空气悬架大客车平顺性仿真分析

应用MSC．ADAMS软件在ADAMS／Car里建立了整车动力学仿真模型，用Pro/E软件和试验的方法获得了整车动力学参数。在ADAMS／Car里进行了空气悬架大客车平顺性仿真分析，并将分析结果与试验结果进行了比较。结果表明，所建立的空气悬架大客车多体系统动力学模型。可以对空气悬架大客车整车性能做出正确的预测。     

大中型悬索管道跨越结构清管动力响应分析

大中型管桥是油气输送管道系统中的关键部位，呈现高次超静定、高柔性的结构特点，清管过程中形成的积液将在管桥处产生强烈的冲击动载荷作用，破坏管桥结构的稳定性。考虑悬索管道跨越结构恒载产生的初始内力、拉索垂度等几何非线性因素，将塔架简化为变截面梁，建立了悬索管桥清管动力分析有限元模型。按照管桥积液流动具有的移动荷栽一时间历程的特性，采用荷载步施加移动载荷。结合实例分析了不同清管工况条件下悬索管桥跨越结构的振动位移、临界积液长度以及临界清管速度，从而为安全清管作业提供指导依据。     

Household Mobility Tool Ownership: Modeling Interactions between Cars and Season Tickets

This paper moves beyond traditional models of car ownership in that we propose a framework for modeling household-level decisions to acquire specific types and numbers of mobility tools to fulfill the mobility needs of household members. The framework is applied to a data set collected during the winter and spring of 2000/2001 in the German city Karlsruhe via an interactive web-based stated response survey in which respondents could optimize their household mobility tool sets through on-line feedback concerning the estimated costs of the sets. In our analysis, bivariate ordered probit models are estimated for three combinations of mobility tools: season tickets (i.e., transit passes) and cars, season tickets and small cars and season tickets and large cars. In all instances, strong substitution effects are found – that is, as the number of season tickets increases, the number of cars decreases. This finding underscores the need to move beyond simple models of car ownership to comprehensive models of mobility tool ownership. As demonstrated by our research, failure to do so is likely to lead to biased results.     

城轨运营中断下应急公交车辆调度模型

随着城市轨道交通运营网络规模扩大和客流迅速增长,运营中断下公交应急联动问题日益被重视.针对城轨运营中断下的公交桥接疏运问题,提出一种灵活调度策略,允许车辆服务于不同的桥接路径,以最小化总疏散时间和平均乘客延误为目标,建立基于灵活路径模式下的多目标应急公交车辆调度模型,使用理想点法和遗传算法进行求解,并进行实例验证.对比传统固定路径车辆调度方案,基于灵活路径的车辆调度方案使总疏散时间和平均乘客延误分别减少了 4.2%和 4.4%.结果表明,本文提出的模型能够提高公交应急桥接疏运效率、降低乘客延误.     

基于APT试验的半刚性基层沥青路面动力响应分析

通过足尺试验路加速加载试验研究半刚性基层沥青路面的动力响应,为路面性能预估及结构设计提供依据。通过修筑水泥稳定碎石基层和二灰稳定碎石基层2种结构APT试验路,埋设水平应变仪、竖向压应力传感器和温度传感器,实时采集路面结构动应力、应变、弯沉和路面温度场,分析温度与结构动应变、应力响应的关系,开展基于FWD弯沉盆的路面结构层模量和力学响应计算分析。研究结果表明,半刚性基层层底拉应变受温度影响较小,半刚性基层厚度对结构弯沉影响较大;半刚性基层厚度越小,其模量衰减越快;随重复荷载作用,半刚性基层层底拉应变响应先较快增加而后缓慢减小;在路面使用初期(2 560万次标准轴载),半刚性基层结构模量衰减较慢,当轮载作用1亿次标准轴载累计当量轴次时,半刚性基层模量约衰减55%。     

Communicating transportation carbon dioxide emissions information: Does gender impact behavioral response?

General concern and knowledge on climate change have been increasingly studied over the past decades. Gender differences have been found for general environmental concern and knowledge, but mixed findings exist with respect to climate change. In transportation, research has examined potential relations between environmental attitudes and transportation behavior, with mixed findings as well. Recently, the use of carbon dioxide (CO₂) emissions information to influence choice has been tested with women being found more willing to pay to reduce their personal impacts, suggesting that women are either more willing to change or that their response to information on climate change is stronger. However, those studies used CO₂ mass and studies that examined understanding of CO₂ information as a mass have found that people struggle to understand it. If concern and knowledge about climate change differ amongst individuals, then, according to theories such as the Transtheoretical Model, the type of information used to motivate choices is likely important. Using a unique data set (n = 236) it is possible to take a first look at how gender might affect concern, knowledge, and action in terms of transportation and climate change. Further, it is also possible to examine behavioral responses to transportation climate change information. Finally, an empirical analysis is conducted of the effect of how the information is presented might differ by gender. Thus, this work aims to investigate whether gender differences might contribute to the explanation of individual behavioral responses (from concern to action) in a transportation climate change context.     

Real time transit demand prediction capturing station interactions and impact of special events

Demand for public transportation is highly affected by passengers’ experience and the level of service provided. Thus, it is vital for transit agencies to deploy adaptive strategies to respond to changes in demand or supply in a timely manner, and prevent unwanted deterioration in service quality. In this paper, a real time prediction methodology, based on univariate and multivariate state-space models, is developed to predict the short-term passenger arrivals at transit stations. A univariate state-space model is developed at the station level. Through a hierarchical clustering algorithm with correlation distance, stations with similar demand patterns are identified. A dynamic factor model is proposed for each cluster, capturing station interdependencies through a set of common factors. Both approaches can model the effect of exogenous events (such as football games). Ensemble predictions are then obtained by combining the outputs from the two models, based on their respective accuracy. We evaluate these models using data from the 32 stations on the Central line of the London Underground (LU), operated by Transport for London (TfL). The results indicate that the proposed methodology performs well in predicting short-term station arrivals for the set of test days. For most stations, ensemble prediction has the lowest mean error, as well as the smallest range of error, and exhibits more robust performance across the test days.     

Guidance for new policy developments on railway noise and vibration

Noise and vibration are two of the main problems associated with railways in residential areas. To ensure quality of life and well-being of inhabitants living in the vicinity of railway paths, it is important to evaluate, understand, control and regulate railway noise and vibration. Much attention has been focused on the impact of noise from railway traffic but the consideration of railway-induced vibration has often been neglected. This paper aims to provide policy guidance based on results obtained from the analyses of relationships estimated from ordinal logit models between human response, railway noise exposure and railway vibration exposure. This was achieved using data from case studies comprised of face-to-face interviews (N = 931), internal vibration measurements (N = 755), and noise calculations (N = 688) collected within the study “Human Response to Vibration in Residential Environments” by the University of Salford, UK. Firstly, the implications of neglecting vibration in railway noise policies are investigated. The findings suggest that it is important to account for railway induced vibrations in future noise and transport policies, as neglecting vibrations results in an underestimation of people highly annoyed by noise. Secondly, implications of neglecting different types of railway sources are presented. It was found that the impact of noise and vibration form maintenance operations should be better understood and should be taken into account when assessing the environmental impact of railways in residential environments. Finally, factors that were found to influence railway vibration annoyance are presented and expressed as weightings. The data shows that factors specific to a particular residential area should also be taken into account in future vibration policies as the literature shows that attitudinal, socio-demographic and situational factors have a large influence on vibration annoyance responses. This work will be of interest to researchers and environmental health practitioners involved in the assessment of vibration complaints, as well as to policy makers, planners and consultants involved in the design of buildings and railways.     

Dynamic charging infrastructure deployment for plug-in hybrid electric trucks

Inspired by the rapid development of charging-while-driving (CWD) technology, plans are ongoing in government agencies worldwide for the development of electrified road freight transportation systems through the deployment of dynamic charging lanes. This en route method for the charging of plug-in hybrid electric trucks is expected to supplement the more conventional charging technique, thus enabling significant reduction in fossil fuel consumption and pollutant emission from road freight transportation. In this study, we investigated the optimal deployment of dynamic charging lanes for plug-in hybrid electric trucks. First, we developed a multi-class multi-criteria user equilibrium model of the route choice behaviors of truck and passenger car drivers and the resultant equilibrium flow distributions. Considering that the developed user equilibrium model may have non-unique flow distributions, a robust deployment of dynamic charging lanes that optimizes the system performance under the worst-case flow distributions was targeted. The problem was formulated as a generalized semi-infinite min-max program, and a heuristic algorithm for solving it was proposed. This paper includes numerical examples that were used to demonstrate the application of the developed models and solution algorithms.