CN113838303A - Parking lot recommendation method and device, electronic equipment and storage medium - Google Patents
Parking lot recommendation method and device, electronic equipment and storage medium Download PDFInfo
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- CN113838303A CN113838303A CN202111126992.9A CN202111126992A CN113838303A CN 113838303 A CN113838303 A CN 113838303A CN 202111126992 A CN202111126992 A CN 202111126992A CN 113838303 A CN113838303 A CN 113838303A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
- G08G1/141—Traffic control systems for road vehicles indicating individual free spaces in parking areas with means giving the indication of available parking spaces
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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- Y02T10/10—Internal combustion engine [ICE] based vehicles
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Abstract
The application provides a parking lot recommendation method and device, electronic equipment and a computer-readable storage medium. The method comprises the following steps: acquiring a parking lot recommendation service request comprising a current position, a destination and a current time; determining at least one target parking lot from all parking lots around the destination; determining an expected time of arrival of the vehicle at each of the target parking lots; calling a prediction model of the target parking lot corresponding to the time period to which the predicted time belongs for each target parking lot, and predicting to obtain the number of free parking spaces of the target parking lot at the predicted time by using the prediction model; and recommending the parking lots according to the number of the idle parking lots of each target parking lot. Compared with the prior art, the number of the idle parking spaces corresponding to different long and short time periods can be predicted for each parking lot, each time period corresponds to one prediction model, the time granularity corresponding to the prediction models is finer, and the prediction accuracy is improved.
Description
Technical Field
The application relates to the technical field of traffic, in particular to a parking lot recommendation method and device, an electronic device and a computer-readable storage medium.
Background
With the improvement of the living standard of people, the vehicle retention rate is greatly increased, and traffic jam and parking difficulty become very concerned problems of people. The parking lot information of the destination is mastered in time, and the method is very important for daily driving and traveling of people.
The parking lot recommendation function in the existing internet map service application, the urban parking application and the internet parking application mainly induces a car owner to park through the parking number, parking fee information, distance information and the like of a parking lot around a destination, and the existing method can not always ensure that the parking lot still has an idle parking space when a user arrives at a parking lot, so that precious time of the user is wasted, and the parking experience of the user is influenced.
Disclosure of Invention
The application aims to provide a parking lot recommendation method and device, electronic equipment and a computer-readable storage medium.
The application provides a parking lot recommendation method in a first aspect, including:
acquiring a parking lot recommendation service request, wherein the parking lot recommendation service request comprises a current position, a destination and a current time;
determining at least one target parking lot from all parking lots around the destination;
determining an expected time of arrival of the vehicle at each of the target parking lots;
calling a prediction model of the target parking lot corresponding to the time period to which the predicted time belongs for each target parking lot, and predicting to obtain the number of free parking spaces of the target parking lot at the predicted time by using the prediction model;
and recommending the parking lots according to the number of the idle parking lots of each target parking lot.
In a possible implementation manner, in the parking lot recommendation method provided in the embodiment of the present application, an establishment process of a prediction model of a parking lot is as follows:
acquiring relevant data of a parking lot, and preprocessing the relevant data of the parking lot;
for each parking lot, training according to the preprocessed parking lot related data and preset time periods to obtain prediction models, wherein each preset time period corresponds to one prediction model;
the preset time period is divided into a short period and a long period, the prediction target of the prediction model corresponding to the short period is the number of idle parking spaces of the parking lot in each hour within 1 day in the future, and the prediction target of the prediction model corresponding to the long period is the number of idle parking spaces of the parking lot in each day within 7 days in the future.
In a possible implementation manner, in the parking lot recommendation method provided in an embodiment of the present application, the preprocessing the parking lot related data includes:
screening out parking lots meeting preset evaluation standards according to the parking lot related data;
carrying out parking lot position correction, parking lot type correction and parking lot total parking space correction on the screened parking lots;
and filling missing values of the leaving time of the vehicles in the screened parking lot.
In a possible implementation manner, in the parking lot recommendation method provided in the embodiment of the present application, the training, according to the preprocessed parking lot related data and according to a preset time period, for each parking lot to obtain a prediction model includes:
aiming at each parking lot, dividing the preprocessed parking lot related data into a training set, a testing set and a testing set according to a preset time period;
for each preset time period, training according to the training set to obtain a plurality of machine learning models;
adopting the test set to test the predicted average error value of each machine learning model in the prediction period, and taking the model with the minimum average error value as an optimal model;
and testing the prediction accuracy of the optimal model according to the test set, and when the prediction accuracy is greater than a certain threshold value, taking the optimal model as the prediction model of the preset time period.
In a possible implementation manner, the parking lot recommendation method provided in the embodiment of the present application further includes:
for each parking lot, periodically calculating the prediction accuracy of the prediction model of each preset time period corresponding to the parking lot;
when the prediction accuracy is smaller than a preset threshold, retraining the prediction model of the parking lot in the preset time period until the prediction accuracy is not smaller than the preset threshold;
the calculation formula of the prediction accuracy y is as follows:
y＝1-|a-b|/c；
wherein, a represents the actual parking space occupation number, b represents the predicted parking space occupation number calculated according to the predicted idle parking space number output by the prediction model, and c represents the total parking space number of the parking lot.
In a possible implementation manner, the parking lot recommendation method provided in the embodiment of the present application further includes:
and when the number of the free parking spaces of each parking lot is predicted, each prediction model of each parking lot runs in parallel.
In a possible implementation manner, in the parking lot recommendation method provided in an embodiment of the present application, the determining at least one target parking lot from all parking lots around the destination includes:
acquiring all parking lots around the destination and the distance between each parking lot and the destination;
taking a parking lot which is within a first preset distance range from the destination as a target parking lot;
and when no parking lot exists in the first preset distance range, determining a parking lot which is within a second preset distance range from the destination as a target parking lot, wherein the second preset distance is greater than the first preset distance.
The second aspect of the present application provides a parking lot recommendation device, including:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a parking lot recommendation service request which comprises a current position, a destination and a current time;
a parking lot determination module for determining at least one target parking lot from all parking lots around the destination;
the arrival time determining module is used for determining the predicted time of the vehicle to arrive at each target parking lot;
the prediction module is used for calling a prediction model of the target parking lot corresponding to the time period to which the predicted time belongs aiming at each target parking lot, and predicting the number of the idle parking spaces of the target parking lot at the predicted time by using the prediction model;
and the recommendation module is used for recommending the parking lots according to the number of the idle parking spaces of each target parking lot.
In a possible implementation manner, the parking lot recommendation device provided in the embodiment of the present application further includes:
a predictive model building module to:
acquiring relevant data of a parking lot, and preprocessing the relevant data of the parking lot;
for each parking lot, training according to the preprocessed parking lot related data and preset time periods to obtain prediction models, wherein each preset time period corresponds to one prediction model;
the preset time period is divided into a short period and a long period, the prediction target of the prediction model corresponding to the short period is the number of idle parking spaces of the parking lot in each hour within 1 day in the future, and the prediction target of the prediction model corresponding to the long period is the number of idle parking spaces of the parking lot in each day within 7 days in the future.
In a possible implementation manner, in the parking lot recommendation device provided in an embodiment of the present application, the prediction model establishing module is specifically configured to:
screening out parking lots meeting preset evaluation standards according to the parking lot related data;
carrying out parking lot position correction, parking lot type correction and parking lot total parking space correction on the screened parking lots;
and filling missing values of the leaving time of the vehicles in the screened parking lot.
In a possible implementation manner, in the parking lot recommendation device provided in an embodiment of the present application, the prediction model establishing module is specifically configured to:
aiming at each parking lot, dividing the preprocessed parking lot related data into a training set, a testing set and a testing set according to a preset time period;
for each preset time period, training according to the training set to obtain a plurality of machine learning models;
adopting the test set to test the predicted average error value of each machine learning model in the prediction period, and taking the model with the minimum average error value as an optimal model;
and testing the prediction accuracy of the optimal model according to the test set, and when the prediction accuracy is greater than a certain threshold value, taking the optimal model as the prediction model of the preset time period.
In a possible implementation manner, in the parking lot recommendation device provided in an embodiment of the present application, the prediction module is further configured to:
for each parking lot, periodically calculating the prediction accuracy of the prediction model of each preset time period corresponding to the parking lot;
when the prediction accuracy is smaller than a preset threshold, retraining the prediction model of the parking lot in the preset time period until the prediction accuracy is not smaller than the preset threshold;
the calculation formula of the prediction accuracy y is as follows:
y＝1-|a-b|/c；
wherein, a represents the actual parking space occupation number, b represents the predicted parking space occupation number calculated according to the predicted idle parking space number output by the prediction model, and c represents the total parking space number of the parking lot.
In a possible implementation manner, in the parking lot recommendation device provided in an embodiment of the present application, the prediction module is further configured to:
and when the number of the free parking spaces of each parking lot is predicted, each prediction model of each parking lot runs in parallel.
In a possible implementation manner, in the parking lot recommendation device provided in an embodiment of the present application, the parking lot determination module is specifically configured to:
determining all parking lots around the destination and the distance between each parking lot and the destination;
and determining at least one target parking lot from all parking lots around the destination according to the distance between each parking lot and the destination.
In a possible implementation manner, in the parking lot recommendation device provided in an embodiment of the present application, the parking lot determination module is further specifically configured to:
determining a parking lot with a distance destination within a first preset distance range as a target parking lot;
when no parking lot exists in the first preset distance range, the parking lot with the distance to the destination within the second preset distance range is determined as the target parking lot, and the second preset distance is larger than the first preset distance.
A third aspect of the present application provides an electronic device comprising: the parking lot recommendation method comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program when running so as to realize the parking lot recommendation method of the first aspect of the application.
A fourth aspect of the present application provides a computer-readable storage medium having computer-readable instructions stored thereon, the computer-readable instructions being executable by a processor to implement the parking lot recommendation method of the first aspect of the present application.
The parking lot recommendation method, the parking lot recommendation device, the electronic equipment and the storage medium acquire a parking lot recommendation service request, and determine at least one target parking lot from all parking lots around a destination; determining an expected time of arrival of the vehicle at each of the target parking lots; calling a prediction model of the target parking lot corresponding to the time period to which the predicted time belongs for each target parking lot, and predicting to obtain the number of free parking spaces of the target parking lot at the predicted time by using the prediction model; and recommending the parking lots according to the number of the idle parking lots of each target parking lot. Compared with the prior art, the number of the idle parking spaces corresponding to each moment point in the short period and the long period can be predicted for each parking lot, each time period corresponds to one prediction model, the time granularity corresponding to the prediction models is finer, and the prediction accuracy is improved.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 shows a flowchart of a parking lot recommendation method provided by the present application;
FIG. 2 is a schematic diagram illustrating a parking lot recommendation provided by the present application;
fig. 3 is a diagram illustrating a process of building a predictive model of a parking lot provided by the present application;
fig. 4 shows a schematic diagram of a parking lot recommendation device provided by the present application;
FIG. 5 illustrates a schematic diagram of an electronic device provided herein;
FIG. 6 shows a schematic diagram of a computer-readable storage medium provided herein.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may 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 disclosure to those skilled in the art.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which this application belongs.
In addition, the terms "first" and "second", etc. are used to distinguish different objects, rather than to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
The embodiment of the application provides a parking lot recommendation method and device, an electronic device and a computer readable storage medium, which are described below with reference to the accompanying drawings.
Fig. 1 shows a flowchart of a parking lot recommendation method provided in an embodiment of the present application, and as shown in fig. 1, the method specifically includes the following steps S101 to S105:
s101, a parking lot recommendation service request is obtained, wherein the parking lot recommendation service request comprises a current position, a destination and current time.
S102, determining at least one target parking lot from all parking lots around the destination;
in practical applications, there are many parking lots around the destination, and for convenience, a parking lot not too far away from the destination is usually selected for parking, in some embodiments of the present application, the step S102 may be implemented as:
determining all parking lots around the destination and the distance between each parking lot and the destination;
at least one target parking lot is determined from all parking lots around the destination according to the distance between each parking lot and the destination.
Further, the step of determining at least one target parking lot from all parking lots around the destination according to the distance between each parking lot and the destination specifically includes:
determining a parking lot with a distance destination within a first preset distance range as a target parking lot;
when no parking lot exists in the first preset distance range, the parking lot with the distance to the destination within the second preset distance range is determined as the target parking lot, and the second preset distance is larger than the first preset distance.
For example, the first preset distance is set to 100 meters, the second preset distance is set to 200 meters, in the present embodiment, a parking lot with a distance destination within a range of 100 meters is first selected as the target parking lot, and when there is no parking lot within a range of 100 meters, a parking lot with a distance destination within a range of 200 meters is selected as the target parking lot. If no parking lot exists within the range of 200 meters, a parking lot farther away from the destination can be recommended to the user, that is, the user traverses stepwise according to the distance from the destination, which is not limited in the present application.
In this embodiment, according to parking area and destination distance, the peripheral parking area in ladder selection destination is as the target parking area, and the selection in target parking area is comparatively reasonable, has promoted user experience.
S103, determining the expected time when the vehicle reaches each target parking lot;
in this step, the time when the vehicle reaches each target parking lot can be estimated by using a relevant method, for example, according to the road condition, the average driving speed of the vehicle, and the like.
S104, calling a prediction model of the target parking lot corresponding to the time period to which the predicted time belongs for each target parking lot, and predicting to obtain the number of free parking spaces of the target parking lot at the predicted time by using the prediction model;
specifically, for each parking lot, the prediction models corresponding to a plurality of time periods are obtained by pre-training according to the historical data related to the parking lot, the time periods are divided into short periods and long periods, and the short periods refer to periods within 1 day in the future, for example, within 1 hour in the future, and within 2 hours to 23 hours in the future. The long cycle is 1 day or more in the future, for example, within 2 days in the future, within 3 days in the future to 7 days in the future.
According to some embodiments of the present application, the short period may be a period of half an hour or an quarter of an hour, and the long period may be a period of 1.5 days or 2 days, which is not limited herein.
In the step, after the preset time when the vehicle reaches the target parking lot is determined, the corresponding prediction model is determined according to the time period to which the preset time belongs, for example, the current time is 9 points, the preset time when the vehicle reaches the target parking lot from the current position is 9 points and 38 points, and the corresponding prediction model is determined to be called to predict the number of the idle parking spaces within 1 hour in the future; if the preset time when the vehicle reaches the target parking lot from the current position is 10: 38 minutes, determining to call a corresponding prediction model for predicting the number of the idle parking spaces in the next 2 hours, and so on.
It should be understood that, because the preset times when the vehicle arrives at different target parking lots are different, the prediction models corresponding to the different target parking lots may be prediction models corresponding to different time periods, the prediction models of the different target parking lots are all independent of each other and run in parallel, and the prediction models corresponding to the time periods are called according to the arrival times. For example, for a certain time period, different machine learning models can be used for training according to the historical data of the parking lot, then the prediction accuracy of each model is tested, and then the prediction model with high prediction accuracy is selected as the prediction model of the time period.
And S105, recommending parking lots according to the number of the free parking spaces of each target parking lot.
In practical applications, the target parking lots may be further classified according to the predicted number of free parking spaces, the predicted arrival time, and the like of each target parking lot, and the parking lot information including the classification information may be sent to the parking lot recommendation requester in step S105.
Specific classification labels are as follows: the parking lot information including the classification label information is sent to the parking lot recommendation requester with the shortest distance, the shortest walking, the shortest time and the like.
Therefore, according to some embodiments of the present application, not only the predicted number of free parking spaces, but also the parking lot recommendation may be performed based on the dimensions of the shortest distance, the shortest walking distance, the shortest time, and the like, which is not limited in the present application. Fig. 2 shows a schematic diagram of a parking lot recommendation.
In this embodiment, the parking area type of recommending is more, increases corresponding categorised label in the parking area of recommending to make things convenient for the user to select, can satisfy different users' demand better, promoted user experience.
In the parking lot recommendation method provided by the embodiment of the application, the method further includes establishing a prediction model of the number of free parking spaces in the parking lot, and fig. 3 shows an establishing process of the prediction model of the parking lot.
Specifically, as shown in fig. 3, the building process of the prediction model of the parking lot is as follows:
s201, acquiring relevant data of a parking lot, and preprocessing the relevant data of the parking lot;
s202, aiming at each parking lot, training according to the preprocessed parking lot related data and preset time periods to obtain prediction models, wherein each preset time period corresponds to one prediction model;
the preset time period comprises at least one short period and at least one long period, the prediction target of the prediction model corresponding to the short period is the number of free parking spaces in the parking lot within 1 day in the future, the prediction target of the prediction model corresponding to the long period is the number of free parking spaces in the parking lot above 1 day in the future, and the short period is within 1 day in the future, such as within 1 hour in the future, within 2 hours in the future to 23 hours in the future. The long cycle is 1 day or more in the future, for example, within 2 days in the future, within 3 days in the future to 7 days in the future. The parking lot related data in the step S201 includes the following types of data:
the parking lot attributes comprise the number of empty parking lots, the capacity of the parking lots, the average number of empty parking lots in the time period, whether the parking lot is located in a large business district or not and the like.
Environmental factors including weather conditions, temperature, holidays or the presence of large activities, etc.
Other factors (e.g., hospital parking), including hospital outpatient volume, hospital admission, etc.
The preprocessing of the relevant data of the parking lot in the step S201 includes:
screening out the parking lots meeting the evaluation standard according to the relevant data of the parking lots; the parking lot meeting the evaluation standard can be screened out according to the time span, the loss rate, the delay condition, the stability of data and the like of the relevant data of the parking lot.
And correcting the position of the parking lot, the type of the parking lot and the total parking space of the parking lot for the screened parking lots.
And filling missing values of the leaving time of the vehicles in the screened parking lot. The method specifically comprises the following two conditions: the first case is that the vehicle has been taken out of the field, and the average parking time of the vehicle in the vehicle entering time period is calculated as the estimated parking time of the vehicle, so as to obtain the time of the vehicle taking out of the field. The second situation is that the vehicle is not on the scene, and the filling is the current time or a certain time in the future.
In step S202, according to the preprocessed parking lot related data, a prediction model is obtained by training according to a preset time period, which specifically includes:
aiming at each parking lot, dividing the preprocessed parking lot related data into a training set, a testing set and a testing set according to a preset time period;
for each preset time period, training according to the training set to obtain a plurality of machine learning models;
adopting the test set to test the predicted average error value of each machine learning model in the prediction period, and taking the model with the minimum average error value as an optimal model;
and testing the prediction accuracy of the optimal model according to the test set after the optimal model is obtained, and taking the optimal model as the prediction model of the preset time period when the prediction accuracy is greater than a certain threshold value.
Specifically, in step S202, model training and optimization are performed on each parking lot according to the time period, and the training models may be divided into short-period prediction models and long-period prediction models. For example, the short-term prediction model includes a model that predicts the 1 st hour to 23 rd hour in the future, and the long-term prediction model includes a model that predicts the 1 st day to 7 th day in the future. That is, at the end of each training, each parking lot will get 30 prediction models (23 short-period prediction models +7 long-period prediction models), each short-period prediction model is one hour, and each long-period prediction model is 1 day, so as to predict the number of idle parking spaces at each time point (for example, one time point every five minutes) in 7 days in the future. Therefore, the parking spaces at each time in 23 hours in the future can be predicted according to the short-period prediction model, and the parking spaces at each time in 7 days in the future can be predicted according to the long-period prediction model. In practical application, the 30 prediction models are continuously operated in the background to calculate and output prediction data of 7 days in the future, and corresponding prediction data can be obtained by determining time. And the idle parking space data output by the prediction model is changed in a rolling manner in real time.
The following model training method may be specifically employed:
for a specific parking lot, dividing a data set of the parking lot related data preprocessed by the parking lot into three parts according to time sequence: training set, test set and test set.
Each sample in the dataset includes: parking lot attributes, environmental factors, and other factors.
In a training set, a series of classical machine learning models (including random forests, XGboost, elastic network regression and mixed linear regression) and deep learning neural network correlation models (including model integration and superposition), model parameters and dimensions are continuously adjusted, and a stable and efficient prediction model is obtained through training. This model can be used to predict the number of free slots at a given time in the future.
This application adopts the automatic optimal model of selecting every model, and is concrete, as shown in fig. 3, after the training is accomplished, use each model on the inspection set, reachs corresponding idle parking stall quantity predicted value, and idle parking stall quantity predicted value is subtracted to total parking stall quantity to obtain the prediction parking stall and hold the number to calculate average error value (total error and peak section error) in this time quantum, the error value ═ actual parking stall holds the number-the prediction parking stall holds the number |. And performing parameter optimization on each model according to the error value to obtain an optimized model for subsequent prediction.
The optimal model of each model selected automatically is used for predicting on a test set, after corresponding prediction is obtained, the prediction accuracy is calculated, the prediction model with the highest prediction accuracy is selected to serve as the prediction model corresponding to the preset time period, the prediction accuracy is 1- | the number of occupied actual parking spaces-the number of occupied prediction parking spaces |/the parking lot capacity (namely the total number of parking lots) so as to evaluate the application effect of the model, and the following table 1 shows the prediction accuracy of various models of a certain parking lot.
Table 1: prediction accuracy of various models of a parking lot
The closer the prediction accuracy is to 1, the closer the predicted value is to the true value. The index not only considers errors, but also brings the capacity of the parking lot into the index, so that the influence of the parking lots of different sizes on the errors is standardized.
By the model training method, the prediction model with the highest accuracy rate in each time period can be obtained, so that the prediction accuracy rate can be improved and the accuracy rate of the parking lot recommendation can be improved compared with the prior art.
In the parking lot recommendation method provided by the embodiment of the application, in order to ensure the accuracy of the prediction of the vacant parking space, the actual verification can be performed by using the actually accessed dynamic parking data. Specifically, a mechanism for realizing periodical review of the prediction accuracy rate can be designed, the published prediction data is tracked by regularly utilizing actual data, and when the prediction accuracy rate data is lower than a certain threshold value, intervention can be performed in time, such as retraining the prediction model of the parking lot, and improving the prediction effect. Therefore, according to some embodiments of the present application, the parking lot recommendation method may further include the following steps:
for each parking lot, periodically calculating the prediction accuracy of the prediction model of each preset time period corresponding to the parking lot;
when the prediction accuracy is smaller than a preset threshold, retraining the prediction model of the parking lot in the preset time period until the prediction accuracy is not smaller than the preset threshold;
the calculation formula of the prediction accuracy y is as follows:
y＝1-|a-b|/c；
wherein, a represents the actual parking space occupation number, b represents the predicted parking space occupation number calculated according to the predicted idle parking space number output by the prediction model, and c represents the total parking space number of the parking lot. And the number of the predicted occupied parking spaces is equal to the total number of the parking spaces minus the number of the predicted idle parking spaces.
In order to accelerate the real-time performance of the free parking space prediction of the parking lot, the method for recommending the parking lot provided by the embodiment of the application can further comprise the following steps:
and when the number of the free parking spaces of each parking lot is predicted, each prediction model of each parking lot runs in parallel.
In practical application, a traffic big data platform (ODPS) is usually adopted to predict the vacant parking spaces, but based on the data service environment of the big data platform, the query efficiency of real-time data is not high, and in order to accelerate the real-time performance of parking space prediction in a parking lot, the following technical improvements can be adopted:
(1) separating the parking record data of the last month from the ODPS to rds (relational database service) and optimizing the data table; due to the fact that the number of the parking lots processed at the same time is too many, the prediction service reads data in a multi-process concurrent mode.
The ODPS converges and fuses urban traffic multi-source data, the data volume is billion levels, the free parking space prediction model in the application is one of model systems, the ODPS is mainly used for large-volume data storage, the data analysis and processing efficiency is not high, the relative efficiency of the rds is higher, the data in one month can be separated to the rds, and the data can be directly used for model analysis and prediction to improve the processing efficiency. Data table optimization refers to: optimizing the table structure, and deleting fields which are irrelevant to prediction, such as population numbers, vehicle types and the like, in the new table; and optimizing data items, namely deleting non-target parking lot data and invalid data in the new table, wherein the optimization aims to reduce the data items and improve the data reading efficiency.
(2) Because data of more than 100 parking lots need to be processed and calculated simultaneously, repeated calculation and serial operation are reduced as much as possible in the actual operation process. In the process of calculating the occupied parking space number, the vehicle entering and exiting records are updated in real time, the records outside one week are not repeatedly calculated, and the records within one week can be continuously used for updating the occupied parking space number so as to ensure the accuracy of calculation. Meanwhile, the for loop is replaced by a matrix form to accelerate the parking space calculation. When the models are trained and the free parking spaces are predicted, the models of different parking lots in different prediction periods (for example, 1 hour for prediction and 7 days for prediction) are operated in parallel, and the different prediction models of the same parking lot are also operated in parallel.
The parking lot recommendation method provided by the embodiment obtains a parking lot recommendation service request, and determines at least one target parking lot from all parking lots around a destination; determining an expected time of arrival of the vehicle at each of the target parking lots; calling a prediction model of the target parking lot corresponding to the time period to which the predicted time belongs for each target parking lot, and predicting to obtain the number of free parking spaces of the target parking lot at the predicted time by using the prediction model; and recommending the parking lots according to the number of the idle parking lots of each target parking lot. Compared with the prior art, the number of the idle parking spaces corresponding to different long and short time periods can be predicted for each parking lot, each time period corresponds to one prediction model, the time granularity corresponding to the prediction models is finer, and the prediction accuracy is improved. In the above embodiment, a parking lot recommendation method is provided, and correspondingly, the application further provides a parking lot recommendation device. The parking lot recommendation device provided by the embodiment of the application can implement the parking lot recommendation method, and can be implemented through software, hardware or a software and hardware combined mode. For example, the parking lot recommendation device may comprise integrated or separate functional modules or units to perform the corresponding steps of the above methods. Please refer to fig. 4, which illustrates a schematic diagram of a parking lot recommendation device according to an embodiment of the present application. Since the apparatus embodiments are substantially similar to the method embodiments, they are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for relevant points. The device embodiments described below are merely illustrative.
As shown in fig. 4, the parking lot recommendation device 10 may include:
the system comprises an obtaining module 101, configured to obtain a parking lot recommendation service request, where the parking lot recommendation service request includes a current location, a destination, and a current time;
a parking lot determination module 102 for determining at least one target parking lot from all parking lots around the destination;
an arrival time determination module 103, configured to determine an expected time when a vehicle arrives at each of the target parking lots;
the prediction module 104 is configured to, for each target parking lot, invoke a prediction model of the target parking lot corresponding to the time period to which the predicted time belongs, and predict, by using the prediction model, the number of free parking spaces of the target parking lot at the predicted time;
and the recommending module 105 is configured to recommend the parking lot according to the number of the free parking spaces of each target parking lot.
In a possible implementation manner, the parking lot recommendation device provided in the embodiment of the present application further includes:
a predictive model building module to:
acquiring relevant data of a parking lot, and preprocessing the relevant data of the parking lot;
for each parking lot, training according to the preprocessed parking lot related data and preset time periods to obtain prediction models, wherein each preset time period corresponds to one prediction model;
the preset time period is divided into a short period and a long period, the prediction target of the prediction model corresponding to the short period is the number of idle parking spaces of the parking lot in each hour within 1 day in the future, and the prediction target of the prediction model corresponding to the long period is the number of idle parking spaces of the parking lot in each day within 7 days in the future.
In a possible implementation manner, in the parking lot recommendation device provided in an embodiment of the present application, the prediction model establishing module is specifically configured to:
screening out parking lots meeting preset evaluation standards according to the parking lot related data;
carrying out parking lot position correction, parking lot type correction and parking lot total parking space correction on the screened parking lots;
and filling missing values of the leaving time of the vehicles in the screened parking lot.
In a possible implementation manner, in the parking lot recommendation device provided in an embodiment of the present application, the prediction model establishing module is specifically configured to:
aiming at each parking lot, dividing the preprocessed parking lot related data into a training set, a testing set and a testing set according to a preset time period;
for each preset time period, training according to the training set to obtain a plurality of machine learning models;
adopting the test set to test the predicted average error value of each machine learning model in the prediction period, and taking the model with the minimum average error value as an optimal model;
and testing the prediction accuracy of the optimal model according to the test set, and when the prediction accuracy is greater than a certain threshold value, taking the optimal model as the prediction model of the preset time period.
In a possible implementation manner, in the parking lot recommendation device provided in the embodiment of the present application, the prediction module 103 is further configured to:
for each parking lot, periodically calculating the prediction accuracy of the prediction model of each preset time period corresponding to the parking lot;
when the prediction accuracy is smaller than a preset threshold, retraining the prediction model of the parking lot in the preset time period until the prediction accuracy is not smaller than the preset threshold;
the calculation formula of the prediction accuracy y is as follows:
y＝1-|a-b|/c；
wherein, a represents the actual parking space occupation number, b represents the predicted parking space occupation number calculated according to the predicted idle parking space number output by the prediction model, and c represents the total parking space number of the parking lot.
In a possible implementation manner, in the parking lot recommendation device provided in the embodiment of the present application, the prediction module 103 is further configured to:
and when the number of the free parking spaces of each parking lot is predicted, each prediction model of each parking lot runs in parallel.
In a possible implementation manner, in the parking lot recommendation device provided in this embodiment of the application, the parking lot determination module 102 is specifically configured to:
determining all parking lots around the destination and the distance between each parking lot and the destination;
and determining at least one target parking lot from all parking lots around the destination according to the distance between each parking lot and the destination.
In a possible implementation manner, in the parking lot recommendation device provided in this embodiment of the application, the parking lot determination module 102 is further specifically configured to:
determining a parking lot with a distance destination within a first preset distance range as a target parking lot;
when no parking lot exists in the first preset distance range, the parking lot with the distance to the destination within the second preset distance range is determined as the target parking lot, and the second preset distance is larger than the first preset distance.
The parking lot recommendation device provided by the embodiment of the application obtains a parking lot recommendation service request, and determines at least one target parking lot from all parking lots around a destination; determining an expected time of arrival of the vehicle at each of the target parking lots; calling a prediction model of the target parking lot corresponding to the time period to which the predicted time belongs for each target parking lot, and predicting to obtain the number of free parking spaces of the target parking lot at the predicted time by using the prediction model; and recommending the parking lots according to the number of the idle parking lots of each target parking lot. Compared with the prior art, the number of the idle parking spaces corresponding to different long and short time periods can be predicted for each parking lot, each time period corresponds to one prediction model, the time granularity corresponding to the prediction models is finer, and the prediction accuracy is improved.
The embodiment of the present application further provides an electronic device corresponding to the parking lot recommendation method provided by the foregoing embodiment, where the electronic device may be a mobile phone, a notebook computer, a tablet computer, a desktop computer, or the like, so as to execute the parking lot recommendation method.
Please refer to fig. 5, which illustrates a schematic diagram of an electronic device according to some embodiments of the present application. As shown in fig. 5, the electronic device 20 includes: the system comprises a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected through the bus 202; the memory 201 stores a computer program that can be executed on the processor 200, and the processor 200 executes the parking lot recommendation method provided in any of the foregoing embodiments when executing the computer program.
The Memory 201 may include a high-speed Random Access Memory (RAM) and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The processor 200 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 200. The Processor 200 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the method in combination with the hardware thereof.
The electronic device provided by the embodiment of the application and the parking lot recommending method provided by the embodiment of the application have the same inventive concept and have the same beneficial effects as the method adopted, operated or realized by the electronic device.
Referring to fig. 6, the computer readable storage medium is an optical disc 30, on which a computer program (i.e., a program product) is stored, and when the computer program is executed by a processor, the computer program executes the parking lot recommendation method according to any of the foregoing embodiments.
It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory, or other optical and magnetic storage media, which are not described in detail herein.
The computer-readable storage medium provided by the above-mentioned embodiment of the present application and the parking lot recommendation method provided by the embodiment of the present application have the same beneficial effects as the method adopted, operated or implemented by the application program stored in the computer-readable storage medium.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present disclosure, and the present disclosure should be construed as being covered by the claims and the specification.
Claims (10)
1. A parking lot recommendation method, comprising:
acquiring a parking lot recommendation service request, wherein the parking lot recommendation service request comprises a current position, a destination and a current time;
determining at least one target parking lot from all parking lots around the destination;
determining an expected time of arrival of the vehicle at each of the target parking lots;
calling a prediction model of the target parking lot corresponding to the time period to which the predicted time belongs for each target parking lot, and predicting to obtain the number of free parking spaces of the target parking lot at the predicted time by using the prediction model;
and recommending the parking lots according to the number of the idle parking lots of each target parking lot.
2. The method of claim 1, wherein the predictive model of the parking lot is built as follows:
acquiring relevant data of a parking lot, and preprocessing the relevant data of the parking lot;
for each parking lot, training according to the preprocessed parking lot related data and preset time periods to obtain prediction models, wherein each preset time period corresponds to one prediction model;
the preset time period is divided into a short period and a long period, the prediction target of the prediction model corresponding to the short period is the number of idle parking spaces of the parking lot in each hour within 1 day in the future, and the prediction target of the prediction model corresponding to the long period is the number of idle parking spaces of the parking lot in each day within 7 days in the future.
3. The method of claim 2, wherein the pre-processing the parking lot-related data comprises:
screening out parking lots meeting preset evaluation standards according to the parking lot related data;
carrying out parking lot position correction, parking lot type correction and parking lot total parking space correction on the screened parking lots;
and filling missing values of the leaving time of the vehicles in the screened parking lot.
4. The method according to claim 2, wherein the training of the prediction model according to the preprocessed parking lot related data and the preset time period for each parking lot comprises:
aiming at each parking lot, dividing the preprocessed parking lot related data into a training set, a testing set and a testing set according to a preset time period;
for each preset time period, training according to the training set to obtain a plurality of machine learning models;
adopting the test set to test the predicted average error value of each machine learning model in the prediction period, and taking the model with the minimum average error value as an optimal model;
and testing the prediction accuracy of the optimal model according to the test set, and when the prediction accuracy is greater than a certain threshold value, taking the optimal model as the prediction model of the preset time period.
5. The method of claim 1, further comprising:
for each parking lot, periodically calculating the prediction accuracy of the prediction model of each preset time period corresponding to the parking lot;
when the prediction accuracy is smaller than a preset threshold, retraining the prediction model of the parking lot in the preset time period until the prediction accuracy is not smaller than the preset threshold;
the calculation formula of the prediction accuracy y is as follows:
y＝1-|a-b|/c；
wherein, a represents the actual parking space occupation number, b represents the predicted parking space occupation number calculated according to the predicted idle parking space number output by the prediction model, and c represents the total parking space number of the parking lot.
6. The method of claim 1, further comprising: and when the number of the free parking spaces of each parking lot is predicted, each prediction model of each parking lot runs in parallel.
7. The method of claim 1, wherein said determining at least one target parking lot from all parking lots around said destination comprises:
acquiring all parking lots around the destination and the distance between each parking lot and the destination;
determining a parking lot which is within a first preset distance range from the destination as a target parking lot;
when no parking lot exists in the first preset distance range, determining a parking lot which is within a second preset distance range from the destination as a target parking lot;
the second preset distance is larger than the first preset distance.
8. A parking lot recommendation device, comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a parking lot recommendation service request which comprises a current position, a destination and a current time;
a parking lot determination module for determining at least one target parking lot from all parking lots around the destination;
the arrival time determining module is used for determining the predicted time of the vehicle to arrive at each target parking lot;
the prediction module is used for calling a prediction model of the target parking lot corresponding to the time period to which the predicted time belongs aiming at each target parking lot, and predicting the number of the idle parking spaces of the target parking lot at the predicted time by using the prediction model;
and the recommendation module is used for recommending the parking lots according to the number of the idle parking spaces of each target parking lot.
9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method according to any of claims 1 to 7.
10. A computer-readable storage medium having computer-readable instructions stored thereon, the computer-readable instructions being executable by a processor to implement the method of any one of claims 1 to 7.
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