Vulnerability analysis for large-scale and congested road networks with demand uncertainty

To assess the vulnerability of congested road networks, the commonly used full network scan approach is to evaluate all possible scenarios of link closure using a form of traffic assignment. This approach can be computationally burdensome and may not be viable for identifying the most critical links in large-scale networks. In this study, an “impact area” vulnerability analysis approach is proposed to evaluate the consequences of a link closure within its impact area instead of the whole network. The proposed approach can significantly reduce the search space for determining the most critical links in large-scale networks. In addition, a new vulnerability index is introduced to examine properly the consequences of a link closure. The effects of demand uncertainty and heterogeneous travellers’ risk-taking behaviour are explicitly considered. Numerical results for two different road networks show that in practice the proposed approach is more efficient than traditional full scan approach for identifying the same set of critical links. Numerical results also demonstrate that both stochastic demand and travellers’ risk-taking behaviour have significant impacts on network vulnerability analysis, especially under high network congestion and large demand variations. Ignoring their impacts can underestimate the consequences of link closures and misidentify the most critical links.     

Recursive estimation of average vehicle time headway using single inductive loop detector data

Vehicle time headway is an important traffic parameter. It affects roadway safety, capacity, and level of service. Single inductive loop detectors are widely deployed in road networks, supplying a wealth of information on the current status of traffic flow. In this paper, we perform Bayesian analysis to online estimate average vehicle time headway using the data collected from a single inductive loop detector. We consider three different scenarios, i.e. light, congested, and disturbed traffic conditions, and have developed a set of unified recursive estimation equations that can be applied to all three scenarios. The computational overhead of updating the estimate is kept to a minimum. The developed recursive method provides an efficient way for the online monitoring of roadway safety and level of service. The method is illustrated using a simulation study and real traffic data.     

A model for the visualization and analysis of elevator traffic

ABSTRACT

Analysis of elevator traffic in high rise buildings is critical to the performance evaluation of elevator group control systems (EGCS). Elevator dispatching methods or parking algorithms in an EGCS can be designed or modified according to analyses of traffic flow. However, interpretation of traffic flow based solely on numerical data may not be explicit and transparent for EGCS experts as well as for other non-expert building administration. In this study, we present a model for visualization and analysis of elevator traffic. First, we present an alternative approach for traffic analysis which we call route visualization. In the proposed approach, we initially decompose elevator traffic into its component parts and investigate each component independently. Then, using superposition of components we obtain a reconstructed model of overall traffic. This modeling approach provides component-based traffic analysis and representation of routes with intensities through data visualization. In the second part we introduce a multi-dimensional analysis of time parameters in ECGS. This approach provides a comparative analysis of several control algorithms such as dispatch or park algorithms for different combinations of traffic components.     

Prediction of travel time variability for cost-benefit analysis

Unreliable travel times cause substantial costs to travelers. Nevertheless, they are often not taken into account in cost-benefit analyses (CBA), or only in very rough ways. This paper aims at providing simple rules to predict variability, based on travel time data from Dutch highways. Two different concepts of travel time variability are used, which differ in their assumptions on information availability to drivers. The first measure is based on the assumption that, for a given road link and given time of day, the expected travel time is constant across all working days (rough information: RI). In the second case, expected travel times are assumed to reflect day-specific factors such as weather conditions or weekdays (fine information: FI). For both definitions of variability, we find that the mean travel time is a good predictor. On average, longer delays are associated with higher variability. However, the derivative of variability with respect to delays is decreasing in delays. It can be shown that this result relates to differences in the relative shares of observed traffic ‘regimes’ (free-flow, congested, hyper-congested) in the mean delay. For most CBAs, no information on the relative shares of the traffic regimes is available. A non-linear model based on mean travel times can then be used as an approximation.     

百色低碳物流发展浅析

文章阐述了百色发展低碳物流的必要性,分析了百色低碳物流发展所面临的问题,提出了针对性的低碳物流发展思路及建议。     

施工最大悬臂状态梁桥P-Δ效应有限元分析

文章以赣江大桥和宁海新桥特大桥为研究对象,利用有限元模型对其主桥施工最大悬臂状态下P-Δ效应进行了计算,分析了考虑P-Δ效应时的桥梁位移与应力变化情况,为今后类似工程的设计计算和施工监控提供参考。     

梧州西江四桥主桥结构设计及受力分析

文章结合梧州西江四桥主桥工程方案,介绍了该主桥桥型的结构受力特性,阐述了主桥各主要构件的设计要点,并通过静力计算及动力分析,验证了该主桥结构设计方案的合理性和可行性。     

永鑫大桥主桥结构设计及受力分析

矮塔斜拉桥因其受力合理、施工简单、造型美观及造价经济的特点,在我国桥梁建设中得到了快速发展。文章以来宾市永鑫大桥主桥设计为例,介绍了该双塔单索面预应力混凝土矮塔斜拉桥各主要部位的结构设计要点,并采用相关模型计算分析了主桥结构的受力情况。     

往复式压缩机气阀密封面失效分析

气阀是压缩机中发生故障最频繁的部件,故障会直接导致机组排气压比失调、排温高等现象,很大程度上影响企业安全生产及设备的使用寿命.文中对压缩机气阀结构及其故障特征进行了简要阐述,并通过实例分析气阀的故障特征,结合HYSYS计算出工艺气压缩过程中的各组分相图,分析了压缩工艺中气带液的产生过程,总结概括了压缩机气阀密封面失效的...     

隧道爆破信号主分量特征提取与毫秒延期识别研究

为了准确揭示隧道爆破振动信号所包含的反映爆破特征参数的重要信息,通过EMD分解与交叉小波变换组合分析方法对实测爆破信号进行分析,得到各分量与原信号的相关性系数,从而准确判别出信号的主分量。再依据相关性系数对包含主分量在内的相关分量重组并进行Hilbert变换取模值,精确识别出隧道采用的各段别雷管的毫秒延期时间。结果表明： 组合方法确定的主分量及相关分量重组信号能够突出爆破信号的主要特征,对隧道爆破信号的分析效果好。