Abstract
With the increasingly significant trend of developing urban land for mixed-use, an increasing number of urban commercial and office complexes have been built. The parking demand characteristics of such buildings are more complex than the parking demand characteristics of single-use buildings due to more diverse influencing factors. As there are complicated linear and nonlinear relationships between parking demand and influencing factors, it is difficult to accurately predict parking demand using a single multiple regression analysis (MRA) model. Hence, in this paper, a combined algorithm based on the MRA model, beetle antennae search (BAS) algorithm, and BP neural network is proposed for demand prediction. In this paper, a two-level and ten-category index system is established and then mixed with the BP algorithm through the MRA model to improve the overall robustness and accuracy of the algorithm. Then, the BAS algorithm is used to search for optimal parameters involved in the BP neural network to avoid local optimization and improve the accuracy and efficiency of prediction. Finally, an instance analysis is carried out for verification, and the result indicates that the parking demand prediction accuracy of the MRA-BAS-BP algorithm is higher than the prediction accuracy of the traditional algorithm.
Highlights
In recent years, urban motorization has made a great process in China. e contradiction between growing parking demands and insufficient parking spots in buildings has become increasingly prominent. erefore, many cities begin to pay more and more attention to the formulation of parking spaces allocated for new buildings
The research on parking allocation indicators mostly focuses on the parking demand model of single business type; when it comes to urban complexes with mixed commercial and office business types, they mostly adopt the simple mode of classified demand superposition and do not fully consider the sharing benefits brought by parking peak staggering, which increases the cost of urban development and operation and restricts the intensity of land compound development
With the increasing number of urban commercial complex buildings, it is of great practical significance to carry out the research on parking demand prediction of a commercial complex
Summary
Urban motorization has made a great process in China. e contradiction between growing parking demands and insufficient parking spots in buildings has become increasingly prominent. erefore, many cities begin to pay more and more attention to the formulation of parking spaces allocated for new buildings. E results showed that the parking demand predicted by the deep learning neural network is greatly improved in comparison with the parking demand predicted by conventional basic models. Roughout research on parking demand prediction methods for existing urban commercial-office complexes at home and abroad, the parking demand of commercial-office complexes and its influencing factors can be found to show a complex linear and nonlinear relationship [5]. Erefore, referring to relevant research and to compensate for the shortcomings of the MRA model, the authors in this paper combine the MRA model with a machine learning algorithm to construct a combinational algorithm with a more significant fitting effect for parking demand prediction of urban commercial-office complexes
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