CN117746616A - Regional parking space management system based on big data analysis and application - Google Patents
Regional parking space management system based on big data analysis and application Download PDFInfo
- Publication number
- CN117746616A CN117746616A CN202311204544.5A CN202311204544A CN117746616A CN 117746616 A CN117746616 A CN 117746616A CN 202311204544 A CN202311204544 A CN 202311204544A CN 117746616 A CN117746616 A CN 117746616A
- Authority
- CN
- China
- Prior art keywords
- parking
- regional
- parking space
- area
- management system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007405 data analysis Methods 0.000 title claims abstract description 21
- 238000000034 method Methods 0.000 claims abstract description 44
- 230000008569 process Effects 0.000 claims abstract description 11
- 238000012545 processing Methods 0.000 claims abstract description 8
- 230000007306 turnover Effects 0.000 claims description 12
- 238000010586 diagram Methods 0.000 claims description 8
- 230000006698 induction Effects 0.000 claims description 6
- YHXISWVBGDMDLQ-UHFFFAOYSA-N moclobemide Chemical compound C1=CC(Cl)=CC=C1C(=O)NCCN1CCOCC1 YHXISWVBGDMDLQ-UHFFFAOYSA-N 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 4
- 230000004069 differentiation Effects 0.000 claims description 3
- 238000012800 visualization Methods 0.000 claims description 2
- 238000007726 management method Methods 0.000 description 21
- 238000012544 monitoring process Methods 0.000 description 8
- 230000000694 effects Effects 0.000 description 6
- 238000012986 modification Methods 0.000 description 6
- 230000004048 modification Effects 0.000 description 6
- 238000012417 linear regression Methods 0.000 description 5
- 238000010276 construction Methods 0.000 description 4
- 238000005457 optimization Methods 0.000 description 3
- 230000008447 perception Effects 0.000 description 3
- 238000002360 preparation method Methods 0.000 description 3
- 238000006467 substitution reaction Methods 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 206010039203 Road traffic accident Diseases 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention mainly relates to a regional parking space management system based on big data analysis, which comprises: the sensing layer is configured to collect initial information in an external environment of the system, namely, an area, classify the collected initial information and then transmit the classified initial information to the service layer; the service layer is configured to integrate, process and analyze the initial information, perform business logic processing to obtain regional statistical information, and provide the regional statistical information for the application layer; an application layer configured to process requests from underlying components of a user interface, network interface, etc., and invoke other lower level services to accomplish operations desired by a user. The system can remarkably improve the parking efficiency, reduce the parking waiting time and traffic jam, improve the regional parking environment, predict the parking data in the region and facilitate regional security personnel to conduct targeted adjustment on the parking management and control method.
Description
Technical Field
The invention mainly relates to the technical field of parking space management systems, in particular to an regional parking space management system based on big data analysis and application thereof.
Background
At present, the automobile keeping amount in China continuously rises, urban parking spaces are more and more tense, partial vehicles are difficult to park in non-parking areas such as two sides of a road and a shop entrance, traffic jam is seriously aggravated, and larger potential safety hazards exist. And public institutions such as universities and hospitals in partial areas need to bear certain social service functions, and social personnel and vehicles need to be received while the personnel and vehicles of the unit are received, so that the traffic pressure of cities is relieved. The area of the university is large, the road is narrow, the mobility of personnel is large and periodic, and a large number of non-motor vehicles shuttle, so that social vehicles are difficult to find a parking space quickly after entering a university campus, the parking space can be randomly parked and disorderly, traffic jam, parking disputes and even traffic accidents are easy to cause, and certain threat exists to the safety of teachers, students and students.
There are many factors for the campus parking difficulty problem, including: 1) The cost is high: the trees in the campus are luxuriant, the monitoring camera is easily affected by the grown trees, the campus trees need to be trimmed during construction, the construction cost is high, whether the trees are shielded or not needs to be observed regularly during later maintenance, and the operation and maintenance cost is high; 2) The accuracy is low: monitoring the parking space state of the camera; 3) Influence the college image: the parking spaces in the school district are distributed scattered, the number of the required monitoring cameras is large, and the high-pole forestation is unfavorable for the later construction of the campus; 4) The management difficulty is large: the manual management difficulty of the non-motor vehicle parking problem in the campus is high, and a real-time notification mode is not available; 5) The efficiency is low: without data support, campus manager can't have new parking stall planning reference, can't discover hidden parking problem in advance. Therefore, the development of a parking space management system with high parking efficiency, low monitoring cost, visualization and easy realization is necessary.
The foregoing background knowledge is intended to assist those of ordinary skill in the art in understanding the prior art that is closer to the present invention and to facilitate an understanding of the inventive concepts and aspects of the present application, and it should be understood that the foregoing background art should not be used to assess the novelty of the technical aspects of the present application without explicit evidence that such matter is disclosed prior to the filing date of the present application.
Disclosure of Invention
In order to solve at least one technical problem mentioned in the background art, the invention aims to provide a regional parking space management system based on big data analysis, which can obviously improve parking efficiency, reduce parking waiting time and traffic jam, improve regional parking environment, predict parking data in a region and facilitate regional security personnel to carry out targeted adjustment on a parking management method.
Regional parking space management system based on big data analysis includes:
the sensing layer is configured to collect initial information in an external environment of the system, namely, an area, classify the collected initial information and then transmit the classified initial information to the service layer;
the service layer is configured to integrate, process and analyze the initial information, perform business logic processing to obtain regional statistical information, and provide the regional statistical information for the application layer;
an application layer configured to process requests from underlying components of a user interface, network interface, etc., and invoke other lower level services to accomplish operations desired by a user.
As a preference for the technical solution of the present application, the initial information collected by the sensing layer includes: non-motor vehicle parking area information, whether a motor vehicle parking space is provided with a vehicle, and whether an in-place motor vehicle is provided with a parking standard.
As a preferable aspect of the present application, the non-motor vehicle parking area information includes a non-motor vehicle parking area empty amount, whether a motor vehicle is parked in the non-motor vehicle parking area, and whether a non-motor vehicle is parked at the periphery of the non-motor vehicle parking area.
As the optimization of the technical scheme, the sensing layer acquires initial information in the external environment, namely the area of the system through three detection modes of geomagnetism, radar and illuminance.
As a preferable implementation of the technical solution of the present application, the service layer is further input with other information in advance, including: a motor vehicle drive line, a location name near the motor vehicle drive line, a non-motor vehicle drive line, a location name near the non-motor vehicle drive line, a distance between the respective location names.
As a preference to the technical solution of the present application, the service layer is further configured to store the regional statistics in a database.
As an preference for the technical scheme of the application, the application layer is further configured to display the residual information of the parking area and guide the vehicle owner to park.
As the optimization of the technical scheme, the application layer is further configured to generate a visual model of regional parking thermodynamic diagram, regional parking time length analysis table, regional car owner portrait and the like.
As a preferred embodiment of the present application, the method for displaying the residual information of the parking area by the application layer includes:
1) Listing the guidance screen for displaying information on a motor vehicle driving route to guide the vehicle to search a proper parking area; and/or
2) The driver sends a request to the system through the mobile terminal equipment to acquire the residual data of each parking area, and can guide the vehicle to the parking area closest to the destination and with the residual after inputting the destination area, and guide the vehicle owner to park.
The sensing layer collects initial information in the area range, such as non-motor vehicle parking area information, whether vehicles are parked in parking spaces, whether motor vehicle parking is standard or not, and the like, the service layer generates an image parking thermodynamic diagram of a regional parking user according to the initial information, the thermodynamic diagram comprises the number of vacancies in each parking space area in the area, and real-time information publishing and inquiring service is provided for the public through the induction screen or mobile terminal equipment, so that the public can conveniently and rapidly find suitable regional parking.
As the optimization of the technical scheme, the service layer receives the information of the non-parked standard of the in-place motor vehicle, and the system automatically sends a notification to the owner of the motor vehicle.
As the preference to the technical scheme of the application, the service layer knows that the vehicle is not parked in the parking space according to the number of the parking spaces in the whole area, the number of the existing vehicles in the whole area and the number of the empty vehicles, and learns the final occurrence position of the illegal vehicle through the induction screen, so as to guide security personnel to go to the position for processing.
As the preference to the technical scheme of the application, security personnel observe that the non-motor vehicle is not parked in a specified area, interact with the campus intranet through scanning the two-dimensional code of the non-motor vehicle, acquire vehicle information, post a notification bill, and meanwhile, the platform notifies teachers and students of issuing a short message through an Arian short message service and processes the short message as soon as possible.
As the technical scheme is optimized, the service layer is further configured to predict parking data through an embedded prediction model and provide prediction results comprising single-day in-out traffic flow, daytime parking space utilization, night parking space utilization, average parking duration and parking space turnover rate for the application layer.
As a preference for the technical solution of the present application, the prediction model is specifically established through the following steps:
1) Obtaining parking data of two periods in an area, wherein the parking data comprises: the method comprises the following steps of (1) single-day vehicle inlet and outlet flow, daytime parking space utilization, night parking space utilization, average parking time length and parking space turnover rate;
2) The parking data is subjected to differential to obtain a first-order differential d 1 ＝[Y 2 -Y 1 ，Y 3 -Y 2 ，...，Y n -Y n-1 ，]Wherein Y is 1 ，Y 2 ，...，Y n Namely, the value of the original parking data Y at each moment is Y= [ Y ] 1 ，Y 2 ，...，Y i ，...，Y n ]，Y i A value indicating the i-th time;
3) AR fitting to a time series after differentiationThe AR (p) model is expressed asWherein c is a constant term, ">Coefficients representing the AR (p) model, namely autoregressive coefficients, d t-i Representing the value of the time series after differentiation at time point t-i, ε t Representing a random error term;
4) Based on the AR fitting, MA terms are added, and the MA (q) model is expressed as follows:
wherein θ j The coefficients representing the MA model, i.e. the moving average coefficients, ε t-j A value representing the random error term at a time point t-j; obtaining a moving average coefficient according to a least square method in autoregressive;
5) Combining the AR and MA parts gives a complete ARIMA (p, d, q) model:
wherein Y is t A value representing the current time t; y is Y t-i-d A value representing the past time t-i-d; d represents a few-order differential, typically a 1-order differential; r represents a special event coefficient; the prediction data in one future period in the parking lot is obtained, wherein the prediction data comprise the single-day vehicle inlet and outlet flow, the daytime parking space utilization rate, the night parking space utilization rate, the average parking time length and the parking space turnover rate, so that regional security personnel can conveniently and specifically adjust a parking management and control method.
As a preference for the technical solution of the present application, the period is at least one of week, month, quarter and year.
As a preferred embodiment of the present application, in the step 3), the autoregressive coefficients of the AR (p) model are estimated using a least squares methodThe goal is to minimize the sum of squares of residuals between observations and model predictions. The method comprises the following specific steps:
1, preparing data: preparing time-series data as y= [ Y ] 1 ，Y 2 ，...，Y n ]Form (iv);
2, setting the AR order: determining the order p of the AR model, namely taking the values of p moments into consideration;
3 building independent variables and dependent variables: constructing an independent variable X 'and an independent variable Y', wherein Y 'is the p-th to n-th elements of the time series data Y, and X' is the p-1-th to n-1-th elements of the time series data Y;
4, performing linear regression: linear regression using least squares method, estimating autoregressive coefficients of AR model
As a preference for the solution of the present application, in said step 3), linear regression and parameter estimation are automatically performed using statsmode in Python or lm function in R language. The order p of the AR model needs to be selected according to the characteristics and fitting effect of the data, and information criteria (such as AIC and BIC) and other indexes can be used for evaluating the advantages and disadvantages of the models with different orders.
As a preference for the technical solution of the present application, in the ARIMA (p, d, q) model, r=0 when no special event is determined to occur in the prediction period; when a special event occurs in the prediction period, the special event coefficient R is expressed by the extremely poor parking data in the last period when the special event occurs. The special event refers to an event that causes the regional parking data to vary drastically. When the area is a college, the special event includes, but is not limited to, at least one of new-born entrance, leave-on, school-held major activities, student communities, or social organization activities. When new entrance or major activities are carried out, the parking data in the period can be changed drastically compared with the period when no activities are carried out, for example, vehicles in a campus are increased in geometric multiple when new entrance is carried out, and vehicles in the campus are obviously reduced when the new entrance is carried out or during the fake-free period, so that special event factors are added into a prediction model, the single-day vehicle entering and exiting flow, the daytime parking space utilization, the night parking space utilization, the average parking time length and the parking space turnover rate in the next period can be predicted more accurately, the method is favorable for the regional security personnel to judge the number of vehicles entering and exiting the region and the parking time in the period when special events occur, and accordingly, the parking management and control method can be adjusted in a targeted manner, traffic jam or even traffic accidents caused by the special activities are avoided, and regional security is maintained to the greatest extent.
The regional parking space management system based on big data analysis is applied to parking management in colleges and universities.
According to the invention, parking information in a region such as a campus is acquired by adopting three detection modes of hardware equipment such as geomagnetism, radar and illuminance, parking spaces in the campus are managed, the parking information is integrated, processed and analyzed after being transmitted to a service layer, and the vacant parking space data are transmitted to a parking guidance screen or mobile terminal equipment of a vehicle owner in a wired or wireless mode, so that the vehicle owner is guided to drive the vehicle to rapidly and safely park in the region with vacant parking spaces. Meanwhile, the system supports a manager to randomly stop a non-motor vehicle or a motor vehicle in an area, the system automatically contacts a vehicle owner, the last position of the vehicle can be obtained through an induction screen, security personnel is guided to go to the position for processing, the security personnel shoots the vehicle through a handheld machine to obtain evidence, the vehicle number plate is associated with information of the vehicle owner in a campus system, and the management is carried out in a mode of off-line bill sticking and on-line short message notification of the vehicle owner.
The platform generates a visual model such as a college parking thermodynamic diagram, a college parking time length analysis table, a college main image and the like by acquiring parking data of a vehicle owner in a campus, further predicts the parking data in one period in the future in an area by utilizing the parking data prediction model, and provides a prediction result comprising single-day vehicle inlet and outlet flow, daily parking space utilization rate, night parking space utilization rate, average parking time length and parking space turnover rate for an application layer, so that regional security personnel can conveniently and specifically adjust a parking management and control method.
The beneficial effects of this application are:
1. the parking efficiency is improved: the parking guidance system can automatically adjust the allocation of the parking spaces by monitoring the use condition of the parking spaces in real time, so that a vehicle can quickly find the proper parking spaces, and the parking time and the waiting time are reduced.
2. Traffic congestion is reduced: the parking guidance system can automatically adjust the allocation of the parking spaces according to the real-time traffic condition, avoid overcrowding the parking spaces and reduce the risks of traffic jam and accidents.
3. The parking environment is improved: the parking guidance system can reasonably distribute the parking spaces by monitoring the use condition of the parking spaces in real time, improve the parking environment and improve the utilization rate of the parking lot.
4. The parking safety is improved: the parking guidance system can timely discover obstacles on the parking space through monitoring the service condition of the parking space in real time, avoid collision of vehicles and improve parking safety.
5. The parking cost is reduced: the parking guidance system can automatically adjust the allocation of the parking spaces by monitoring the use condition of the parking spaces in real time, reduce the parking times of vehicles and reduce the parking cost.
Drawings
To make the above and/or other objects, features, advantages and examples of the present invention more comprehensible, the accompanying drawings which are needed in the detailed description of the present invention are simply illustrative of the present invention and other drawings can be obtained without inventive effort for those skilled in the art.
FIG. 1 shows an overall system architecture diagram of an regional parking space management system based on big data analysis;
FIG. 2 shows an overall business flow diagram of an regional parking space management system based on big data analysis;
fig. 3 shows a flow chart of the treatment when it is found that there is stopping without specification.
Detailed Description
Suitable substitutions and/or modifications of the process parameters will be apparent to those skilled in the art from the disclosure herein, however, it is to be expressly pointed out that all such substitutions and/or modifications are intended to be encompassed by the present invention. While the products and methods of preparation of the present invention have been described in terms of preferred embodiments, it will be apparent to those skilled in the relevant art that variations and modifications can be made in the products and methods of preparation described herein without departing from the spirit and scope of the invention.
Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The present invention uses the methods and materials described herein; other suitable methods and materials known in the art may be used. The materials, methods, and examples described herein are illustrative only and not intended to be limiting. All publications, patent applications, patents, provisional applications, database entries, and other references mentioned herein, and the like, are incorporated herein by reference in their entirety. In case of conflict, the present specification, including definitions, will control.
Unless specifically stated otherwise, the materials, methods, and examples described herein are illustrative only and not intended to be limiting. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described herein.
The present invention is described in detail below.
Example 1:
the regional parking space management system based on big data analysis comprises a perception layer, a service layer and an application layer from bottom to top, wherein the service layer is also called a cloud platform layer, the perception layer, the service layer and the application layer are connected in a wired and/or wireless mode, and the whole system architecture is shown in figure 1.
The sensing layer is used as a bottom layer of the system and is configured to collect initial information in an external environment, namely an area, of the system in three detection modes of geomagnetism, radar and illuminance, wherein the initial information comprises: the method comprises the steps of positioning a non-motor vehicle parking area, leaving a non-motor vehicle parking area, judging whether a motor vehicle is parked in the non-motor vehicle parking area, judging whether a non-motor vehicle is parked at the periphery of the non-motor vehicle parking area, judging whether a vehicle is parked in a motor vehicle parking space, and judging whether a motor vehicle is parked in place; and classifying the acquired initial information, and then transmitting the initial information to a service layer. As shown in fig. 2, the overall business flow of the system includes: the sensing layer detects whether a car exists in the parking space state, the data packet is wirelessly transmitted to a service layer of the system, the service layer is used for wirelessly and/or wiredly transmitting parking data in each area to a control card of a service layer guidance screen after finishing parking data arrangement, and the guidance screen displays residual information through a control card protocol to guide a car owner to park.
The service layer is a cloud platform layer and is configured to integrate, process and analyze the initial information transmitted by the perception layer and other information input in advance, wherein the other information comprises: a motor vehicle driving route, a place name near the motor vehicle driving route, a non-motor vehicle driving route, a place name near the non-motor vehicle driving route, and a distance between the place names; and then carrying out service logic processing to obtain regional statistical information, and providing the regional statistical information for an application layer.
The application layer is further configured to display the residual information of the parking area and guide the vehicle owner to park, and the method comprises the following steps:
1) Listing the guidance screen for displaying information on a motor vehicle driving route to guide the vehicle to search a proper parking area; or alternatively
2) The driver sends a request to the system through the mobile terminal equipment to acquire the residual data of each parking area, and can guide the vehicle to the parking area closest to the destination and with the residual after inputting the destination area, and guide the vehicle owner to park. Through the display of the induction screen, the vehicle can quickly find a proper parking area with a residual position, so that the vehicle is prevented from staying on a motor vehicle driving route for a long time, and the parking is prevented from being too crowded; the further system can guide the driver to go to the parking area closest to the destination after the driver inputs the destination, so that not only is the parking efficiency improved, but also the vehicle can be parked in the parking area near the destination of the driver, the walking distance and time after the driver parks are reduced, and the working efficiency is improved.
The service layer is further configured to store the region statistics in a database.
The application layer is further configured to generate a visual model of regional parking thermodynamic diagrams, regional parking duration analysis tables, regional vehicle owner representations and the like.
The service layer receives information of the non-parked standard of the in-place motor vehicle, and the system automatically sends notification to the owner of the motor vehicle.
The service layer obtains that vehicles are not parked in the parking spaces according to the number of the parking spaces in the whole area, the number of existing vehicles in the whole area and the number of empty vehicles, obtains the final occurrence position of the illegal vehicles through the induction screen, and guides security personnel to go to the position for processing.
As shown in fig. 3, for a non-motor vehicle which is not parked according to the regulations, if security personnel observe that the non-motor vehicle is not parked in the regulated area, the security personnel can obtain vehicle information by scanning two-dimension codes of the non-motor vehicle to interact with the campus intranet, and post a notice, and meanwhile, the platform notifies teachers and students of issuing a short message through an ali cloud short message service to process as soon as possible.
An application layer configured to process requests from underlying components of a user interface, network interface, etc., and invoke other lower level services to accomplish operations desired by a user.
Example 2:
based on the foregoing embodiment, the regional parking space management system based on big data analysis is perfected, specifically, a prediction model is embedded in a service layer to predict parking data including single-day vehicle in-out flow, daytime parking space utilization, night parking space utilization, average parking time length and parking space turnover rate, and a prediction result is provided to an application layer.
The predictive model is specifically built via the following steps 1) to 5).
Step 1), obtaining parking data of last two months in an area, which specifically comprises the following steps:
traffic flow in and out on a single day: the number of vehicles entering and exiting the area in a single day;
daytime parking space utilization rate: the ratio of the actual number of parking spaces to the total number of the parking spaces in the parking lot in the daytime (08:00-20:00), counting each hour, and finally taking an average value to represent the current day;
the utilization rate of parking spaces at night: the ratio of the actual number of the parking spaces to the total number of the parking spaces in the parking lot at night (20:00-the next day 08:00), counting every hour, and finally taking an average value to represent the current day;
average parking duration: calculating the average residence time of the daily departure vehicle in the parking lot;
the parking space turnover rate represents the average turnover number of the vehicle per day calculated by dividing the number of vehicles entering the parking lot per day by the number of berths in the parking lot.
Step 2), differentiating the parking data in the step 1) to obtain a first-order difference d 1 ＝[Y 2 -Y 1 ，Y 3 -Y 2 ，...，Y n -Y n-1 ，]Wherein Y is 1 ，Y 2 ，...，Y n Namely, the value of the original parking data Y at each moment is Y= [ Y ] 1 ，Y 2 ，...，Y i ，...，Y n ]，Y i A value indicating the i-th time.
Step 3), AR fitting is carried out on the time sequence after the difference, and an AR (p) model is expressed asWherein c is a constant term, ">Coefficients representing the AR (p) model, namely autoregressive coefficients, d t-i Representing the value of the time series after differentiation at time point t-i, ε t Representing a random error term.
Estimating autoregressive coefficients of an AR (p) model using least squaresThe goal is to minimize the sum of squares of residuals between observations and model predictions. The method comprises the following specific steps:
3.1 preparation of data: preparing time-series data as y= [ Y ] 1 ，Y 2 ，...，Y n ]Form (iv);
3.2 setting AR order: determining the order p of the AR model, namely taking the values of p moments into consideration;
3.3 construction of independent and dependent variables: constructing an independent variable X 'and an independent variable Y', wherein Y 'is the p-th to n-th elements of the time series data Y, and X' is the p-1-th to n-1-th elements of the time series data Y;
3.4 performing linear regression: linear regression using least squares method, estimating autoregressive coefficients of AR model
Step 4), adding MA items on the basis of AR fitting, wherein the MA (q) model is expressed as follows:
wherein θ j The coefficients representing the MA model, i.e. the moving average coefficients, ε t-j A value representing the random error term at a time point t-j; the moving average coefficient is obtained from the least square method in autoregressive.
Step 5), combining the AR and MA parts to obtain a complete ARIMA (p, d, q) model:
wherein Y is t A value representing the current time t; y is Y t-i-d A value representing the past time t-i-d; d represents a few-order differential, typically a 1-order differential; r represents a special event coefficient; the prediction data in one future period in the parking lot is obtained, wherein the prediction data comprise the single-day vehicle inlet and outlet flow, the daytime parking space utilization rate, the night parking space utilization rate, the average parking time length and the parking space turnover rate, so that regional security personnel can conveniently and specifically adjust a parking management and control method.
In the ARIMA (p, d, q) model, R=0 when no special event is determined to occur in a prediction period; when a special event occurs in the prediction period, the special event coefficient R is expressed by the extremely poor of the parking data in the period which is the last period when the special event occurs, and the special event occurs only at the moment t. If for certain universities, 1-4 days of 9 months each year are new entrance time, the single-day vehicle entering and exiting flow, the daytime parking space utilization rate and the average parking time length in the period of several days are rapidly increased, and for predicting the 9-month parking data of the present year, the extremely poor of the 9-month parking data of the last year is considered on the basis of the 9-month 1-4-day parking data prediction result, and the application of special event coefficients can finely quantify the influence of special events on regional parking, so that regional security personnel can easily predict the parking data when the special events occur, thereby pertinently adjusting a parking management and control method and maintaining regional security.
The conventional technology in the above embodiments is known to those skilled in the art, and thus is not described in detail herein.
The specific embodiments described herein are offered by way of example only to illustrate the spirit of the invention. Various modifications or additions to the described embodiments may be made by those skilled in the art to which the invention pertains or may be substituted in a similar manner without departing from the spirit of the invention or beyond the scope of the appended claims.
While the invention has been described in detail and with reference to specific embodiments thereof, it will be apparent to one skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope thereof.
While the above detailed description has shown, described, and pointed out novel features as applied to various embodiments, it will be understood that various omissions, substitutions, and changes in the form and details of the device or method illustrated may be made without departing from the spirit of the disclosure. In addition, the various features and methods described above may be used independently of one another, or may be combined in various ways. All possible combinations and subcombinations are intended to fall within the scope of this disclosure. Many of the embodiments described above include similar components, and thus, these similar components are interchangeable in different embodiments. While the invention has been disclosed in the context of certain embodiments and examples, it will be understood by those skilled in the art that the invention extends beyond the specifically disclosed embodiments to other alternative embodiments and/or uses and obvious modifications and equivalents thereof. Therefore, the present invention is not intended to be limited by the specific disclosure of the preferred embodiments herein.
The invention is a well-known technique.
Claims (10)
1. Regional parking stall management system based on big data analysis, its characterized in that includes:
the sensing layer is configured to collect initial information in an external environment of the system, namely, an area, classify the collected initial information and then transmit the classified initial information to the service layer;
the service layer is configured to integrate, process and analyze the initial information, perform business logic processing to obtain regional statistical information, and provide the regional statistical information for the application layer;
an application layer configured to process requests from underlying components of a user interface, network interface, etc., and invoke other lower level services to accomplish operations desired by a user.
2. The regional parking space management system based on big data analysis of claim 1, wherein: the sensing layer acquires initial information in an external environment, namely an area of the system in three detection modes of geomagnetism, radar and illuminance.
3. The regional parking space management system based on big data analysis according to claim 1 or 2, wherein: the application layer is also configured to display the residual information of the parking area and guide the vehicle owner to park.
4. The regional parking space management system based on big data analysis of claim 3, wherein: the method for displaying the residual information of the parking area by the application layer comprises the following steps:
1) Listing the guidance screen for displaying information on a motor vehicle driving route to guide the vehicle to search a proper parking area; and/or
2) The driver sends a request to the system through the mobile terminal equipment to acquire the residual data of each parking area, and can guide the vehicle to the parking area closest to the destination and with the residual after inputting the destination area, and guide the vehicle owner to park.
5. The regional parking space management system based on big data analysis of claim 1, 2 or 4, wherein: the application layer is further configured to generate a regional parking thermodynamic diagram, a regional parking duration analysis table, and a regional vehicle owner representation visualization model.
6. The regional parking space management system based on big data analysis of claim 1, 2 or 4, wherein: the service layer obtains that vehicles are not parked in the parking spaces according to the number of the parking spaces in the whole area, the number of existing vehicles in the whole area and the number of empty vehicles, obtains the final occurrence position of the illegal vehicles through the induction screen, and guides security personnel to go to the position for processing.
7. The regional parking space management system based on big data analysis of claim 1, 2 or 4, wherein: the service layer is further configured to predict parking data through an embedded prediction model, and provide prediction results including single-day in-out traffic flow, daytime parking space utilization, night parking space utilization, average parking duration and parking space turnover rate to the application layer.
8. The regional parking space management system based on big data analysis of claim 7, wherein: the prediction model is specifically built through the following steps:
1) Obtaining parking data of two periods in an area, wherein the parking data comprises: the method comprises the following steps of (1) single-day vehicle inlet and outlet flow, daytime parking space utilization, night parking space utilization, average parking time length and parking space turnover rate;
2) The parking data is subjected to differential to obtain a first-order differential d 1 ＝[Y 2 -Y 1 ,Y 3 -Y 2 ,…,Y n -Y n-1 ,]Wherein Y is 1 ,Y 2 ,…,Y n Namely, the value of the original parking data Y at each moment is Y= [ Y ] 1 ,Y 2 ,…,Y i ,…,Y n ]，Y i A value indicating the i-th time;
3) AR fitting is performed on the time series after the difference, and an AR (p) model is expressed as Wherein c is a constant term, ">Coefficients representing the AR (p) model, namely autoregressive coefficients, d t-i Representing the value of the time series after differentiation at time point t-i, ε t Representing a random error term;
4) Based on the AR fitting, MA terms are added, and the MA (q) model is expressed as follows:
wherein θ j The coefficients representing the MA model, i.e. the moving average coefficients, ε t-j A value representing the random error term at a time point t-j; obtaining a moving average coefficient according to a least square method in autoregressive;
5) Combining the AR and MA parts gives a complete ARIMA (p, d, q) model:
wherein Y is t A value representing the current time t; y is Y t-i-d A value representing the past time t-i-d; d represents the differential order; r represents a special event coefficient; the prediction data in one future period in the parking lot is obtained, wherein the prediction data comprise the single-day vehicle inlet and outlet flow, the daytime parking space utilization rate, the night parking space utilization rate, the average parking time length and the parking space turnover rate, so that regional security personnel can conveniently and specifically adjust a parking management and control method.
9. The regional parking space management system based on big data analysis of claim 8, wherein: in the ARIMA (p, d, q) model, R=0 when no special event is determined to occur in a prediction period; when a special event occurs in the prediction period, the special event coefficient R is expressed by the extremely poor parking data in the last period when the special event occurs.
10. Use of the regional parking space management system based on big data analysis of any of claims 1-9 in parking management at universities.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311204544.5A CN117746616A (en) | 2023-09-18 | 2023-09-18 | Regional parking space management system based on big data analysis and application |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311204544.5A CN117746616A (en) | 2023-09-18 | 2023-09-18 | Regional parking space management system based on big data analysis and application |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117746616A true CN117746616A (en) | 2024-03-22 |
Family
ID=90251467
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311204544.5A Pending CN117746616A (en) | 2023-09-18 | 2023-09-18 | Regional parking space management system based on big data analysis and application |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117746616A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118280152A (en) * | 2024-03-29 | 2024-07-02 | 深圳市前海智慧园区有限公司 | Visual parking lot parking space quantity management system and method |
Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101188014A (en) * | 2007-09-28 | 2008-05-28 | 北京邮电大学 | Bicycle theft-prevention and inquiry management system and method based on 2D code |
| CN102938092A (en) * | 2012-10-08 | 2013-02-20 | 珠海派诺科技股份有限公司 | Prediction method of building energy consumption in festivals and holidays based on neural network |
| DE202014004003U1 (en) * | 2014-05-14 | 2014-06-11 | Felix Winkler | QR code on a carrier element, for mounting in or on the car / truck, for arranging, transferring and storing individualized data of the vehicle owner / driver |
| CN104239982A (en) * | 2014-10-12 | 2014-12-24 | 刘岩 | Method for predicting energy consumption of buildings during holidays and festivals on basis of time series and neural networks |
| CN105489056A (en) * | 2015-12-28 | 2016-04-13 | 中兴软创科技股份有限公司 | Parking requirement prediction method based on OD matrix |
| CN108182823A (en) * | 2017-12-14 | 2018-06-19 | 特斯联（北京）科技有限公司 | A kind of blocking wisdom management in garden parking stall and guide service system |
| CN109118815A (en) * | 2018-08-28 | 2019-01-01 | 浙江工业大学 | A kind of campus vehicle management optimization method based on generalized information system |
| CN111461786A (en) * | 2020-04-03 | 2020-07-28 | 中南大学 | Goods sales prediction method and device based on Prophet-CEEMDAN-ARIMA |
| CN214670731U (en) * | 2021-01-15 | 2021-11-09 | 杭州目博科技有限公司 | Parking charging system based on 5G technology |
| CN113920482A (en) * | 2021-12-13 | 2022-01-11 | 江西科技学院 | Vehicle illegal parking detection method and system |
| CN114218483A (en) * | 2021-12-16 | 2022-03-22 | 城云科技（中国）有限公司 | Parking recommendation method and application thereof |
| CN115019509A (en) * | 2022-06-22 | 2022-09-06 | 同济大学 | Parking lot vacant parking space prediction method and system based on two-stage attention LSTM |
| CN218276759U (en) * | 2022-08-22 | 2023-01-10 | 河池学院 | Campus electric vehicle management system based on two-dimensional code |
| CN115796936A (en) * | 2022-12-19 | 2023-03-14 | 昆明理工大学 | Cigarette sales prediction method and system based on combined model and storage medium |
| CN115798252A (en) * | 2022-11-04 | 2023-03-14 | 广西北投信创科技投资集团有限公司 | Public parking space acquisition method, system and device based on road |
| CN116108991A (en) * | 2023-02-13 | 2023-05-12 | 中国银联股份有限公司 | Data processing method, device, equipment and storage medium |
| CN116167489A (en) * | 2022-12-08 | 2023-05-26 | 江苏鼋博群智能技术有限公司 | Building energy data analysis and prediction method and system |
| CN116167486A (en) * | 2022-12-02 | 2023-05-26 | 中国水利水电科学研究院 | Drought prediction method and system based on ARIMA-regression model |
| CN116524714A (en) * | 2023-04-13 | 2023-08-01 | 智慧互通科技股份有限公司 | Method and device for predicting and determining parking saturation and trend of urban parking |
| CN116597687A (en) * | 2023-06-06 | 2023-08-15 | 重庆大学 | Dynamic parking allocation method based on parking occupancy prediction under cloud control system |
-
2023
- 2023-09-18 CN CN202311204544.5A patent/CN117746616A/en active Pending
Patent Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101188014A (en) * | 2007-09-28 | 2008-05-28 | 北京邮电大学 | Bicycle theft-prevention and inquiry management system and method based on 2D code |
| CN102938092A (en) * | 2012-10-08 | 2013-02-20 | 珠海派诺科技股份有限公司 | Prediction method of building energy consumption in festivals and holidays based on neural network |
| DE202014004003U1 (en) * | 2014-05-14 | 2014-06-11 | Felix Winkler | QR code on a carrier element, for mounting in or on the car / truck, for arranging, transferring and storing individualized data of the vehicle owner / driver |
| CN104239982A (en) * | 2014-10-12 | 2014-12-24 | 刘岩 | Method for predicting energy consumption of buildings during holidays and festivals on basis of time series and neural networks |
| CN105489056A (en) * | 2015-12-28 | 2016-04-13 | 中兴软创科技股份有限公司 | Parking requirement prediction method based on OD matrix |
| CN108182823A (en) * | 2017-12-14 | 2018-06-19 | 特斯联（北京）科技有限公司 | A kind of blocking wisdom management in garden parking stall and guide service system |
| CN109118815A (en) * | 2018-08-28 | 2019-01-01 | 浙江工业大学 | A kind of campus vehicle management optimization method based on generalized information system |
| CN111461786A (en) * | 2020-04-03 | 2020-07-28 | 中南大学 | Goods sales prediction method and device based on Prophet-CEEMDAN-ARIMA |
| CN214670731U (en) * | 2021-01-15 | 2021-11-09 | 杭州目博科技有限公司 | Parking charging system based on 5G technology |
| CN113920482A (en) * | 2021-12-13 | 2022-01-11 | 江西科技学院 | Vehicle illegal parking detection method and system |
| CN114218483A (en) * | 2021-12-16 | 2022-03-22 | 城云科技（中国）有限公司 | Parking recommendation method and application thereof |
| CN115019509A (en) * | 2022-06-22 | 2022-09-06 | 同济大学 | Parking lot vacant parking space prediction method and system based on two-stage attention LSTM |
| CN218276759U (en) * | 2022-08-22 | 2023-01-10 | 河池学院 | Campus electric vehicle management system based on two-dimensional code |
| CN115798252A (en) * | 2022-11-04 | 2023-03-14 | 广西北投信创科技投资集团有限公司 | Public parking space acquisition method, system and device based on road |
| CN116167486A (en) * | 2022-12-02 | 2023-05-26 | 中国水利水电科学研究院 | Drought prediction method and system based on ARIMA-regression model |
| CN116167489A (en) * | 2022-12-08 | 2023-05-26 | 江苏鼋博群智能技术有限公司 | Building energy data analysis and prediction method and system |
| CN115796936A (en) * | 2022-12-19 | 2023-03-14 | 昆明理工大学 | Cigarette sales prediction method and system based on combined model and storage medium |
| CN116108991A (en) * | 2023-02-13 | 2023-05-12 | 中国银联股份有限公司 | Data processing method, device, equipment and storage medium |
| CN116524714A (en) * | 2023-04-13 | 2023-08-01 | 智慧互通科技股份有限公司 | Method and device for predicting and determining parking saturation and trend of urban parking |
| CN116597687A (en) * | 2023-06-06 | 2023-08-15 | 重庆大学 | Dynamic parking allocation method based on parking occupancy prediction under cloud control system |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118280152A (en) * | 2024-03-29 | 2024-07-02 | 深圳市前海智慧园区有限公司 | Visual parking lot parking space quantity management system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Rudskoy et al. | Digital twins in the intelligent transport systems | |
| Cao et al. | Impacts of the urban parking system on cruising traffic and policy development: the case of Zurich downtown area, Switzerland | |
| Boyce et al. | Design and implementation of ADVANCE: The Illinois dynamic navigation and route guidance demonstration program | |
| Hounsell et al. | Data management and applications in a world-leading bus fleet | |
| CN110705747A (en) | Intelligent public transport cloud brain system based on big data | |
| Arriagada et al. | Modeling bus bunching using massive location and fare collection data | |
| DE102014209453A1 (en) | Method for simplifying the search for a free parking space | |
| CN117746616A (en) | Regional parking space management system based on big data analysis and application | |
| Millonig et al. | Monitoring Pedestrian Spatio-Temporal Behaviour. | |
| Banik et al. | Mapping of bus travel time to traffic stream travel time using econometric modeling | |
| Beura et al. | Unsignalized intersection level of service: A bicyclist’s perspective | |
| Wang et al. | Field evaluation of connected vehicle-based transit signal priority control under two different signal plans | |
| Mukherjee et al. | Proactive pedestrian safety evaluation at urban road network level, an experience in Kolkata City, India | |
| Makhloga | IMPROVING INDIA’S TRAFFIC MANAGEMENT USING INTELLIGENT TRANSPORTATION SYSTEMS | |
| CHU et al. | Typical intelligent transportation applications | |
| Gordon et al. | Deployment of intelligent transportation systems: A summary of the 2016 national survey results | |
| Horijon | Paving the road towards intelligent transportation systems: A governmentality analysis of smart traffic management in the Netherlands | |
| Musa et al. | The benefits of National Intelligent Transportation Management Centre (NITMC) establishment in Malaysia | |
| Ahmed | Planning and Management of Connected and Autonomous Vehicles (CAVs) Implementation for Dense Event | |
| Minnikhanov et al. | Unified ITS Environment in the Republic of Tatarstan | |
| Ebrahim | Development of a cost-effective method to implement traffic management principles to a small city environment | |
| Khalifeh | RITThe Contributions of Traffic Management Centers in life Enhancement | |
| Weinberger et al. | Trip generation data collection in urban areas. | |
| Kunjumon Mathew | Conception and design of a dynamic parking guidance system in Wismar | |
| GATERA | Towards improved road traffic safety: A modelling and IoT integration approach |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |