term traffic flow prediction process, the matching degree of the prediction result and the actual result is not high, so that it is difficult to provide accurate and reliable reference data for people to get back in holidays.
Therefore, how to study and design a road traffic flow rolling prediction method, system, terminal and medium capable of overcoming the defects is a problem which needs to be solved at present.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a road traffic flow rolling prediction method, a system, a terminal and a medium, which are used for analyzing the input quantity of vehicles with target points at target time by corresponding target entrance stations according to the traffic flow data of vehicles which are driven out of the target exit stations at different times in a rolling way, accurately predicting the traffic flows at different times and providing reliable reference data for making return plans in advance for people.
The technical aim of the invention is realized by the following technical scheme:
in a first aspect, a rolling prediction method for traffic flow of road traffic is provided, including the following steps:
acquiring holiday time length of a holiday event, generating return probability sequences of different stay time ranges under each driving mileage range according to the holiday time length, and forming a return probability matrix by a plurality of return probability sequences;
determining virtual points on the same road section on the opposite lane according to the target points on the traffic lane, and selecting all high-speed exit stations of the corresponding road section within a preset distance from the virtual points on the opposite lane as target exit stations;
extracting traffic flow data which are driven out at different times of each target exit station after the route virtual point positions;
determining the vehicle input amount of a target entrance station corresponding to the opposite side of a corresponding target exit station to a target point position at target time according to the return probability matrix and the vehicle flow data of a single target exit station;
calculating according to the sum of the vehicle input amounts of the target points at the target time by the plurality of target entrance stations to obtain the total running vehicle of the target points at the target time;
and determining the vehicle running density of the target point at the target time according to the total running vehicles of the target point and the number of lanes of the target point, and converting the vehicle running density into the congestion degree of the target point at the target time according to the standard running density.
Further, the expression of the return probability matrix is specifically:；
wherein,representing a return probability matrix; />Is indicated in the range +.>Lower and residence time in the range +.>The corresponding return probability; />Indicate->The driving mileage range corresponding to each mileage interval; />Indicate->A residence time range corresponding to each time interval; />Representing the mileage width of a single mileage interval; />A time width representing a single time interval; />Representing the division number of the mileage intervals; />Representing the number of divisions of the time interval.
When the invention generates the return probability matrix, the interval segmentation processing is carried out on the driving mileage and the stay time, so that the data operation amount of the return probability can be reduced, and the calculation of the total driving vehicle can be simpler.
Specifically, the calculation formula of the return probability is:wherein (1)>Indicating the residence time range +.>Average residence time of (2); />Representing a time pole function determined by the range; />Representing the coefficients of a function, defined by the divisions of time intervalsDetermining the division number and the division number of mileage intervals; />The representation function constant is determined by the dividing number of the time intervals and the dividing number of the mileage intervals; />Indicating a residence time lower limit; />Representing a lower limit value of the driving mileage;indicating the range +.>Is a vehicle having a vehicle speed, and is a vehicle speed.
Further, the preset distance is positively correlated with the holiday duration.
Further, the calculation formula of the vehicle input amount of the target entrance station corresponding to the opposite side of the target exit station to the target point at the target time is specifically as follows:
wherein,representing the target exit station +.>A target entry station corresponding to the opposite side; />Representing target entry station->For the target point position->At the target time +.>Is a vehicle input amount; />A start time representing a holiday duration; />An expiration time representing a holiday duration; />Representing the target exit station +.>At time->The number of vehicles that are driven out; />Representing the distance covered by the actual distance coveredAnd actual residence time->The matching return probability; />Representing mileage positions of high-speed entrance stations to which vehicle departure points belong; />Representing the target exit station +.>Is a mileage location of (a).
Further, the calculation formula of the total running vehicle of the target point position at the target time is specifically as follows:wherein (1)>Indicating all destination entry stations to destination point location +.>At the target time +.>Is a vehicle for traveling in the vehicle; />Representing target entry station->For the target point position->At the target time +.>Is a vehicle input amount; />Indicating the number of all target ingress stations.
Further, the method further comprises:
acquiring congestion degrees of a plurality of target points distributed at intervals in a traffic road section at the same target time;
and performing curve fitting on the plurality of congestion degrees by adopting a least square method to obtain a congestion trend line of the traffic road section, and performing visual display on the congestion trend line.
In a second aspect, a road traffic vehicle flow rolling prediction system is provided, comprising:
the probability generation module is used for acquiring holiday time of a holiday event, generating return probability sequences of different stay time ranges under each driving mileage range according to the holiday time, and forming a return probability matrix by a plurality of return probability sequences;
the station port analysis module is used for determining virtual points positioned on the same road section on the opposite lane according to the target points on the traffic lane, and selecting all high-speed exit stations of the corresponding road section within a preset distance from the virtual points on the opposite lane as target exit stations;
the data extraction module is used for extracting traffic flow data which are driven out at different times at each target exit station after the route virtual point positions;
the vehicle analysis module is used for determining the vehicle input quantity of the target entrance station corresponding to the opposite side of the corresponding target exit station to the target point at the target time according to the return probability matrix and the vehicle flow data of the single target exit station;
the vehicle summation module is used for calculating the total running vehicle of the target point position at the target time according to the sum of the vehicle input amounts of the target point positions at the target time by the plurality of target entrance stations;
the congestion analysis module is used for determining the vehicle running density of the target point at the target time according to the total running vehicles of the target point and the number of lanes of the target point, and converting the vehicle running density into the vehicle running density of the target point at the target time according to the standard running density
In a third aspect, a computer terminal is provided, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing a road traffic flow rolling prediction method according to any one of the first aspects when executing the program.
In a fourth aspect, a computer readable medium is provided, on which a computer program is stored, the computer program being executable by a processor to implement a road traffic flow rolling prediction method according to any one of the first aspects.
Compared with the prior art, the invention has the following beneficial effects:
1. the road traffic flow rolling prediction method provided by the invention carries out global analysis on the return situation of the vehicle travel in all holiday events, generates return probability matrixes of different travel mileage ranges and different stay time ranges under the condition of considering the travel mileage, and carries out rolling analysis on the input quantity of the corresponding target entrance station to the target point at the target time according to the traffic flow data of the target exit station at different times, thereby accurately predicting the traffic flow at different times and providing reliable reference data for making a return schedule for people in advance;
2. according to the invention, traffic accidents are not considered in the process of predicting the traffic flow, and the input quantity of the vehicle is obtained by carrying out accumulation operation according to the return probability in the return process of each vehicle, so that the error fluctuation is smaller when the vehicle flow prediction method is suitable for long-time traffic flow prediction;
3. when the invention generates the return probability matrix, the interval segmentation processing is carried out on the driving mileage and the stay time, so that the data operand of the return probability can be reduced, and the calculation of the total driving vehicle can be simpler;
4. the invention considers not only the influence of the residence time and the driving mileage on the return probability, but also the influence of the driving mileage on the change trend of the residence time, thereby generating the return probability of self-adaptive change;
5. according to the method, the congestion trend lines of the traffic road sections are obtained after curve fitting is carried out on the plurality of congestion degrees by adopting the least square method, and the prediction difficulty can be reduced when the road sections with a larger range are predicted.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention. In the drawings:
FIG. 1 is a flow chart in embodiment 1 of the present invention;
FIG. 2 is a schematic diagram showing the distribution of target points in embodiment 1 of the present invention;
fig. 3 is a system block diagram in embodiment 2 of the present invention.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
Example 1: a rolling prediction method for road traffic flow, as shown in figure 1, comprises the following steps:
s1: acquiring holiday time length of a holiday event, generating return probability sequences of different stay time ranges under each driving mileage range according to the holiday time length, and forming a return probability matrix by a plurality of return probability sequences;
s2: determining virtual points on the same road section on the opposite lane according to the target points on the traffic lane, and selecting all high-speed exit stations of the corresponding road section within a preset distance from the virtual points on the opposite lane as target exit stations;
s3: extracting traffic flow data which are driven out at different times of each target exit station after the route virtual point positions;
s4: determining the vehicle input amount of a target entrance station corresponding to the opposite side of a corresponding target exit station to a target point position at target time according to the return probability matrix and the vehicle flow data of a single target exit station;
s5: calculating according to the sum of the vehicle input amounts of the target points at the target time by the plurality of target entrance stations to obtain the total running vehicle of the target points at the target time;
s6: and determining the vehicle running density of the target point at the target time according to the total running vehicles of the target point and the number of lanes of the target point, and converting the vehicle running density into the congestion degree of the target point at the target time according to the standard running density.
The invention does not consider the occurrence of traffic accidents in the process of predicting the traffic flow, and the input quantity of the vehicle is obtained by carrying out accumulation operation according to the return probability in the return process of each vehicle, so that the error fluctuation is smaller when the invention is suitable for predicting the traffic flow for a longer time. The traffic road may be an expressway.
As shown in fig. 2, assume thatFor the target point location->Is->Virtual point of->Then it is the target exit station, and +.>Then it is the target entry station.
In this embodiment, the expression of the return probability matrix is specifically:；
wherein,representing a return probability matrix; />Is indicated in the range +.>Lower and residence time in the range +.>The corresponding return probability; />Indicate->The driving mileage range corresponding to each mileage interval; />Indicate->A residence time range corresponding to each time interval; />Representing the mileage width of a single mileage interval; />A time width representing a single time interval; />Representing the division number of the mileage intervals; />Representing the number of divisions of the time interval.
When the invention generates the return probability matrix, the interval segmentation processing is carried out on the driving mileage and the stay time, so that the data operation amount of the return probability can be reduced, and the calculation of the total driving vehicle can be simpler.
Specifically, the calculation formula of the return probability is:wherein (1)>Indicating the residence time range +.>Average residence time of (2); />Representing a time pole function determined by the range; />Representing function coefficients, wherein the function coefficients are determined by the dividing number of time intervals and the dividing number of mileage intervals; />The representation function constant is determined by the dividing number of the time intervals and the dividing number of the mileage intervals; />Indicating a residence time lower limit; />Representing a lower limit value of the driving mileage;indicating the range +.>Is a vehicle having a vehicle speed, and is a vehicle speed.
The invention not only considers the influence of the residence time and the driving mileage on the return probability, but also considers the influence of the driving mileage on the change trend of the residence time, thereby generating the return probability of self-adaptive change.
It should be noted that the preset distance is positively correlated with the holiday duration.
In this embodiment, a calculation formula of the vehicle input amount of the target entrance station corresponding to the opposite side of the target exit station to the target point at the target time is specifically:
wherein,representing the target exit station +.>A target entry station corresponding to the opposite side; />Representing target entry station->For the target point position->At the target time +.>Is a vehicle input amount; />A start time representing a holiday duration; />An expiration time representing a holiday duration; />Representing the target exit station +.>At time->The number of vehicles that are driven out; />Representing the distance covered by the actual distance coveredAnd actual residence time->The matching return probability; />Representing mileage positions of high-speed entrance stations to which vehicle departure points belong; />Representing the target exit station +.>Is a mileage location of (a).
In addition, the calculation formula of the total running vehicle of the target point position at the target time is specifically as follows:wherein (1)>Indicating all destination entry stations to destination point location +.>At the target time +.>Is a vehicle for traveling in the vehicle; />Representing target entry station->For the target point position->At the target time +.>Is a vehicle input amount; />Indicating the number of all target ingress stations.
In order to adapt to the traffic flow prediction of a longer road section, the road traffic flow rolling prediction method disclosed by the invention further comprises the following steps: acquiring congestion degrees of a plurality of target points distributed at intervals in a traffic road section at the same target time; and performing curve fitting on the plurality of congestion degrees by adopting a least square method to obtain a congestion trend line of the traffic road section, and performing visual display on the congestion trend line.
The above-described degree of congestion does not relate to analysis of a vehicle congestion condition, but is limited to a congestion condition caused by an increase in the vehicle flow rate.
Example 2: a road traffic flow rolling prediction system for implementing a road traffic flow rolling prediction method described in embodiment 1, as shown in fig. 3, includes a probability generation module, a station analysis module, a data extraction module, a vehicle analysis module, a vehicle summation module, and a congestion analysis module.
The probability generation module is used for acquiring holiday time of a holiday event, generating return probability sequences of different stay time ranges under each driving mileage range according to the holiday time, and forming a return probability matrix by the multiple return probability sequences; the station port analysis module is used for determining virtual points positioned on the same road section on the opposite lane according to the target points on the traffic lane, and selecting all high-speed exit stations of the corresponding road section within a preset distance from the virtual points on the opposite lane as target exit stations; the data extraction module is used for extracting traffic flow data which are driven out at different times at each target exit station after the route virtual point positions; the vehicle analysis module is used for determining the vehicle input quantity of the target entrance station corresponding to the opposite side of the corresponding target exit station to the target point at the target time according to the return probability matrix and the vehicle flow data of the single target exit station; the vehicle summation module is used for calculating the total running vehicle of the target point position at the target time according to the sum of the vehicle input amounts of the target point positions at the target time by the plurality of target entrance stations; the congestion analysis module is used for determining the vehicle running density of the target point at the target time according to the total running vehicles of the target point and the number of lanes of the target point, and converting the vehicle running density into the congestion degree of the target point at the target time according to the standard running density.
Working principle: according to the invention, global analysis is carried out on the return situation of the vehicle traveling in all holiday events, return probability matrixes of different traveling mileage ranges and different stay time ranges are generated under the condition of considering traveling mileage, and the input quantity of the corresponding target entrance station to the vehicle with the target point at the target time is analyzed in a rolling way according to the traffic flow data of the target exit station which is driven out at different times, so that the traffic flows of different times can be accurately predicted, and reliable reference data can be provided for making a return plan in advance for people.
It will be appreciated by those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or 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, 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.
The foregoing detailed description of the invention has been presented for purposes of illustration and description, and it should be understood that the invention is not limited to the particular embodiments disclosed, but is intended to cover all modifications, equivalents, alternatives, and improvements within the spirit and principles of the invention.
Claims (10)
1. The rolling prediction method for the traffic flow of the road is characterized by comprising the following steps of:
acquiring holiday time length of a holiday event, generating return probability sequences of different stay time ranges under each driving mileage range according to the holiday time length, and forming a return probability matrix by a plurality of return probability sequences;
determining virtual points on the same road section on the opposite lane according to the target points on the traffic lane, and selecting all high-speed exit stations of the corresponding road section within a preset distance from the virtual points on the opposite lane as target exit stations;
extracting traffic flow data which are driven out at different times of each target exit station after the route virtual point positions;
determining the vehicle input amount of a target entrance station corresponding to the opposite side of a corresponding target exit station to a target point position at target time according to the return probability matrix and the vehicle flow data of a single target exit station;
calculating according to the sum of the vehicle input amounts of the target points at the target time by the plurality of target entrance stations to obtain the total running vehicle of the target points at the target time;
and determining the vehicle running density of the target point at the target time according to the total running vehicles of the target point and the number of lanes of the target point, and converting the vehicle running density into the congestion degree of the target point at the target time according to the standard running density.
2. The method for predicting traffic flow of road according to claim 1, wherein the expression of the return probability matrix is specifically:
；
wherein,representing a return probability matrix; />Is indicated in the range +.>Lower and residence time in the range +.>The corresponding return probability; />Indicate->The driving mileage range corresponding to each mileage interval; />Indicate->A residence time range corresponding to each time interval; />Representing the mileage width of a single mileage interval; />A time width representing a single time interval; />Representing the division number of the mileage intervals; />Representing the number of divisions of the time interval.
3. The rolling prediction method of road traffic flow according to claim 2, wherein the calculation formula of the return probability is specifically:wherein (1)>Indicating the residence time range +.>Average residence time of (2); />Representing a time pole function determined by the range; />Representing function coefficients, wherein the function coefficients are determined by the dividing number of time intervals and the dividing number of mileage intervals; />The representation function constant is determined by the dividing number of the time intervals and the dividing number of the mileage intervals; />Indicating a residence time lower limit; />Representing a lower limit value of the driving mileage; />Indicating the range +.>Is a vehicle having a vehicle speed, and is a vehicle speed.
4. The method for predicting traffic flow of road traffic as recited in claim 1, wherein the preset distance is positively correlated with the holiday duration.
5. The method for predicting traffic flow of road according to claim 1, wherein the calculation formula of the input amount of the vehicle of the target entrance station corresponding to the opposite side of the target exit station to the target point at the target time is specifically as follows:；
wherein,representing the target exit station +.>A target entry station corresponding to the opposite side; />Representing target entry station->For the target point position->At the target time +.>Is a vehicle input amount; />A start time representing a holiday duration; />An expiration time representing a holiday duration; />Representing the target exit station +.>At time->The number of vehicles that are driven out; />Representing the actual driving distance +.>And actual residence time->The matching return probability; />Representing mileage positions of high-speed entrance stations to which vehicle departure points belong; />Representing the target exit station +.>Is a mileage location of (a).
6. The method for predicting traffic flow of road according to claim 5, wherein the calculation formula of the total running vehicles at the target point location at the target time is specifically as follows:wherein (1)>Indicating all destination entry stations to destination point location +.>At the target time +.>Is a vehicle for traveling in the vehicle; />Representing target entry station->For the target point position->At the target time +.>Is a vehicle input amount; />Indicating the number of all target ingress stations.
7. The method of claim 1, further comprising:
acquiring congestion degrees of a plurality of target points distributed at intervals in a traffic road section at the same target time;
and performing curve fitting on the plurality of congestion degrees by adopting a least square method to obtain a congestion trend line of the traffic road section, and performing visual display on the congestion trend line.
8. A road traffic vehicle flow rolling prediction system, comprising:
the probability generation module is used for acquiring holiday time of a holiday event, generating return probability sequences of different stay time ranges under each driving mileage range according to the holiday time, and forming a return probability matrix by a plurality of return probability sequences;
the station port analysis module is used for determining virtual points positioned on the same road section on the opposite lane according to the target points on the traffic lane, and selecting all high-speed exit stations of the corresponding road section within a preset distance from the virtual points on the opposite lane as target exit stations;
the data extraction module is used for extracting traffic flow data which are driven out at different times at each target exit station after the route virtual point positions;
the vehicle analysis module is used for determining the vehicle input quantity of the target entrance station corresponding to the opposite side of the corresponding target exit station to the target point at the target time according to the return probability matrix and the vehicle flow data of the single target exit station;
the vehicle summation module is used for calculating the total running vehicle of the target point position at the target time according to the sum of the vehicle input amounts of the target point positions at the target time by the plurality of target entrance stations;
the congestion analysis module is used for determining the vehicle running density of the target point at the target time according to the total running vehicles of the target point and the number of lanes of the target point, and converting the vehicle running density into the congestion degree of the target point at the target time according to the standard running density.
9. A computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements a road traffic flow rolling prediction method as claimed in any one of claims 1 to 7 when the program is executed by the processor.
10. A computer readable medium having stored thereon a computer program, wherein execution of the computer program by a processor implements a road traffic flow rolling prediction method according to any one of claims 1-7.
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| CN117994989A (en) * | 2024-04-02 | 2024-05-07 | 云南省公路科学技术研究院 | Traffic flow monitoring method, system, terminal and medium based on target deduplication |
| CN118314733A (en) * | 2024-06-07 | 2024-07-09 | 云南省交通科学研究院有限公司 | Traffic regulation and control method, system, terminal and medium based on vehicle carbon emission prediction |
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