Large-scale evacuation network optimization: a bi-level control method with uncertain arterial demand

To improve the efficiency of large-scale evacuations, a network aggregation method and a bi-level optimization control method are proposed in this paper. The network aggregation method indicates the uncertain evacuation demand on the arterial sub-network and balances accuracy and efficiency by refining local road sub-networks. The bi-level optimization control method is developed to reconfigure the aggregated network from both supply and demand sides with contraflow and conflict elimination. The main purpose of this control method is to make the arterial sub-network to be served without congestion and interruption. Then, a corresponding bi-objective network flow model is presented in a static manner for an oversaturated network, and a Genetic Algorithm-based solution method is used to solve the evacuation problem. The numerical results from optimizing a city-scale evacuation network for a super typhoon justify the validity and usefulness of the network aggregation and optimization control methods.     

Vulnerability analysis for large-scale and congested road networks with demand uncertainty

To assess the vulnerability of congested road networks, the commonly used full network scan approach is to evaluate all possible scenarios of link closure using a form of traffic assignment. This approach can be computationally burdensome and may not be viable for identifying the most critical links in large-scale networks. In this study, an “impact area” vulnerability analysis approach is proposed to evaluate the consequences of a link closure within its impact area instead of the whole network. The proposed approach can significantly reduce the search space for determining the most critical links in large-scale networks. In addition, a new vulnerability index is introduced to examine properly the consequences of a link closure. The effects of demand uncertainty and heterogeneous travellers’ risk-taking behaviour are explicitly considered. Numerical results for two different road networks show that in practice the proposed approach is more efficient than traditional full scan approach for identifying the same set of critical links. Numerical results also demonstrate that both stochastic demand and travellers’ risk-taking behaviour have significant impacts on network vulnerability analysis, especially under high network congestion and large demand variations. Ignoring their impacts can underestimate the consequences of link closures and misidentify the most critical links.     

基于BP人工神经网络的滑坡预测分析

文章以汶川地震引发的滑坡为研究对象,以震中距、地震烈度、坡度、前缘高程、坡高和岩性等影响坡体稳定性的因素为切入点,利用BP人工神经网络对实际坡体的稳定性进行了预测分析。结果表明,BP人工神经网络方法能有效预测坡体的稳定情况。     

大跨悬索桥MTMD抖振控制的参数优化

针对悬索桥抖振控制问题,建立有限元模型,应用神经网络和遗传算法对多重调频质量阻尼器(MTMD)进行双参数优化。以某大跨悬索桥为例,利用神经网络改进的谐波合成模拟方法(RBF-WAWS法)对脉动风速进行模拟,并换算成抖振力作用主梁上,通过时程分析及后处理获取主跨跨中横桥向响应值。将响应值的均方差作为优化目标函数,以MTMD总质量、个数及阻尼比作为优化变量和约束条件,采用神经网络拟合目标函数并应用改进的自适应遗传算法进行寻优。结果表明,优化后的MTMD能有效控制悬索桥在脉动风作用下的抖振响应,减振率达48%。提出的理论与计算方法对悬索桥中MTMD的设置及参数选取具有实际工程意义。     

船舶电子证书应用及监管思考

对于如何保证船舶证书和技术文件的完整可查,提出相应的船舶电子证书应用及监管思考。介绍船舶电子证书可进一步拓宽无纸化办公领域、提高检验过程的便利性、提高港口检查的高效性,并可大幅减少证书制作成本、缩短寄送时间,有效提升工作效率;同时,可促进相关部门船舶智能化管理的优化升级。     

Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems

Map-matching (MM) algorithms integrate positioning data from a Global Positioning System (or a number of other positioning sensors) with a spatial road map with the aim of identifying the road segment on which a user (or a vehicle) is travelling and the location on that segment. Amongst the family of MM algorithms consisting of geometric, topological, probabilistic and advanced, topological MM (tMM) algorithms are relatively simple, easy and quick, enabling them to be implemented in real-time. Therefore, a tMM algorithm is used in many navigation devices manufactured by industry. However, existing tMM algorithms have a number of limitations which affect their performance relative to advanced MM algorithms. This paper demonstrates that it is possible by addressing these issues to significantly improve the performance of a tMM algorithm. This paper describes the development of an enhanced weight-based tMM algorithm in which the weights are determined from real-world field data using an optimisation technique. Two new weights for turn-restriction at junctions and link connectivity are introduced to improve the performance of matching, especially at junctions. A new procedure is developed for the initial map-matching process. Two consistency checks are introduced to minimise mismatches. The enhanced map-matching algorithm was tested using field data from dense urban areas and suburban areas. The algorithm identified 96.8% and 95.93% of the links correctly for positioning data collected in urban areas of central London and Washington, DC, respectively. In case of suburban area, in the west of London, the algorithm succeeded with 96.71% correct link identification with a horizontal accuracy of 9.81 m (2σ). This is superior to most existing topological MM algorithms and has the potential to support the navigation modules of many Intelligent Transport System (ITS) services.     

基于VRML的虚拟现实技术在数字管道中的应用

介绍了虚拟现实建模语言(VRML),将基于VRML的虚拟现实技术和HTML、 JavaScript相结合,并应用到数字管道中,构建了真实感强、交互性强的可视化虚拟石油管道场景和管道泄漏模型,实现了石油管道虚拟场景的漫游,以及用户与虚拟场景的交互,并及时、直观地反映管道泄漏.应用结果表明:将基于VRML的网络交互式虚拟现实技术应用到数字管道,促进了数字管道的信息共享,提高了数字管道的决策管理效率.     

Using on-board logging devices to study the longer-term impact of an eco-driving course

In this paper the long-term impact of an eco-driving training course is evaluated by monitoring driving behavior and fuel consumption for several months before and after the course. Cars were equipped with an on-board logging device that records the position and speed of the vehicle using GPS tracking as well as real time as electronic engine data extracted from the controller area network. The data includes mileage, number of revolutions per minute, position of the accelerator pedal, and instantaneous fuel consumption. It was gathered over a period of 10 months for 10 drivers during real-life conditions thus enabling an individual drive style analysis. The average fuel consumption four months after the course fell by 5.8%. Most drivers showed an immediate improvement in fuel consumption that was stable over time, but some tended to fall back into their original driving habits.     

一种基于独立成分分析和径向基神经网络的人脸识别新方法

提出了一种新的基于独立成分分析和径向基神经网络的人脸识别方法。独立成分分析可以从高阶上消除特征数据的相关性,改进了主成分分析方法只能从2阶上消除数据相关性的弱点;最终特征数据的分类由RBF神经网络来实现。在人脸数据库上的实验结果表明该新方法的识别性能较其他方法有了很大提高。     

人的可靠性分析方法及其在军事活动中的应用

回顾了人的可靠性分析发展的历史和现状,重点对现在已存在的人的可靠性分析方法进行了介绍分析,提出并简要介绍了两种在军事活动中适用的人的可靠性评估方法的基本原理：基于粗糙集的人的可靠性评估法和基于贝叶斯的人的可靠性评估法,最后分析了人的可靠性研究中存在的问题。