高压齿轮泵浮动侧板的动态平衡机理

针对高压齿轮泵浮动侧板磨损问题,在解析齿轮泵内部流场的基础上重点研究了浮动侧板反推力作用点变化规律和导致侧板产生倾覆力矩的关键因素。以某型号高压大排量齿轮泵为模型,运用CFD软件解析齿轮泵内部流场并根据侧板结构特点建立压紧力和反推力的求解微分方程,求得一个轮齿啮合周期内的侧板倾覆力矩变化规律,同时通过建立齿轮泵工作腔压力测试系统对该理论分析结果进行验证,额定工况下：试验值与理论值误差为4.18%,当轮齿转角φ=14°时倾覆力矩达到最大值Ms=82.16N?m。该研究为高压大排量齿轮泵浮动侧板倾覆力矩计算和侧板结构优化设计提供了理论基础和技术支撑。     

Bush-based sensitivity analysis for approximating subnetwork diversion

Subnetwork analysis is often used in traffic assignment problems to reduce the size of the network being analyzed, with a corresponding decrease in computation time. This is particularly important in network design, second-best pricing, or other bilevel problems in which many equilibrium runs must be solved as a subproblem to a master optimization program. A fixed trip table based on an equilibrium path flow solution is often used, but this ignores important attraction and diversion effects as drivers (globally) change routes in response to (local) subnetwork changes. This paper presents an approach for replacing a regional network with a smaller one, containing all of the subnetwork, and zones. Artificial arcs are created to represent “all paths” between each origin and subnetwork boundary node, under the assumption that the set of equilibrium routes does not change. The primary contribution of the paper is a procedure for estimating a cost function on these artificial arcs, using derivatives of the equilibrium travel times between the end nodes to create a Taylor series. A bush-based representation allows rapid calculation of these derivatives. Two methods for calculating these derivatives are presented, one based on network transformations and resembling techniques used in the analysis of resistive circuits, and another based on iterated solution of a nested set of linear equations. These methods are applied to two networks, one small and artificial, and the other a regional network representing the Austin, Texas metropolitan area. These demonstrations show substantial improvement in accuracy as compared to using a fixed table, and demonstrate the efficiency of the proposed approach.     

Application of genetic algorithms for joint optimization of signal setting parameters and dynamic traffic assignment for the real network data

This paper presents the joint optimization of signal setting parameters and dynamic user equilibrium (DUE) traffic assignment for the congested urban road network. The simulation-based approach is employed to obtain the DUE condition for the case of multiple-origin multiple-destination traffic flows. The dynamic traffic assignment simulation program (DTASP), developed in C language is used to assign the traffic dynamically on the road network, whereas method of successive averages (MSA) is modified and used to arrive at the DUE condition. The artificial intelligence technique of genetic algorithms (GAs) is applied to obtain the optimal signal setting parameters and path flow distribution factor for DUE condition. The methodology developed in such a way that joint optimization of signal setting parameters with DUE is obtained. The proposed method is applied to the real network data of Fort Area of Mumbai city comprising of 17 nodes and 56 unidirectional links with 72 Origin–Destination pairs, where all the 17 nodes are signalized intersections. The traffic flow condition for the optimized signal setting parameters is considerably improved compared to the existing signal settings. The results prove that the GA is an effective technique to solve the joint optimization problem for the real network data.     

Is equilibrium in transport pure Nash,mixed or Stochastic?

The classical theory of transport equilibrium is based on the Wardrop’s first principle that describes a Nash User Equilibrium (UE), where in no driver can unilaterally change routes to improve his/her travel times. A growing number of economic laboratory experiments aiming at testing Nash-Wardrop equilibrium have shown that the Pure Strategy Nash Equilibrium (PSNE) is not able to explain the observed strategic choices well. In addition even though Mixed Strategy Nash Equilibrium (MSNE) has been found to fit better the observed aggregate choices, it does not explain the variance in choices well. This study analyses choices made by users in three different experiments involving strategic interactions in endogenous congestion to evaluate equilibrium prediction. We compare the predictions of the PSNE, MSNE and Stochastic User Equilibrium (SUE). In SUE, the observed variations in choices are assumed to be due to perception errors. The study proposes a method to iteratively estimate SUE models on choice data with strategic interactions. Among the three sets of experimental data the SUE approach was found to accurately predict the average choices, as well as the variances in choices. The fact that the SUE model was found to accurately predict variances in choices, suggests its applicability for transport equilibrium models that attempt to evaluate reliability in transportation systems. This finding is fundamental in the effort to determining a behaviourally consistent paradigm to model equilibrium in transport networks. The study also finds that Fechner error which is the inverse of the scale parameter in the SUE model is affected by the group sizes and the complexity of the cost function. In fact, the larger group sizes and complexity of cost functions increased the variability in choices. Finally, from an experimental design standpoint we show that it is not possible to estimate a noise parameter associate to Fechner error in the case when the choices are equally probable.     

Sensitivity analysis on stochastic equilibrium transportation networks using genetic algorithm

This study deals with the sensitivity analysis of an equilibrium transportation networks using genetic algorithm approach and uses the bi‐level iterative sensitivity algorithm. Therefore, integrated Genetic Algorithm‐TRANSYT and Path Flow Estimator (GATPFE) is developed for signalized road networks for various level of perceived travel time in order to test the sensitivity of perceived travel time error in an urban stochastic road networks. Level of information provided to drivers correspondingly affects the signal timing parameters and hence the Stochastic User Equilibrium (SUE) link flows. When the information on road system is increased, the road users try to avoid conflicting links. Therefore, the stochastic equilibrium assignment concept tends to be user equilibrium. The GATPFE is used to solve the bi‐level problem, where the Area Traffic Control (ATC) is the upper‐level and the SUE assignment is the lower‐level. The GATPFE is tested for six‐junction network taken from literature. The results show that the integrated GATPFE can be applied to carry out sensitivity analysis at the equilibrium network design problems for various level of information and it simultaneously optimize the signal timings (i.e. network common cycle time, signal stage and offsets between junctions).     

Integrated planning of static and dynamic charging infrastructure for electric vehicles

This paper investigates the optimal deployment of static and dynamic charging infrastructure considering the interdependency between transportation and power networks. Static infrastructure means plug-in charging stations, while the dynamic counterpart refers to electrified roads or charging lanes enabled by charging-while-driving technology. A network equilibrium model is first developed to capture the interactions among battery electric vehicles’ (BEVs) route choices, charging plans, and the prices of electricity. A mixed-integer bi-level program is then formulated to determine the deployment plan of charging infrastructure to minimize the total social cost of the coupled networks. Numerical examples are provided to demonstrate travel and charging plans of BEV drivers and the competitiveness of static and dynamic charging infrastructure. The numerical results on three networks suggest that (1) for individual BEV drivers, the choice between using charging lanes and charging stations is more sensitive to parameters including value of travel time, service fee markup, and battery size, but less sensitive to the charging rates and travel demand; (2) deploying more charging lanes is favorable for transportation networks with sparser topology while more charging stations can be more preferable for those denser networks.     

日常路径选择行为的模拟研究

假设司机只能根据自己的历史经验做出判断，本文据此设计了日常路径选择行为中的四种路径更新规则。应用基于多智能体的模拟环境-SeSAm，在简单网络上对四种规则进行模拟，比较和分析了模拟结果。     

Extending stated choice analysis to recognise agent-specific attribute endogeneity in bilateral group negotiation and choice: a think piece

This paper is a think piece on variations in the structure of stated preference studies when modelling the joint preferences of interacting agents who have the power to influence the attribute levels on offer. The approach proposed is an extension of standard stated choice methods, known as ‘stated endogenous attribute level’ (SEAL) analysis. It allows for interactive agents to adjust attribute levels off a base stated choice specification that are within their control, in an effort to reach agreement in an experimental setting. This accomplishes three goals: (1) the ability to place respondents in an environment that more closely matches interactive settings in which some attribute levels are endogenous to a specific agent, should the modeller wish to capture such behaviour; (2) the improved ability of the modeller to capture the behaviour in such settings, including a greater wealth of information on the related interaction processes, rather than simply outcomes; and (3) the expansion of the set of situations that the modeller can investigate using experimental data.

The use of the bicycle

The rediscovery of the bicycle by the public, by politicians and by professional urban transportation planners as a mode of transport which is perfectly in harmony with the goals of environmental protection, energy saving and personal fitness has stimulated this empirical study on the actual use of the bicycle by various population groups for obligatory and discretionary trip purposes. The influence on bicycle usage of such factors as age, education, car availability, residential density and town size, topography and time of year is analysed in this paper for selected population groups. For housewives from motorized households logit‐models were designed and calibrated to model their modal choice for shopping trips with special references to the bicycle. From the empirical results, the groups with the largest potentials for cycling are identified and the extent to which the potentials could be activated by specific policies is discussed. The research is based on a large sample held to be representative for the Federal Republic of Germany in 1976 and is supplemented by more recent surveys in selected German cities conducted by SOCIALDATA Munich.     

给定周期条件下环交叉口优化控制方法研究

环形交叉口左转二次控制理论自提出以来,在国内得到了一定的应用,同时也暴露出诸多问题,如原对称式控制方法易导致饱和度不均衡、部分进口绿灯时间利用不充分等。本文在环形交叉口左转二次控制理论的基础上,针对环型交叉口各流向的不对称性,以环型交叉口各流向饱和度均衡为优化目标,提出了基于非对称性的环型交叉口优化控制方法。并以各进口道饱和度方差为评价指标,对该优化方法进行评价。文章的最后,以该方法在厦门市的实际应用为例,说明该方法的正确性及实用价值。