三维空间路线连续性衰退对公路安全性影响分析

为深入分析公路三维空间线形连续性衰退对事故频数的影响,采集广东省内3条高等级公路243 km道路设计资料,共1 768条有效事故数据,利用Tobit回归模型分析三维空间路线连续性衰退对公路安全性的影响.应用微分几何空间的基本理论,提出公路线形连续性衰退表征参数,利用Tobit回归模型对数据样本中单元内总曲率差(TCD)...     

基于偏最小二乘回归的疏浚产量预测方法

针对绞吸式挖泥船产量预测困难的问题,对挖泥船作业实时反馈的数据进行研究。利用Relief权重算法提取出影响挖泥船产量的主要工艺参量,并在此基础上采用偏最小二乘回归,建立主要工艺参量与产量之间的数学模型,实现对挖泥船产量的预测。结果表明,利用偏最小二乘回归建立的数学模型能够很好地对挖泥船的产量进行预测,可为预测挖泥船的产量提供一种有效的方法。     

Assessment of factors associated with travel time reliability and prediction: an empirical analysis using probabilistic reasoning approach

Significant efforts have been made in modeling a travel time distribution and establishing measures of travel time reliability (TTR). However, the literature on evaluating the factors affecting TTR is not well established. Accordingly, this paper presents an empirical analysis to determine potential factors that are associated with TTR. This study mainly applies the Bayesian Networks model to assess the probabilistic association between road geometry, traffic data, and TTR. The results from this model reveal that land use characteristics, intersection factors, and posted speed limits are directly associated with TTR. Evaluating the strength of the association between TTR and the directly related variables, the log odds ratio analysis indicates that the land use factor has the highest impact (0.83) followed by the intersection factor (0.57). The findings from this study can provide valuable resources to planners and traffic operators in their decision-making to improve TTR with quantitative evidence.     

基于HMM-SVR的船舶动力设备故障模式识别与状态预测研究

船舶动力设备因故障监测信号样本少、变化缓慢且数据特征呈非线性,使得设备故障模式的准确识别和状态预测比较难。鉴于此,文章研究了基于隐马尔科夫模型的故障模式识别方法,利用该模型将微弱变化的信号特征转换为变化较大的对数似然概率对故障模式实现有效识别。在此基础上进一步提出基于HMM-SVR的设备状态预测模型,将遗传算法用于支持向量回归模型参数寻优,并结合隐马尔科夫模型,实现对设备状态的预测。对船用柴油机进行仿真,结果表明上述模型具有较高的识别率,能准确预测船舶动力设备的当前状态。     

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.     

Long-term impact of the Shinkansen on rail and air demand: analysis with data from Northeast Japan

This paper analyses how the high-speed rail construction in Northeast Japan (Tohoku) has affected total demand and interregional travel patterns. We use annual interregional passenger data from 1989 to 2012 and apply regression analysis with the demand between Tokyo and the Tohoku prefectures as the dependent variable. We distinguish particularly between the ‘Full-’ and the ‘Mini-’ Shinkansen, where the latter are branch services running with reduced speed. We find that the ‘Full-Shinkansen’ quickly increases rail and total public transport trips and generates additional rail demand year on year. The ‘Mini-Shinkansen’ impacts are less pronounced. Furthermore, our analysis shows that the Shinkansen has shifted some demand from air to rail once it started operation and increased rail share gradually. We therefore suggest that predictions of demand impacts should carefully distinguish immediate from gradual impacts. We also discuss differences in regional demand in that not all prefectures have gained equally from Shinkansen construction.     

基于最优回归理论的舰船辐射噪声指向性统计模型及分析方法研究

文章在已有单航次的舰船噪声空间特性建模分析方法等研究成果的基础上,进一步研究了若干航次条件下的统计建模及分析方法。该研究主要以单一部位垂向剖面的噪声指向性为例,建立了基于最优回归理论的舰船目标辐射噪声特性的统计模型及分析方法,并开展了海上实测研究,验证了该模型及分析方法的正确性和可行性。该项成果为深入分析不同航行工况下各类舰船目标的噪声空间特性及其统计规律等创造了条件。     

轨道交通与城市协同发展的空间差异性分析：以北京市为例

为识别轨道交通与城市协同发展程度的空间差异性,以及不同空间范围内城市发展水平对轨道交通客流影响的差异性,引入描述空间集聚特征的莫兰指数Moran's I 建立轨道站点周边城市发展水平评价方法,将轨道站点周边城市发展水平和站点客流空间集聚分布特征作为轨道交通与城市发展协同程度判断标准,划定若干研究区域,利用地理加权回归GWR 模型研究两者空间相关性和城市发展因素对轨道交通客流的影响. 实证研究表明：轨道站点周边城市发展水平与站点客流协同程度较高的区域,城市发展水平对站点客流影响更大;将城市发展因素对站点客流影响程度进行排序,密度高于交通便捷程度、功能多样化程度和发展紧凑度.     

内蒙古道路货运与现代物流业的融合

通过对内蒙古道路货运发展的现状分析,找出当前内蒙古道路货运业存在的问题,针对问题提出传统道路货运与现代物流融合的思路及具体措施。