Research On The Rational Scale Of Parking In Central Business District Based On The Capacity Of Road Network

With the rapid growth of urban economy, more and more people dependent on vehicle travel. It not only leads to an increase in the number of motor vehicles, but also contributes to the growth in the travel’s times of people. It brought difficulties to the urban traffic system, and also prompts people to have to face up to the city’s parking problems. Especially in Central Business District of city, the land resources stay in shortage, contradiction between motor vehicles and parking has been aggravated. As people’s more understanding of the urban traffic system and parking planning, they pay more and more attention to effect of parking area size which comes from the road network capacity of urban road network. Given the deforestation, with parking lot size of the city central area as the research object, the paper analyzes road network capacity to affect the city parking reasonable scale, and starts a study on the reasonable road network capacity of the city parking lot size.First, through the analysis of city central area and road network capacity, the paper gives a new definition of the city’s road network. Through analyzing the influence factors of road network capacity in central region on the basis of characteristics of the city road network capacity, new roadway taking-up parking will influence the road network capacity. The calculation model of road network capacity is established on the basis of the consumption of time and space method. At the same time, it improves the calculation precision of the road network capacity.Immediately following, the paper analyzes the classification of the parking and the definition and behavior characteristics of the city parking demand, summarizes the city parking demand characteristics, and detailed analyzes parking characteristic parameters. On that basis, it analyzes influence factors of parking demand, and focuses on analyzing the mechanism of the road network capacity influence central area parking lot size.Then, the paper uses classification method to predict the city size of parking after analyzing all kinds of parking demand forecasting model. By weighted processing of car growth rate and economic growth rate, it gets the average growth rate that is used to predict the city dedicated parking lot size of special parking lot. At the same time, it chooses model of traffic flow that based on the travel of Origin and Destination as basic model to predict public parking lot size. By increasing the impact factor of the road network capacity, the paper builds a public parking lot parking demand forecasting model over the through traffic restrained, and uses the average turnover rate of peak hour’s parking demand and the ratio of between30th parking demand and average annual parking demand to amend. By the analysis of the main influence factors of inside road parking, the paper choose the average growth rate of inside road parking to predict the inside road parking of city. And eventually creates the rational scale of parking model in central business district based on the capacity of road network.Immediately following, through the analysis of establishing the principle of evaluation indexes and evaluation indexes of existing parking lot planning, it establishes an evaluation index system on account of the predicted results by using related principles and methods of systems engineering, and adopts Fuzzy Mathematics Theory, comparative analysis method and analytic hierarchy method to establish the accuracy of the parking prediction results hierarchy model, and evaluates the accuracy of forecasting results.Finally, the paper takes the Chongqing Guan yin bridge business circle as an example, and analyzes the parking problem of the Chongqing Guan yin bridge business circle, and uses the rational scale of parking model in central business district based on the capacity of road network to predict parking scale of Central Business District of city. At the same time, it verifies the accuracy of the model, and provides a better basis for future parking scale prediction.