基于安全可靠性的多类用户交通分配模型

为探究出行安全对用户出行选择行为的影响,提出了考虑事故风险成本和旅行时间的多类用户交通分配模型。针对事故发生的随机性特征,定义了路径出行安全可靠性概念,并以此计算用户的事故风险成本预算,体现出行者的安全偏好。基于考虑事故机会和事故风险的基础事故预测模型,针对路段和交叉口的不同特征,分别定义了路段和交叉口的事故风险成本分布。构建的交叉口事故风险成本模型,体现了交叉口不同转向的事故风险成本的差异性。为了求解基于安全可靠性的多类用户交通分配模型,采用路径配流法和相继平均法设计了相应的求解算法,并通过算例分析了模型和算法的有效性。研究结果表明：安全可靠性在用户出行选择中具有重要影响。当在广义出行费用中考虑事故风险成本时,出行者会更多地选择事故风险成本较小的路径;不同风险倾向的用户会有不同的选择特征,保守型出行者倾向于选择路径事故风险成本标准差相对较小的路径,即事故风险成本波动小的路径,而中立型出行者倾向于选择事故风险成本均值相对较低的路径;考虑交叉口的事故风险成本与否会直接影响流量分配结果,即路径交叉口数量和转向的差异性同样会影响出行者的选择。所提出的模型对于客流预测和网络安全评价与管理具有潜在的应用价值。     

MMFX-2钢筋性能综述和抗腐蚀性现场实验

水泥混凝土桥面钢筋腐蚀是一普遍且复杂的问题,采用替代钢筋能有效地预防或延缓钢筋腐蚀,而近年应用的MMFX-2钢被认为具有更好的抗锈蚀性能。印第安纳州交通厅对现有的MMFX-2钢的研究成果进行了综合分析,并修建了现场试验桥面以长期评价MMFX钢筋抗腐蚀能力。同普通碳钢相比,MMFX-2钢的屈服强度和抗拉强度更高、抗腐蚀性更好,但没有明显的屈服点。试验桥面铺装的实验结果表明,施工竣工不久至竣工两年期间,MMFX-2钢筋腐蚀很少,混凝土中氯化物含量没有达到钢筋去钝化水平,但在桥面铺装上部氯离子浓度显著增加。笔者建议对于氯化物影响较大场合的混凝土桥面铺装,如果希望提供二次腐蚀保护时,可考虑使用MMFX-2钢。     

机械电子技术在未来铁道车辆中的应用与发展(待续)

全面介绍了机械电子技术在未来铁道车辆特别是悬挂系统、牵引系统和制动系统中的应用和发展。     

浅水中航行船舶的水动力系数

本文利用切片理论数值研究了浅水中前进船舶的水动力系数.为计及水深的影响,采用简单格林函数方法求解剖面水动力系数,提出了满足辐射条件的一种较方便的数值方法.计算了一数学船型的水动力系数,并与有关文献结果比较验证了数值方法的可行性.数值结果显示了水底效应对航行船舶水动力的影响,为进一步研究浅水航行船舶的耐波性提供了基础.     

轨道交通的产学研新模式——麻省理工学院/波多黎各地铁项目简介

波多黎各于20世纪90年代初才开始发展城市轨道交通,但因为善于利用后发优势,通过产学研的结合,充分考虑了地铁工程的多学科特性,走出一条有波多黎各特色的道路,成为美国和国际公共交通领域技术交流的典范.主要介绍了其产学研的项目,它由负责地铁项目的政府主管部门、波多黎各大学和美国麻省理工学院共同组织实施,历经地铁前期规划、修建、调试、运营和政策分析等全部过程,共13年的时间.这种方式提高了工程的质量和施工效率,推动当地轨道交通辅助产业的发展,加快了新技术的应用及研究,培养了一大批轨道交通的规划运营人才,为拉丁美洲城市轨道交通建设提供了一个新模式.波多黎各的经验对于我国当前的城市轨道交通建设同样具有借鉴意义.     

A traffic analysis zone definition: a new methodology and algorithm

This paper develops a comprehensive approach to the definition of transportation analysis zones (TAZ), and therein, presents a new methodology and algorithm for the definition of TAZ embedded in geographic information systems software, improves the base algorithm with several local algorithms, and comprehensively analyses the obtained results. The results obtained are then compared to these presently used in the transportation analysis process of the Lisbon Metropolitan Area. The proposed algorithm presents a new methodology for TAZ design based on a smoothed density surface of geocoded travel demand data. The algorithm aims to minimise the loss of information when moving from a continuous representation of the origin and destination of each trip to their discrete representations through zones, and focuses on the trade-off between the statistical precision, geographical error, and the percentage of intra-zonal trips of the resulting OD matrix. The results for the Lisbon Metropolitan Area case study suggest a significant improvement in OD matrix estimates compared to current transportation analysis practises based on administrative units.

A copula-based joint multinomial discrete–continuous model of vehicle type choice and miles of travel

In this paper, a joint model of vehicle type choice and utilization is formulated and estimated on a data set of vehicles drawn from the 2000 San Francisco Bay Area Travel Survey. The joint discrete–continuous model system formulated in this study explicitly accounts for common unobserved factors that may affect the choice and utilization of a certain vehicle type (i.e., self-selection effects). A new copula-based methodology is adopted to facilitate model estimation without imposing restrictive distribution assumptions on the dependency structures between the errors in the discrete and continuous choice components. The copula-based methodology is found to provide statistically superior goodness-of-fit when compared with previous estimation approaches for joint discrete–continuous model systems. The model system, when applied to simulate the impacts of a doubling in fuel price, shows that individuals are more likely to shift vehicle type choices than vehicle usage patterns.

The economics of airport noise: How to manage markets for noise licenses

This paper discusses various issues in the implementation of a local market for aircraft noise licenses to solve the noise externalities harming the residents located near airports. The design of such markets is affected by aircraft heterogeneity, wind contingencies, peak times, runways capacity constraints, hub strategies, and airport planning is discussed.     

Inferring attribute non-attendance from stated choice data: implications for willingness to pay estimates and a warning for stated choice experiment design

There is a growing interest in traveller behaviour research to explore alternative information processing strategies (often referred to as heuristics or rules) adopted by individuals when assessing packages of attributes describing alternatives in a choice set, and making a choice. One popular attribute processing rule relates to attributes not being considered (i.e., being ignored), for all manner of reasons, referred to in the small but growing literature as attribute non-attendance or non-preservation. Researchers have used a mixture of methods to study the role of attribute non-attendance, including supplementary questions on whether each attribute is ignored or not, and methods in which the functional form of the utility expressions defining an alternative can recognise the possibility, up to a probability, of an attribute being ignored. Although supplementary questions are worthy of further consideration, despite the controversy as to the reliability of the response, recent interest has focused on ways to establish the incidence of attribute non-attendance without recourse to such evidence. In this paper we use an existing data set of choice amongst four attributes describing alternative car non-commuting trips, to illustrate the proposed method, and to compare values of travel time savings under each possible combination of non-attendance attributes relative to a model in which all attributes are assumed to be fully attended to. The paper reveals a major concern with the way that attribute levels and ranges are selected in the design of choice experiments, which can induce non-attendance situations where willingness to pay estimates cannot be obtained.     

Can scale and coefficient heterogeneity be separated in random coefficients models?

There is growing interest in the notion that a significant component of the heterogeneity retrieved in random coefficients models may actually relate to variations in absolute sensitivities, a phenomenon referred to as scale heterogeneity. As a result, a number of authors have tried to explicitly model such scale heterogeneity, which is shared across coefficients, and separate it from heterogeneity in individual coefficients. This direction of work has in part motivated the development of specialised modelling tools such as the G-MNL model. While not disagreeing with the notion that scale heterogeneity across respondents exists, this paper argues that attempts in the literature to disentangle scale heterogeneity from heterogeneity in individual coefficients in discrete choice models are misguided. In particular, we show how the various model specifications can in fact simply be seen as different parameterisations, and that any gains in fit obtained in random scale models are the result of using more flexible distributions, rather than an ability to capture scale heterogeneity. We illustrate our arguments through an empirical example and show how the conclusions from past work are based on misinterpretations of model results.