Short-time parking demand prediction method based on GRU model

Abstract

The invention discloses a short-time parking demand prediction method based on a GRU model. The short-time parking demand prediction method comprises the following steps: 1), obtaining historical dataof parking lot facilities, processing the historical data, and obtaining the parking space occupancy data at each time point; 2) setting a GRU neural network structure by using a deep learning Kerasframework packet, and obtaining model optimal parameters by using a GridSearch function in the Keras packet; and 3) training a GRU model by using the training set data, storing the model and predicting the berth occupancy of the next step length. Compared with the prior art, under the background of obtaining continuous parking data, the short-time parking demand prediction method utilizes the bigdata processing technology, applies the latest algorithm of deep learning, provides a more advanced and more accurate parking information induction publishing method, and can consider the relevance ofthe parking demand in the time dimension while the cell module has a simpler control door structure, so that training efficiency can be improved to a great extent, and then the utilization rate of parking facilities is increased, and satisfaction of users with parking requirements is improved, and unnecessary traffic of the users is avoided, and traffic pressure of roads is reduced, and effectivetraffic control of traffic management departments during peak hours of traffic flow is facilitated.

Description

technical field [0001] The invention relates to a parking demand forecasting method, in particular to a short-term parking demand forecasting method based on a GRU model. Background technique [0002] The rapid development of the automobile industry is in serious conflict with the limited urban space resources, the contradiction between supply and demand is becoming increasingly acute, and the problem of difficult parking has become a challenge for major cities. Parking Guidance System (PGS) is an effective way to alleviate traffic congestion, but the short-term accurate prediction of parking demand as a key technology for the release of vacant parking spaces has not been effectively resolved. During the use of the existing guidance information, there will be a problem that the guidance information display is inaccurate. When the driver sees the guidance information, it shows that there are still parking spaces, but when they arrive at the parking lot, it shows that there ar...

AI Technical Summary

Problems solved by technology

According to previous studies, the accuracy of these methods cannot meet the requirements of refined management of parking lots for parking demand prediction. Therefore, it is necessary to explore a machine learning algorithm with higher accuracy.

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