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This paper reports on an analysis aiming to understand differences across individual people in their willingness to accept increased commuting time in return for higher salary, using Hierarchical Bayes (HB) analysis of a dataset collected in Sweden. We find that socio-demographic and attitudinal differences are significant in explaining the variations in values of time for individuals, in particular income, who drives when carpooling and hours worked per week. Additionally we also examine the values of individuals when their choices also impact on the salary and commute of their partner, finding that incomes, income differentials, driving behaviour when carpooling, division of housework and car user decisions significantly explain the values assigned to others and variations in an individual’s own values once their partner is affected. The overall richness of the results reflect the benefits that posterior analysis can bring, and highlight the computational efficiency of Bayesian methods in producing such conditionals at an individual level. 相似文献
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应用多层线性模型研究了降雨强度对城市道路交通流平均行驶车速的影响.研究资料包括道路特征数据(25个路段)、广州市城市道路交通流平均行驶车速数据(有效车速样本量超过160万)和降雨量数据(样本量93 462).采集时间从2011年6月至2012年4月一共330 d.基于以上大规模数据,应用多层线性模型(HLM),建立了一个降雨条件下道路交通流车速的短时动态预测模型.结果表明:交通流信息存在嵌套、分层的数据结构,相较于传统交通流研究方法,多层线性模型对交通流影响因素的研究将更为合理;降雨对车速的影响具有普遍的空间变异性,这是因为每个路段具有不同的物理特征;交通流的影响因素之间存在着显著的跨层交互效应,这与传统理论对交通流的认识不同;本文提出的降雨条件下交通流车速的短时动态预测模型具有路段适用性广的突出优点,这与大多数传统交通流模型适用范围较为狭窄的特点较为不同. 相似文献
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城市路网交通状态分析方法研究 总被引:8,自引:0,他引:8
城市路网交通状态分析是智能交通系统中具有理论和实际应用双重性的基础问题,是涉及多尺度、多变量、高度随机和时变的复杂系统分析问题。其难点在于如何建立交通路网模型、如何定义交通状态变量和如何分析路网交通状态。本文对这些问题进行了研究,从中观角度把交通路口参数归入到路段参数,从路段的角度再定义新的路段交通状态系数,并建立用于交通状态分析的路网交通模型。提出了基于可达矩阵的路口分层方法,使用该方法能够得出在不同路段拥挤程度之下的路口层次,得出路口可达性、路段连通性以及整体路网交通状态的判别结果,可以找出路网中关键路口、关键路段,可以得到交通状态的演变方式, 进而为研究交通拥堵形成机理奠定基础。这种分析方法可以直接应用于交通状态分析中,对交通诱导、旅行信息发布也有重要价值。 相似文献
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What neighborhood are you in? Empirical findings of relationships between household travel and neighborhood characteristics 总被引:1,自引:0,他引:1
In recent years, there have been studies of the influence of neighborhood or built environment characteristics on residential
location choice and household travel behavior. Interestingly, there is no uniform definition of neighborhood in the literature
and the definition is often vague. This paper presents an alternative way of defining neighborhood and neighborhood type,
which involves innovative usage of public data sources. Furthermore, the paper investigates the interaction between neighborhood
environment and household travel in the US. A neighborhood here is spatially identical to a census tract. A neighborhood type
identifies a group of neighborhoods with similar neighborhood socio-economic, demographic, and land use characteristics. This
is accomplished by performing log-likelihood clustering on the Census Transportation Planning Package (CTPP) 2000 data. Five
household travel measures, i.e., number of trips per household, mode share, average travel distance and time per trip, and
vehicle miles of travel (VMT), are then compared across the resulting 10 neighborhood types, using the 2001 National Household
Travel Survey (NHTS) household and trip files. It is found that household life cycle status and residential location are not
independent. Transit availability at place of residence tends to increase the transit mode share regardless of household automobile
ownership and income level, and job-housing trade-offs are evident when mobility is not of concern. The study also reveals
racial preference in residential location and contrasting travel characteristics among ethnic groups.
Dr. Jie Lin (Jane) is an assistant professor in Department of Civil and Materials Engineering and a researcher with the Institute for Environmental Science and Policy at University of Illinois at Chicago. Her research is focused on transportation demand analysis, data mining, and transportation sustainability in private, freight, and public transportation systems. Dr. Liang Long received a Doctorate degree in Civil Engineering from the University of Illinois at Chicago and a Master’s degree in Civil Engineering (Transportation Engineering) from Tongji University. She is currently with Cambridge Systematics as a transportation modeler with expertise in travel demand forecasting, geographic information systems (GIS) and market research. 相似文献
Liang LongEmail: |
Dr. Jie Lin (Jane) is an assistant professor in Department of Civil and Materials Engineering and a researcher with the Institute for Environmental Science and Policy at University of Illinois at Chicago. Her research is focused on transportation demand analysis, data mining, and transportation sustainability in private, freight, and public transportation systems. Dr. Liang Long received a Doctorate degree in Civil Engineering from the University of Illinois at Chicago and a Master’s degree in Civil Engineering (Transportation Engineering) from Tongji University. She is currently with Cambridge Systematics as a transportation modeler with expertise in travel demand forecasting, geographic information systems (GIS) and market research. 相似文献
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The focus of the current research was to evaluate how the individual’s social characteristics and urban infrastructure impacts the usage of Private Motorized Modes (PMM). Based on individual and urban characteristics a multilevel analysis was conducted on the possibility of commuting trip by private motorized modes on the rush time of 78 cities around the world. Also the selected cities were classified through a principal component analysis, and based on the classification the impact of and urban variables on the possibility of commuting trips made by private motorized modes (PCTP) was verified. Results showed a diverse range of variables related to the usage of PMM, as well as the urban structure and railway lengths being an important variable in travel behavior. 相似文献
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Zhang K. Yang H. He M. Sun J. Li D. Cheng Z. Zhao Q. Yin S. Kang L. Wang X. Zhu J. 《现代隧道技术》2022,(1):69-79
In order to further evaluate the intelligent degree of the coal mining face scientifically and reasonably, this paper constructs a gray relational analysis based comprehensive evaluation model for the intelligent degree of mining face by deeply analyzing the influencing factors on the intelligent degree of mining face, such as surrounding rock detection, mining equipment, production system, supporting production system, organization and management, etc. By quantifying and scoring factors affecting the intelligence degree of mining face, using the hierarchical analysis method to obtain the weights and determine the correlations, the intelligence degrees of mining face are classified into four levels: excellent, good, medium and poor. Using the comprehensive evaluation model to empirically verify the 802 working face of Shaanxi Huangling Mining Co., Ltd., the calculated correlation is 0.765 8, which indicates that the intelligent degree of 802 working face is excellent according to the rating criteria, and it is consistent with the actual situation. Therefore, the comprehensive evaluation model based on gray relational analysis can accurately and objectively evaluate the intelligent degree of the mining face. © 2022, Editorial Office of "Modern Tunnelling Technology". All right reserved. 相似文献
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基于多层隐类贝叶斯网络的舰船生存能力评估模型研究 总被引:1,自引:1,他引:0
生存能力是舰船设计时需要考虑的一个重要性能指标,然而其涉及的因素较多,因素之间的关系错综复杂,不确定信息充斥其间.在系统分析舰船生存能力评估要素的基础上,针对评估过程中的不确定性信息难以量化处理的特点,引入基于贝叶斯网络的多层隐类模型算法对舰船生存能力进行评估,给出了模型评分原理.最后以实例说明了建模方法与评估过程,并结合专家意见分析了模型的优劣,说明该多层隐类模型的算法符合实际情况. 相似文献