地表堆载下桩体结构承载力分析

以通用有限元程序AN SY S为基础,通过建模、加载计算及后处理方面的分析计算过程,探讨分析地表堆载下桩体结构承载力,为桩基础设计、施工、运营、管理及维修诸环节提供理论基础及依据。     

海底隧道安全监测问题的几点思考

安全监测是海底隧道施工的重要保障措施之一。由于海底隧道具有与陆地隧道不同的环境特点,因此,也就带来了海底隧道安全监测的复杂性与高风险。为了切实解决海底隧道的安全监测问题,结合陆地隧道安全监测经验和海底隧道的环境特点,提出了海底隧道安全监测的基本原则和技术方法,包括海水渗透超前探测、岩层岩性超前探测、围压监测、隧道形变监测、隧道空间位置导向与定位监测等,为海底隧道安全监测方法设计提供了有益的基础依据。     

基于因子分析法的公路运输绩效评价研究

公路运输是国民经济的基础产业,提高公路运输绩效是发展国民经济的根本途径。在构建公路运输绩效评价指标体系的基础上,利用2003年的调研统计资料,用因子分析法对全国各省市的公路运输系统绩效进行综合评价,指出影响道路运输系统绩效的主要因素,将各省的公路运输系统绩效进行比较,探索影响排名的因素,明确提高公路运输绩效的方向。     

Efficient missing data imputing for traffic flow by considering temporal and spatial dependence

The missing data problem remains as a difficulty in a diverse variety of transportation applications, e.g. traffic flow prediction and traffic pattern recognition. To solve this problem, numerous algorithms had been proposed in the last decade to impute the missed data. However, few existing studies had fully used the traffic flow information of neighboring detecting points to improve imputing performance. In this paper, probabilistic principle component analysis (PPCA) based imputing method, which had been proven to be one of the most effective imputing methods without using temporal or spatial dependence, is extended to utilize the information of multiple points. We systematically examine the potential benefits of multi-point data fusion and study the possible influence of measurement time lags. Tests indicate that the hidden temporal–spatial dependence is nonlinear and could be better retrieved by kernel probabilistic principle component analysis (KPPCA) based method rather than PPCA method. Comparison proves that imputing errors can be notably reduced, if temporal–spatial dependence has been appropriately considered.     

改进振动法在地下工程动力时程计算中的应用

地下工程受到周围土体的约束作用,其动力反映与地面工程有所区别,如将源于地面工程的振动法用于地下工程的动力时程计算分析,需要进行相应的改进.文章通过计算分析发现,振动法用于地下工程动力时程计算时,在计算模型边界条件和波动传播时间效应方面不能反映波动对地下工程作用的实质;通过对比分析,在这两个方面对常规振动法进行了改进;提出了适用于地下工程动力时程计算的改进振动法.该方法的计算模型采用左右两侧粘弹性底部固定的人工边界条件,波动输入采用能够反映波动传播时间效应的分层加速度动态输入法,其计算分析结果与理论解吻合较好,适用于地下工程的动力时程计算分析.     

Sensitivity-based uncertainty analysis of a combined travel demand model

Travel demand forecasting is subject to great uncertainties. A systematic uncertainty analysis can provide insights into the level of confidence on the model outputs, and also identify critical sources of uncertainty for enhancing the robustness of the travel demand model. In this paper, we develop a systematic framework for quantitative uncertainty analysis of a combined travel demand model (CTDM) using the analytical sensitivity-based method. The CTDM overcomes limitations of the sequential four-step procedure since it is based on a single unifying rationale. The analytical sensitivity-based method requires less computational effort than the sampling-based method. Meanwhile, the uncertainties stemming from inputs and parameters can be treated separately so that the individual and collective effects of uncertainty on the outputs can be clearly assessed and quantified. Numerical examples are finally used to demonstrate the proposed sensitivity-based uncertainty analysis method for the CTDM.     

Optimization models for differentiating quality of service levels in probabilistic network capacity design problems

This paper develops various chance-constrained models for optimizing the probabilistic network design problem (PNDP), where we differentiate the quality of service (QoS) and measure the related network performance under uncertain demand. The upper level problem of PNDP designs continuous/discrete link capacities shared by multi-commodity flows, and the lower level problem differentiates the corresponding QoS for demand satisfaction, to prioritize customers and/or commodities. We consider PNDP variants that have either fixed flows (formulated at the upper level) or recourse flows (at the lower level) according to different applications. We transform each probabilistic model into a mixed-integer program, and derive polynomial-time algorithms for special cases with single-row chance constraints. The paper formulates benchmark stochastic programming models by either enforcing to meet all demand or penalizing unmet demand via a linear penalty function. We compare different models and approaches by testing randomly generated network instances and an instance built on the Sioux–Falls network. Numerical results demonstrate the computational efficacy of the solution approaches and derive managerial insights.     

Improving the estimation of total and direction-based heavy-duty vehicle annual average daily traffic

Abstract

The estimation of annual average daily traffic (AADT) is an important parameter collected and maintained by all US departments of transportation. There have been many past research studies that have focused on ways to improve the estimation of AADT. This paper builds upon previous research and compares eight methods, both traditional and cluster-based methodologies, for aggregating monthly adjustment factors for heavy-duty vehicles (US Department of Transportation Federal Highway Administration (FHWA) vehicle classes 4–13). In addition to the direct comparison between the methodologies, the results from the analysis of variance show at the 95% confidence level that the four cluster-based methods produce statistically lower variance and coefficient of variation over the more traditional approaches. In addition to these findings – which are consistent with previous total volume studies – further analysis is performed to compare total heavy-duty monthly adjustment factors, both directions of traffic, with direction-based monthly adjustment factors. The final results show that the variance as well as the coefficient of variation improve on average by 25% when directional aggregate monthly adjustment factors are used instead of total direction.     

Strategies to achieve deep reductions in metropolitan transportation GHG emissions: the case of Philadelphia

ABSTRACT

This paper investigates strategies that could achieve an 80% reduction in transportation emissions from current levels by 2050 in the City of Philadelphia. The baseline daily lifecycle emissions generated by road transportation in the Greater Philadelphia Region in 2012 were quantified using trip information from the 2012 Household Travel Survey (HTS). Emissions were projected to the year 2050 accounting for population growth and trends in vehicle technology for both the Greater Philadelphia Region and the City of Philadelphia. The impacts of vehicle technology and shifts in travel modes on greenhouse gas (GHG) emissions in 2050 were quantified using a scenario approach. The analysis of 12 different scenarios suggests that 80% reduction in emissions is technically feasible through a combination of active transportation, cleaner fuels for public transit vehicles, and a significant market penetration of battery-electric vehicles. The additional electricity demand associated with greater use of electric vehicles could amount to 10.8 TWh/year. The use of plug-in hybrid electric vehicles (PHEV) shows promising results due to high reductions in GHG emissions at a potentially manageable cost.     

Extracting driving characteristics from heavy goods vehicle tachograph charts

European Union regulations require haulage companies of member states like the UK to keep records of their drivers’ hours of work. All heavy goods vehicles (HGV's) over 7.5 tonnes are fitted with tachographs which record a driver's operating activities (periods of driving, other work and rest). These records are etched onto a laminated chart by various styli, one of which records the vehicle's speed. This paper describes the development and testing of a new technique for extracting individual driving characteristics from the speed trace of an HGV tachograph chart to calculate four parameters: distance travelled, average speed, time travelled and speed variability.

The average speed, time travelled and speed variability were analysed statistically using one‐way analysis of variance tests. Speed variability was found to be particularly useful for identifying differences between individual driver's behaviour. Once differences in behaviours can be identified it may be possible to link certain driving habits to factors such as component wear, accident rates and excessive fuel usage.