Strategic planning support for road safety measures based on accident data mining

When actions and measures to increase road safety are to be planned by the police and local authorities, it is necessary to consider the specific accident circumstances as well as their historical, current, and predicted course. In particular, combinations of accident circumstances not contained in existing police statistics are often neglected, but may nevertheless be relevant, e. g., due to an increasing frequency. In order to identify these undiscovered interesting combinations, we propose a framework to support strategic planning of road safety measures based on several consecutive data mining stages. The scope, type, and location of road safety measures must be planned at a strategic level several months in advance to be fully effective. Therefore, it is essential to investigate and predict the accident circumstances and the temporal changes in their frequency comprehensively. Only with the knowledge, e. g., about the temporal pattern, locations, conditions of roads or speeds, meaningful actions can be derived. The embedded data mining approaches, i. e., frequent itemset mining, time series clustering, time series classification, forecasting, and scoring, are carefully selected, coordinated, and aligned. As a result, the framework provides police users with information about circumstances of accidents that are of interest in the future and presents their previous temporal and local patterns in a dashboard. In this study, the framework is applied in four different geographical regions. Thereby, default parameter settings for all approaches are found that are particularly suitable for the framework to investigate novel geographic regions.     

Vortex induced vibrations of short side by side cylinders

Staggered cylinders of different diameters is a common configuration in offshore design, including e.g. clamped risers, piggy-back solutions and piping systems. Current design practice is not clearly formulated in relevant design codes such as DNVGL-RP-F105 and DNVGL-RP-C205. Conservatively, it is often assumed that flow acceleration due to a neighboring cylinder causes up to a doubling of the incoming flow velocity depending on incidence angle and difference in cylinder diameter. Doubling the incoming particle velocity compared to free stream would decrease the required support distance by around 30%. Particularly in conditions with significant diameter disparity, flow acceleration effects due to the larger cylinder of the two can thus cause strict requirements to support distances on the smaller. Designers may want to reduce the number of support points due to cost, maintenance requirements and rigid structure availability, particularly in piping system arrangements. Recent research on piggy-back pipeline vortex induced vibrations have complemented traditional understanding of such configurations, allowing for improved design guidelines. In the present study, a detailed meta study of available laboratory and numerical work is performed, and the resulting findings are translated into simple design guidelines for piggy-back and other staggered arrangements. The present paper provides clear recommendations for improvements to the relevant recommended practices.     

基于SVM的隧道衬砌空洞填充物雷达图像识别研究北大核心CSCD

衬砌背后空洞及其填充物对隧道结构安全具有重要影响,开展空洞探测识别对于结构安全评估和病害处置具有重要意义。首先采用室内试验和FDTD正演模拟相结合的方法,获得了空洞内填充空气、水、干砂、湿砂条件下的雷达图谱数据,并对不同填充物波形规律进行对比分析;然后,基于支持向量机算法对波形特征进行提取和分类识别,建立了一种空洞填充物的人工智能辨识方法。研究结果表明,采用傅里叶变换前的平均值、方差、平均绝对离差和傅里叶变换后的最大幅度值max(fft(X))四个统计量作为支持向量机的识别特征,可以有效区分出衬砌背后填充物的六种类型;当采取单一倾向数据时,识别准确率较好,六种物质二分类问题准确率均可以达到90%以上。     

Learning aircraft operational factors to improve aircraft climb prediction: A large scale multi-airport study

Ground-based aircraft trajectory prediction is a major concern in air traffic control and management. A safe and efficient prediction is a prerequisite to the implementation of new automated tools.In current operations, trajectory prediction is computed using a physical model. It models the forces acting on the aircraft to predict the successive points of the future trajectory. Using such a model requires knowledge of the aircraft state (mass) and aircraft intent (thrust law, speed intent). Most of this information is not available to ground-based systems.This paper focuses on the climb phase. We improve the trajectory prediction accuracy by predicting some of the unknown point-mass model parameters. These unknown parameters are the mass and the speed intent. This study relies on ADS-B data coming from The OpenSky Network. It contains the climbing segments of the year 2017 detected by this sensor network. The 11 most frequent aircraft types are studied. The obtained data set contains millions of climbing segments from all over the world. The climbing segments are not filtered according to their altitude. Predictive models returning the missing parameters are learned from this data set, using a Machine Learning method. The trained models are tested on the two last months of the year and compared with a baseline method (BADA used with the mean parameters computed on the first ten months). Compared with this baseline, the Machine Learning approach reduce the RMSE on the altitude by 48% on average on a 10 min horizon prediction. The RMSE on the speed is reduced by 25% on average. The trajectory prediction is also improved for small climbing segments. Using only information available before the considered aircraft take-off, the Machine Learning method can predict the unknown parameters, reducing the RMSE on the altitude by 25% on average.The data set and the Machine Learning code are publicly available.     

Predicting and planning airport acceptance rates in metroplex systems for improved traffic flow management decision support

Efficient planning of Airport Acceptance Rates (AARs) is key for the overall efficiency of Traffic Management Initiatives such as Ground Delay Programs (GDPs). Yet, precisely estimating future flow rates is a challenge for traffic managers during daily operations as capacity depends on a number of factors/decisions with very dynamic and uncertain profiles. This paper presents a data-driven framework for AAR prediction and planning towards improved traffic flow management decision support. A unique feature of this framework is to account for operational interdependency aspects that exist in metroplex systems and affect throughput performance. Gaussian Process regression is used to create an airport capacity prediction model capable of translating weather and metroplex configuration forecasts into probabilistic arrival capacity forecasts for strategic time horizons. To process the capacity forecasts and assist the design of traffic flow management strategies, an optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal AARs in GDPs, an overall delay reduction of up to 9.7% is achieved. The results also reveal that incorporating robustness in the design of the traffic flow management plan can contribute to decrease delay costs while increasing predictability.     

Prediction of arrival times of freight traffic on US railroads using support vector regression

Variability of travel times on the United States freight rail network is high due to large network demands relative to infrastructure capacity, especially when traffic is heterogeneous. Variable runtimes pose significant operational challenges if the nature of runtime variability is not predictable. To address this issue, this article proposes a data-driven approach to predict estimated times of arrival (ETAs) of individual freight trains, based on the properties of the train, the properties of the network, and the properties of potentially conflicting traffic on the network. The ETA prediction problem from an origin to a destination is posed as a machine learning regression problem and solved using support vector regression trained and cross validated on over two years of detailed historical data for a 140 mile section of track located primarily in Tennessee, USA. The article presents the data used in this problem and details on feature engineering and construction for predictions made across the full route. It also highlights findings on the dominant sources of runtime variability and the most predictive factors for ETA. Improvement results for ETA exceed 21% over a baseline prediction method at some locations and average 14% across the study area.     

Development of a driving simulator based eco-driving support system

This research developed an eco-driving feedback system based on a driving simulator to support eco-driving training. This support system could provide both dynamic and static feedback to improve drivers’ eco-driving behavior. In the process of driving, drivers could get voice prompts (e.g., please avoid accelerating rapidly) once non-eco-driving behavior appeared, and also could see the real-time CO₂ emissions curves. After driving, drivers could receive an eco-driving evaluation report including their fuel consumption rank, potential of fuel saving and driving advice corresponding to their driving behavior. In this support system, five items of non-eco-driving behavior (i.e., quick accelerate, rapid decelerate, engine revolutions at a high level, too fast or unstable speed on freeways and idling for a longer time) were defined and could be detected. To validate this support system’s effectiveness in reducing fuel consumption and emissions, 22 participants were recruited and three driving tests were conducted, first without using the support system, then static feedback and then dynamic feedback utilized respectively. A reduction of 5.37% for CO₂ emissions and 5.45% for fuel consumption was obtained. The results indicated that the developed eco-driving support system was an effective training tool to improve drivers’ eco-driving behavior in reducing emissions and fuel consumption.     

Principal components analysis and track quality index: A machine learning approach

Track geometry data exhibits classical big data attributes: value, volume, velocity, veracity and variety. Track Quality Indices-TQI are used to obtain average-based assessment of track segments and schedule track maintenance. TQI is expressed in terms of track parameters like gage, cross-level, etc. Though each of these parameters is objectively important but understanding what they collectively convey for a given track segment often becomes challenging. Several railways including passenger and freight have developed single indices that combines different track parameters to assess overall track quality. Some of these railways have selected certain parameters whilst dropping others. Using track geometry data from a sample mile track, we demonstrate how to combine track geometry parameters into a low dimensional form (TQI) that simplifies the track properties without losing much variability in the data. This led us to principal components. To validate the use of principal components as TQI, we employed a two-phase approach. First phase was to identify a classic machine learning technique that works well with track geometry data. The second step was to train the identified machine learning technique on the sample mile-track data using combined TQIs and principal components as defect predictors. The performance of the predictors were compared using true and false positive rates. The results show that three principal components were better at predicting defects and revealing salient characteristics in track geometry data than combined TQIs even though there were some correlations that are potentially useful for track maintenance.     

浅谈VTS机务管理

立足VTS机务管理实际工作,从五个方面探讨了如何开展好VTS机务管理工作,以提高系统的可用率和完好率,为VTS运行提供可靠的物质保障。     

航空电子系统BIT综述

近20年来,随着现代航空电子系统功能先进化程度不断提高,其结构变得越来越复杂,导致航空电子系统的故障检测和维修难度增大、测试时间变长,增加维护费用,严重影响其可测性、维修性和战备完好性。而BIT (Built-in Test,简称机内测试)技术在航空电子系统中的应用解决了上述难题,并成为提高其测试性、维修性有效途径。本文从BIT的基本理论、现状应用和提高航空电子系统BIT诊断能力关键技术途径入手,对应用于航空电子领域中的BIT作—个较为深入分析和研究,以期为我国军事工业领域特别是航空领域上对BIT的深入研究与应用提供具有一定价值的参考。