小型铣刨机车体升降油缸动态过程的分析

通过对小型铣刨机车体升降系统的简化,建立了液压缸运动速度与流量的传递函数,并且在系统参数静态设计方法的基础上,考虑了系统参数对其动态特性的影响.     

大跨度钢管混凝土拱桥地震反应分析

利用有限元法分析大跨度钢管混凝土拱桥的地震反应，考虑了剪切变形的影响，用子空间迭代法分析了其振动规律和动力特性，应用反应谱法和时程分析法分析了该拱桥的地震反应，得出了一些有价值的结论。     

数值模拟在土钉支护公路边坡工程设计的应用

采用岩土工程分析软件，结合某公路边坡开挖的具体实例，对土钉支护进行数值模拟，对边坡开挖和土钉支护过程中的土体位移场、应力场及土钉的受力变化等进行了模拟与分析。结果表明计算值与实测值较为接近，建立的计算模型对土钉支护的受力变形分析是适用的。同时，在改变模型参数的基础上初步探讨了土钉长度、土钉倾角对边坡位移与土钉最大轴力的影响，得出了一些在土钉支护工程中可资应用的结论。     

采用不同方法处理山区高速公路软基的效果分析

以GZ40勉宁高速公路为例,采用抛石挤淤法、粉喷桩法、孔内深层强夯挤密法和冲填法对不同路段软基进行了处理,通过现场的静荷载试验和面波剖面测试对不同方法的处理效果进行了分析,同时对4种方法进行了社会经济效益评价。分析表明以上4种方法均可适用于山区公路软基处理,而其中孔内深层强夯挤密法处理效果较好,且具有施工方便、速度快、无污染、对周围环境影响小和工程造价低等优点,具有广阔的应用前景和推广价值。     

大直径钢管桩承载力恢复过程试验研究

针对目前预制桩承载力恢复特性研究与工程应用中的不足,依托西非某海工工程,提出高应变法。采用对同一钢管桩进行初打与不同休止时间复打相结合的试验方法,研究了大直径钢管桩沉桩后的承载力恢复过程。结合地质情况、沉桩与试验结果,得到了钢管桩承载力、侧阻力及端阻力随时间变化的一般规律及影响因素,并通过静载试验对研究结果进行了验证。该研究在提高项目施工效率、降低成本的同时,还得到了有意义的规律与结论,可为后续类似项目提供参考。     

Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach

Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.     

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss- and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation.     

Estimating traffic volumes for signalized intersections using connected vehicle data

Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimate traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.     

Modeling real-time human mobility based on mobile phone and transportation data fusion

Even though a variety of human mobility models have been recently developed, models that can capture real-time human mobility of urban populations in a sustainable and economical manner are still lacking. Here, we propose a novel human mobility model that combines the advantages of mobile phone signaling data (i.e., comprehensive penetration in a population) and urban transportation data (i.e., continuous collection and high accuracy). Using the proposed human mobility model, travel demands during each 1-h time window were estimated for the city of Shenzhen, China. Significantly, the estimated travel demands not only preserved the distribution of travel demands, but also captured real-time bursts of mobility fluxes during large crowding events. Finally, based on the proposed human mobility model, a predictive model is deployed to predict crowd gatherings that usually cause severe traffic jams.     

桩基建模方法对自升式钻井平台动力响应的影响

随着海洋油气开采作业水深的不断增加,海洋平台固有频率随之降低,易与相同频率成分的波浪产生共振,因此对平台动力响应特性研究是非常有必要的,而桩基模型在平台动力响应分析中发挥着关键作用。以某400英尺自升式钻井平台研究对象,基于等效建模的思想,分别建立了固支桩基、铰接桩基、线性弹簧桩基及非线性弹簧桩基四种有限元模型,并对其进行静力分析、模态分析及瞬态动力学分析,通过对比得到的振动频率、动力放大因子及波流惯性力,得出弹簧桩基模型在平台动力响应分析中的有效性及合理性结论。