A shockwave profile model for traffic flow on congested urban arterials

In this paper a new traffic flow model for congested arterial networks, named shockwave profile model (SPM), is presented. Taking advantage of the fact that traffic states within a congested link can be simplified as free-flow, saturated, and jammed conditions, SPM simulates traffic dynamics by analytically deriving the trajectories of four major shockwaves: queuing, discharge, departure, and compression waves. Unlike conventional macroscopic models, in which space is often discretized into small cells for numerical solutions, SPM treats each homogeneous road segment with constant capacity as a section; and the queuing dynamics within each section are described by tracing the shockwave fronts. SPM is particularly suitable for simulating traffic flow on congested signalized arterials especially with queue spillover problems, where the steady-state periodic pattern of queue build-up and dissipation process may break down. Depending on when and where spillover occurs along a signalized arterial, a large number of queuing patterns may be possible. Therefore it becomes difficult to apply the conventional approach directly to track shockwave fronts. To overcome this difficulty, a novel approach is proposed as part of the SPM, in which queue spillover is treated as either extending a red phase or creating new smaller cycles, so that the analytical solutions for tracing the shockwave fronts can be easily applied. Since only the essential features of arterial traffic flow, i.e., queue build-up and dissipation, are considered, SPM significantly reduces the computational load and improves the numerical efficiency. We further validated SPM using real-world traffic signal data collected from a major arterial in the Twin Cities. The results clearly demonstrate the effectiveness and accuracy of the model. We expect that in the future this model can be applied in a number of real-time applications such as arterial performance prediction and signal optimization.     

The multinomial logit model revisited: A semi-parametric approach in discrete choice analysis

The multinomial logit model in discrete choice analysis is widely used in transport research. It has long been known that the Gumbel distribution forms the basis of the multinomial logit model. Although the Gumbel distribution is a good approximation in some applications such as route choice problems, it is chosen mainly for mathematical convenience. This can be restrictive in many other scenarios in practice. In this paper we show that the assumption of the Gumbel distribution can be substantially relaxed to include a large class of distributions that is stable with respect to the minimum operation. The distributions in the class allow heteroscedastic variances. We then seek a transformation that stabilizes the heteroscedastic variances. We show that this leads to a semi-parametric choice model which links the linear combination of travel-related attributes to the choice probabilities via an unknown sensitivity function. This sensitivity function reflects the degree of travelers’ sensitivity to the changes in the combined travel cost. The estimation of the semi-parametric choice model is also investigated and empirical studies are used to illustrate the developed method.     

Capacity drops at merges: An endogenous model

The Newell-Daganzo merge model is not only very simple but also accurately reproduces experimental findings. However, the capacity downstream of the merge is an exogenous variable in the model. This is a serious limitation for merges that behave as active bottlenecks because their downstream capacity is a direct consequence of the merging behavior. This paper proposes an analytical model that extends the Newell-Daganzo model by incorporating, endogenously, the capacity drop related to the merging process. Two cases are investigated depending on the traffic states on the on-ramp. The model properties are analyzed and a sensitivity analysis is performed to quantify the relative contribution of the each parameter in the capacity drop. Finally, the extended Newell-Daganzo model is validated with experimental data coming from an active merge bottleneck on the M6 freeway in UK.     

运用灰色理论预测隧道变形的探讨

隧道变形监测对于隧道的安全有着重要的作用.运用灰色理论GM模型对其变形数据进行预测分析,发现灰色理论对隧道的变形有一定的预测效果,同时了解到不同样本数据其预测精度存在差异.为此,提出使用二次拟合参数法对其进行改进,得出二次拟合参数法对低精度的预测模型有一定的改进效果,而对于较高精度的预测模型效果并不显著.     

磁性液体密封结构中漏磁的研究

为了研究磁性液体密封结构几何参数的变化对漏磁场的影响规律,选取不同的密封间隙、极齿宽度、齿槽宽度、齿槽深度及密封级数建立物理模型,用有限元方法计算各模型的漏磁场.结果表明在密封结构的轴向和径向距离上存在某一临界值,在临界值的两侧,漏磁场磁感应强度随密封结构几种几何参数的变化趋势不同：在轴向距离大于临界值时,轴向漏磁场磁感应强度随间隙的增大而增大,随级数的增加而减小,随齿宽的增大而减小,随槽宽和槽深的增大而增大;在径向距离小于临界值时,径向漏磁场磁感应强度随间隙的增大而减小,随级数的增加而增大,随齿宽的增大而增大,随槽宽的增大而增大,随槽深的增大而减小;径向距离大于临界值时的情况与小于临界值时的情况刚好相反.     

视角受限传感器网络强栅栏覆盖判定算法

栅栏覆盖研究如何分配网络时空资源来确保移动目标穿越监控区域时被监测．本文通过视角受限节点传感方向的调整,设计一种分布式算法以判定网络能否提供强栅栏覆盖．算法主要思想是利用节点及邻居节点的物理位置关系构建强／弱栅栏对．另外,本文设计贪心策略优先选择邻居节点数多的候选节点构建强栅栏路径．仿真实验证明：本算法可以较小代价判定并改善网络强栅栏覆盖性能．     

混合动力汽车动力系统控制策略试验分析

介绍了一种混合动力客车动力系统的结构组成,阐述了该混合动力系统的各种工作模式、整车控制系统组成及控制策略,并通过其相关试验测试了数据信息。最后以该车能量消耗量试验过程数据为基础,分析了整车能耗状况、发动机工作区域及机械自动变速器与电机协调换挡控制策略。     

模拟退火算法在供应链生产调度中的应用

研究一个简单供应链中的生产调度问题,目的是找到一个合理生产排序使得单位时间的成本最小,建立该问题的单目标非线性规划模型。运用模拟退火算法,结合邻域搜索进行求解,最后举例验证算法的有效性。     

灰色模型在中长期用水量预测中的应用

为实现供水管网经济、可靠、科学的规划改扩建,给出一种基于记录时间较短、历史数据较少的用水量序列的GM(1,1)预测方法。该预测方法把原始用水量序列累加处理生成新序列后,用指数关系式拟合,通过构造参数矩阵,确定辩识参数,建立灰色模型的微分方程。通过对灰色预测方法建模机理的研究建立城市用水量预测GM(1,1)模型,并以东北某大型城市用水量为原始数据进行实际预测,模型精度检验的结果表明该模型的预测等级为高精度预测。该预测方法应用于S市的中长期用水量预测,为S市供水规划改扩建提供有效依据。     

城市公交站点自行车对公交车的干扰行为研究

以北京市两个不同类型的典型公交站点为例,研究了公交站点处自行车对公交车的干扰行为,并建立了不同因素影响下自行车对公交车的干扰行为模型,进而对其进行验证.最后,提出了几点减少公交站点处自行车对公交车干扰的建议.