滑模摊铺机施工混凝土路面工艺原理及设备选型

从滑模摊铺水泥混凝土路面施工应用的角度，以不配备布料机，单台滑模摊铺机施工时的工艺理论及机构设计上，对如何适应和满足中国高等级公路水泥混凝土路面施工的需要，提出了滑模摊铺机施工工艺原理以及各施工单位在选择滑模摊铺机械时应该注意的工艺技术细节和随机附件的选配要求。     

大洋航线气候区划

根据大洋航线的气候特征，尤其是用风、雾、浪、流的时空分布进行分区，把各大洋航线划分为极地航区、温带航区、副热带航区、信风航区、赤道航区，每个航区又划分出若干亚区，对远洋航行航线选择，实现安全经济航行是十分重要的。     

编组站进路调度优化算法

分析编组站作业进路选排问题的本质,以各任务的延误时间加权值总和最小为最优目标,以任务的前后工序选择路径为动态约束,建立编组站作业进路调度数学模型,采用遗传算法求解。编码采用定长染色体,长度为任务的工序数,每个工序采用2段制,编码中的顺序唯一地确定了每工序对指定进路占用的起讫时间和指标递推,设计基于优先规则的编码算法步骤。为保证解的可行性,将编码合法化,对工序进行拓扑排序。递推计算工序开始时间和结束时间,进而计算编码的目标值,并将其转化为适应值。采用轮盘赌与最优性相结合的方法进行选择,设计基于位置成组移位的杂交算子和随机交换的变异算子。以某编组站为例进行模拟计算,结果证明该算法满足编组站进路调度工作的要求。     

铁路专用线选线控制因素分析

为系统分析出铁路专用线选线时各种控制要素,尝试探寻铁路专用线选线的独特规律,通过对铁路专用线选线功能及作用的分析,系统阐述铁路专用线选线时"路内"、"路外"各种控制要素和关键优缺点,同时理论与实践相结合,以华能铜川电厂铁路专用线工程为实例,对铁路专用线选线时涉及的运输通道、技术标准、接轨方案等进行多角度比选分析,使推荐的线路方案满足各方需求,工程合理可行。铁路专用线选线与国铁干线选线相比,既有着相同的特点也有着独特的考虑因素,为铁路专用线选线提供系统、全面、科学的控制因素及比选方法。     

应用模糊自适应PID和预瞄策略的自主车辆转向控制

文中结合智能车辆路径跟踪过程中转向控制方面的特点，提出了一种将模糊逻辑、预瞄规律和自适应PID控制相结合的控制策略，建立起了相应的模型，验证了该方法的可行性和有效性，并提出了进一步改进的方向。     

小波变换在管道泄漏点定位中的应用

当管道发生泄漏时，泄漏诱发的负压波传播到管道上下游监测点时会使该点压力信号幅值产生瞬态下降，利用小波变换模极大值检测压力信号的突变点，从而计算出管道泄漏诱发的负压波传播到上下游监测点的时间差。在MATLAB6．1软件环境下，以某次实验数据为例进行管道泄漏点定位研究。在计算过程中小波基函数选取为Haar小波，尺度水平为6，计算结果表明该方法可以计算出管道泄漏点处诱发的负压波传播到管道上游监测点的时间差，从而为准确定位泄漏点提供了基础。     

Driving cycle prediction model based on bus route features

Bus fuel economy is deeply influenced by the driving cycles, which vary for different route conditions. Buses optimized for a standard driving cycle are not necessarily suitable for actual driving conditions, and, therefore, it is critical to predict the driving cycles based on the route conditions. To conveniently predict representative driving cycles of special bus routes, this paper proposed a prediction model based on bus route features, which supports bus optimization. The relations between 27 inter-station characteristics and bus fuel economy were analyzed. According to the analysis, five inter-station route characteristics were abstracted to represent the bus route features, and four inter-station driving characteristics were abstracted to represent the driving cycle features between bus stations. Inter-station driving characteristic equations were established based on the multiple linear regression, reflecting the linear relationships between the five inter-station route characteristics and the four inter-station driving characteristics. Using kinematic segment classification, a basic driving cycle database was established, including 4704 different transmission matrices. Based on the inter-station driving characteristic equations and the basic driving cycle database, the driving cycle prediction model was developed, generating drive cycles by the iterative Markov chain for the assigned bus lines. The model was finally validated by more than 2 years of acquired data. The experimental results show that the predicted driving cycle is consistent with the historical average velocity profile, and the prediction similarity is 78.69%. The proposed model can be an effective way for the driving cycle prediction of bus routes.     

基于改进BP-GA方法的FPSO舷侧结构耐撞性能优化设计

基于遗传算法和ABAQUS参数化有限元仿真技术,对传统的BP-GA优化方法进行改进,并采用改进的BP-GA方法对浮式生产储油卸油装置(FPSO)舷侧结构的耐撞性能进行优化,以验证其可行性和准确性。结果表明,与传统的BP神经网络相比,经遗传算法优化的BP神经网络具有更高的预测精度和更强的泛化能力;改进的BP-GA优化方法可在结构减重的基础上进一步提高结构的耐撞性能,能较好地适用于复杂的FPSO舷侧结构耐撞性优化设计。采用的优化方法具有通用性,可为抗爆性能的优化设计提供参考。     

Highway voting system: Embracing a possible paradigm shift in traffic data acquisition

The integration of internet and mobile phones has opened the door to a new wave of utilizing private vehicles as probes not only for performance evaluation but for traffic control as well, gradually replacing the role of traffic surveillance systems as the dominant source of traffic data. To prepare for such a paradigm shift, one needs to overcome some key institutional barriers, in particular, the privacy issue. A Highway Voting System (HVS) is proposed to address this issue in which drivers provide link- and/or path-based vehicle data to the traffic management system in the form of “votes” in order to receive favorable service from traffic control. The proposed HVS offers a platform that links data from individual vehicles directly with traffic control. In the system, traffic control responds to voting vehicles in a way similar to the current system responding to prioritized vehicles and providing the requested services accordingly. We show in the paper that the proposed “voting” system can effectively resolve the privacy issue which often hampers traffic engineers from getting detailed data from drivers. Strategies to entice drivers into “voting” so as to increase the market penetration level under all traffic conditions are discussed. Though the focus of the paper is on addressing the institutional issues associated with data acquisition from individual vehicles, other research topics associated with the proposed system are identified. Two examples are given to demonstrate the impact of the proposed system on algorithm development and traffic control.     

On the variance of recurrent traffic flow for statistical traffic assignment

This paper generalizes and extends classical traffic assignment models to characterize the statistical features of Origin-Destination (O-D) demands, link/path flow and link/path costs, all of which vary from day to day. The generalized statistical traffic assignment (GESTA) model has a clear multi-level variance structure. Flow variance is analytically decomposed into three sources, O-D demands, route choices and measurement errors. Consequently, optimal decisions on roadway design, maintenance, operations and planning can be made using estimated probability distributions of link/path flow and system performance. The statistical equilibrium in GESTA is mathematically defined. Its multi-level statistical structure well fits large-scale data mining techniques. The embedded route choice model is consistent with the settings of O-D demands considering link costs that vary from day to day. We propose a Method of Successive Averages (MSA) based solution algorithm to solve for GESTA. Its convergence and computational complexity are analyzed. Three example networks including a large-scale network are solved to provide insights for decision making and to demonstrate computational efficiency.