CN116524714A - Method and device for predicting and determining parking saturation and trend of urban parking - Google Patents
Method and device for predicting and determining parking saturation and trend of urban parking Download PDFInfo
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Abstract
The invention relates to a method and a device for predicting and determining parking saturation and trend of urban parking, wherein the method comprises the steps of collecting historical data of each parking lot through parking lot collecting equipment, summarizing the obtained historical data of all the parking lots, and summarizing the collected historical data Each of which is Carrying out data preprocessing on historical data; extracting trend features from the acquired historical data, and carrying out feature comprehensive analysis by combining external features; after data preprocessing, modeling historical data by adopting an ARIMA time sequence analysis model; predicting a parking result by using an ARIMA time sequence analysis model to model a modeled modeling result index; error analysis and parking lot optimization are carried out on the prediction result; and visually displaying the prediction result. The accuracy of the model and the prediction accuracy are improved.
Description
Technical Field
The invention belongs to the field of parking data analysis, and particularly relates to a method and a device for predicting and determining parking saturation and trend of urban parking.
Background
With the increase of urban population and vehicle quantity, the contradiction between supply and demand of parking resources is increasingly prominent, and the problem of urban traffic jam is also more serious. Parking saturation is an important indicator for measuring the efficiency of use of a parking lot or parking area. The method has the advantages of accurately predicting the parking saturation and trend, and has important significance for formulating a reasonable parking strategy, improving the use efficiency of a parking lot and relieving the urban traffic jam. Existing parking saturation and trend prediction methods mainly include a method based on time series analysis and a method based on machine learning. The traditional prediction method is a time sequence analysis method, and a time sequence model is constructed by analyzing historical data so as to predict future parking saturation and trend. The machine learning method is a novel prediction method developed in recent years, and the regularity in the historical data is automatically learned through a training model, so that future parking saturation and trend are predicted. Although both methods have advantages and disadvantages, they have been widely used in both parking saturation and trend prediction.
Disclosure of Invention
The invention aims to provide a method and a device for predicting and determining parking saturation and trend of urban parking, which solve the technical problems that: the method helps the method to better optimize the layout and management strategy of the parking lot, improves the efficiency and convenience of urban parking, and better formulates parking service. The accuracy of the model and the prediction accuracy are improved. And ensuring timeliness of the prediction result at the data application level.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a parking saturation and trend prediction determining method for urban parking comprises the following steps:
historical data of each parking lot are collected through parking space collection equipment;
collecting historical data of all parking lots and preprocessing;
modeling the preprocessed data, and predicting a parking result according to a modeling result;
and carrying out error analysis and parking lot optimization on the parking prediction result, and outputting the prediction result.
As a further improvement to the method of the present invention, the preprocessing of the data comprises:
cleaning abnormal constant values and repeated values in historical data;
denoising the cleaned data and filling the missing value to obtain the preprocessed data.
As a further improvement of the method, a time sequence analysis model is adopted to model the preprocessed data, and the parking result is predicted according to the index characteristics of the parking lot.
As a further improvement of the method of the invention, error analysis and parking lot optimization are also carried out on the parking prediction result.
As a further improvement to the method, the ARI MA time sequence analysis model is utilized to predict the result of the modeled parking indexes after modeling, including predicting the saturation indexes and trend indexes of the future parking lots by using the historical data of the parking lots and the parameters of the ARI MA time sequence analysis model.
The invention also discloses a parking saturation and trend prediction determining device for urban parking, which comprises
The collection module is used for collecting historical data of each parking lot through the parking space collection equipment;
the preprocessing module is used for summarizing the historical data of all parking lots and preprocessing the historical data;
the modeling module is used for modeling the preprocessed data;
the prediction module is used for predicting a parking result according to the modeling result;
the optimizing module is used for carrying out error analysis and parking lot optimization on the parking prediction result;
and the output module is used for outputting a prediction result according to the error analysis and the parking lot optimization result.
As a further improvement to the apparatus of the present invention, the preprocessing module is further configured to clean the historical data for abnormal values and repeated values; and denoising the cleaned data and filling the missing value to obtain the preprocessed data.
As a further improvement of the device, the modeling module is also used for modeling the preprocessed data by adopting a time sequence analysis model and predicting the parking result according to the index characteristics of the parking lot.
As a further improvement of the device, the optimization module is also used for carrying out error analysis and parking lot optimization on the parking prediction result, and specifically adopting average absolute error or average absolute percentage error index to analyze the prediction result.
As a further improvement of the device, the prediction module is also used for predicting the saturation index and the trend index of the future parking lot according to the historical data of the parking lot and the parameters of the ARI MA time sequence analysis model.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, various factors in the historical parking data are comprehensively considered, the parking saturation and the trend are predicted, and the utilization efficiency of the parking lot is improved: through predicting indexes such as the future weekly parking amount and the parking space utilization rate, a parking lot manager can timely adjust the management strategy and decision of the parking lot, so that the parking space utilization efficiency is improved, the idle parking spaces are reduced, and the income is increased.
Optimizing parking lot management: by analyzing and predicting the historical data of the parking lot, a parking lot manager can know the operation condition and future trend of the parking lot, and make more scientific and reasonable parking lot management strategies and decisions, so that the efficiency and level of parking lot management are improved.
Improving user experience: through predicting the parking stall service condition in parking area in advance, the user can find the vacant parking stall more conveniently, reduces and seeks the time, improves the parking experience, strengthens user satisfaction.
Reducing traffic congestion: by improving the utilization efficiency of the parking lot and reducing the time for searching the parking space in the city and waiting for parking in the vehicle queue, the urban traffic jam phenomenon can be effectively reduced, and the traffic efficiency of urban roads is improved.
Drawings
FIG. 1 is a block diagram of a method for determining parking saturation and trend prediction for urban parking according to the present invention;
fig. 2 is a schematic diagram of the structure of the parking saturation and trend prediction determining apparatus for urban parking according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Examples
As shown in fig. 1, a method for determining parking saturation and trend prediction of urban parking includes:
s101, historical data of each parking lot are collected through parking space collection equipment;
the parking space acquisition equipment comprises high-order videos, geomagnetism and the like, and the historical data comprise parking record data, parking lot data and other related data. The actual use condition of the parking lot, the type, the position, the area and other information of the parking lot, weather, holidays and other factors on the parking lot, and the like.
S102, extracting trend features from the acquired historical data, and comprehensively analyzing features by combining external features; collecting historical data of all parking lots and preprocessing;
preprocessing data, namely cleaning, denoising, filling missing values and normalizing the data by utilizing a big data technology hive tool to obtain preprocessed data; specific data cleaning comprises denoising and filling missing values; data cleaning and normalization are in parallel relation; normalization is to avoid the problem of uneven distribution intervals (N/(max-min)) of the maximum and minimum distribution data.
The data cleaning mainly removes abnormal values and repeated values, and ensures the accuracy and reliability of the data; the denoising is mainly to smooth data by adopting methods such as moving average, exponential smoothing and the like, so that the data is more stable; filling the missing value is mainly to complement the missing value by adopting methods such as interpolation method, regression model and the like so as to reduce errors and influences of data.
S103, a parking saturation and trend prediction model is established according to the pretreatment result;
s104, predicting a parking result according to the modeling result, and outputting a prediction result;
in the embodiment, parking result prediction is performed on the modeled modeling result indexes by using an ARIMA time sequence analysis model, and the saturation indexes and trend indexes of the parking lot at the future time are predicted.
Specifically, predicting indexes such as the parking amount, the parking space utilization rate, the parking space turnover rate and the like of a specified period of a parking lot in each hour; wherein: parking volume per hour = entry time due to parking record number; parking space usage = parking duration/available parking space duration; parking space turnover rate = amount of parking per hour/number of parking places;
for the saturation of the parking lot, the following indexes are adopted for prediction: the average hourly parking amount, the parking space utilization rate, the parking space turnover rate and the like are predicted, and the predicted value is equivalent to the predicted use trend of a parking lot, so that corresponding adjustment is better made for the parking resource planning.
For the trend of the parking lot, the following indexes are adopted for prediction: zhou Junzhi, weekly tendencies, zhou Jijie, etc. Meanwhile, the prediction result can be further analyzed and adjusted according to factors such as the type, the position and the area of the parking lot, so that the prediction accuracy is improved.
Week trend: the weekly trend is used to describe the trend of time series over a long period of time, which reveals the increase or decrease in data over a week. The circumferential trend can be calculated by a linear regression method. For each day of each week, its position throughout the time series is calculated (e.g., day 1, day 8, day 15, etc.), and then these values are used to fit a linear regression line, where the slope of the regression line represents the weekly trend.
Peri-seasonal: zhou Junzhi is first calculated and then the daily observations are divided by the corresponding Zhou Junzhi, which results are indicative of the ratio of daily observations to weekly averages. This ratio can be used to measure seasonality.
The time series analysis model is a method specially used for modeling and analyzing time series data, and mainly comprises an autoregressive model, a moving average model, an ARIMA model and the like.
As a further improvement to the present embodiment, error analysis and parking lot optimization of the parking prediction result are also required; the model error analysis of the prediction result comprises the following steps: average absolute error (MAE), average absolute percent error (MAPE), and the like.
Mean Absolute Error (MAE) is a measure of the magnitude of the difference between a predicted value and an actual observed value. It calculates the absolute error (i.e., the absolute value of the difference) between the predicted value and the actual observed value and takes their average value. The mathematical formula is as follows:
mae= \frac {1} { n } \sum } { i=1 } { n } |y_i- \hat { y_i } | $, where $ y_i $ is the $ i-th actual observation, $\hat { y_i $ is the $ i $ predicted value, and $ n is the number of samples.
Mean Absolute Percent Error (MAPE) is another measure of prediction error and represents the relative error magnitude between the predicted value and the actual observed value. It calculates the percentage error between the predicted value and the actual observed value (i.e., the absolute value of the ratio of the difference divided by the actual observed value) and takes the average value thereof. The mathematical formula is $ $ MAPE = \frac {1} { n } \sum } { i=1 } { n } \l eft|\frac { y_i- \hat { y_i } { r light|\t imes 100% $;
where y_i is the actual observation of the i-th $, hat { y_i $ is the predicted value of the i-th $ and n is the number of samples.
In particular, management and planning of parking lots need to be adjusted and optimized according to actual conditions and predicted results, the trend features include seasonality, periodicity, trend, etc., and the external features include time (holidays, weather), place, type of parking lot, etc.
The prediction result provided by the invention can be displayed in a visual mode, comprises a chart and a report, and helps a decision maker to intuitively know the operation condition and future trend of the parking lot.
The time sequence analysis and prediction technology is applied to urban parking lot management, and features such as parking lot historical data, holidays, weather and the like are combined to predict indexes such as parking amount, parking space utilization rate and the like of a future week parking lot, and corresponding parking lot management suggestions are provided, so that the utilization efficiency of the parking lot is improved, the parking lot management is optimized, the user experience is improved, and the effects of traffic jam and the like are reduced.
Advanced technologies such as deep learning and the like are adopted to improve the accuracy of the model based on the ARI MA model time sequence model; in addition, in the prediction model, besides factors such as holidays and weather, more characteristic factors such as traffic conditions and activities can be introduced, so that the prediction accuracy is improved. Ensuring timeliness and accuracy of the prediction results in the data application level can consider further optimizing functions of the real-time updating part, such as introducing a faster and more accurate data collection and processing mode. The parking lot saturation and trend prediction model can be applied to wider fields, such as traffic jam prediction, market passenger flow prediction and the like, and can further explore and expand the application scenes.
As shown in fig. 2, the present invention also discloses a determining device for parking saturation and trend prediction of urban parking comprising,
the collection module 1 is used for collecting historical data of each parking lot through parking space collection equipment;
the preprocessing module 2 is used for summarizing the historical data of all parking lots and preprocessing the historical data;
a modeling module 3 for modeling the data after preprocessing;
the prediction module 4 is used for predicting a parking result according to the modeling result;
the optimizing module 5 is used for carrying out error analysis and parking lot optimization on the parking prediction result;
and the output module 6 is used for outputting a prediction result according to the error analysis and the parking lot optimization result.
As a further improvement to the apparatus of the present invention, the preprocessing module is further configured to clean the historical data for abnormal values and repeated values; and denoising the cleaned data and filling the missing value to obtain the preprocessed data.
As a further improvement of the device, the modeling module is also used for modeling the preprocessed data by adopting a time sequence analysis model and predicting the parking result according to the index characteristics of the parking lot.
As a further improvement of the device, the optimization module is also used for carrying out error analysis and parking lot optimization on the parking prediction result, and specifically adopting average absolute error or average absolute percentage error index to analyze the prediction result.
As a further improvement of the device, the prediction module is also used for predicting the saturation index and the trend index of the future parking lot according to the historical data of the parking lot and the parameters of the ARI MA time sequence analysis model.
In the description of the present specification, reference to the term "in one embodiment," "in another embodiment," "exemplary," or "in a particular embodiment," etc., means 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, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While the invention has been described in detail in the foregoing general description, embodiments and experiments, it will be apparent to those skilled in the art that modifications and improvements can be made thereto. Accordingly, such modifications or improvements may be made without departing from the spirit of the invention and are intended to be within the scope of the invention as claimed.
Claims (10)
1. A method for predicting and determining parking saturation and trend of urban parking, comprising:
historical data of each parking lot are collected through parking space collection equipment;
collecting historical data of all parking lots and preprocessing;
establishing a parking saturation and trend prediction model according to the pretreatment result;
and predicting the parking result according to the modeling result, and outputting the prediction result.
2. The method of claim 1, wherein preprocessing the data comprises:
cleaning abnormal constant values and repeated values in historical data;
denoising the cleaned data and filling the missing value to obtain the preprocessed data.
3. The method of claim 2, wherein the pre-processed data is modeled using a time series analysis model, and the parking result prediction is performed based on the parking lot index feature.
4. A method according to claim 3, wherein the result prediction of the modeled parking indicator using the ARIMA time series analysis model comprises predicting a future parking lot saturation indicator and trend indicator from historical data of the parking lot and parameters of the ARIMA time series analysis model.
5. The method of any one of claims 1 to 4, further comprising error analysis and parking lot optimization of the parking prediction results.
6. A parking saturation and trend prediction determining device for urban parking comprises
The collection module is used for collecting historical data of each parking lot through the parking space collection equipment;
the preprocessing module is used for summarizing the historical data of all parking lots and preprocessing the historical data;
the modeling module is used for modeling the preprocessed data;
the prediction module is used for predicting a parking result according to the modeling result;
the optimizing module is used for carrying out error analysis and parking lot optimization on the parking prediction result;
and the output module is used for outputting a prediction result according to the error analysis and the parking lot optimization result.
7. The apparatus of claim 6, wherein the preprocessing module is further configured to clean the historical data for abnormal values and duplicate values; and denoising the cleaned data and filling the missing value to obtain the preprocessed data.
8. The apparatus of claim 7, wherein the modeling module is further configured to model the preprocessed data using a time series analysis model, and to predict the parking result based on the parking lot indicator feature.
9. The apparatus of claim 8, wherein the optimization module is further configured to perform error analysis and parking lot optimization on the parking prediction results.
10. The apparatus according to any one of claims 6 to 9, wherein the prediction module is further configured to predict a saturation index and a trend index of the future parking lot based on historical data of the parking lot and parameters of the ARIMA time series analysis model.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117746616A (en) * | 2023-09-18 | 2024-03-22 | 杭州目博科技有限公司 | Regional parking space management system based on big data analysis and application |
| CN117831338A (en) * | 2023-12-26 | 2024-04-05 | 武汉理工大学 | Data collaborative sharing method based on intelligent guidance terminal of parking lot |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117746616A (en) * | 2023-09-18 | 2024-03-22 | 杭州目博科技有限公司 | Regional parking space management system based on big data analysis and application |
| CN117831338A (en) * | 2023-12-26 | 2024-04-05 | 武汉理工大学 | Data collaborative sharing method based on intelligent guidance terminal of parking lot |
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