Public parking lot berth quantity confirming method

Abstract

The invention discloses a public parking lot berth quantity confirming method. The method includes a first step of confirming factors which influence the berth quantity of a public parking lot, investigating data of the factors and dividing the data into a training data group and a prediction data group, a second step of building a back propagation (BP) neural network which comprises an input layer, a hide layer and an output layer, a third step of carrying out preprocessing to the data of the training data group, a fourth step of inputting preprocessed training data into the BP neural network to generate the data of the output layer, comparing the data of the output layer with an actual value, adjusting the variable quantity and the weight of the hide layer, and carrying out iteration training until errors meet the accuracy requirement, and a fifth step of inputting data of the prediction data group into the BP neural network to be subjected to processing and generate the data of the output layer. The data is the berth quantity of the public parking lot. The public parking lot berth quantity confirming method is simple in modeling, parameters are easy to obtain and collect, and intelligent processing to the big data is achieved. The public parking lot berth quantity confirming method can be widely applied to urban construction, large commercial district programming or district programming.

Description

technical field [0001] The invention relates to a method for determining the number of berths in a public parking lot, in particular to a method for determining the number of berths in a public parking lot by using a BP neural network, and belongs to the fields of urban traffic planning and parking lot planning. Background technique [0002] With the rapid growth of the national economy and the acceleration of the urbanization process, the level of urban motorization in my country has developed rapidly in recent decades, and the demand for transportation has increased sharply. However, due to the lack and lag of urban transportation infrastructure in our country, the rapid development of urban motor vehicles not only seriously aggravates the road load and brings traffic congestion and other problems, but also brings serious shortage of parking facilities and random parking of vehicles. And other issues. [0003] The social public parking lot is one of the most important par...

AI Technical Summary

Problems solved by technology

[0004] At present, parking demand forecasting methods include parking generation rate model, land use and traffic impact analysis model, multiple regression analysis forecasting model and parking demand standard, etc., but the existing methods do not consider factors such as population and economy. The scale of the proposed parking lot obtained in the model is first calculated from the industry standard, and second based on the model regression

However, the scale of the obtained parking lot is not based on factors such as the population economy of the proposed area, and there are disagreements about whether it can meet the parking problem in the proposed area. In addition, the existing models are relatively complicated, and the parameters are difficult to obtain, and Algorithms without fast convergence are not applicable in engineering practice

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