基于低碳出行的城市公共停车场选址研究

发布时间：2018-03-24 18:35

本文选题：城市交通　切入点：交通结构　出处：《北京交通大学》2017年硕士论文

【摘要】：随着国民经济水平不断提高,我国城市交通进入快速发展期,机动车保有量持续高速增长,导致“城市交通环境污染”和“停车难”问题日益凸显。本文从我国居民出行结构出发,建立以低碳为目标的交通结构优化模型,通过优化交通出行结构减少机动车出行,在降低交通CO2排放的同时,从根本上压缩了基本停车需求,针对需求进一步研究城市公共停车场选址建设问题。论文研究内容主要包括以下几个方面:第一,本文在分析城市公共停车场类型和特征指标的基础上,根据实际调研数据研究对比了居住类、商业类、医院类、P+R类等不同类别停车场的供需关系,总结了北京市的重点停车问题,并进一步探讨了影响城市公共停车场选址的主要因素。研究发现,居住类和大型医院类停车场的泊位周转率和高峰停放指数偏高,P+R停车场的停放特征指数与其使用性质及管理政策有关,而部分商业类停车场利用率相对较低。因此,停车场停车特征影响城市公共停车场选址,不同类别停车场的停车特征存在一定差异。第二,考虑到机动车保有量的快速增长是导致停车需求不能得到满足的最直接原因,本文在分析城市交通碳排放测算方法的基础上,建立了低碳目标的城市客运交通结构优化模型,并以北京市为例,结合低碳目标的交通结构优化模型探讨了低碳交通出行模式和基本情景下出行模式碳排放情况。结果表明,2020年在低碳交通出行模式下CO2排放量将比基本情景减少12.3%,小汽车出行比例将降低5%,公共交通出行比例将提高8%。基于低碳目标的城市客运交通结构优化能显著降低目标年机动车保有量和出行率,居民的出行方式将以公共交通出行为主导,出行方式更加合理,为进行更为合理的停车规划奠定了基础。第三,停车位需求与居民出行方式相关,传统的停车需求预测方法并未考虑居民出行结构。本文提出了基于居民出行的停车位需求预测方法,以城市公共停车场选址原则为依据,建立了城市公共停车场选址模型,提出了基于matlab求解产销不平衡运输问题的算法设计。案例分析结果表明,低碳情景下的城市交通出行结构能够显著减少停车位需求和建设费用,而基础情景下的停车位缺口约为低碳模式下的3.7倍,说明改善城市交通出行结构是改善交通污染和停车问题的重要途径。
[Abstract]:With the continuous improvement of the national economic level, the urban traffic in China has entered a period of rapid development, and the number of motor vehicles has continued to grow at a high speed. The problems of "pollution of urban traffic environment" and "parking difficulty" become more and more prominent. Starting from the travel structure of residents in China, this paper establishes an optimization model of traffic structure aiming at low carbon, which can reduce the travel of motor vehicles by optimizing the structure of traffic travel. At the same time of reducing the traffic CO2 emission, the basic parking demand is reduced fundamentally, and the problem of urban public parking location construction is further studied in the light of the demand. The main contents of this paper include the following aspects: first, Based on the analysis of the types and characteristics of urban public parking lots, this paper studies and compares the supply and demand relationships among different types of parking lots, such as residential, commercial, hospital and so on, according to the actual investigation data. This paper summarizes the key parking problems in Beijing, and further discusses the main factors that affect the location of urban public parking lots. The berth turnover rate and peak parking index of residential and large hospital car parks are relatively high. The parking characteristic index of P R car park is related to the nature of use and management policy, while the utilization ratio of some commercial car parks is relatively low. Parking characteristics affect the location of urban public parking, and the parking characteristics of different types of parking are different. Second, considering that the rapid growth of vehicle ownership is the most direct reason leading to the parking demand can not be met. In this paper, based on the analysis of urban traffic carbon emission measurement method, a low carbon target urban passenger transport structure optimization model is established, and taking Beijing as an example, Based on the model of low carbon target traffic structure optimization, the carbon emissions of low carbon transport travel mode and basic travel mode are discussed. The results show that CO2 emissions will be less in 2020 than in the basic scenario in the low carbon transport travel mode. 12.3The proportion of car trips will be reduced by 5%, the proportion of public transportation travel will be increased by 8%. The optimization of urban passenger transport structure based on low-carbon targets can significantly reduce the number of motor vehicles and the travel rate in the target year. Residents' travel mode will be dominated by public transportation, and the travel mode will be more reasonable, which will lay the foundation for more reasonable parking planning. Third, the demand for parking space is related to the residents' travel mode. The traditional parking demand forecasting method does not consider the resident travel structure. This paper puts forward the parking space demand forecasting method based on the resident travel, and establishes the urban public parking location model based on the urban public parking location principle. An algorithm for solving unbalanced transportation problem based on matlab is proposed. The results of case study show that the urban traffic travel structure can significantly reduce the demand for parking spaces and construction costs under low carbon scenarios. The gap of parking space in the basic scenario is about 3.7 times higher than that in the low-carbon model, which indicates that improving the urban transportation structure is an important way to improve the traffic pollution and parking problems.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U491.7

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