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Indoor parking lot remaining parking space prediction method

A prediction method and parking lot technology, which is applied in the direction of indicating, forecasting, and traffic flow detection of each open space in the parking lot. It can solve the problems of weak stability and low single-model prediction accuracy, and achieve high accuracy.

Active Publication Date: 2019-03-19
ZHEJIANG UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0006] Aiming at the shortcomings of low prediction accuracy and weak stability of single model, the present invention combines the advantages of first-order filtering algorithm for smoothing and denoising and gray model algorithm for weakening sequence randomness, and proposes a dynamic weighted combination model prediction method

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  • Indoor parking lot remaining parking space prediction method

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Embodiment Construction

[0027] The present invention overcomes the above-mentioned shortcomings of the prior art and provides a method for predicting parking spaces in large indoor parking lots. The invention proposes a prediction method of a dynamic weighted integrated model aiming at the fact that the prediction accuracy of a single model is low and the stability is weak.

[0028] A method for predicting remaining parking spaces in an indoor parking lot of the present invention comprises the following steps:

[0029] Step 1. Obtain the sequence of vacant berths in the parking lot and perform data preprocessing.

[0030] Obtain the historical data of vacant berths in the parking lot, extract the data at intervals of 5 minutes, and obtain the time series of vacant berths in the parking lot, denoted as X={X i |i=1,2,…,n}, where X i is the number of vacant berths in the parking lot in the i-th time period, n and i are natural numbers, and n is the total number of time periods in the time series. Che...

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Abstract

An indoor parking lot remaining parking space prediction method comprises the following steps that 1, acquiring a parking lot unoccupied parking space sequence, and carrying out data preprocessing; step 2, performing denoising processing on the training set of the initial sequence to obtain a time sequence with noise removed, recording the time sequence as a smooth sequence, and dividing the smooth sequence into a test set and a training set; Using the smooth sequence training set to train an LSTM neural network; step 3, constructing and training a gray residual neural network model by using the initial sequence; and step 4, weighting and combining the two prediction models to obtain a final prediction model.

Description

Technical field: [0001] The invention relates to a method for predicting remaining parking spaces in a parking lot. Background technique: [0002] With the improvement of people's living standards, the number of motor vehicles in cities has increased year by year, but the number of parking spaces cannot meet the demand, and parking difficulties have become a big problem day by day. In recent years, big data technology has been applied more and more in the field of intelligent transportation, and many artificial intelligence algorithms have been quite effective in predicting the number of berths. The current mainstream research is to analyze the historical data of the parking lot, and then make accurate predictions on the parking space information of the parking lot. With the accurate prediction results, users can be provided with reliable travel information, help users make travel plans, and at the same time reduce the number of users looking for The time wasted in parking ...

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Application Information

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IPC IPC(8): G06Q10/04G06F16/2458G08G1/01G08G1/14
CPCG06Q10/04G08G1/0129G08G1/14
Inventor 岑跃峰李向东岑岗张宇来马伟峰程志刚徐昶孔颖周扬林雪芬徐增伟王佳晨
Owner ZHEJIANG UNIVERSITY OF SCIENCE AND TECHNOLOGY
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