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Prediction method of road parking spaces based on optimized lstm model

A prediction method and berth technology, applied in prediction, biological neural network model, indicating the direction of each open space in the parking lot, etc., can solve the problems of low accuracy and unstable prediction results of the remaining parking berths, so as to improve the accuracy and weaken the Stochastic volatility, high efficiency effects

Active Publication Date: 2020-03-24
TAIHUA WISDOM IND GRP CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] In view of this, the present invention provides a road parking berth prediction method based on an optimized LSTM model, which solves the problems in the prior art that the prediction result of the remaining parking berths in the parking lot is unstable and the accuracy is low

Method used

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  • Prediction method of road parking spaces based on optimized lstm model
  • Prediction method of road parking spaces based on optimized lstm model

Examples

Experimental program
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Embodiment 1

[0065] This embodiment provides a road parking berth prediction method based on an optimized LSTM model, which is used to predict the number of parking berths in the target parking lot, such as figure 1 is a flow chart of the road parking berth prediction method based on the optimized LSTM model; the method comprises the following steps:

[0066] Step S1: receiving a parking space prediction request;

[0067] Wherein, the parking space prediction request is used to request to predict the remaining parking spaces per unit time interval in the predetermined time period in the target parking lot, for example, the current time is August 3, and the predetermined time period can be from August 3 to August On the 4th, the unit time is one hour, and the target parking lot is A parking lot, then the parking space prediction request is to predict the remaining parking spaces of A parking lot every hour from August 3rd to August 4th ask.

[0068] Step S2: Obtain the historical parking ...

Embodiment 2

[0101] On the basis of Embodiment 1, this embodiment provides a preferred road parking parking space prediction method based on an optimized LSTM model, such as figure 2 Shown is the flow chart of the road parking space prediction method based on the optimized LSTM model.

[0102] Specifically, the method includes the following steps:

[0103] S201: receiving a parking space prediction request,

[0104] Wherein, the parking space prediction request is used to request the remaining number of parking spaces per unit time interval in a predetermined time period within a predetermined date, for example, the current time is August 3, and the predetermined time period can be from August 3 to August 4 , the unit time is one hour, and the target parking lot is A parking lot, then the parking space prediction request is a request to predict the remaining parking spaces of A parking lot at intervals of one hour from August 3 to August 4.

[0105] S202: determine that the scheduled da...

Embodiment 3

[0151] On the basis of Embodiment 1 and Embodiment 2, this embodiment provides another road parking parking space prediction method based on an optimized LSTM model. The general content is the same as that of Embodiment 2, and the description of Embodiment 2 may be referred to. The difference is that the sample set x is obtained by calculating the historical remaining number of parking spaces per unit time in the target parking lot based on historical parking data (0) ={x 1 , x 2 ,...,x k}, the sample set is obtained by the following method:

[0152] S301: Count the number A of vehicles whose driving target is to enter the parking lot in the i-th unit time i ;

[0153] The unit time is an artificially set period of time, for example, it can be 1 hour or 30 minutes or 20 minutes. Taking one hour as the unit time as an example, for example, from 8:00 am to 12:00 am on August 3, you can Divided into 4 unit time, the first unit time is 8:00 to 9:00, and so on, the last unit ...

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Abstract

The invention discloses a road parking berth prediction method based on an optimized LSTM model, comprising the following steps: obtaining historical parking data of a target parking lot, calculating the number of parking berths per interval unit time in the target parking lot to obtain a sample set; Divide it into a training set and a test set and perform normalization processing respectively; input the normalized training set into the neural network model for training, and obtain the first prediction result set, if the result in the first prediction result set is less than the first error threshold, then Input the normalized test set into the neural network model to obtain the second prediction result set, denormalize the second prediction result set, and calculate the difference between the result obtained after the denormalization processing and the number of remaining parking spaces Error: if the error is smaller than the second predetermined error threshold, output the data corresponding to the predetermined time period in the second prediction result set. The present invention can more accurately predict the number of remaining parking spaces in the parking lot.

Description

technical field [0001] The present invention relates to the field of prediction of road parking spaces, and more particularly, relates to a method for predicting road parking spaces based on an optimized LSTM model. Background technique [0002] With the rapid development of my country's economy, more and more attention has been paid to the construction of urban public parking facilities, while the number of urban vehicles has increased rapidly, foreign vehicles have increased in large numbers, public parking facilities have seriously lagged behind, and parking facilities are managed by multiple parties. The lack of connection and coordination makes parking more difficult, especially in central business districts. [0003] With the increase of the number of vehicles, due to the lack of effective management, the phenomenon of random parking of vehicles on urban roads or town roads is becoming more and more serious, and many vehicles even park in prohibited areas. This phenome...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/14G06N3/04G06Q10/04
CPCG06N3/049G06Q10/04G08G1/14G06N3/044Y02T10/40
Inventor 刘菲沈海南辛国茂周永利郝敬全
Owner TAIHUA WISDOM IND GRP CO LTD
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