Reliable sensor deployment for network traffic surveillance

New sensor technologies enable synthesis of disaggregated vehicle information from multiple locations. This paper proposes a reliable facility location model to optimize traffic surveillance benefit from synthesized sensor pairs (e.g., for travel time estimation) in addition to individual sensor flow coverage (e.g., for traffic volume statistics), while considering probabilistic sensor failures. Customized greedy and Lagrangian relaxation algorithms are proposed to solve this problem, and their performance is discussed. Numerical results show that the proposed algorithms solve the problem efficiently. We also discuss managerial insights on how optimal sensor deployment and surveillance benefits vary with surveillance objective and system parameters (such as sensor failure probabilities).     

城市公路客运站选址与班线分配方法研究

以旅客出行的时间消耗降最低为目标,兼顾客运站的建设成本和经营效益,建立公路客运站的选址与班线分配模型。通过遗传算法求解,并请专家对最好的几组计算结果予以评估和修正,得到客运站的最优选址和班线分配方案。     

考虑诱导信息影响的可变信息标志优化选址研究——以北京南站为例

可变信息标志(VMS Variable Message Signs)系统是交通诱导系统的重要组成部分.VMS通过发布相关信息,引导车辆选择最合理的路径,避免局部路段拥堵,均衡路网交通流量,在宏观上对城市交通管理起到重要作用.本文概述了城市交通诱导系统的发展及应用,介绍了基于VMS的交通诱导系统的功能、信息生成等,同时对国内外关于VMS的交通诱导系统研究现状进行了比较与分析;通过对VMS用户需求以及布点影响因素的分析,结合可变信息标志的选址原则,依据VMS诱导信息对路径选择的影响分析,改进了VMS数学规划选址模型.结合北京南站地区实际交通状况,分析VMS选址的影响.     

山区高速公路事故多发点的组合鉴别方法

基于国内外交通事故鉴别方法,结合山区高速公路特点及其事故特点,提出改进的鉴别指数与非定长分段法相结合的适合山区高速公路的混合鉴别方法,最后在包茂高速(西安—榆林)铜川段进行实例验证,证明了该方法的有效性。     

铁路枢纽内客运站布局优化分析

铁路枢纽内客运站布局的优化,对铁路的运营发展以及整个运输系统效率的发挥具有重要意义。在总结有关研究的基础上,通过对客运站布局影响因素的分析和优化问题的相关描述,将整个网络系统简化,略去从一个客运站到另外一个客运站的运输过程,仅考虑向客流消失地疏散或从产生地集中到客运站的旅客出行过程,建立铁路枢纽内客运站布局优化模型,并设计相应的算法。     

考虑路段失效的军事物流配送中心选址模型

针对战场保障网络常常遭受敌人打击的现象,建立了考虑路段失效的军事配送中心可靠选址模型.模型为双层规划形式,上层规划的目标是优化系统在无路段失效和出现路段失效时所覆盖的总的需求量,下层规划的目标是最小化某一路段失效时系统覆盖的需求量.采用启发式算法求解下层规划,运用遗传算法求解整个模型.通过一个算例验证模型的优越性和算法的有效性.算例结果表明,尽管模型所得选址方案在无路段失效时覆盖的需求量,小于不考虑路段失效的最大覆盖模型选址方案覆盖的需求量,但在最佳路段失效时模型选址方案的覆盖效果优于后者.     

船舶与海洋工程中的焊接变形预测方法研究概述

文章介绍了焊接变形预测理论的发展及现状,并将其归纳为基于实验和理论相结合的经验计算阶段、基于计算机数值模拟的模型分析阶段、基于新兴数据挖掘方法的研究应用阶段三个标志性阶段,详细论述了每个阶段预测技术在实际中应用的优劣.着重以BP、RBF人工神经网络为例阐述了神经网络等数据挖掘方法在焊接变形预测中的应用前景,并指出了目前存在的不足及未来亟待研究的方向.     

基于尺寸精度的铝合金减震器塔结构设计及优化

铝合金作为一种优质轻量化材料在轻量化车身上的应用越来越广泛,为了确保铝合金零部件的使用功能和性能,设计人员在设计前期就需要对零部件的精度进行设计分析和优化。文章基于一种压铸铝合金减震器塔的零部件,首先对该铝合金零部件精度进行分析摸底并识别出影响该铝合金零部件精度的两个关键要素,再分别针对两个关键要素进行优化设计,重点一是优化加强筋结构,以增加减震器塔本体的刚度和抗变形能力,另外是增加热处理工艺中夹具支撑块结构,通过外力支撑来增加抗变形能力。结果表明:合理的加强筋布置能有效提升减震器塔本体的刚度、强度和尺寸精度;热处理时需设计专用支撑夹具,且支撑夹具要保证所有重要安装面的支撑,避免铝合金减震器塔局部出现悬臂,通过外力支撑来防止变形。     

连续-钢桁组合结构钢桁下节点安装定位施工工艺

以西宁跨兰西高速公路特大桥(80+168+80)m连续-钢桁组合结构钢桁下节点安装定位工程施工为例,详细介绍了下节点在连续梁砼施工过程中如何精确定位和安装,确保在连续梁施工完成后下节点的精度满足钢桁结构的安装要求,保证连续-钢桁组合结构的整体施工要求。     

Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.