车用消声器的研究和试验方法

介绍了目前国内外常用的各种消声器,讨论了它们的特点。论述了几种典型及新兴的研究理论和计算方法;最后系统地介绍了车用消声器试验的一般情况和试验内容。     

动态OD反推理论中的关键问题研究

针对ITS研究中动态OD矩阵难以获得的问题,回顾动态OD反推理论的发展历程,在此基础上明确了该领域研究中的四个关键问题:状态变量的选择、系统方程的确定、OD量与路段流量的关系和OD矩阵动态特性的体现,在对其进行分析的基础上提出每个关键问题的解决方法,为动态OD反推理论的进一步研究垫定基础。     

全液压推土机关键技术参数研究

研究了全液压推土机的关键技术参数———滑转率、效率、牵引比和比功率。通过分析牵引效率在滑转曲线上的配置,给出了全液压推土机的额定滑转率为12%～15%。从理论和试验两个方面深入分析了压力和排量对泵—马达系统效率的影响,得到了典型泵—马达效率的试验数据拟合公式。在对全液压推土机驱动系统的匹配目标设计时,除了满足扭矩和功率上的匹配要求外,还应考虑整机的效率。变量泵的变化范围最好控制在βp=0.4～1,变量马达的变化范围最好控制在βm=0.3～1,才能保证整机有较好的牵引性能。通过对典型全液压推土机的统计分析,确定牵引比均值为1.48,比功率为7.2kW/t,具体取值应该稍大于或等于该均值。     

体外预应力高强混凝土薄壁箱梁试验研究

进行了体外预应力高强混凝土薄壁箱梁从预应力钢绞线张拉到承载力极限破坏这一全过程的试验研究,研究了体外预应力损失及应力增量、跨中截面应力—应变分布以及跨中挠度和抗裂性能等问题。研究结果表明:体外预应力高强混凝土薄壁箱梁预应力损失实测值与现行规范计算值基本吻合,探讨了其截面受压翼缘有效分布宽度和体外预应力筋应力增量的变化规律,初步揭示了体外预应力高强混凝土薄壁箱梁在混凝土开裂前和受拉非预应力钢筋屈服后混凝土受压翼缘存在不同的剪力滞效应,并提出了相应状态下的受压翼缘有效分布宽度系数。     

露石水泥混凝土路面关键技术研究

研究了露石水泥混凝土路面铺筑过程中必然要遇到的材料选择、冲洗时间、成熟度、双层铺筑混凝土强度等关键技术问题,分析了冲洗时间、成熟度的主要影响因素,发现温度越高,冲洗时间越早,最早冲洗时间与最迟冲洗时间有较大的间隔,常温下可达十多个小时,温度升高,间隔减小,成熟度随温度的升高而增大,相隔一定时间分两层振捣的水泥混凝土试件强度与一次振捣的水泥混凝土试件强度基本相同。结果表明:成熟度指标、试验法是评价、检测露石工艺性质的有效手段,两层铺筑方法是降低此种路面成本的有效途径。     

改性沥青伸缩缝结合料与混合料低温性能研究

为改善改性沥青桥梁伸缩缝材料的低温性能,使其在寒冷地区中小跨径桥梁上获得成功应用,通过添加热塑性橡胶类改性剂和橡胶类改性剂,对美佳改性沥青结合料进行二次改性,并通过对改性前后结合料和混合料低温性能的试验对比分析,重点研究了伸缩缝用改性沥青结合料和混合料的低温性能,通过实桥伸缩缝验证了研究成果的可行性。结果表明:该研究成果可在-40°C以上地区的中小跨径桥梁伸缩缝中使用。     

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.