CN113643529B - Parking lot lane congestion prediction method and system based on big data analysis - Google Patents

Parking lot lane congestion prediction method and system based on big data analysis Download PDF

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CN113643529B
CN113643529B CN202110755035.6A CN202110755035A CN113643529B CN 113643529 B CN113643529 B CN 113643529B CN 202110755035 A CN202110755035 A CN 202110755035A CN 113643529 B CN113643529 B CN 113643529B
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parking lot
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acquiring
traffic
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CN113643529A (en
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吴福森
修晓英
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Xiamen Road & Bridge Information Co ltd
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    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125Traffic data processing
    • G08G1/0129Traffic data processing for creating historical data or processing based on historical data
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125Traffic data processing

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Abstract

The invention discloses a parking lot lane congestion prediction method, medium, equipment and system based on big data analysis, wherein the method comprises the following steps: acquiring historical data of a parking lot, and preprocessing the historical data of the parking lot; acquiring lane congestion basic data in the preprocessed parking lot historical data according to the traffic volume of each lane; acquiring inter-lane flow relation information, and determining inter-lane influence coefficients, remaining parking space influence coefficients and a traffic pressure threshold; acquiring real-time parking lot data, acquiring corresponding preprocessed parking lot historical data according to the current time corresponding to the real-time parking lot data, and calculating the corresponding traffic pressure of each lane; judging whether the traffic pressure corresponding to each lane is greater than a traffic pressure threshold value or not, and generating alarm information if the judgment result is yes; the method can automatically and effectively predict the users in the lanes of the parking lot, improve the accuracy of prediction results and reasonably guide the users to go out.

Description

Parking lot lane congestion prediction method and system based on big data analysis
Technical Field
The invention relates to the technical field of parking lot management, in particular to a parking lot lane congestion prediction method based on big data analysis, a computer readable storage medium, computer equipment and a parking lot lane congestion prediction system based on big data analysis.
Background
In the related art, when the congestion of the parking lot lane is predicted, the traffic volume of the corresponding lane in the future period is predicted according to the historical traffic volume of a single lane, and whether the lane is congested in the future period is predicted. However, the method does not consider a plurality of factors of lane congestion (such as remaining parking spaces in the scene, influence of traffic of other lanes and the like), and only predicts the traffic based on the historical traffic of a single lane; and further, the final prediction result is inaccurate, and reasonable guidance on the trip of the user is difficult.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the art described above. Therefore, one purpose of the present invention is to provide a parking lot lane congestion prediction method based on big data analysis, which can automatically and effectively predict users in a parking lot lane, improve the accuracy of prediction results, and reasonably guide the travel of the users.
A second object of the invention is to propose a computer-readable storage medium.
A third object of the invention is to propose a computer device.
The fourth purpose of the invention is to provide a parking lot lane congestion prediction system based on big data analysis.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides a method for predicting parking lot lane congestion based on big data analysis, including the following steps: acquiring historical data of a parking lot, and preprocessing the historical data of the parking lot, wherein the preprocessed historical data of the parking lot comprises a data generation time period, remaining parking spaces of the parking lot corresponding to the data generation time period and traffic volume of each lane; acquiring lane congestion basic data in the preprocessed parking lot historical data according to the traffic volume of each lane; acquiring inter-lane flow relation information, and determining inter-lane influence coefficients, remaining parking space influence coefficients and traffic pressure threshold values according to the inter-lane flow relation information and the lane congestion basic data; acquiring real-time parking lot data, acquiring corresponding preprocessed parking lot historical data according to current time corresponding to the real-time parking lot data, and calculating corresponding traffic pressure of each lane according to the real-time parking lot data, the corresponding preprocessed parking lot historical data, the remaining parking space influence coefficient and the inter-lane influence coefficient; and judging whether the traffic pressure corresponding to each lane is greater than the traffic pressure threshold value or not, and generating alarm information if the judgment result is yes.
According to the method for predicting the traffic jam of the parking lot lane based on the big data analysis, firstly, historical data of the parking lot are obtained, and the historical data of the parking lot are preprocessed, wherein the preprocessed historical data of the parking lot comprise a data generation time period, remaining parking spaces of the parking lot corresponding to the data generation time period and traffic volume of each lane; secondly, acquiring lane congestion basic data in the preprocessed parking lot historical data according to the traffic volume of each lane; then, acquiring inter-lane flow relation information, and determining inter-lane influence coefficients, remaining parking space influence coefficients and traffic pressure threshold values according to the inter-lane flow relation information and the lane congestion basic data; then, acquiring real-time parking lot data, acquiring corresponding preprocessed parking lot historical data according to the current time corresponding to the real-time parking lot data, and calculating the traffic pressure corresponding to each lane according to the real-time parking lot data, the corresponding preprocessed parking lot historical data, the remaining parking space influence coefficient and the inter-lane influence coefficient; then, judging whether the traffic pressure corresponding to each lane is greater than the traffic pressure threshold value, and generating alarm information if the judgment result is yes; therefore, effective prediction of the users in the lanes of the parking lot is automatically realized, the accuracy of the prediction result is improved, and reasonable guidance is provided for the traveling of the users.
In addition, the parking lot lane congestion prediction method based on big data analysis according to the above embodiment of the present invention may further have the following additional technical features:
optionally, acquiring lane congestion basic data in the preprocessed parking lot historical data according to the traffic volume of each lane, including: judging whether the traffic volume of the lane corresponding to any lane in any data generation time period is 0 or not; and if so, taking the parking lot historical data in the preset section as the lane congestion basic data.
Optionally, the traffic pressure corresponding to each lane is calculated according to the following formula:
Figure BDA0003145143660000021
wherein, P i Indicating the traffic pressure of lane i, Q i Representing the historical traffic volume, Q, of the lane i corresponding to the current time j Represents the historical traffic volume, k, of the lane j corresponding to the current time j Indicating lane k j Coefficient of influence on lane i, Z represents the real-time remaining space, k 0 And representing the influence coefficient of the remaining parking space.
In order to achieve the above object, a second embodiment of the present invention provides a computer-readable storage medium, on which a big data analysis-based parking lot lane congestion prediction program is stored, which when executed by a processor implements the big data analysis-based parking lot lane congestion prediction method as described above.
According to the computer-readable storage medium of the embodiment of the invention, the parking lot lane congestion prediction program based on big data analysis is stored, so that the processor can realize the parking lot lane congestion prediction method based on big data analysis when executing the parking lot lane congestion prediction program based on big data analysis, thereby realizing automatic effective prediction of users in lanes of the parking lot, improving the accuracy of prediction results and reasonably guiding the users to go out.
In order to achieve the above object, a third embodiment of the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for predicting the lane congestion of the parking lot based on the big data analysis.
According to the computer equipment provided by the embodiment of the invention, the parking lot lane congestion prediction program based on the big data analysis is stored through the memory, so that the processor can realize the parking lot lane congestion prediction method based on the big data analysis when executing the parking lot lane congestion prediction program based on the big data analysis, thereby realizing the automatic effective prediction of users in the parking lot lane, improving the accuracy of prediction results and reasonably guiding the travel of the users.
In order to achieve the above object, a fourth aspect of the present invention provides a system for predicting lane congestion of a parking lot based on big data analysis, including: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring historical data of a parking lot and preprocessing the historical data of the parking lot, and the preprocessed historical data of the parking lot comprises a data generation time period, remaining parking spaces of the parking lot corresponding to the data generation time period and traffic volume of each lane; the second acquisition module is used for acquiring lane congestion basic data in the preprocessed parking lot historical data according to the traffic volume of each lane; the first calculation module is used for acquiring inter-lane flow relation information and determining inter-lane influence coefficients, remaining parking space influence coefficients and traffic pressure threshold values according to the inter-lane flow relation information and the lane congestion basic data; the second calculation module is used for acquiring real-time parking lot data, acquiring corresponding preprocessed parking lot historical data according to the current time corresponding to the real-time parking lot data, and calculating the traffic pressure corresponding to each lane according to the real-time parking lot data, the corresponding preprocessed parking lot historical data, the remaining parking space influence coefficient and the inter-lane influence coefficient; and the judging module is used for judging whether the traffic pressure corresponding to each lane is greater than the traffic pressure threshold value or not and generating alarm information if the judging result is yes.
According to the parking lot lane congestion prediction system based on big data analysis, a first acquisition module is arranged for acquiring historical data of a parking lot and preprocessing the historical data of the parking lot, wherein the preprocessed historical data of the parking lot comprise a data generation time period, remaining parking spaces of the parking lot corresponding to the data generation time period and traffic volume of each lane; the second acquisition module is used for acquiring lane congestion basic data in the preprocessed parking lot historical data according to the traffic volume of each lane; the first calculation module is used for acquiring inter-lane flow relation information and determining an inter-lane influence coefficient, a remaining parking space influence coefficient and a traffic pressure threshold according to the inter-lane flow relation information and the lane congestion basic data; the second calculation module is used for acquiring real-time parking lot data, acquiring corresponding preprocessed parking lot historical data according to the current time corresponding to the real-time parking lot data, and calculating the corresponding traffic pressure of each lane according to the real-time parking lot data, the corresponding preprocessed parking lot historical data, the remaining parking space influence coefficient and the inter-lane influence coefficient; the judging module is used for judging whether the traffic pressure corresponding to each lane is greater than the traffic pressure threshold value or not and generating alarm information when the judging result is yes; therefore, effective prediction of the users in the lanes of the parking lot is automatically realized, the accuracy of the prediction result is improved, and reasonable guidance is provided for the traveling of the users.
In addition, the parking lot lane congestion prediction system based on big data analysis according to the above embodiment of the present invention may further have the following additional technical features:
optionally, acquiring lane congestion basic data in the preprocessed parking lot historical data according to the traffic volume of each lane, including: judging whether the traffic volume of the lane corresponding to any lane in any data generation time period is 0 or not; and if so, taking the parking lot historical data in the preset section as the lane congestion basic data.
Optionally, the traffic pressure corresponding to each lane is calculated according to the following formula:
Figure BDA0003145143660000041
wherein, P i Indicating the traffic pressure of lane i, Q i Representing the historical traffic volume, Q, of the lane i corresponding to the current time j Represents the historical traffic volume, k, of the lane j corresponding to the current time j Indicating lane k j Coefficient of influence on lane i, Z represents the real-time remaining space, k 0 And representing the influence coefficient of the remaining parking space.
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Fig. 1 is a schematic flowchart of a parking lot lane congestion prediction method based on big data analysis according to an embodiment of the present invention;
fig. 2 is a block diagram illustrating a parking lot lane congestion prediction system based on big data analysis according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
In the related technology, when the parking lot lane congestion is predicted, the prediction is simply carried out according to the historical traffic volume of a single lane; further, the final prediction result is inaccurate, and reasonable guidance on the trip of the user is difficult to perform; according to the method for predicting the traffic jam of the parking lot lane based on the big data analysis, firstly, historical data of the parking lot are obtained, wherein the preprocessed historical data of the parking lot comprise a data generation time period, remaining parking spaces of the parking lot corresponding to the data generation time period and traffic volume of each lane; secondly, acquiring lane congestion basic data in the preprocessed parking lot historical data according to the traffic volume of each lane; then, acquiring inter-lane flow relation information, and determining inter-lane influence coefficients, remaining parking space influence coefficients and traffic pressure threshold values according to the inter-lane flow relation information and the lane congestion basic data; then, acquiring real-time parking lot data, acquiring corresponding preprocessed parking lot historical data according to the current time corresponding to the real-time parking lot data, and calculating the traffic pressure corresponding to each lane according to the real-time parking lot data, the corresponding preprocessed parking lot historical data, the remaining parking space influence coefficient and the inter-lane influence coefficient; then, judging whether the traffic pressure corresponding to each lane is greater than the traffic pressure threshold value, and generating alarm information if the judgment result is yes; therefore, the method can automatically and effectively predict the users in the lane of the parking lot, improve the accuracy of the prediction result and reasonably guide the travel of the users.
In order to better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Fig. 1 is a flowchart illustrating a parking lot lane congestion prediction method based on big data analysis according to an embodiment of the present invention, and as shown in fig. 1, the parking lot lane congestion prediction method based on big data analysis includes the following steps:
s101, obtaining historical data of the parking lot, wherein the preprocessed historical data of the parking lot comprise data generation time periods, remaining parking spaces of the parking lot corresponding to the data generation time periods and traffic volumes of all lanes.
As an example, historical data of a parking lot is collected, then, the historical data of the parking lot is processed in a segmented manner according to a preset time period, traffic volumes of lanes (including entrance lanes and exit lanes) corresponding to each time period are counted, and remaining parking spaces of the parking lot in the time period are counted (specifically, the remaining parking spaces corresponding to each minute in the time period can be counted, and an average value of the remaining parking spaces corresponding to all minutes is taken as the remaining parking space corresponding to the time period); the preprocessed parking lot historical data comprise data generation time periods, remaining parking spaces of the parking lot corresponding to the data generation time periods and traffic volume of each lane, and follow-up data processing is facilitated.
As another example, first, the parking lot historical data is intercepted in a sliding window manner to obtain corresponding time period data; specifically, it is assumed that the parking lot history data is generated once per minute, and the generated history data per minute is arranged in sequence before and after the time; if the length of the sliding window is 30 minutes, intercepting the sliding window once every translation minute, and then acquiring data of a plurality of time periods; for example, after the sliding window intercepts data from 10:00 to 10:30 as time interval data, the sliding window translates for one minute and continues intercepting to obtain data from 10:01 to 10:31 as time interval data; and then, respectively counting the remaining parking spaces of the parking lot and the traffic volume of each lane corresponding to each time period to finish the preprocessing of the historical data. Therefore, analysis can be refined in this way, and the final prediction accuracy is improved.
And S102, acquiring lane congestion basic data in the preprocessed parking lot historical data according to the traffic volume of each lane.
In some embodiments, obtaining the lane congestion basic data in the parking lot history data according to the traffic of each lane includes: judging whether the traffic volume of the lane corresponding to any lane in any data generation time period is 0 or not; and if so, taking the parking lot historical data in the preset section as the lane congestion basic data.
As an example, it is assumed that the preprocessed parking lot historical data is time period data of a preset length acquired through a sliding window; further, it can be judged whether or not the traffic volume of the lane in the time zone data is 0; if so, determining that the data lane is jammed in the time interval; secondly, the preprocessed parking lot historical data in the preset interval corresponding to the time interval data are used as the basic data of lane congestion; specifically; if the traffic volume of the lane in the time period data of 10:01 to 10:30 is 0; the time period data of the corresponding preset interval 9:30 to 10:00 is used as the lane congestion basic data.
S103, obtaining the inter-lane flow relation information, and determining an inter-lane influence coefficient, a remaining parking space influence coefficient and a traffic pressure threshold according to the inter-lane flow relation information and the lane congestion basic data.
That is, inter-lane flow relationship information is acquired to determine whether traffic flows between lanes affect each other, and then an inter-lane influence coefficient, a remaining parking space influence coefficient, and a traffic pressure threshold are determined based on the inter-lane flow relationship information and the lane congestion basic data.
And S104, acquiring real-time parking lot data, acquiring corresponding preprocessed parking lot historical data according to the current time corresponding to the real-time parking lot data, and calculating the traffic pressure corresponding to each lane according to the real-time parking lot data, the corresponding preprocessed parking lot historical data, the remaining parking space influence coefficient and the inter-lane influence coefficient.
As an example, real-time parking lot data is acquired, wherein the real-time parking lot data comprises remaining parking spaces of a parking lot, lane traffic volume of each entrance lane, and lane traffic volume of each exit lane; then, acquiring corresponding preprocessed parking lot historical data according to the current time; specifically, assuming that the current time is 10:00 of 2021/4/12, time period data of the previous month corresponding thereto, i.e., time period data of 10:01 to 10:30 of 2021/3/12, is acquired according to the current time; or, assuming that the current time is 10:00 of 2021/4/12 and is Monday, acquiring the data corresponding to the current time in the same period of previous two weeks, namely the data of 10:01 to 10:30 of 2021/4/5 and the data of 10:01 to 10:30 of 2021/3/29; further, calculating the corresponding traffic pressure of each lane according to the real-time parking lot data, the corresponding preprocessed parking lot historical data, the remaining parking space influence coefficient and the inter-lane influence coefficient; specifically, the corresponding period data may be regarded as traffic data of a future period with reference to the current time; to calculate the traffic volume of the future time period according to the time period data; for example, assuming that the remaining space at the current time is 85, and the total traffic volume of the lane of the entrance lane at 10:01 and the total traffic volume of the lane of the exit lane at 10:01 to 10:30 in the corresponding time period data 2021/3/12 are 8 and 10, the obtained predicted data is 87 for the remaining space at 10:01 in the future time; or, assuming that the remaining parking space at the current time is 85, and in the data of 10:01 to 10:30 of corresponding time period data 2021/4/5 and the data of 10:01 to 10:30 of 2021/3/29, the inlet traffic of 10:01 of 2021/4/5 is 15 and the outlet traffic is 10; 2021/3/29, 10:01, has an inlet traffic of 8 and an outlet traffic of 10; furthermore, the corresponding data can be subjected to weight calculation according to the proximity degree of time; for example, let 2021/4/5 be 0.7 weighted and 2021/3/29 be 0.3 weighted; the final calculated future time 10:01 has an inlet traffic of 0.7 x 15+0.3 x 8-12.9, taking value 13; outlet throughput is 0.7 × 10+0.3 × 10 ═ 10; the remaining space at future time 10:01 is 82.
The calculation method of the traffic pressure can be various.
In some embodiments, the traffic pressure for each lane is calculated according to the following formula:
Figure BDA0003145143660000061
wherein, P i Indicating a lanei passing pressure, Q i Representing the historical traffic volume, Q, of the lane i corresponding to the current time j Represents the historical traffic volume, k, of the lane j corresponding to the current time j Indicating lane k j Coefficient of influence on lane i, Z represents the real-time remaining space, k 0 And representing the influence coefficient of the remaining parking space.
And S105, judging whether the traffic pressure corresponding to each lane is greater than a traffic pressure threshold value or not, and generating alarm information if the judgment result is yes.
In summary, according to the method for predicting the traffic jam of the parking lot lane based on big data analysis in the embodiment of the present invention, first, historical data of the parking lot is obtained, and the historical data of the parking lot is preprocessed, wherein the preprocessed historical data of the parking lot includes a data generation time period, remaining parking spaces of the parking lot corresponding to the data generation time period, and traffic volume of each lane; secondly, acquiring lane congestion basic data in the preprocessed parking lot historical data according to the traffic volume of each lane; then, acquiring inter-lane flow relation information, and determining inter-lane influence coefficients, remaining parking space influence coefficients and traffic pressure threshold values according to the inter-lane flow relation information and the lane congestion basic data; then, acquiring real-time parking lot data, acquiring corresponding preprocessed parking lot historical data according to the current time corresponding to the real-time parking lot data, and calculating the traffic pressure corresponding to each lane according to the real-time parking lot data, the corresponding preprocessed parking lot historical data, the remaining parking space influence coefficient and the inter-lane influence coefficient; then, judging whether the traffic pressure corresponding to each lane is greater than the traffic pressure threshold value, and generating alarm information if the judgment result is yes; therefore, effective prediction of the users in the lanes of the parking lot is automatically realized, the accuracy of the prediction result is improved, and reasonable guidance is provided for the traveling of the users.
In order to implement the above embodiments, an embodiment of the present invention proposes a computer-readable storage medium on which a large data analysis-based parking lot lane congestion prediction program is stored, which, when executed by a processor, implements the large data analysis-based parking lot lane congestion prediction method as described above.
According to the computer-readable storage medium of the embodiment of the invention, the parking lot lane congestion prediction program based on big data analysis is stored, so that the processor can realize the parking lot lane congestion prediction method based on big data analysis when executing the parking lot lane congestion prediction program based on big data analysis, thereby realizing automatic effective prediction of users in lanes of the parking lot, improving the accuracy of prediction results and reasonably guiding the users to go out.
In order to implement the above embodiments, an embodiment of the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the parking lot lane congestion prediction method based on big data analysis as described above is implemented.
According to the computer equipment provided by the embodiment of the invention, the parking lot lane congestion prediction program based on the big data analysis is stored through the memory, so that the processor can realize the parking lot lane congestion prediction method based on the big data analysis when executing the parking lot lane congestion prediction program based on the big data analysis, thereby realizing the automatic effective prediction of users in the parking lot lane, improving the accuracy of prediction results and reasonably guiding the travel of the users.
In order to implement the foregoing embodiment, an embodiment of the present invention provides a parking lot lane congestion prediction system based on big data analysis, and as shown in fig. 2, the parking lot lane congestion prediction system based on big data analysis includes: the device comprises a first acquisition module 10, a second acquisition module 20, a first calculation module 30, a second calculation module 40 and a judgment module 50.
The first obtaining module 10 is configured to obtain historical parking lot data and preprocess the historical parking lot data, where the preprocessed historical parking lot data includes a data generation time period, remaining parking spaces of the parking lot corresponding to the data generation time period, and traffic volume of each lane;
the second obtaining module 20 is configured to obtain lane congestion basic data in the preprocessed parking lot historical data according to traffic volume of each lane;
the first calculation module 30 is configured to obtain inter-lane flow relationship information, and determine an inter-lane influence coefficient, a remaining parking space influence coefficient, and a traffic pressure threshold according to the inter-lane flow relationship information and the lane congestion basic data;
the second calculating module 40 is configured to obtain real-time parking lot data, obtain corresponding preprocessed parking lot historical data according to current time corresponding to the real-time parking lot data, and calculate traffic pressure corresponding to each lane according to the real-time parking lot data, the corresponding parking lot historical data, remaining parking space influence coefficients, and inter-lane influence coefficients;
the judging module 50 is configured to judge whether the traffic pressure corresponding to each lane is greater than a traffic pressure threshold, and generate alarm information if the judgment result is yes.
In some embodiments, obtaining lane congestion basic data in the preprocessed parking lot history data according to the traffic of each lane comprises: judging whether the traffic volume of the lane corresponding to any lane in any data generation time period is 0 or not; and if so, taking the preprocessed parking lot historical data in the preset section as the lane congestion basic data.
In some embodiments, the traffic pressure for each lane is calculated according to the following formula:
Figure BDA0003145143660000081
wherein, P i Indicating the traffic pressure of lane i, Q i Representing the historical traffic volume, Q, of the lane i corresponding to the current time j Represents the historical traffic volume, k, of the lane j corresponding to the current time j Indicating lane k j Coefficient of influence on lane i, Z represents the real-time remaining space, k 0 And representing the influence coefficient of the remaining parking space.
In summary, according to the parking lot lane congestion prediction system based on big data analysis in the embodiment of the present invention, the first obtaining module is configured to obtain the parking lot historical data, and preprocess the parking lot historical data, where the preprocessed parking lot historical data includes a data generation time period, remaining parking spaces in the parking lot corresponding to the data generation time period, and traffic volume of each lane; the second acquisition module is used for acquiring lane congestion basic data in the preprocessed parking lot historical data according to the traffic volume of each lane; the first calculation module is used for acquiring inter-lane flow relation information and determining an inter-lane influence coefficient, a remaining parking space influence coefficient and a traffic pressure threshold according to the inter-lane flow relation information and the lane congestion basic data; the second calculation module is used for acquiring real-time parking lot data, acquiring corresponding preprocessed parking lot historical data according to the current time corresponding to the real-time parking lot data, and calculating the corresponding traffic pressure of each lane according to the real-time parking lot data, the corresponding preprocessed parking lot historical data, the remaining parking space influence coefficient and the inter-lane influence coefficient; the judging module is used for judging whether the traffic pressure corresponding to each lane is greater than the traffic pressure threshold value or not and generating alarm information when the judging result is yes; therefore, effective prediction of the users in the lanes of the parking lot is automatically realized, the accuracy of the prediction result is improved, and reasonable guidance is provided for the traveling of the users.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
In the description of the present invention, it is to be understood that the terms "first", "second" and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or may be connected through the use of two elements or the interaction of two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above should not be understood to necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (8)

1. A parking lot lane congestion prediction method based on big data analysis is characterized by comprising the following steps:
acquiring historical data of a parking lot, and preprocessing the historical data of the parking lot, wherein the preprocessed historical data of the parking lot comprises a data generation time period, remaining parking spaces of the parking lot corresponding to the data generation time period and traffic volume of each lane;
acquiring lane congestion basic data in the preprocessed parking lot historical data according to the traffic volume of each lane;
acquiring inter-lane flow relation information, and determining inter-lane influence coefficients, remaining parking space influence coefficients and traffic pressure threshold values according to the inter-lane flow relation information and the lane congestion basic data;
acquiring real-time parking lot data, acquiring corresponding preprocessed parking lot historical data according to current time corresponding to the real-time parking lot data, and calculating the corresponding traffic pressure of each lane according to the real-time parking lot data, the corresponding preprocessed parking lot historical data, the remaining parking space influence coefficient and the inter-lane influence coefficient;
and judging whether the traffic pressure corresponding to each lane is greater than the traffic pressure threshold value or not, and generating alarm information if the judgment result is yes.
2. The method for predicting the traffic jam of the parking lot based on the big data analysis as claimed in claim 1, wherein the step of obtaining the lane jam basic data in the preprocessed parking lot historical data according to the traffic volume of each lane comprises the following steps:
judging whether the traffic volume of the lane corresponding to any lane in any data generation time period is 0 or not;
and if so, taking the preprocessed parking lot historical data in the preset section as the lane congestion basic data.
3. The parking lot lane congestion prediction method based on big data analysis as claimed in claim 1, wherein the traffic pressure corresponding to each lane is calculated according to the following formula:
Figure FDA0003145143650000011
wherein, P i Indicating the traffic pressure of lane i, Q i Representing the historical traffic volume, Q, of the lane i corresponding to the current time j Represents the historical traffic volume, k, of the lane j corresponding to the current time j Indicating lane k j Coefficient of influence on lane i, Z represents the real-time remaining space, k 0 And representing the influence coefficient of the remaining parking space.
4. A computer-readable storage medium, on which a big data analysis-based parking lot lane congestion prediction program is stored, which when executed by a processor implements the big data analysis-based parking lot lane congestion prediction method according to any one of claims 1 to 3.
5. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the big data analysis based parking lot lane congestion prediction method according to any one of claims 1-3.
6. A parking lot lane congestion prediction system based on big data analysis is characterized by comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring historical data of a parking lot and preprocessing the historical data of the parking lot, and the preprocessed historical data of the parking lot comprises a data generation time period, remaining parking spaces of the parking lot corresponding to the data generation time period and traffic volume of each lane;
the second acquisition module is used for acquiring lane congestion basic data in the preprocessed parking lot historical data according to the traffic volume of each lane;
the first calculation module is used for acquiring inter-lane flow relation information and determining inter-lane influence coefficients, remaining parking space influence coefficients and traffic pressure threshold values according to the inter-lane flow relation information and the lane congestion basic data;
the second calculation module is used for acquiring real-time parking lot data, acquiring corresponding preprocessed parking lot historical data according to the current time corresponding to the real-time parking lot data, and calculating the corresponding traffic pressure of each lane according to the real-time parking lot data, the corresponding parking lot historical data, the remaining parking space influence coefficient and the inter-lane influence coefficient;
and the judging module is used for judging whether the traffic pressure corresponding to each lane is greater than the traffic pressure threshold value or not and generating alarm information when the judging result is yes.
7. The big data analysis-based parking lot lane congestion prediction system according to claim 6, wherein acquiring lane congestion basic data in the pre-processed parking lot history data according to the traffic volume of each lane comprises:
judging whether the traffic volume of the lane corresponding to any lane in any data generation time period is 0 or not;
and if so, taking the preprocessed parking lot historical data in the preset section as the lane congestion basic data.
8. The parking lot lane congestion prediction system based on big data analysis of claim 6, wherein the traffic pressure corresponding to each lane is calculated according to the following formula:
Figure FDA0003145143650000021
wherein, P i Indicating the traffic pressure of lane i, Q i Representing the historical traffic volume, Q, of the lane i corresponding to the current time j Represents the historical traffic volume, k, of the lane j corresponding to the current time j Indicating lane k j Coefficient of influence on lane i, Z represents the real-time remaining space, k 0 And representing the influence coefficient of the remaining parking space.
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