改进估算编制方法 提高估算编制质量

分析铁路工程估算编制存在的问题,进而探讨改进估算编制的方法,提出制订铁路工程估算编制办法的思路及主要技术原则。     

The estimation of wave elevation and wave disturbance caused by the wave orbital motion of a fully submerged hydrofoil craft

The current control system of a fully submerged hydrofoil craft has manual input of fore-foil depth and control mode selection to improve the performance of the control system. However, the manual input needs skillful human operation and observation of waves the encountered to work well over a wide range of waves. In order to use information about the waves encountered in the control system, we considered the estimation of wave elevation and wave disturbance which was caused by the orbital motion of the waves in irregular waves. First, we investigated the wave disturbance by a fully submerged hydrofoil craft, in a state-space model of wave disturbance, and in hydrofoil craft motion, etc. We than considered estimations of the wave elevation and wave disturbance using a shaping filter, a Kalman filter, an autoregressive (AR) model, etc. Finally, we confirmed through simulations that the estimation results and estimation error of wave elevation and wave disturbance were valid.     

辽宁地区农村公路桥梁病害及养护评价指标的研究

通过对辽宁地区农村公路桥梁的病害现状调查,总结出本地区农村公路桥梁的病害特点;提出适合辽宁地区的农村公路桥梁养护质量评价指标.     

Travel time estimation for urban road networks using low frequency probe vehicle data

The paper presents a statistical model for urban road network travel time estimation using vehicle trajectories obtained from low frequency GPS probes as observations, where the vehicles typically cover multiple network links between reports. The network model separates trip travel times into link travel times and intersection delays and allows correlation between travel times on different network links based on a spatial moving average (SMA) structure. The observation model presents a way to estimate the parameters of the network model, including the correlation structure, through low frequency sampling of vehicle traces. Link-specific effects are combined with link attributes (speed limit, functional class, etc.) and trip conditions (day of week, season, weather, etc.) as explanatory variables. The approach captures the underlying factors behind spatial and temporal variations in speeds, which is useful for traffic management, planning and forecasting. The model is estimated using maximum likelihood. The model is applied in a case study for the network of Stockholm, Sweden. Link attributes and trip conditions (including recent snowfall) have significant effects on travel times and there is significant positive correlation between segments. The case study highlights the potential of using sparse probe vehicle data for monitoring the performance of the urban transport system.     

利用Hough变换和稳健估计计算道路线形参数

道路线形参数计算对于公路现状调查、道路优化设计、道路信息管理、安全分析和建立公路的三维空间模型均具有重要意义。根据道路线形的特点,利用基于Hough变换的直线和圆曲线提取算法对数据进行分组,然后利用稳健估计对数据进行迭代平差,计算出道路各段线形参数,从而实现道路线形识别的目的。算法由Visual C 语言实现,在实践中证明是很有效的。     

Improved Speed Estimation From Single-Loop Detectors With High Truck Flow

Single-loop detectors are the most common sensors employed by freeway traffic management agencies. The data are used for traffic management and traveler information. Single-loop detectors can only measure flow and occupancy. Although speed is often the most useful metric, it can only be estimated at conventional single-loop detectors. Typically this estimate comes from the quotient of flow and occupancy multiplied by the fixed, assumed average effective vehicle length. This conventional approach is limited because the actual average effective vehicle length will vary from sample to sample. Many researchers have proposed alternatives to address this problem, and although many of the methods work well under normal conditions, there has been limited research into methods that yield reliable estimates under heavy truck traffic. Heavy truck flows may arise as a function of location or time of day, for example, with proximity to a trucking facility or in early mornings when the number of passenger vehicles drops, respectively. This article presents a new methodology to estimate speed from single-loop detectors in conditions where trucks comprise a large percentage of the fleet. While the focus is on single loop detectors, the work is equally applicable to side-fire microwave radar detectors that emulate single-loop detectors.     

Short-Term Highway Traffic State Prediction Using Structural State Space Models

This research proposes a short-term highway traffic state prediction method based on a structural state space model, with the intention to provide a robust approach for obtaining accurate forecasts of traffic state under both recurring and non-recurring conditions. True traffic state is decomposed to three components, namely, regular traffic pattern, structural deviation, and random fluctuation. Particularly, the structural deviation term reflects the change of true traffic state from regular (historical) pattern, due to unexpected capacity reduction and/or demand variations. A polynomial trend is adopted to describe the temporal evolution of structural deviations across different time intervals. We derive an analytical form of structural deviations in a single bottleneck case based on cumulative flow count diagrams. The proposed model is incorporated in a Kalman filtering-based algorithmic framework, together with an adaptive scheme for determining the variances of random errors. A set of numerical experiments was conducted on two test beds in the northern Taiwan highway network. Experimental results show that the proposed approach is particularly superior to an ordinary Kalman filtering method presented in the literature under non-recurring conditions, highlighting the advantage of combining both the polynomial trend model and historical pattern into the proposed short-term traffic state prediction approach.