网络计划工期－费用的动态规划决策

关键路线法中网络计划工期－费用优化，可以化为一系列单阶段决策，构成确定性离散优化决策问题，本文试用动态规划处理这一过程。     

堤防水泥固化培土强度的试验研究

水泥固化土是近年来用于堤防防渗、提高边坡稳定性及提高抗冲刷能力的一种新技术，为了提高水泥土的应用水平，通过室内试验研究了水泥土的无侧限强度与水泥掺人比、水灰比、龄期的关系，认为满足工程应用的最佳水泥掺量比大于8％。     

适于TBM施工的HSP法实时预报技术设计与实现

为实现TBM施工隧道的实时地质预报,以TBM刀盘滚刀破岩震动作为震源的HSP法地质预报技术为基础,通过分析TBM施工工艺与机身结构特点,对HSP系统硬件和软件进行优化设计,使其小型化、自动化和智能化,并搭载于TBM上进行智能控制。在TBM掘进过程中,首先,通过隧道轮廓位置的检波器连续或高频次地采集地震反射波信号; 然后,经系统软件对数据的自动处理,实时获取掌子面前方地层反射特征参数图谱; 其次,通过智能识别技术完成对不良地质反射界面的有效拾取与判识; 最后,实现不良地质的探测。通过对适于TBM施工的HSP法实时预报技术进行优化设计,实时探查前方地层不良地质(体)位置、规模与性质,为TBM的高效施工起到一定的推助作用。     

基于时间便利性的行程选择

出行者基于广义成本最小化选择最佳的出行方式.广义成本包含资金成本、时间成本与舒适成本.随着社会的发展,出行者对时间成本看的越来越重.时间成本与时间长度和时间起始点有关,时间成本不仅包含出行者的时间价值也包括早到或晚到的损失.相对于频率较快的地铁与公交,满足乘客长距离出行的火车的时间点对乘客出行的影响很大.本文通过对杭州与上海之间的城际高铁后,出行者在汽车、自驾车及高铁三种方式之间的选择进行调查.得出列车行程安排应考虑旅行时间长度来安排起讫点的时间点,并通过两个例子来佐证合理的时间安排可增加列车的竞争力.优化出行结构同时也保证了列车的客源.本文的研究对于其他的城际列车有借鉴作用.     

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自然灾害经常造成运输过程中断,其损失的评估是一项十分重要而复杂的工作。本文从灾害造成的运输中断时间出发,对时间价值的内涵进行了分析;将灾害引起的运输中断及其导致的生产价值损失时间分为两个波及区,即生产人员损失价值和库存中断造成的损失价值。通过引入资源时间价值概念,分析了灾害中断运输所造成的直接与间接经济损失内涵,建立了基于资源时间价值的运输线路中断后社会生产价值损失估算模型,并结合算例给出了损失计算方法。本文研究可以为运输企业计算灾害带来的自身直接经济损失以及运输中断后引起的社会间接经济损失提供一种定量分析与估算方法。     

Estimation of delay variability at signalized intersections for urban arterial performance evaluation

Urban arterial performance evaluation has been broadly studied, with the major focus on average travel time estimation. However, in view of the stochastic nature of interrupted flow, the ability to capture the characteristics of travel time variability has become a critical step in determining arterial level of service (LOS). This article first presents a stochastic approach that integrates classic cumulative curves and probability theories in order to investigate delay variability at signalized intersections, as a dominant part of the link travel time variability. This serves as a basis for arterial travel time estimation, which can be obtained through a convolution of individual link travel time distributions. The proposed approach is then applied in the estimation of travel time along one arterial in Shanghai, China, with abundant automatic vehicle identification (AVI) data sources. The travel time variability is evaluated thoroughly at 30-min intervals, with promising results achieved in comparison to the field measurements. In addition, the estimated travel time distributions are utilized to illustrate the probability of multiple LOS ranges, namely, reliability LOS. The results provide insights into how we might achieve a more reliable and informative understanding of arterial performance.     

Assessment of factors associated with travel time reliability and prediction: an empirical analysis using probabilistic reasoning approach

Significant efforts have been made in modeling a travel time distribution and establishing measures of travel time reliability (TTR). However, the literature on evaluating the factors affecting TTR is not well established. Accordingly, this paper presents an empirical analysis to determine potential factors that are associated with TTR. This study mainly applies the Bayesian Networks model to assess the probabilistic association between road geometry, traffic data, and TTR. The results from this model reveal that land use characteristics, intersection factors, and posted speed limits are directly associated with TTR. Evaluating the strength of the association between TTR and the directly related variables, the log odds ratio analysis indicates that the land use factor has the highest impact (0.83) followed by the intersection factor (0.57). The findings from this study can provide valuable resources to planners and traffic operators in their decision-making to improve TTR with quantitative evidence.     

SIR-20地质雷达在工程中的应用及图谱解释

介绍了地质雷达探测的基本原理,并结合工程实例和模拟试验说明了地质雷达的具体应用。通过对地质雷达检测图谱的分析和归类处理,找出目标体的地质雷达图谱特征,为地质雷达在工程中的应用提供了判别依据。     

An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks

As intelligent transportation systems (ITS) approach the realm of widespread deployment, there is an increasing need to robustly capture the variability of link travel time in real-time to generate reliable predictions of real-time traffic conditions. This study proposes an adaptive information fusion model to predict the short-term link travel time distribution by iteratively combining past information on link travel time on the current day with the real-time link travel time information available at discrete time points. The past link travel time information is represented as a discrete distribution. The real-time link travel time is represented as a range, and is characterized using information quality in terms of information accuracy and time delay. A nonlinear programming formulation is used to specify the adaptive information fusion model to update the short-term link travel time distribution by focusing on information quality. The model adapts good information by weighing it higher while shielding the effects of bad information by reducing its weight. Numerical experiments suggest that the proposed model adequately represents the short-term link travel time distribution in terms of accuracy and robustness, while ensuring consistency with ambient traffic flow conditions. Further, they illustrate that the mean of a representative short-term travel time distribution is not necessarily a good tracking indicator of the actual (ground truth) time-dependent travel time on that link. Parametric sensitivity analysis illustrates that information accuracy significantly influences the model, and dominates the effects of time delay and the consistency constraint parameter. The proposed information fusion model bridges key methodological gaps in the ITS deployment context related to information fusion and the need for short-term travel time distributions.     

On the income elasticity of the value of travel time

Transport infrastructure is long-term and in appraisal it is necessary to value travel time savings for future years. This requires knowing how the value of time (VTT) will develop over time as incomes grow. This paper investigates if the cross-sectional income elasticity of the VTT is equal to inter-temporal income elasticity. The study is based on two identical stated choice experiments conducted with a 13 year interval. Results indicate that the relationship between income and the VTT in the cross-section has remained unchanged over time. As a consequence, the inter-temporal income elasticity of the VTT can be predicted based on cross-sectional income elasticity. However, the income elasticity of the VTT is not a constant but increases with income. For this reason, the average income elasticity of the VTT in the cross-sections has increased between the two survey years and can be expected to increase further over time.