A Comparative Study of Three Multivariate Short-Term Freeway Traffic Flow Forecasting Methods With Missing Data

Short-term traffic flow forecasting is a critical function in advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS). Accurate forecasting results are useful to indicate future traffic conditions and assist traffic managers in seeking solutions to congestion problems on urban freeways and surface streets. There is new research interest in short-term traffic flow forecasting due to recent developments in intelligent transportation systems (ITS) technologies. Previous research involves technologies in multiple areas, and a significant number of forecasting methods exist in the literature. However, most studies used univariate forecasting methods, and they have limited forecasting abilities when part of the data is missing or erroneous. While the historical average (HA) method is often applied to deal with this issue, the forecasting accuracy cannot be guaranteed. This article makes use of the spatial relationship of traffic flow at nearby locations and builds up two multivariate forecasting approaches: the vector autoregression (VAR) and the general regression neural network (GRNN) based forecasting models. Traffic data collected from U.S. Highway 290 in Houston, TX, were used to test the model performance. Comparison of performances of the three models (HA, VAR, and GRNN) in different missing ratios and forecasting time intervals indicates that the accuracy of the VAR model is more sensitive to the missing ratio, while on average the GRNN model gives more robust and accurate forecasting with missing data, particularly when the missing data ratio is high.     

不同数值积分方式对海洋结构物疲劳裂纹扩展模拟结果的影响

海洋结构物的疲劳寿命预报是当前的研究热点,文章根据第二作者率领的课题组提出的海洋结构物疲劳寿命预报统一方法比较了疲劳裂纹扩展模拟的三种数值积分方法：逐周数值积分法、△N积分法和△a积分法,并分析了每种方法的适用条件。随后,以中心表面裂纹承受单向交变拉伸载荷的平板为例,分别运用三种方法计算了裂纹扩展寿命和最终裂纹尺寸。同时,研究了△N和△a取值不同对结果的影响。最后,综合考虑计算耗时和计算结果精度,给出了△N积分法和△a积分法的建议值,即△N/N≤1.0%,△a≤0.1 mm。     

独头巷道中炮烟散发规律及浓度预测模型试验研究

在钻爆法开采的地下金属矿山中,监测和预测炮烟中有毒气体散发过程对于保障作业人员安全和提高生产效率有重要意义。文章对某地下矿山独头巷道强制通风状态下的炮烟浓度变化规律进行了模型试验研究;通过数据拟合发现,巷道中炮烟的浓度变化可按e指数规律衰减;炮烟散发过程中各巷道截面位置处的最大浓度与炮烟抛掷区理论初始浓度有较强的线性关系。研究结果可为预测响炮后巷道内达到CO安全浓度所需时间及组建硬件系统时传感器的选择提供依据。     

新建开发区交通需求预测模型研究及应用

提出新建开发区交通需求预测模型并将其应用于实际规划工作.提出交通需求预测的体系框架,建立居民出行生成预测模型,将所建立的模型应用于营口沿海产业基地的综合交通规划中.     

Aural comfort prediction method for high-speed trains under complex tunnel environments

Aural comfort is negatively affected during a train’s passage through various tunnel environments. The objective of this study was to propose a prediction model for determining optimal operation parameter combinations to improve train occupants’ aural comfort. High-speed train model tests, combined with a mathematical transfer model, were used to obtain the interior pressure transients under varying speeds, tunnel lengths and seal indexes. Then, a middle ear finite element model was used to simulate the dynamic responses under the pressure transients, and three indicators were employed to assess the severity of aural sensations. Meanwhile, the aural discomfort were classified into four groups according to the duration. Based on the simulation results, the ordinal regression analysis method was used to reveal the effects of the considered factors on aural comfort. The results indicate that aural discomfort sensations begin when a train runs in the middle of a tunnel but are mitigated when it approaches the tunnel exit. Furthermore, aural discomfort is positively correlated with the train speed and the distance from the driver cabin of the head car but negatively correlated with the seal index and tunnel length. As a conclusion, a mathematical prediction model was established that incorporates factors including the train speed, seal index, tunnel length and car position. It can not only forecast aural sensations under certain operation parameters and tunnel environments but also be used for determining the optimal operation parameters to ensure the best aural sensations for high-speed-train occupants.     

基坑开挖对邻近高铁路基变形影响的预测方法研究

依托软土地区某典型基坑工程,研究基坑开挖对邻近高铁路基变形影响的预测方法。结合该工程土体修正摩尔–库伦模型参数,建立96个不同工况下的有限元模型。通过对有限元计算结果的分析和拟合,推导能够综合考虑基坑开挖深度、支撑系统刚度和基坑距路基坡脚距离3个因素的高铁路基最大水平位移和最大沉降的简化计算公式,提出受基坑开挖影响的路基水平位移、沉降的预测曲线,并给出相应的预测流程。结果表明:在双对数坐标中,当基坑距路基坡脚距离相同时,路基最大水平位移与开挖深度的比值(δhmax/H)、路基最大沉降与开挖深度的比值(δvmax/H)均与支撑系统刚度ρ基本呈线性关系;可用图10中的折线ABC、图13中的折线DEFG分别预测路基水平位移、路基沉降;简化分析方法能较好地预测软土地区类似依托工程土层条件下受基坑开挖影响的高铁路基变形。     

高速铁路桥梁声屏障插入损失五声源预测模式研究

研究一种高速铁路桥梁声屏障插入损失的五声源预测模式,可应用于时速300 km以上高速铁路声屏障声学设计。对高速铁路噪声源进行现场辨识测试,分析其声源特性,将高速铁路噪声源简化为轮轨区、车体下部、车体上部、集电系统、桥梁结构5个等效噪声源。根据单声源模式的声屏障插入损失预测公式,结合不同车速下声源等效频率和噪声贡献量,同时考虑桥梁翼板对声传播的影响,形成五声源模式的声屏障插入损失预测公式。采用该方法计算2.15 m声屏障插入损失并与现场测试数据对比,结果显示距离线路25~50 m处受声点插入损失预测结果与实测结果吻合度最高。     

渡口河特大桥的施工监控

结合渡口河特大桥施工控制实践,阐述了悬臂浇筑施工过程中的施工监控原理和方法,以及有限元计算、线形监测、应变测量等方面的技术问题,研究了箱梁的线形预测控制方法和混凝土应变分布特性,科学地指导了施工。施工数据表明该特大桥的施工监控方法和计算方法的有效性以及合理性。     

小流量下短时交通量预测最佳窗口长度与时间间隔

探讨了小流量情况下，路段短时交通量预测中的可变时间间隔及预测窗口长度对预测精度的影响，分析了不同间隔序列在反映交通流特性方面的差别和最适合的预测窗口长度，并建立神经网络模型对预测效果进行了定量比较，得到序列用于预测的最佳窗口长度和一组最优时间间隔，有助于预测算法的改进和预测精度的提高。     

Data Fusion-Based Traffic Density Estimation and Prediction

Traffic congestion has become a major challenge in recent years in many countries of the world. One way to alleviate congestion is to manage the traffic efficiently by applying intelligent transportation systems (ITS). One set of ITS technologies helps in diverting vehicles from congested parts of the network to alternate routes having less congestion. Congestion is often measured by traffic density, which is the number of vehicles per unit stretch of the roadway. Density, being a spatial characteristic, is difficult to measure in the field. Also, the general approach of estimating density from location-based measures may not capture the spatial variation in density. To capture the spatial variation better, density can be estimated using both location-based and spatial data sources using a data fusion approach. The present study uses a Kalman filter to fuse spatial and location-based data for the estimation of traffic density. Subsequently, the estimated data are utilized for predicting density to future time intervals using a time-series regression model. The models were estimated and validated using both field and simulated data. Both estimation and prediction models performed well, despite the challenges arising from heterogeneous traffic flow conditions prevalent in India.