The effects of planned and unplanned transfers on public transport users' perception of transfer routes

Many authorities are investing in new infrastructure to improve the quality of public transport (PT) services in the hope to increase mode switch from cars. The goal is to provide users with an integrated multimodal PT network by facilitating transfers. There exists a lack in the clarity of the attributes which defines a planned transfer and the effects of planned transfers on users' willingness to use routes with transfers. The present study provides approximate effects of ‘planned’ and ‘unplanned’ transfers on PT users' decisions to use transfer routes. The study focuses on two attributes of ‘planned’ transfers, integrated physical connection of transfers and information integration. A user preference survey was undertaken in Auckland, New Zealand. Analysis of the results shows that physical integration is more important than information integration for current PT users. Results also suggest that information integration has a greater influence on users of transfer services which are more closely aligned to being ‘unplanned’.     

圆形隧道施工对不同深度地层沉降影响的模型试验研究

为了预测圆形隧道施工引起地表以下不同埋深地层沉降特征,首先,通过理论推导不同地层最大沉降位移与沉降槽宽度系数的函数关系;然后,建立包括试验台架、地层模型、圆形隧道开挖模型以及测量地层变形装置的平面应变模型试验系统。通过理论解析和模型试验可知:1)地表以下地层的最大沉降位移与沉降槽宽度系数成反比;2)不同深度地层的沉降位移随着地层埋深的增加而增大,且地表以下地层沉降槽曲线仍然符合正态分布;3)通过对模型试验数据进行回归分析,得到黏土地表以下不同深度地层沉降槽宽度系数的计算公式,从而为预测圆形隧道施工地表以下不同深度地层竖向位移提供了一种可靠的计算方法。     

Power network parameter estimation method based on data mining technology

The parameter values which actually change with the circumstances, weather and load level etc.produce great effect to the result of state estimation. A new parameter estimation method based on data mining technology was proposed. The clustering method was used to classify the historical data in supervisory control and data acquisition (SCADA) database as several types. The data processing technology was impliedto treat the isolated point, missing data and yawp data in samples for classified groups. The measurement data which belong to each classification were introduced to the linear regression equation in order to gain the regression coefficient and actual parameters by the least square method. A practical system demonstrates the high correctness, reliability and strong practicability of the proposed method.     

基于支持向量机回归树的内燃机振动信号趋势预测

针对标准回归树建立在统计分析基础上所存在的缺陷,提出一种基于支持向量机的回归树预测模型。首先,根据原始振动信号趋势序列构建回归树;然后,针对回归树上包含样本数过少的节点,利用支持向量机,建立能够反映重要变量与响应变量之间映射关系的回归模型。仿真结果表明:即便由于设备出现异常,导致振动信号趋势序列出现非平稳、突变情况,该方法也能准确地预测,性能优于标准分析方法,具有一定的工程实用性。     

Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures

Due to the difficulty of obtaining accurate real-time visibility and vehicle based traffic data at the same time, there are only few research studies that addressed the impact of reduced visibility on traffic crash risk. This research was conducted based on a new visibility detection system by mounting visibility sensor arrays combined with adaptive learning modules to provide more accurate visibility detections. The vehicle-based detector, Wavetronix SmartSensor HD, was installed at the same place to collect traffic data. Reduced visibility due to fog were selected and analyzed by comparing them with clear cases to identify the differences based on several surrogate measures of safety under different visibility classes. Moreover, vehicles were divided into different types and the vehicles in different lanes were compared in order to identify whether the impact of reduced visibility due to fog on traffic crash risk varies depending on vehicle types and lanes. Log-Inverse Gaussian regression modeling was then applied to explore the relationship between time to collision and visibility together with other traffic parameters. Based on the accurate visibility and traffic data collected by the new visibility and traffic detection system, it was concluded that reduced visibility would significantly increase the traffic crash risk especially rear-end crashes and the impact on crash risk was different for different vehicle types and for different lanes. The results would be helpful to understand the change in traffic crash risk and crash contributing factors under fog conditions. We suggest implementing the algorithms in real-time and augmenting it with ITS measures such as VSL and DMS to reduce crash risk.     

Development and evaluation of a statistically reliable traffic counting program

This paper reviews the existing procedures to compute the seasonal factors, growth factors, and the number of automatic traffic recorder stations (ATRs) for developing a statistically reliable traffic counting program. The review is based on, (i) theoretical evaluation of the procedures, (ii) analysis of the results obtained from applying these procedures to extensive real life data, and (iii) experience of past researchers with these methods. The paper primarily deals with existing statistical procedures for determining seasonal factors, since it is believed that these factors play the most important role in estimating Annual Average Daily Traffic (AADTs) from short counts. The procedure of cluster analysis (which is most commonly used to determine seasonal factors) is critically reviewed. The shortcomings of this method motivated the development of a different statistical procedure, based on regression analysis, to determine seasonal factors. It is shown that this newly developed method gives better results than the cluster analysis techniques. Furthermore, separate sections are dedicated to a thorough analysis of determination of number of ATR stations and computation of growth factors.     

基于GRNN的GSM-R场强覆盖预测算法

在比较Hata模型修正方法和GRNN算法场强覆盖预测效果的基础上,仿真分析了训练集构成和平滑因子选择对GRNN算法预测效果的影响,给出了训练集构成和平滑因子选择的方法;提出了电波传播环境相似系数来表征GRNN模型在不同环境下的适用性。仿真结果表明,两种传播环境的相似系数越大,由一种环境下测试数据确定的GRNN在另一环境下的预测精度越高。     

级配碎石回弹变形特性

级配碎石具有非线性的应力-应变特性,回弹模量是有效描述这一特性的参数,通过室内动三轴试验能够得到较好的模拟级配碎石回弹模量的模型。分析了影响级配碎石回弹变形特性的各种因素,并进行了试验验证。通过数据拟合发现,Uzan模型能更好地反映级配碎石的非线性回弹变形特性,且模型中的回归参数与级配中小于0.075mm筛孔的质量分数有一定的关联性。并针对影响级配碎石回弹模量的两个重要参数：级配中小于0.075mm筛孔的质量分数和含水质量分数,提出相应的取值范围建议。     

汽车操纵稳定性主客观评价数据的一种处理方法

汽车操纵稳定性主客观评价之间的相关性分析是汽车技术的重要课题。提出了汽车操纵稳定性主客观评价数据的一种处理方法．通过回归分析筛选客观参数，运用BP神经网络建立相关性模型，验证结果说明了该方法的可行性。     

基于非参数回归的快速路行程速度短期预测算法

基于北京市快速路上的检测器所采集的历史数据，经过数据筛选，剔除判别，小波滤噪平稳处理，聚类分析等过程，建立了交通状态演变系列的历史样本数据库。基于所构建的历史数据库，通过数值试验，确定了状态向量、距离匹配原则，K近邻值等参量，构建了一种基于K近邻的非参数回归短时交通预测模型，实现了对路段行程速度的短时预测。最后，利用随机选取的历史数据系列对预测模型的精度进行了检验。结果表明，预测算法的精度可以达到90％以上，可以很好地满足ITS应用系统对于交通预测数据的精度要求。