超限严格管制下公路货运运力结构预测研究

针对传统的超限运输运力结构预测模型中,将车辆超限部分轴载转化为同一车型满轴载通过假设的不足,本文基于运力均衡的思想建立了超限严格管制下公路货运运力结构预测模型。在轴载调查的基础上,模型以单车装载量的累积概率分布需同单位货物重量的累积概率分布相一致为求解目标,推测了各轴型车辆总重谱的偏移情况,进而对超限管制变动下的公路货运车型结构及运量结构进行预测。模型在公路安徽世行项目II———超载运输研究中的使用结果表明,在超限运输严格管制条件下,2-S2将取代SU2成为公路货运的主力车型。     

极限平衡法与有限元法结合分析平阿高速公路高边坡稳定性

结合平安至阿岱高速公路高边坡工程实例,先用极限平衡法计算得出边坡的稳定安全系数,再用有限元法分析边坡的应力、应变和位移,这两种方法相结合既能反映边坡的稳定和变形之间的关系,又能用安全系数来评价边坡的稳定性。采用预应力锚索等加固,高边坡处于稳定状态。     

初始条件对驾驶员逐日路径选择行为和网络交通流演化的影响

从查阅到的文献可以看到,在驾驶员逐日路径选择行为及网络交通流演化的研究中,均假定驾驶员第1天对路径的理解行程时间相同,也即初始条件中没有考虑驾驶员的个体差异性。首先,对初始条件和驾驶员逐日路径选择过程建模,在2条平行路径的简单路网中,运用Agent仿真方法模拟了不同初始条件下驾驶员逐日路径选择过程。结果表明：路网达到平衡所需的时间与驾驶员对历史信息的依赖程度显著相关,而与第1天驾驶员对路径行程时间理解的相关差异性不显著;路网平衡和用户平衡的差别与两者均显著相关。虽然在不同情况下路网均能够达到近似的用户平衡状态,但是平衡时驾驶员对2条路径的理解行程时间存在较大差异。     

弹性需求下高速公路超限补偿费率优化模型

为了准确刻画超限需求弹性对补偿费率的影响,从用户和系统的角度分别对超限运输的收益和成本进行分析和建模,构造超限运输弹性需求函数,反映超限运输需求量与其运输效益之间的变化关系,将超限运输业者运输行为的选择归结为弹性需求下的用户均衡配流问题,进而利用高速公路管理者与超限运输业者之间的Stackelberg博弈关系,建立了基于弹性需求的高速公路超限补偿费率的双层规划优化模型,并设计了基于模拟退火算法求解的优化算法。结果表明：模型能较好地优化超限补偿费率,使之对超限运输业者进行合理补偿收费和适度惩罚,有效遏制超限运输,从而产生更大的社会与经济效益。     

贝叶斯与遗传神经网络相融合的柴油机故障诊断研究

利用小波包分解提取缸盖振动信号的特征向量,提出选择因子对改进误差反向传播(BP)神经网络和超1-依赖贝叶斯算法的诊断结果进行融合,克服贝叶斯和神经网络的不足。通过在WD615柴油机上的实验检测,证明了该方法的有效性。     

Dynamic activity-travel assignment in multi-state supernetworks

The integration of activity-based modeling and dynamic traffic assignment for travel demand analysis has recently attracted ever-increasing attention. However, related studies have limitations either on the integration structure or the number of choice facets being captured. This paper proposes a formulation of dynamic activity-travel assignment (DATA) in the framework of multi-state supernetworks, in which any path through a personalized supernetwork represents a particular activity-travel pattern (ATP) at a high level of spatial and temporal detail. DATA is formulated as a discrete-time dynamic user equilibrium (DUE) problem, which is reformulated as an equivalent variational inequality (VI) problem. A generalized dynamic link disutility function is established with the accommodation of different characteristics of the links in the supernetworks. Flow constraints and non-uniqueness of equilibria are also investigated. In the proposed formulation, the choices of departure time, route, mode, activity sequence, activity and parking location are all unified into one time-dependent ATP choice. As a result, the interdependences among all these choice facets can be readily captured. A solution algorithm based on the route-swapping mechanism is adopted to find the user equilibrium. A numerical example with simulated scenarios is provided to demonstrate the advantages of the proposed approach.     

Stability of transportation networks under adaptive routing policies

Growing concerns regarding urban congestion, and the recent explosion of mobile devices able to provide real-time information to traffic users have motivated increasing reliance on real-time route guidance for the online management of traffic networks. However, while the theory of traffic equilibria is very well-known, fewer results exist on the stability of such equilibria, especially in the context of adaptive routing policy. In this work, we consider the problem of characterizing the stability properties of traffic equilibria in the context of online adaptive route choice induced by GPS-based decision making. We first extend the recent framework of “Markovian Traffic Equilibria” (MTE), in which users update their route choice at each intersection of the road network based on traffic conditions, to the case of non-equilibrium conditions, while preserving consistency with known existence and uniqueness results on MTE. We then exhibit sufficient conditions on the network topology and the latency functions for those MTEs to be stable in the sense of Lyapunov for a single destination problem. For various more restricted classes of network topologies motivated by the observed properties of travel patterns in the Singapore network, under certain assumptions we prove local exponential stability of the MTE, and derive analytical results on the sensitivity of the characteristic time of convergence to network and traffic parameters. The results proposed in this work are illustrated and validated on synthetic toy problems as well as on the Singapore road network with real demand and traffic data.     

Equilibrium analysis and route guidance in large-scale networks with MFD dynamics

Recent studies have demonstrated that Macroscopic Fundamental Diagram (MFD), which provides an aggregated model of urban traffic dynamics linking network production and density, offers a new generation of real-time traffic management strategies to improve the network performance. However, the effect of route choice behavior on MFD modeling in case of heterogeneous urban networks is still unexplored. The paper advances in this direction by firstly extending two MFD-based traffic models with different granularity of vehicle accumulation state and route choice behavior aggregation. This configuration enables us to address limited traffic state observability and to scrutinize implications of drivers’ route choice in MFD modeling. We consider a city that is partitioned in a small number of large-size regions (aggregated model) where each region consists of medium-size sub-regions (more detailed model) exhibiting a well-defined MFD. This paper proposes a route guidance advisory control system based on the aggregated model as a large-scale traffic management strategy that utilizes aggregated traffic states while sub-regional information is partially known. In addition, we investigate the effect of equilibrium conditions (i.e. user equilibrium and system optimum) on the overall network performance, in particular MFD functions.     

不确定条件下机场群系统均衡配流模型研究

为探索客流在机场群的分布规律，以整个航空出行链为视角，综合考虑空中交通、地面交通和旅客出行选择偏好，构建机场群系统均衡配流模型.以路径时间、机场停留时间、出行票价和旅客类型为主要参考属性，提出机场群系统旅客出行成本模型.由于路径时间与流量相关且存在不确定性，基于预算超出时间对路径时间的均值与方差进行统一度量.综合路径时间、出行票价、机场停留时间与旅客类型，基于Logit模型建立旅客对航空出行路径的选择模型，将机场群系统的客流均衡条件转换为变分不等式.基于投影收缩算法对变分不等式求解，从而得出客流在机场群间的均衡分布状态.算例结果表明，机场群系统均衡配流模型有效建立了客流分布状态，调整路径时间、机场服务水平、票价等因素均可促进客流在机场群间的均衡分布.     

Learning marginal-cost pricing via a trial-and-error procedure with day-to-day flow dynamics

This paper investigates the convergence of the trial-and-error procedure to achieve the system optimum by incorporating the day-to-day evolution of traffic flows. The path flows are assumed to follow an ‘excess travel cost dynamics’ and evolve from disequilibrium states to the equilibrium day by day. This implies that the observed link flow pattern during the trial-and-error procedure is in disequilibrium. By making certain assumptions on the flow evolution dynamics, we prove that the trial-and-error procedure is capable of learning the system optimum link tolls without requiring explicit knowledge of the demand functions and flow evolution mechanism. A methodology is developed for updating the toll charges and choosing the inter-trial periods to ensure convergence of the iterative approach towards the system optimum. Numerical examples are given in support of the theoretical findings.