王进
入学时间:2008级
答辩时间:2012年
论文题目:信号控制交叉口群时空协调设计基础问题研究
张毅
入学时间:2008级
答辩时间:2011年
论文题目:基于多维影响因素的通勤出行决策行为
中文摘要
摘要
随着城镇化、机动化水平的提高,我国城市居民的日常活动范围不断扩大、个体机动化出行趋势日益加剧,对有限的城市道路资源带来巨大压力,引发城市交通的诸多问题。为此,我国政府提出了“优先发展城市公共交通”的战略,意在调控和引导出行行为、平衡交通需求和供给。但在实际操作中,以公共交通发展为导向的交通需求管理在我国尚处于起步阶段,战略层面的“公交优先”并没有转化成战术层面的交通需求管理措施,公交方式在出行方式结构中的比例不升反降情况有之。造成这一局面的原因非常复杂,其中重要原因之一是我国出行决策行为的理论研究对实践应用尚缺乏有效支撑。
关于出行决策特征,家庭或个体的出行决策行为包含若干连续的链状出行行为,从居住\工作地点选择和交通工具拥有等长期决策行为,到出行生成和出行方式选择等短期决策行为,符合行为决策理论中连续决策的特征。对出行决策行为展开研究,从决策过程角度更真实地模拟一系列出行行为,能够突破以往仅仅关注单一出行行为的局限性。
关于出行影响因素,出行决策行为受到家庭、个人、交通系统、土地利用和出行意愿等多个维度因素的影响。建成环境(Built Environment)整合了土地利用、道路设施、交通服务等方面的物质环境,以及该物质环境中的人类活动模式,在研究中引入建成环境特征的因素能够为解释出行决策行为提供新的视角。同时,解决通勤时段的交通问题对改善现有的城市交通具有关键影响。
因此,为了解析通勤出行决策行为,围绕上述问题及其有机关系,基于家庭、个人和建成环境特征维度的因素与通勤出行决策行为的显著相关性,本文展开了“多维影响因素——通勤出行决策行为”的研究。研究的主要内容和结论包括:
(1)研究的核心是解析多维影响因素与通勤出行决策行为之间的内在关系。为此,本研究:①引入了建成环境特征的影响因素,与常用的家庭、个人特征影响因素共同构成多维影响因素;②选择了三种具有一定连续性和相关性的通勤出行决策行为作为研究对象:家庭交通工具拥有行为、家庭分方式通勤出行生成行为和个体通勤出行方式选择行为;③确定科学合理的“多维影响因素——通勤出行决策行为”研究方法框架体系,包括“收集数据®确定变量®建立模型®标定模型®解析结果”等五个环节;④并以沿海中等发达城市——中山市为案例对研究方法进行实证应用;⑤最后通过实证案例研究的结论对不同层面交通需求管理提出了政策、措施建议。本文采用多维影响因素突破了研究视角,选取三种通勤出行决策行为拓展了研究思路,构建方法框架体系丰富了研究手段,采集一手案例数据应用了研究方法,通过实证案例研究获得了实践反馈,上述创新点提升了研究的理论意义和实用价值。
(2)在确定变量环节,明确了建成环境特征指标体系,采用了基于GIS数据的多元统计分析方法将建成环境影响因素定量划分为特征指标、特征因子和邻里类型三个子维度,作为模型的自变量输入。首先通过GIS软件对建成环境原始数据进行加工获得特征指标,其次通过因子分析法将特征指标转化为特征因子,最后通过聚类分析法以特征因子为依据将研究范围内的邻里转化为若干具有代表性的邻里类型。根据该方法对中山市建成环境特征进行分析,确定了十种特征指标、五种特征因子和六类邻里类型。确定科学的建成环境特征定量研究方法,以我国城市为背景进行方法的应用,具有一定的创新。
(3)在模型建立环节,介绍了多元线性回归模型和非集计模型的建模方法、适用条件和选择方法。通过对因变量数据分布特征的检验,确定采用泊松回归模型模拟家庭交通工具拥有行为、采用负二项回归模型模拟家庭分方式通勤出行生成行为。通过对被选择方案“独立不相关”特性的检验,确定采用多项非集计模型模拟个体通勤出行方式选择行为。
(4)在模型标定环节,探讨了用于标定的统计分析软件和标定结果的统计特征分析方法。采用Stata 10.0软件,模型的标定结果清晰地表明了多维影响因素与通勤出行决策行为之间的定量关系,并证明了研究假设——家庭、个人特征维度的因素是通勤出行决策行为的主要影响因素,作用机理和力度比较稳定;建成环境特征维度的因素是次要影响因素,能够增加模型对通勤出行决策行为的解释能力。同时,对标定结果进行统计特征分析发现:研究对模型和变量的选取均科学合理。
(5)在结果解析环节,以弹性系数分析为依据,从定量和定性相结合的角度研究了多维影响因素的作用机理。家庭特征维度中,收入特征是最主要的影响因素,直接所用于家庭交通工具拥有行为,并间接作用于家庭分方式通勤出行生成行为和个体通勤出行方式选择行为。个人特征维度中,收入和机动化特征是主要的影响因素,直接所用于个体通勤出行方式选择行为。基于家庭和个人特征维度因素的发展趋势,中山市居民的通勤出行决策行为将呈现“个体机动化为主导”的特征,表现为:个体机动化交通工具逐步取代非机动化交通工具,部分非机动化或公交通勤出行转化为个体机动化通勤出行,出行者对个体机动化方式的选择概率压过非机动化或公交方式。
(6)建成环境特征维度中,在特征指标和因子层面,高密度和混合度的土地开发、便捷的目的地可达性等特征能够促进通勤出行决策中对非机动化方式和公交方式的选择,抑制对个体机动化方式的选择,具体表现为:更高的家庭非机动化交通工具拥有量、家庭非机动化或公交通勤出行生成量和个体非机动化或公交方式选择概率。发达的公交设施会促进通勤出行决策中对公交方式的选择,抑制对自行车、电动车或摩托车方式的选择;发达的道路设施会促进对个体机动化方式的选择,抑制对非机动化方式和公交方式的选择。在邻里类型层面,位于城市的两类邻里与步行方式和自行车方式的通勤出行决策行为均具有显著正相关性,位于城市和郊区的四类邻里与摩托车方式和小汽车方式的交通工具拥有行为和通勤出行生成行为具有显著正相关性,位于农村的两类邻里与公交方式的通勤出行决策行为具有显著正相关性。研究结论为从城市总体规划和城市综合交通规划层面制定有针对性的交通需求管理措施奠定了理论基础。
(7)在实践应用环节,在城市总体规划层面,提出保持土地开发的适宜强度、引导土地利用的混合开发和推动“工宿平衡”的格局构建等政策措施;在城市综合交通规划层面,提出重视
系统的规划设计、加快“微循环”系统的建设完善和贯彻多方式的综合与衔接等政策措施;在公共政策层次,提出形成城市小汽车拥有的动态配额机制、制定小汽车拥有和使用的费用政策、提供合理有效的出行信息等政策措施。
最后,在总结论文主要研究成果和创新点的基础上,指出了进一步的研究方向。
关键词:多维影响因素,通勤出行决策行为,建成环境,多元线性回归模型,非集计模型
英文摘要
ABSTRACT
Along with the rapid economic development, the levels of urbanization and motorization are increasing at a high speed, which led to the expansion of daily activities and the proportion of motorized travel. This brings enormous pressure on the limited urban road infrastructure and caused severe urban transportation problems. The Chinese government has put forward the public transport priority strategy and a series of transportation demand management policies to handle those problems. However, due to the lack of profound research of travel mode choice, these strategy and policies didn’ play expected roles. At the present time, the research on travel behavior in the Chinese context is limited in terms of range, depth, research ideas and methodologies. Moreover, the research findings couldn’t fully support the transportation strategy and policies. Those have led to the slow development of travel behavior theoretically and practically.
From the view of distinct characteristics, the travel decision behavior includes a sequential series of travel behaviors, from long-term behaviors like resident/job location choice, vehicle ownership choice to short-term behaviors like trip generation to mode choice, fitting the characteristics of sequential decision-making in behavioral decision theory. The research on travel decision behavior simulates the decision-making process more vividly and breaks the limitation of merely focusing on one travel behavior in previous research.
From the view of factors, the travel decision behavior is influenced by household, personal, transportation system, land-use, and attitude factors. Built environment is a complex of physical features like transportation system, land-use, urban form and the human activities patterns among the complex. Introducing the built environment factors into travel decision behavior research will provide new angle to understand travel decision behavior.
Nowadays, it is crucial to urban transportation development to handle the urban transportation problems during commute peak hours. Therefore, it is of great practical value to analyze commute travel decision behavior. Based on the views above, this dissertation conducts a research on the relationship between multidimensional factors and commute travel decision behavior. The hypothesis of the dissertation is household, personal and built environment factors are significantly associated with commute travel decision behavior and are able to explain commute travel decision behavior to some extent. The main content and conclusions of the dissertation include:
(1) The research introduces built environment factors and defines the household, personal and built environment factors as multidimensional factors. The research focus on three travel behaviors of commute travel decision behavior, which are household vehicle ownership behavior, mode-specific household commute trip generation behavior and commute mode choice behavior. To profoundly analyze the relationship between multidimensional factors and commute travel decision behavior, this dissertation then build a proper research methods framework , including the data collection, factors definition, modeling, model calibration and results analysis. Based on the research methods framework, this dissertation applies the methods to a developed medium-sized city, Zhongshan as a case study. According to the results of the case study, the dissertation finally comes to several policy implications for transportation demand management. The innovations of research content, method framework and application of theoretical methods to real case study enhance the theoretical and practical value of the dissertation.
(2) In the part of factors definition, the dissertation employs a GIS data-based multivariant statistical method to define three sub-dimensions of built environment factors. Firstly, the dissertation processes the raw data in GIS software to define the simple indicators. Then, factor analysis is used to come up with abstracted indicators. Finally, cluster analysis is employed to obtain the representative neighborhood types. According to the data of Zhongshan, ten simple indicators, five abstracted indicators and six neighborhood types were attained for further research.
(3)In the part of modeling, the dissertation elaborates the modeling method, feasibility and selection rules of multivariant linear regression models and logit models. Judging on the distribution of dependent variables, the dissertation chose Poisson regression modelfor the modeling of household vehicle ownership behavior and Negative Binomial regression model for the modeling of mode-specific household commute trip generation behavior. After examing the IIA features of the choice set, the dissertation chose Multinomiol Logit Model for the modeling of commute mode choice behavior.
(4)In the part ofmodelcalibration, the dissertation introduces the statistical software for calibration and the statistical characteristic analysis method. Using Stata 10.0 software, the calibration results clearly demonstrate the quantitative relationship between multidimensional factors and commute travel decision behavior. The results also prove the research hypothesis. The results of statistical characteristic analysis show that the model and independent variables are properly chosen.
(5)In the part ofresults analysis, based on the elasticity, the dissertation studies the profound impact of multidimensional factors quantitatively and qualitatively. In household factors, the income plays the most important role, directly influencing the household vehicle ownership behavior and indirectly influencing the mode-specific household commute trip generation behavior and commute mode choice behavior. In personal factors, the income and motorized features play the most important role, directly influencing the commute mode choice behavior. Considering the future development trends of household factors and personal factors, the commute travel decision behavior will be dominated by the motorized feature. The non-morotized vehicle ownership will be replaced by morotized vehicle ownership. Some of thenon-morotized mode and bus commute trip generation will turn into morotized mode commute trip generation. The probability of choosing morotized modes will be higher than the probability of choosing non-morotized modes and bus in commute trips.
(6) In the dimension of built environment factors, high-dense mixed land-use development and convenient destination accessibility will promote the choice ofnon-morotized modes and bus and restrain thethe choice ofmorotized modesin commute travel decision behavior, including higher ownership of non-morotized vehicles, more non-morotized and bus commute trip generation and higher probability of choosing non-morotized modes and bus in commute trips. Convenient bus service willpromote the choice ofbus and restrain thethe choice ofbicycle, moped or motorcycle in commute travel decision behavior. Well-connected road network willpromote the choice ofmorotized modes and restrain thethe choice ofnon-morotized modes and bus in commute travel decision behavior. From the aspect of neighborhood types, two urban neighborhood types are positively associated with the commute travel decision behavior in terms of walking and bicycling.Four urban and suburban neighborhood types are positively associated with the household ownership and trip generation behaviors of motorcycle and private car. Two rural neighborhood types are positively associated with the commute travel decision behavior of bus.
(7) In the part of practical application, the dissertation proposes three policies on the general planning level, including keeping high-density of land-use development, guiding mixed development and promoting the land-use pattern of jobs-housing balancing. On the comprehensive transportation planning level, the dissertation proposes two policies, including enhacing the design and construction of transit system and pay extra attention to the improvement of lower level road network. On the level of transportation management, control and organization, the dissertation proposes four policies, including the dynamic quota mechanism of private car ownership, enhancing the cost of private car ownership and usage and providing efficient and proper traveler information service.
At last, after the conclusion of main research findings and innovations, the dissertation points out the direction for future research.
Key Words:Multidimensional Factors, Commute Travel Decision Behavior, Built Environment, Multivariant Linear Regression Model, Logit Model
朱伟权
入学时间:2008级
答辩时间:2011年
论文题目:公共汽车交通运营监管基础问题研究
中文摘要:
摘要
公交是一种重要的公用事业,公交行业存在着自然垄断、经营性与公益性之间的矛盾、信息不对称以及外部性等市场失灵的问题。这些市场失灵问题的存在为政府监管的产生和发展提供了基本的理论依据,政府应该针对这些市场失灵的问题对公交行业实施监管。国际上在公交以及其他公用事业的激励性监管实践和相关实证研究都表明,引入激励性监管机制并以科学的绩效评价作为其决策支持手段对实现有效的监管具有重要意义。在我国优先发展公共交通的现实背景下,针对这些市场失灵的问题,公交监管部门要构建科学的运营监管机制和完善监管手段,提高整体监管水平,以实现对公交行业的有效监管,保障公交事业的可持续健康发展。本论文从我国公交运营监管实践出发,在交通工程学、激励性监管、绩效评价等理论和方法的指导下,重点围绕我国公交运营监管机制的构建以及支撑机制运作的公交线路的运营成本评价和管理效率评价方法展开研究,取得了一系列的研究成果。
在我国公交运营监管机制的构建研究方面,为了减轻公交行业存在的自然垄断、信息不对称问题的影响,本论文提出了在公交的成本监管以及运营绩效监管方面应引入基于标尺竞争的监管机制的总体思路并构建了总体框架。然后,一方面,根据公交企业的经营性亏损的影响因素差异,将经营性亏损划分为公交企业自身的管理因素和短期内不可控的外部运营环境因素而造成的两部分。在此基础上,为了减轻经营性与公益性之间的矛盾、信息不对称问题的影响,以及考虑到公交线路的外部运营环境的差异性,提出了完善基于成本监管的补贴机制的思路。此外,对于公交企业的主要直接运营成本的补贴,构建了应用前沿分析方法进行的公交线路运营成本评价与激励性补贴为主要手段,以实现正向激励与合理分配补贴为目的的基于成本监管的补贴机制;另一方面,为了减轻信息不对称、外部性问题的影响,构建了以公交行风考评、应用前沿分析方法进行的管理效率评价及有效奖惩为主要手段,以实现正向激励为目的的基于运营绩效评价的奖惩机制。
在公交线路的运营成本评价方法研究方面,考虑公交服务的异质性产出特性和外部运营环境的影响,基于随机前沿成本函数方法构建了公交线路的运营成本评价模型,并从公交线路的微观层面引入了四个表征其外部运营环境的指标,构建了公交线路的运营成本的评价指标体系。结合案例应用该模型对公交线路的运营成本进行了评价,获得了随机前沿成本函数和成本无效率模型的参数估计结果,
还获得了公交线路的成本效率值和可能达到的最小总运营成本等评价结果。
在公交线路的管理效率评价方法研究方面,考虑公交服务的异质性产出特性构建了对公交线路的运营效率和质量效率进行独立评价的模型框架,再基于能考虑外部运营环境影响的三阶段方法,提出了公交线路的管理效率评价的基本方法,并构建了公交线路的管理效率的评价指标体系。结合案例应用该模型对公交线路的管理效率进行了评价,获得了公交线路的运营效率值和质量效率值,以及外部运营环境因素分别对运营效率和质量效率的影响等评价结果。
本论文的研究成果在理论层面拓展了公交的激励性运营监管以及公交线路的运营成本评价和管理效率评价方法研究;在应用层面为我国公交运营监管部门构建科学的运营监管机制和完善监管手段提供了方法指导。由于公交运营监管研究的复杂性以及本人在知识结构和能力方面的欠缺,本论文还有许多不足之处有待于进一步的研究。
关键词:公交,公交线路,激励性监管,监管机制,标尺竞争,外部运营环境,运营成本,管理效率
英文摘要
ABSTRACT
Bus serviceisone of the important public utilities, which includes manyissues of market failure, suchas natural monopoly, the conflict between for-profits and non-profits,information asymmetry, and externalities and so on. These issues can be considered as the basic arguments for the motivation and evolution of government regulation. The government should regulate the bus service based on these issues of market failure. Both the practice of incentive regulation and the empirical research on regulation of international bus and other public utilities have indicated that it is significant for the efficient regulation to introduce the incentive regulatory mechanisms and scientific performance measurement as a decision making tool. Under the background of giving priority to public transport development in China, considering theseissues of market failure,the government regulator of bus need to establish the scientific regulatory mechanisms and improve the regulatory instruments, as well as increase the integral level of regulation. The purpose is to ensure the efficient regulation and the sustainable development of bus industry. The dissertation research focused on the establishment of regulatory mechanisms of bus, the measurement methodologies of operating costs and managerial efficiency of bus route, which based on the practice of bus operating regulation in China, and according to the theories and methodologies on traffic engineering, incentive regulation, and performance measurement and so on. At the same time, a series of research achievements have been obtained.
In the aspect of the establishment of regulatory mechanisms of bus in China, the dissertation proposed the regulatory mechanisms based on yardstick competition should be introduced in the costs and operating performance regulation, which in order to lighten the influences of natural monopolyand information asymmetry.
Accordingly, a general framework of the regulatory mechanisms was established. Then on the one hand, the operating deficit could be divided into the following two categories which according to the different impact factors: caused by the management factors of bus agency and by the external operating environment factors which are outside the direct control of agency in the short run.Onthisbasis,in order to lighten the influences of conflict between for-profits and non-profits,and information asymmetry, as well as considering differences of the external operating environment factors of bus route, an opinion of bettering the subsidy mechanism based on costs regulation wassuggested. Additionally, for the subsidy of main direct operating costs of bus agency, the subsidy mechanism based on costs regulation was developed. The measurement of operating costs of bus route which applies frontier analysis and incentive subsidy areservedas its main instruments. The purpose of the subsidy mechanism is to achieve active incentive and reasonable subsidy allocation. On the other hand, in order to lighten the influences of information asymmetry and externalities, a reward and punishment mechanism based on the operatingperformance measurement was designed. The measurement of bus service normalization andmanagerial efficiencywhich appliesfrontieranalysis,as well as theefficientreward and punishment areservedas its maininstruments. The purpose of the reward and punishment mechanism is to achieve active incentive.
With regard to the measurement methodology research of operating costs of bus route,considering theheterogeneous outputs of bus service and the influences of external operating environment factors, a measurement model of operating costs of bus route was established based on the stochastic frontier cost function. In addition, four new operating environment factors at the microscopic level of bus route were introduced, and a set of performance measurement indicators of operating costs of bus route was established. Then, the model was applied in theempirical research, the parameter estimate results of stochastic frontier cost function andcost inefficiency model wereacquired, and the cost efficiency scores and minimum attainable total operating costs, etc. were also obtained.
With regard to the measurement methodology research ofmanagerial efficiencyof bus route, a model framework for the independent measurement of operating efficiency and quality efficiency of bus route was developed, whichconsidering theheterogeneous outputs of bus service. Next, the basic measurement methodology of themanagerial efficiencyof bus route were proposedaccording to the three-stageanalysisapproachwhich can consider the influence of external operating environment, and a set of performance measurement indicators ofmanagerial efficiencyof bus route was established. Then, the model was applied in theempirical research, the operating efficiency and quality efficiency scoreswereacquired, and the measurement results of the effects of the external operating environment on operating efficiency and quality efficiency, etc.wereobtained.
The research achievements of the dissertation have doublesignificance.
Theoretically, theyextend theresearches on the incentive operating regulationof bus, and the measurement methodologies of operating costsandmanagerial efficiencyof bus route.In practice, they provide the methodology guidance for theregulator to establishthe scientific regulatory mechanisms andimprove their regulatoryinstruments. Nevertheless, since the complexity of theregulationresearchof bus, and thelimitation of knowledge structureandcompetence of the author, there are also many shortages in this research, which need further research.
Key Words: bus,bus route, incentive regulation, regulatorymechanism, yardstick competition, external operating environment, operating costs,managerial efficiency
李慧兵
入学时间:2008级
答辩时间:2011年
论文题目:基于融合与挖掘的间断流行程时间估计与预测方法
中文摘要
摘 要
智能交通系统作为改善交通问题的最佳途径之一,具有两项很重要的功能——智能交通信息服务和智能交通管理,实现智能交通管理和信息服务的关键是能够对实时交通状态进行处理(如交通状态估计、预测)。行程时间是描述城市交通网络状态的重要参数之一,它包括行程时间估计值与预测值。其中,行程时间估计值是指当前时段的道路行程时间,而行程时间预测值是指下一(多)时段的道路行程时间,行程时间估计值是行程时间预测研究的一个最重要的输入参数。行程时间的估计与预测可以改善城市道路交通的运行状况,能够实时地向用户提供交通信息服务。同时,行程时间的估计与预测还能够为交通管理部门提供数据支持,以便进行交通异常状态的判别、交通事件的自动检测,以此来缓解交通压力,提高行车速度,保证道路的安全畅通。因此,行程时间的估计与预测研究具有极其重要的意义。
间断流是有外部固定因素影响的周期性中断的交通流,一般来说,间断流是指行驶在城市地面道路上的交通流。由于间断流受到外部固定因素的影响,其交通状态具有复杂多变的时空特性,因此间断流行程时间的估计与预测一直以来都是国内外学者的一个研究难点。
随着电子、通信、计算机等高新技术在交通运输领域的推广使用,交通信息在道路交通系统中发挥着越来越大的作用,大量的多源交通数据被采集。同时,数据融合与挖掘理论与实践应用的逐渐成熟使得面向间断流行程时间估计及预测的数据融合与挖掘成为一个迫切的研究任务。
基于实测数据,本文遵循“数据分析与理论模型结合”的行程时间估计与预测机制—即以数据为主,在分析数据需求及特性的基础上,建立(应用)合适的理论模型来应用这些数据,其中数据分析与模型建立之间存在闭环反馈的关系(下同),利用数据融合和挖掘的理论和方法来估计与预测间断流行程时间,保证了估计与预测模型的真实性、科学性和有用性,突破了传统间断流行程时间估计与预测的机理与方法。
本论文以国家高技术研究发展计划(863计划):“城市交通状态智能预测及管理支持系统”、国家高技术研究发展计划(863计划):“交通状态全息感知与交通战略实验室以及工业与信息化部2010年电子信息产业发展基金:“基于动态信息的智能交通系统研发及应用示范系统设计”中的“数据融合与挖掘”部分为依托,围绕面向间断流行程时间估计及预测的数据融合及挖掘的理论和应用问题展开研究。
当前城市路网普遍采用的数据主要有两种:固定型数据和移动型数据。固定型数据指可以描述路段断面交通特征的数据,比如线圈数据、视频数据等;移动型数据指可以描述车辆运行过程的时空特征数据,比如RFID(Radio Frequen-
cy Identification)数据、浮动车数据等。支持论文研究的实测数据主要有杭州市的固定型数据(环形线圈数据和视频OD数据)与移动型数据(浮动车数据)和以及南京市的移动型数据(浮动车数据和试验车数据)。
通过融合多源数据以及挖掘移动型数据(浮动车数据)来对间断流行程时间进行估计与预测研究,本文首先研究面向行程时间预测的数据融合与挖掘基础问题分析,本研究属于论文的基础部分。传统的融合与挖掘模型均属于基于理论解析的模型,考虑到已有融合与挖掘模型的复杂性以及数量有限性,并且现实世界中的交通流特性复杂多变,这种基于理论解析的融合与挖掘模型将使得实测数据无法得到充分的利用,因此需要对融合与挖掘的数据需求、融合与挖掘策略等基础问题进行研究。
其次,对异类传感器不同(同类)参数间的数据融合方法进行研究,本研究属于面向行程时间估计与预测的数据融合关键技术研究。传统异类传感器同类参数融合方法可分为加权式融合方法和修正式融合方法,然而这两种融合方法均具有各自严格的适用范围与条件,使得大量融合数据无法得到充分的利用;与异类传感器同类参数相比,异类传感器不同参数之间具有更强的互补特性。因此,本文对异类传感器不同(同类)参数间的数据融合方法进行研究。
最后,对基于移动型数据(浮动车数据)挖掘的单车路段(路径)行程时间估计(预测)方法进行研究,该研究属于面向行程时间估计与预测的数据挖掘关键技术研究。行程时间的估计与预测是诱导系统的一项重要研究任务,传统行程时间指通过路段(路径)全体车辆的行程时间平均值,然而诱导系统是一个以服务单辆车或单个出行者为目的的交通服务系统,因此,本研究以单辆车的行程时间作为研究对象,对路段(路径)行程时间进行估计(预测)研究。其中,路段行程时间估计是路径行程时间预测研究的基础。本论文的主要研究内容简述如下:
(1)面向行程时间预测的数据融合与挖掘基础问题分析。在总结分析传统行程时间预测机制的基础上,提出一种“基于数据分析与理论模型结合”的行程时间预测机制,基于此机制,从基于融合与挖掘的行程时间预测原理、融合与挖掘的数据需求、数据融合与挖掘策略以及预测模型选择等四方面来分析面向行程时间预测的数据融合与挖掘的基础问题。本研究为本论文的后续研究提供了理论支持与依据,奠定了论文研究的基础。
(2)异类传感器不同参数间的数据融合研究,本研究对两个关键的、基于异类传感器不同参数间融合的技术问题进行研究。首先对移动型数据(浮动车数据)和固定型数据(线圈数据)的预处理方法进行研究,然后研究了两个异类传感器不同参数间的数据融合问题,它们分别是:(1)面向浮动车取样偏差修正的数据融合方法。浮动车数据样本量很低;而线圈数据是一种近全样本的数据,因此这两种数据存在很强的互补特性,通过线圈流量数据来修正浮动车行程时间,实验结果表明该方法获取的行程时间估计值准确度远大于传统浮动车方法。(2)信号配时信息缺失下的数据融合方法。在分析行程时间估计影响因素的基础上,提出一种BP神经网络融合模型,选择浮动车样本量以及浮动车行程时间估计值,线圈数据的路段流量与路段平均密度作为该模型的输入变量,选择实测路段行程时间作为模型输出变量,实验结果表明该模型获取的行程时间估计值平均相对误差相可达到7.36%。
(3)异类传感器同类参数间的数据融合研究。本研究提出一种基于模糊回归的异类传感器同类参数融合模型,主要借助于浮动车行程时间数据,同时融合了少量的历史视频行程时间数据;考虑了相邻路段行程时间的连续性,仅需要少量的数据就可以对间断流行程时间进行较准确的预测。本研究模型究具有一个较大的缺权狮是属于加权狮子方法时间,实验结果表明,引进一种新的异类传感器同类参数间的数据融合模式,拓展了面向行程时间估计与预测的融合数据的适用范围与条件,补充异类传感器同类参数的融合理论。
(4)基于移动型数据(浮动车数据)挖掘的单车路段行程时间估计方法。在分析浮动车行程时间的特性的基础上,提出一种基于浮动车数据挖掘的单车路段行程时间估计方法。该方法由四个模块组成——两相邻交叉口间路段的动态划分模块、影响单车行程时间估计的路段范围分析模块、浮动车数据提取模块与单车路段行程时间估计模块。本研究改进了传统基于浮动车数据的单车路段行程时间估计的直接法与间接法,在间断流行程时间估计的理论与应用层面均具有创新和拓展的意义。
(5)单车路径行程时间预测方法。提出一个基于浮动车数据挖掘的时间模式识别模型来预测单车路径行程时间。本研究在较大程度上突破了传统的路径行程时间预测理念,指出了一个路径行程时间预测研究的新方向。
最后,对全文进行总结,指出论文的创新点,对有待于进一步研究的主要问题进行展望:
(1)需要深入研究面向行程时间预测的数据融合与挖掘基础问题,在融合与挖掘数据需求、融合与挖掘策略等方面不断改进,并能从定量(公式推演)的角度提出面向行程时间预测的数据融合与挖掘的基础理论。
(2)需要研究基于浮动车数据与交叉口信号配时信息融合的单车路段行程时间估计问题。
关键词:融合方法 挖掘方法 间断流 行程时间估计 行程时间预测
英文摘要
ABSTRACT
Intelligent Transportation System (ITS) has been regarded as one of the best ways to ameliorate traffic problems and Intelligent Traffic service and management systems are two main sub-systems in ITS. The key of implementing these two sub-systems is to estimate/predict real-time traffic state. As a key parameter that could describe traffic state on the road network, Travel Time contains two kinds of parameters, they are---Estimated Travel Times and Predicted Travel Times. Estimated Travel Time is travel time during the current interval while Predicted Travel Time is travel time during the next one or multiple intervals and Estimated Travel Time is an essential input parameter for travel time prediction. Travel time estimation and prediction could improve travelling situation for travelers on road network, could provide travelers with real-time traffic information service. At the same time, travel time estimation and prediction could provide traffic management departments with all kinds of data support, so that they could differentiate abnormal traffic states and detect different traffic incidents, as a result, traffic pressure could be greatly reduced, traveling speed could be increased and traffic safety could be ensured. Therefore, Research on Travel Time Estimation and Prediction is very important.
Interrupted flow is traffic flow(mostly vehicle flow) whose movements get impeded by the external items, usually interrupted flow means traffic flow on surface road in cities. Due to the fact that interrupted flow is being affected by traffic control facilities, its state has a very complicated and ficklespatio-temporalcharacteristic, therefore, interrupted flow travel time estimation and prediction has always been a difficult point for home and abroad researchers.
With the development of electronic, communication and computer technologies in transportation field, traffic data plays a very important role in transportation system and a lot of multi-source traffic data is being collected. At the same time, the maturity of theories and practical applications of data fusion and mining has facilitated the study of interrupted flow travel time estimation and prediction based on data fusion and mining.
Based on the field data, the paper follows the travel time estimation(prediction) mechanism of ‘data analysis and theoretical modeling combination’, that is: emphasize on data, and establish(or look for) the right model(s) after some data analysis, where data analysis and modeling have a close-loop feedback relationship. Then the paper utilizes data fusion and mining theories and methods to estimate/predict travel times, which ensures the authenticity, scientific nature and usefulness of the models and it innovates traditional travel time estimation and prediction mechanism and methods to some extent.
Study for the paper relies on the part of “data fusion and mining” from Ministry of Industry and Information Technology of People’s Republic of China, which is based on City of Nanjing, 863 project (2008AA11Z205)---Intelligent Forecasting and Traffic Management System Based on the Traffic State of Traffic Network in Cities and 863 project(SS2012AA112306)---Traffic State Perception based on Big Data and Traffic Strategical Laboratory. And it revolves around data fusion and mining theories and applications problems on interrupted flow travel time estimation and prediction.
Most prevalent data on road network in cities contains two kinds: fixed data and mobile data. Fixed data is the data that could depict traffic characteristics on road segments, it contains loop detector data, some video detector data and so on. mobile data is the data that could depict vehicles’ temporal-spatial characteristics, it contains RFID(Radio Frequency Identification), floating car data and so on. Field data in the paper is consisted of fixed data(loop detector data and OD data from video detectors) and mobile data(floating car data) that is collected in Hangzhou; mobile data(floating car data and experimental car data) in Nanjing.
The paper undertakes interrupted flow travel time estimation and prediction study by fusing multi-source data and mining the floating car data, And the paper firstly does some research on fundamental problems of data fusion and mining based on travel time prediction and it is the foundation for the paper. Traditional fusion and mining models are models based on theoretical analysis, considering that existing fusion and mining models are very complicated and their amount is very limited, and traffic flow characteristics are very complicated and unpredictable, therefore, it is inevitable that traditional travel time mechanism will not be able to explore the field data to its best, and it is necessary to do some research on some fusion and mining fundamental problems such as data demand, strategies and so on.
Then the paper does some research on different parameters fusion and same parameter fusion from different sensors and it is data fusion key technology studies for travel time estimation and prediction. Traditional same parameter fusion methods from different sensors can be put into two categories, first one is weighted fusion method and second one is modification fusion method.And these two kinds of fusion methods both have their own application scopes,which will make fusion data not be able to be exploited fully; Compared with same parameter from different sensors, different parameters from different sensors have more complementary characteristics, therefore, this part does some study on different parameter (same parameter) fusion from different sensors.
At last the paper does some research on Link(Path) travel time estimation(prediction) method based on mobile data(floating car data) mining for a single vehicle and it is data mining key technology studies for travel time estimation and prediction. Travel time estimation and prediction is a essential task in route guidance system, and traditional travel time is the arithmetic mean of travel times of all population vehicles that traverse the link(path), however, route guidance system is a traffic service system that is aiming to serve the individual passenger(vehicle),therefore, the research does some link(path) travel time estimation(prediction) study based on single vehicle, where Link travel time estimation study is the foundation of path travel time prediction study. The following will sketch the main study content of every part briefly:
(1)Study on data fusion and mining fundamental problems based on travel time prediction. After summarizing the traditional travel time prediction mechanism, the paper brings forward a travel time estimation(prediction) mechanism of ‘data analysis and theoretical modeling combination’. Based on the mechanism, the paper studies some data fusion and mining fundamental problems from four perspectives, which are respectively travel time prediction axiom based on data fusion and mining, data demand, data fusion and mining strategies and prediction model selection. The study provides the theoretical support for the whole paper.
(2)Study on different parameters fusion from different sensors, the study does some research on two important different parameters fusion from different sensors.
Firstly, pre-processing of mobile data(floating car data) and fixed data(loop detector data) is being studied, then paper studies different parameter fusion methods from different sensors, which are mainly consisted of two parts: (1) Data Fusion method on modifying sampling bias of floating cars. Sampling size of floating car data is very small; loop detector data is near-all-population data. Therefore, these two data sets have very strong complementary characteristics. The study tries to modify estimated travel time based on floating car data with help of loop detector data(traffic volume data),and the results show that fusion method could yield much more accurate estimated travel times compared with traditional floating car method.(2)Data fusion method in shortage of traffic signal control timing information. After analyzing the factors that affect travel time estimation, the paper brings forward a BP neural network model, selecting sampling size and estimated travel times from floating car data, traffic volume and link traffic density from loop detector data as model inputs, and actual travel times as model output, the experimental results show that MRE(Mean Relative Error) of estimated travel times based on the fusion model is 7.36%.The study works on two key different parameter fusion problems from different sensors, and it supplements different parameter fusion theories from different sensors.
(3)Study on same parameter fusion from different sensors. This part brings up a Fuzzy Regression Model to fuse travel times from both floating car data and historical video detector data. The model takes traffic-state continuity characteristic between two adjacent links into consideration, and could yield some very accurate link travel time prediction values with help of little data resources. The study brings forward a new data fusion pattern for same parameter fusion from different sensors, it expends the application scope of same parameter fusion methods from different sensors and supplements same parameter fusion theories from different sensors.
(4)Link travel time estimation method based on mobile data(floating car data) mining for a single vehicle. After analyzing some characteristics of travel times based on floating car data, paper brings forward a travel time estimation method based on floating car data mining for single vehicle. The method is consisted of 4 components ----Dynamic Link Division Component between Two Adjacent Intersections, Link Influential Segment Analysis Component based on Link Travel Time Estimation for Single Vehicle, Probe Data Extraction Component and Link Travel Time Estimation Component for Single Vehicle. The study improves the performance of traditional direct and indirect methods for link travel time estimation based on floating car data, and it is very meaningful on innovating and expending interrupted flow travel time estimation theories and applications.
(5)Path link travel time prediction method for a single vehicle. Paper brings forward a temporal pattern recognition model based on floating car data mining to predict path travel time for single vehicle. The study makes a somewhat breakthrough on traditional path travel time prediction theories, and points out a new study direction for the future path travel time prediction.
At last, the paper does some conclusion work on my study, points out the innovative work from the study, and brings up the future work that is needed to be done.
Firstly, deep and thorough research on data fusion and mining fundamental problems based on travel time prediction needs to be continued, and a lot of improvement on data demand and data fusion and mining strategies needs to be done. What’s more, data fusion and mining fundamental theories based on travel time prediction need to be brought forward from a quantitative perspective.
Secondly, the paper only uses floating car data to estimate link travel time for single vehicle in part(4), the future work should involve link travel time estimation based on fusion of floating car data and traffic signal control timing data.
Key words:DataFusion Method, Data Mining Method, Interrupted Flow,Travel Time Estimation, Travel Time Prediction