Research on Improvement of Parking Generation Rate Model Based on Behavior Selection

Abstract

The traditional parking generation rate model can no longer forecast the demand of parking slots accurately under the pattern of shared parking. Shared parking, which can make full use of the free time of private or non-private parking slots, has become an effective way to ease the pressure of urban parking. Therefore, shared parking behavior selection generation (SPBSG) model is established, based on the analysis of residents’ shared parking selection behavior. The SPBSG model fully simulates residents’ parking choice preferences, shared slots management, parking time differences between different land types, and walking distance after parking. Experiment shows that the SPBSG model can reduce parking slots by 24.45% compared with the traditional parking demand prediction method.