公铁建设占用保护性地类的法规解读及解决对策

我国土地资源十分紧缺,减少公铁建设对重要地类的占用对保护我国土地资源具有重要的意义。本文在对我国保护性地类的概念、发展、管理等方面进行梳理的基础上,对公铁建设占用基本农田、生态公益林和基本草地时涉及的有关法规条例进行解读,根据不同的保护性地类的法规条例要求,阐述了公铁建设占用保护性地类的处理原则和方法,提出了部分对策和建议。     

学区尺度下小学生通学事故风险评估及影响因素

为评估学区尺度下小学生通学事故风险并获取其影响因素,融合交通事故数据、道路运行数据和学区划分数据,构建学区尺度下通学事故风险评估方法,并运用随机森林模型分析其影响因素.以小学生在学区内部小学通学为原则,构建基于交通事故数据和道路长度的道路暴露度模型评估小学生通学事故风险.以深圳市中心城区为例进行验证.结果表明:深圳市中...     

基于机器学习的船舶机舱设备状态监测方法

[目的]为实现船舶机舱设备的智能状态监测,引入机器学习算法,提出一种结合流形学习和孤立森林的船舶机舱设备状态监测方法.[方法]由于船舶机舱设备的状态监测数据是多维度数据,基于该监测系统,通过流形学习来提取有效的数据特征,实现对原始数据的降维,减少数据复杂度.基于孤立森林算法,在仅利用正常工况数据集的情况下,训练并构建多...     

基于自然驾驶跟车数据的驾驶人差异性分析与辨识

为研究跟车工况下个体驾驶行为特性及其辨识,以驾驶人自然驾驶数据为基础,通过统计分析,频域分析及时频分析,多尺度对比驾驶人加速度、碰撞时间倒数、跟车时距等跟车轨迹特征参数分布的差异性;利用统计方法和离散小波变换提取能够表征驾驶人跟车习性差异的特征参数,分析不同参数输入结果,确定最优参数组合,建立基于随机森林的驾驶人差异性...     

人口老龄化背景下香港地铁服务满意度分析与改进策略研究

我国的人口老龄化趋势日益凸显,受到政府和社会的高度关注。实施积极应对人口老龄化国家战略,满足老年人多层次、多样化需求成为国家的中心工作之一。地铁是老年人重要的出行方式和移动性保障,本研究基于270名老年人对香港地铁(MTR)服务满意度的问卷调查数据,建立随机森林模型关联整体满意度与属性满意度,并构建“重要性-满意度”指数识别地铁属性适老化改进的优先级。研究发现：老年人对地铁服务基本满意;老年人重点关注的地铁服务属性是候车空间、优先座位和行车稳定性;老年人较少关注的属性是发车频率和准时性;改进优先级最高的地铁服务属性是优先座位、车厢温度和站点可达性。据此,针对性提出地铁适老化改进策略。     

Forecasting road traffic conditions using a context-based random forest algorithm

With the ability to accurately forecast road traffic conditions several hours, days and even months ahead of time, both travellers and network managers can take pro-active measures to minimise congestion, saving time, money and emissions. This study evaluates a previously developed random forest algorithm, RoadCast, which was designed to achieve this task. RoadCast incorporates contexts using machine learning to forecast more accurately contexts such as public holidays, sporting events and school term dates. This paper evaluates the potential of RoadCast as a traffic forecasting algorithm for use in Intelligent Transport System applications. Tests are undertaken using a number of different forecast horizons and varying amounts of training data, and an implementation procedure is recommended.     

A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction

With the availability of large volumes of real-time traffic flow data along with traffic accident information, there is a renewed interest in the development of models for the real-time prediction of traffic accident risk. One challenge, however, is that the available data are usually complex, noisy, and even misleading. This raises the question of how to select the most important explanatory variables to achieve an acceptable level of accuracy for real-time traffic accident risk prediction. To address this, the present paper proposes a novel Frequent Pattern tree (FP tree) based variable selection method. The method works by first identifying all the frequent patterns in the traffic accident dataset. Next, for each frequent pattern, we introduce a new metric, herein referred to as the Relative Object Purity Ratio (ROPR). The ROPR is then used to calculate the importance score of each explanatory variable which in turn can be used for ranking and selecting the variables that contribute most to explaining the accident patterns. To demonstrate the advantages of the proposed variable selection method, the study develops two traffic accident risk prediction models, based on accident data collected on interstate highway I-64 in Virginia, namely a k-nearest neighbor model and a Bayesian network. Prior to model development, two variable selection methods are utilized: (1) the FP tree based method proposed in this paper; and (2) the random forest method, a widely used variable selection method, which is used as the base case for comparison. The results show that the FP tree based accident risk prediction models perform better than the random forest based models, regardless of the type of prediction models (i.e. k-nearest neighbor or Bayesian network), the settings of their parameters, and the types of datasets used for model training and testing. The best model found is a FP tree based Bayesian network model that can predict 61.11% of accidents while having a false alarm rate of 38.16%. These results compare very favorably with other accident prediction models reported in the literature.     

Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service

The primary objective of this study was to evaluate the risks of crashes associated with the freeway traffic flow operating at various levels of service (LOS) and to identify crash-prone traffic conditions for each LOS. The results showed that the traffic flow operating at LOS E had the highest crash potential, followed by LOS F and D. The traffic flow operating at LOS B and A had the lowest crash potential. For LOS A and B, the vehicle platoon and abrupt change in vehicle speeds were major contributing factors to crash occurrences. For LOS C, crash risks were correlated with lane-change maneuvers, speed variation, and small headways in traffic. For LOS D, crash risks increased with an increase in the temporal change in traffic flow variables and the frequency of lane-change maneuvers. For LOS E, crash risks were mainly affected by high traffic volumes and oscillating traffic conditions. For LOS F, crash risks increased with an increase in the standard deviation of flow rate and the frequency of lane-change maneuvers. The findings suggested that the mechanism of crashes were quite different across various LOS. A Bayesian random-parameters logistic regression model was developed to identify crash-prone traffic conditions for various LOS. The proposed model significantly improved the prediction performance as compared to the conventional logistic regression model.     

俄罗斯原木关税调整的影响及辽宁省的对策

本文在分析俄原木的出口关税率提高对我国的影响和两国森林资源合作开发的基础上,就我省面临的问题提出对策和建议。     

基于随机森林算法的电力系统暂态稳定性评估

利用随机森林算法,通过组合多棵基于随机向量的决策树对电力系统的暂态稳定性分类,提出了一种暂态稳定评估模型.在IEEE 16机和IEEE 50机测试系统进行的仿真验证了该模型对暂态稳定评估的有效性,其评估性能较经典决策树算法、人工神经网络、支持向量机和K最近邻方法均有提高.