组团城市停车需求预测研究

发布时间：2018-03-25 00:35

本文选题：组团城市　切入点：停车需求　出处：《重庆交通大学》2014年硕士论文

【摘要】：在我国城市土地资源高度紧缺和汽车保有量快速增长背景下，大多数城市停车供需矛盾日益突出，现有的停车设施已不能满足人们日益增长的停车需求。“停车难”等问题的解决迫在眉睫，，相关部门也开始着手城市停车规划的研究。然而，要做好一个城市的停车规划，就必须要对城市的停车需求进行准确合理的预测。组团城市利用“分散集中”的城市空间结构，把城市交通流分散到各个组团，从根源上改变了城市形态和内部相互作用关系，从而导致组团内外出行分布特征的改变。停车需求源于交通出行，因此，停车需求也会随之改变。本文旨在从组团式空间结构角度入手，在总结分析停车需求预测影响因素的基础上，结合组团城市形态、密度、功能布局等空间结构特性对停车需求分布的影响，探讨建立基于区位优势的组团城市停车需求预测模型，为组团城市停车需求预测提供理论支撑和技术指导。主要包括以下几个方面：首先，系统回顾和总结了组团城市相关概念、形成机理及空间结构特性，深入分析了组团城市的交通特性，在此基础上以重庆市主城区调查数据为依托，阐述和总结了组团城市的停车需求特性。其次，详细分析了停车需求的影响因素，从交通出行的角度，结合重庆市主城区居民出行调查数据，总结分析空间结构对停车需求分布的影响。之后，对常用停车需求预测方法进行了归纳总结，对比分析各类方法的适应性和优缺点，并将其与组团城市的适应性进行分析。最后，在分析现有区位理论模型的基础上，建立了以交通优势（交通便捷度、路网结构、道路服务水平）和综合优势（土地开发利用强度、职住匹配度、配套设施完善度）作为量化指标的区位优势模型。然后以停车产生率模型为基础模型，针对不同用地类型分别利用不同优化因子对其进行差异化修正，构建了基于区位优势的组团城市停车需求预测优化模型。即引入机动车增长率来优化居住用地的停车需求；引入区位优势来量化组团空间结构对商业金融等其它用地类型停车需求的影响。并采用重庆主城区停车调查数据和规划相关数据对优化模型进行参数标定和应用，验证了优化模型的实用性及合理性。
[Abstract]:Under the background of the high shortage of urban land resources and the rapid growth of vehicle ownership, the contradiction between supply and demand of parking in most cities is becoming increasingly prominent. The existing parking facilities can no longer meet the increasing demand for parking. It is urgent to solve the problems such as "parking difficulties", and the relevant departments have begun to study the urban parking planning. However, to do a good job of parking planning in a city, The parking demand of the city must be predicted accurately and reasonably. By using the "decentralized and centralized" urban spatial structure, the regiment cities disperse the urban traffic flow to the various groups, which changes the form of the city and the internal interaction relationship from the root. As a result, the characteristics of travel distribution inside and outside the group are changed. The parking demand is derived from the traffic travel, so the parking demand will change with it. The purpose of this paper is to analyze the influence factors of parking demand prediction from the angle of cluster spatial structure, and combine the spatial structure characteristics such as form, density and function layout of cluster city to influence the distribution of parking demand. In order to provide theoretical support and technical guidance for the prediction of parking demand of group cities, this paper discusses the establishment of a forecasting model of parking demand based on regional advantages. It mainly includes the following aspects:. First of all, the related concepts, formation mechanism and spatial structure characteristics of the regiment city are reviewed and summarized systematically, and the traffic characteristics of the regiment city are deeply analyzed, which is based on the survey data of Chongqing main urban area. The characteristics of parking demand of the tour city are expounded and summarized. Secondly, the influence factors of parking demand are analyzed in detail. From the point of view of traffic travel, combined with the survey data of residents in Chongqing's main urban area, the influence of spatial structure on parking demand distribution is summarized and analyzed. The common methods of parking demand prediction are summarized, the adaptability, advantages and disadvantages of these methods are compared and analyzed, and the adaptability of these methods is analyzed with that of group cities. Finally, on the basis of analyzing the existing location theory model, the paper establishes the traffic advantage (traffic convenience, road network structure, road service level) and the comprehensive advantage (land development and utilization intensity, occupation and housing matching degree). Then based on the parking production rate model, different optimization factors are used to modify different land types. In this paper, an optimization model of parking demand prediction based on location advantage is established, that is, the vehicle growth rate is introduced to optimize the parking demand of residential land. The location advantage is introduced to quantify the influence of cluster space structure on the parking demand of other land types, such as commercial finance, and the parameters of the optimization model are calibrated and applied by using the parking survey data and planning data of Chongqing main urban area. The practicability and rationality of the optimization model are verified.
【学位授予单位】：重庆交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U491.7

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