环境承载力约束下的城市最大乘用车保有量预测研究

发布时间：2018-05-21 00:02

本文选题：乘用车保有量 + 双层规划模型　；参考：《大连海事大学》2015年博士论文

【摘要】：在高速发展的城市化与机动化的双重效用下,中国的大城市都出现了空间范围无序蔓延、住宅价格高涨、汽车保有量快速增长、道路交通量持续增多、交通拥堵日益严重等城市问题。目前,在中国的城市内大量的汽车出行在改善居民出行质量的同时,诱发了严重的环境污染问题。因此,很多大城市不得不采用限行/限号的措施来控制道路交通流量,以达到缓解拥堵,降低环境污染的目的。本文把道路交通环境负荷纳入到城市空气环境质量指标中,把城市空气环境质量满足某类标准作为城市交通的环境承载能力,研究为满足规定的空气质量,城市状态静止不变时城市可保有的最大乘用车保有数量,以及随着城市动态发展和不同政策组合的实施,可保有的乘用车数量的变化。本研究对制定可持续的道路交通政策具有重要的理论意义和实际价值。首先,为确定道路交通对空气质量的影响,构建城市道路交通污染物浓度预测模型。模型采用人工神经网络技术,模拟道路交通流、大气环境等因素与某高度处交通污染物浓度之间的关联关系,基于“排放-扩散”一体化的方式,基于道路交通流特征,计算道路两侧各种高度位置的道路交通污染物浓度。为获取道路交通流的属性特征,基于虚拟线圈技术开发采集道路交通流特征数据的方法,克服了用交通流调查方法获取数据费时费力的缺点。其次,建立环境承载力约束下城市最大乘用车保有量预测模型。在城市状态静止不变的情况下,考虑机动车(乘用车+巴士)出行需求与城市道路交通污染程度之间的均衡关系,以出行小区为空间单位,预测环境承载力约束下城市可保有的最大乘用车数量。模型采用双层规划方法分析机动车出行需求与环境污染程度、环境承载力之间的相互关系,其中上层模型以环境承载力为约束,以城市乘用车数量最大化为目标,基于乘用车与巴士的出行分担率计算各小区的乘用车保有数量。下层模型基于用户均衡原则,计算乘用车+巴士出行在道路上的均衡状态,得到出行者的出行时间、道路网上的交通流特征数据。同时,用污染物浓度预测模型,根据交通流特征计算道路环境污染浓度,并将污染浓度数值反馈到上层模型。上下层模型之间通过交通方式划分和污染物浓度预测相互联系。最后,考虑到在城市发展是动态的,其环境承载力会发生变化,而某些交通规划和管理措施也会改变乘用车和公交车的分担率与道路交通特征,因此拓展静态乘用车保有能力预测模型。将城市发展脚本、交通规划与管理政策作为影响变量,拓展静态环境承载力约束下的最大乘用车保有量模型,使之能够预测城市发展政策所导致的环境承载力与出行行为变化时,城市能够保有的最大乘用车数量的变化。进而预判各种发展脚本和政策方案,城市可以保有的最大乘用车数量,测试城市发展政策对城市机动化和道路交通系统的影响效果。
[Abstract]:Under the dual utility of urbanization and motorization in high speed, large cities in China have appeared disorderly spread of space, high housing price, rapid growth of car ownership, increasing traffic volume, increasing traffic congestion and so on. At present, a large number of cars travel in China's cities to improve residents' travel. The quality has caused serious environmental pollution problems. Therefore, many big cities have to adopt the limit / limit measures to control the road traffic flow in order to alleviate the congestion and reduce the environmental pollution. In this paper, the road traffic environment load is included in the urban air environmental quality index, and the quality of the urban air environment is satisfied. As the environmental bearing capacity of urban traffic, some types of standards are studied to meet the required air quality, the number of maximum passenger cars available in the city when the city is still at rest and the change of the number of passenger cars that can be maintained with the dynamic development of the city and the implementation of different policy combinations. The general policy has important theoretical significance and practical value. First, in order to determine the influence of road traffic to air quality, the prediction model of urban road traffic pollutant concentration is constructed. The model adopts artificial neural network technology to simulate the relationship between road traffic flow, atmospheric environment and other factors of traffic pollutant concentration at a high degree. In the integration of "emission and diffusion", based on the characteristics of road traffic flow, the concentration of road traffic pollutants on both sides of the road is calculated. In order to obtain the characteristics of road traffic flow, the method of collecting the characteristic data of road traffic flow is developed based on the virtual coil technology, and the data fee is overcome by the traffic flow survey method. Secondly, the prediction model of the maximum passenger car is established under the constraint of environmental carrying capacity. Under the condition of the static state of the city, the equilibrium relationship between the travel demand of motor vehicle (passenger car + bus) and the pollution degree of urban road traffic is considered, and the space unit is taken out as the space unit and the environmental bearing capacity is predicted. The maximum number of passenger cars available in the next city. The model uses a double deck programming method to analyze the relationship between vehicle travel demand and environmental pollution level and environmental carrying capacity. The upper model is constrained by environmental carrying capacity, maximizes the number of urban passenger cars, and calculates the rate of travel sharing based on passenger car and bus. The lower level model is based on the user equilibrium principle, calculates the equilibrium state of the passenger car and bus travel on the road, gets the traveler's travel time, and the traffic flow characteristic data on the road network. At the same time, the pollutant concentration prediction model is used to calculate the pollution concentration of the road environment according to the traffic flow characteristics, and the pollution is thicker. In the end, the environmental carrying capacity of the city will change, and some traffic planning and management measures also change the sharing rate of passenger cars and buses and the characteristics of road traffic. This paper expands the prediction model of the capacity of the static passenger car, and takes the city development script, traffic planning and management policy as the influence variable to expand the maximum passenger car ownership model under the constraint of static environment carrying capacity, so that it can predict the urban development policy caused by the environment carrying capacity and travel behavior change, the city can maintain the most. The change in the number of Mahayana vehicles, and then a variety of development scripts and policy plans, the largest number of passenger cars available in the city, and the impact of urban development policies on urban mobility and road traffic systems.
【学位授予单位】：大连海事大学
【学位级别】：博士
【学位授予年份】：2015
【分类号】：U491

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