基于出行活动的城市居民低碳交通出行模型及算法

发布时间：2018-01-14 08:18

本文关键词：基于出行活动的城市居民低碳交通出行模型及算法　出处：《北京交通大学》2015年博士论文　论文类型：学位论文

【摘要】：随着社会的发展,人类消耗的能源和产生的污染物逐年上升,已经危害到全球生态环境与人类生活安全。国家能源战略和国家应对气候变化规划都提出了对能源消耗和碳排放量的约束要求。交通运输活动能源使用量大、排放强度高,是城市能源和污染物排放的主要来源,也成为实现节能减排的重要领域。不同交通方式的能耗和排放存在较大差异,出行结构的优化对于降低能耗和污染物排放具有重要作用。若北京出行结构模式转变为以轨道交通为主的模式,能源消耗、污染物和碳排放分别能够实现降低10%-50%,对于环境改善将起到积极作用。因此,解析交通行为,研究基于能源环境约束的出行结构优化调整,对于积极应对政府对能耗排放的约束考核,满足对蓝天白云的期许具有重要的现实意义,对于完善出行结构及出行行为研究方法也具有推动作用。本论文分别从宏观的出行结构优化、中观的出行活动调整、微观的交通方式选择三个方面研究基于低碳目标的出行结构和出行行为优化调整的理论与方法。为了支持模型构建,进一步拓展研究了出行行为调查技术和多源数据分析方法。论文的主要内容包括以下4方面:(1)基于多源异构数据的交通出行行为调查技术和数据分析方法。分析了出行行为调查方法的优缺点和适用性,构建以入户调查为核心,道路流量、GPS定位、手机APP为校核,手机定位数据、公共交通调查、出租汽车调查为互补,意愿调查为共生关系的出行者交通出行综合调查体系。对调查获取数据特征进行分析,针对多源数据,给出加权扩样和综合校核的数据分析技术路线,并以北京市第四次全市交通综合调查为例,进行了调查项目设置和多源数据分析的实证研究。(2)基于低碳目标的城市交通出行结构优化模型研究。从宏观出行结构调整目标层面,以降低碳排放和提高政府交通建设资金投入产出比为目标,构建基于社会成本投入的城市交通出行结构优化模型。将居民出行社会效益作为模型的目标函数,将能耗、排放降低水平、公共交通满载率等作为约束条件,使用分枝定界法对模型进行求解。开展实证研究,分析北京市出行结构、出行费用、政府投资、能耗和排放约束等指标数据,经模型测算得到低碳目标下的城区最优出行结构。(3)基于居民出行活动的出行组合链预测模型研究。首先分析居民一日出行数据,将出行活动和交通方式按照发生序列串联起来,构成组合链,组合链包含一日活动序列、每次活动目的、采取的交通方式等信息。而后对组合链进行编码,转化计算机能够识别和计算的0-1代码模式。之后应用神经网络预测模型,通过训练数据对组合链模型进行模拟训练,预测居民出行活动,获取出行总量变化。最后应用模型开展实证分析,对人均收入加倍及取消小汽车摇号措施进行分析,测算居民出行量和交通碳排放量的变化。(4)基于低碳政策的个体出行者交通方式选择模型研究。应用北京市第四次交通综合调查数据,分析影响个体进行交通方式选择的因素,构建基于Mixed Logit模型的个体出行者交通方式选择模型,进行基于优化退火算法的模型参数求解。开展实证分析,对拥堵收费和公交提高运行速度两项措施对出行方式转变和碳排放量变化效果进行预测和评估分析。
[Abstract]:With the development of society, the human consumption of energy and pollutants increased year by year, has been harmful to the safety of ecological environment and human life world. The national energy strategy and the national climate change plan are put forward on energy consumption and carbon emissions constraints. Transport activities using a large amount of energy, the emission intensity is high. The main source of city energy and pollutant emissions, has also become an important field for energy conservation. There is a big difference between the energy consumption and emissions of different transport modes, optimization of travel structure plays an important role in reducing energy consumption and pollutant discharge. If the energy consumption changes of Beijing travel structure model to track traffic patterns, pollutants and carbon emissions can be reduced to 10%-50%, to improve the environment will play a positive role. Therefore, analysis of traffic behavior, based on energy and environmental constraints Optimization and adjustment of travel structure, to actively respond to the government on the assessment of energy consumption and emissions constraints, has important practical significance to meet the expectations of the blue sky and white clouds, but also play an important role in improving the travel structure and travel behavior research methods. This paper respectively from the macro meso structure optimization of travel, travel activities, study three aspects of selection the microscopic traffic model structure and travel behavior optimization theory and method of adjustment based on low carbon target. In order to build support model, to further expand the research on travel behavior survey technology and multi-source data analysis methods. The main contents of this paper include the following 4 aspects: (1) travel behavior survey and data analysis method of multi-source heterogeneous technology based on the data analysis. The advantages and disadvantages and the applicability of the investigation methods of travel behavior and the household survey as the core, road traffic, construction of GPS positioning, hand APP check, the mobile phone location data, public traffic survey, car rental survey to complement each other, the survey for the symbiotic relationship between traveler traffic comprehensive survey system. The data obtained were analyzed according to the characteristics of investigation, multi-source data, nuclear data gives weighted sampling expansion and comprehensive analysis of technical route, and in Beijing city fourth times the city's traffic survey as an example, makes an empirical research on the investigation and analysis of the project settings and multi-source data. (2) research of traffic structure optimization model based on the goal of low carbon city. From the macro travel structure adjustment target level, to reduce carbon emissions and improve government transportation construction funds input-output ratio as the goal, to build traffic the travel structure optimization model of social cost investment. Based on the city residents social benefit as the objective function, the model will reduce energy consumption, emission level, public traffic load ratio As the constraints, the model was solved using branch and bound method. To carry out empirical research, analysis of Beijing city travel structure, travel cost, government investment, energy consumption and emission constraint index data, the model estimates obtained in optimal travel structure of low carbon target. (3) model of the combination of travel chain residents travel activities based on the prediction analysis of residents. First day of travel data, travel and transportation activities in accordance with the sequence together, constitute a combination of chain, chain combination comprises a day activity sequence, each event to take the information traffic way. And then the encoding of the combination of transformation chain, the computer can recognize and calculate the 0-1 code mode after the application of neural network prediction model, through simulation training of combination chain model training data, predict the residents travel activities, access to travel amount changes. Finally the application of model To carry out the empirical analysis, the per capita income doubled and cancel the car Yaohao measures for analysis, measure changes in residents travel and traffic emissions. (4) low carbon policy individual traveler traffic mode choice model based on the application of data traffic. A comprehensive survey of fourth in Beijing City, analysis the influence factors of traffic mode choice of the individual Mixed Logit, the construction model of the individual traveler traffic mode choice model based on the solution of parameter optimization algorithm based on annealing. Carry out empirical analysis, the effect of change speed two measures on travel mode change and carbon emissions of congestion charging and bus analysis prediction and assessment.

【学位授予单位】：北京交通大学
【学位级别】：博士
【学位授予年份】：2015
【分类号】：U491

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