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Short-time prediction method for future parking space demand quantity in a closed area

A closed area, prediction method technology, applied in prediction, data processing applications, instruments, etc., to achieve the effect of effectively utilizing parking resources and alleviating traffic congestion

Inactive Publication Date: 2019-03-19
HANGZHOU DIANZI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

At present, it has been proved that LSTM is an effective technology to solve the problem of long-order dependence, and the universality of this technology is very high, resulting in a lot of possibilities.

Method used

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  • Short-time prediction method for future parking space demand quantity in a closed area
  • Short-time prediction method for future parking space demand quantity in a closed area
  • Short-time prediction method for future parking space demand quantity in a closed area

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Embodiment Construction

[0054] The number of remaining berths in the closed area has the property of real-time change, and the number of remaining berths varies greatly between different closed areas. The present invention takes a university in Hangzhou as the specific research object. In order to describe the present invention more specifically, the technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0055] See the forecast flow chart figure 2

[0056] Step 1, obtain the historical data of vehicle entry and exit of the university, and perform data preprocessing on the obtained data;

[0057] Step 1.1, desensitize the original data, first classify the same items in the license plate number column, and then modify the items with different license plate numbers into desensitized information in the form of A00001, A00002, A00003... ;

[0058] Step 1.2, match the vehicle entry and exit records, write a ...

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Abstract

The invention discloses a short-time prediction method for future parking space demand quantity in a closed area, which is used for predicting the parking space demand quantity of a certain closed area in future hours, so that managers can effectively manage and schedule facilities of parking lots on roads in the area. The invention provides a deep learning theory-based recurrent neural network parking space demand prediction model of an LSTM (Long Short-Term Memory) unit, and the parking space demand corresponding to a subsequent time point is predicted according to the parking space demand variations of previous hours. Certain Hangzhou University is taken as an experimental object, and data of two random days and two specific days are adopted for practical verification. Results show thatthe LSTM recurrent neural network module is adopted to predict the parking demand in the area, the result is closer to the actual value than that of a traditional method, the precision is satisfactory, and it is indicated that the prediction method is feasible and effective.

Description

technical field [0001] The present invention proposes a cyclic neural network berth demand forecasting model based on LSTM units based on deep learning theory, and predicts the berth demand corresponding to subsequent time points according to the change in berth demand in the first few hours. It is a method for predicting the number of berths in a short period of time in the future in a closed area. Background technique [0002] The prediction of berths in a region is an important prerequisite for traffic regulation and control in a region, and it is one of the key technologies to realize intelligent parking. [0003] Prediction and early warning of changes in the number of berths in a closed area can help to predict in advance that there will be too many cars or even road congestion in the area, and make corresponding countermeasures to alleviate it, which is helpful for alleviating traffic congestion in the area and effectively using parking spaces Resources play an impor...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/26G06N3/04
CPCG06Q10/04G06Q50/26G06N3/045
Inventor 周后盘胡进吴辉裘瑞清
Owner HANGZHOU DIANZI UNIV
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