Probe vehicle data sampled by time or space: Consistent travel time allocation and estimation

A characteristic of low frequency probe vehicle data is that vehicles traverse multiple network components (e.g., links) between consecutive position samplings, creating challenges for (i) the allocation of the measured travel time to the traversed components, and (ii) the consistent estimation of component travel time distribution parameters. This paper shows that the solution to these problems depends on whether sampling is based on time (e.g., one report every minute) or space (e.g., one every 500 m). For the special case of segments with uniform space-mean speeds, explicit formulae are derived under both sampling principles for the likelihood of the measurements and the allocation of travel time. It is shown that time-based sampling is biased towards measurements where a disproportionally long time is spent on the last segment. Numerical experiments show that an incorrect likelihood formulation can lead to significantly biased parameter estimates depending on the shapes of the travel time distributions. The analysis reveals that the sampling protocol needs to be considered in travel time estimation using probe vehicle data.     

Predicting travel time reliability using mobile phone GPS data

Estimates of road speeds have become commonplace and central to route planning, but few systems in production provide information about the reliability of the prediction. Probabilistic forecasts of travel time capture reliability and can be used for risk-averse routing, for reporting travel time reliability to a user, or as a component of fleet vehicle decision-support systems. Many of these uses (such as those for mapping services like Bing or Google Maps) require predictions for routes in the road network, at arbitrary times; the highest-volume source of data for this purpose is GPS data from mobile phones. We introduce a method (TRIP) to predict the probability distribution of travel time on an arbitrary route in a road network at an arbitrary time, using GPS data from mobile phones or other probe vehicles. TRIP captures weekly cycles in congestion levels, gives informed predictions for parts of the road network with little data, and is computationally efficient, even for very large road networks and datasets. We apply TRIP to predict travel time on the road network of the Seattle metropolitan region, based on large volumes of GPS data from Windows phones. TRIP provides improved interval predictions (forecast ranges for travel time) relative to Microsoft’s engine for travel time prediction as used in Bing Maps. It also provides deterministic predictions that are as accurate as Bing Maps predictions, despite using fewer explanatory variables, and differing from the observed travel times by only 10.1% on average over 35,190 test trips. To our knowledge TRIP is the first method to provide accurate predictions of travel time reliability for complete, large-scale road networks.     

城市轻轨车辆最佳运行控制方案的研究

本文给出一种利用微型机实现城市轨道车辆最佳运行的控制方案,即在变化的外界条件下确定断电和制动时刻,以指导司机驾驶车辆,实现能耗最小和准时运行.该控制方案的特点是不断地利用实测值并结合估算确定断电和制动时刻,简单易行.     

FAULT IDENTIFICATION IN HETEROGENEOUS NETWORKS USING TIME SERIES ANALYSIS

Fault management is crucial to provide quality of service grantees for the future networks, and fault identification is an essential part of it. A novel fault identification algorithm is proposed in this paper, which focuses on the anomaly detection of network traffic. Since the fault identification has been achieved using statistical information in management information base, the algorithm is compatible with the existing simple network management protocol framework. The network traffic time series is verified to be non-stationary. By fitting the adaptive autoregressive model, the series is transformed into a multidimensional vector. The training samples and identifiers are acquired from the network simulation. A k-nearest neighbor classifier identifies the system faults after being trained. The experiment results are consistent with the given fault scenarios, Which prove the accuracy of the algorithm. The identification errors are discussed to illustrate that the novel fault identification algorithm is adaptive in the fault scenarios with network traffic change.     

钢框架结构钢骨消能支撑的抗震性能研究

通过对钢框架、钢支撑钢框架和钢骨消能支撑钢框架结构体系的弹性反应谱分析、弹性以及弹塑性时程分析,对比研究了3种钢框架结构体系的抗震性能。分析结果表明,钢骨消能支撑钢框架结构体系能有效地消散输入结构的地震能量,减少结构地震反应对钢框架梁柱的非线性性能的需求。同时由于钢骨消能支撑的作用,避免了钢框架结构的局部失稳,提高了结构的弹塑性变形能力和延性。     

控制时滞对半主动悬挂车辆动力学性能的影响

利用天棚控制原理和SIMPACK动力学软件建立了具有二系横向和垂向半主动悬挂车辆模型,分析在不同速度下时滞10、30ms车体横向和垂向的平稳性性能、1位轮对的轮轨横向力,同时还分析在速度为240km/h时,时滞对车体横向加速度振动幅频特性、前构架和1位轮对横向加速度功率谱密度的影响.分析表明时滞10ms时上述各项指标变化不是很明显,当时滞30ms时许多指标恶化得较为严重,半主动控制失效,说明时滞不能过大.为了突显半主动悬挂的优势,对控制系统时滞进行研究和限制显得尤为必要.     

铁路运输中货车周转与货物运送时间的分析

对铁路货车周转时间及货物运送时间进行了较详细的探讨,对造成周转时间与货物运送时间延长的各时间因素及其影响程度进行了分析计算,并提出了相应的建议.     

延迟型高度阀特性参数的研究

根据高度阀的工作原理建立了力学方程和气体流量方程,利用Simulink进行了高度阀的不感带、流量以及延迟时间的时域仿真,并研究了高度阀的结构参数如阻尼、缓冲弹簧刚度、不感带幅值、杠杆比以及节流面积等对其性能的影响,最终得到了高度阀特性与其结构参数的关系式。     

新型NCV1系列电压传感器

分析了高导磁环形镍铁合金线圈在方波电压下的工作过程，论证了这种线圈构成的电压传感器有极快的响应时间；介绍了该系列电压传感器的主要技术参数以及运用情况。     

海水过滤器长效保护新技术

通过对船舶海水过滤器使用环境、现有保护方法分析,提出全新解决海水过滤器防蚀防污技术。