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.     

Marginal costs of freeway traffic congestion with on-road pollution exposure externality

The health cost of on-road air pollution exposure is a component of traffic marginal costs that has not previously been assessed. The main objective of this paper is to introduce on-road pollution exposure as an externality of traffic, particularly important during traffic congestion when on-road pollution exposure is highest. Marginal private and external cost equations are developed that include on-road pollution exposure in addition to time, fuel, and pollution emissions components. The marginal external cost of on-road exposure includes terms for the marginal vehicle’s emissions, the increased emissions from all vehicles caused by additional congestion from the marginal vehicle, and the additional exposure duration for all travelers caused by additional congestion from the marginal vehicle. A sensitivity analysis shows that on-road pollution exposure can be a large portion (18%) of marginal social costs of traffic flow near freeway capacity, ranging from 4% to 38% with different exposure parameters. In an optimal pricing scenario, excluding the on-road exposure externality can lead to 6% residual welfare loss because of sub-optimal tolls. While regional pollution generates greater costs in uncongested conditions, on-road exposure comes to dominate health costs on congested freeways because of increased duration and intensity of exposure. The estimated marginal cost and benefit curves indicate a theoretical preference for price controls to address the externality problem. The inclusion of on-road exposure costs reduces the magnitudes of projects required to cover implementation costs for intelligent transportation system (ITS) improvements; the net benefits of road-pricing ITS systems are increased more than the net benefits of ITS traffic flow improvements. When considering distinct vehicle classes, inclusion of on-road exposure costs greatly increases heavy-duty vehicle marginal costs because of their higher emissions rates and greater roadway capacity utilization. Lastly, there are large uncertainties associated with the parameters utilized in the estimation of health outcomes that are a function of travel pollution intensity and duration. More research is needed to develop on-road exposure modeling tools that link repeated short-duration exposure and health outcomes.     

海上巡逻救助飞机若干问题探讨

根据海上巡逻搜救任务对巡逻救助飞机在安全系数、操作性能、搜救能力、适应程度等方面的要求,论证了采用专业飞机执行海上巡逻救助任务的必要性;通过巡逻搜救飞机和船舶在应急反应时间、搜寻范围、救助能力以及成本费用对比,提出了巡逻搜救飞机的适用条件和船机配合执行任务的模式;通过对巡逻搜救飞机和机场自建-自用、建设-托管、租赁-使用三种模式的优缺点和费用的分析,提出了巡逻搜救飞机的机场建设的管理模式。     

高架桥下交叉路口交通放行时间和空间资源的综合利用

本文对高架桥下交叉口的时空资源进行了分析综合利用,提出了一种多相位半幅路权系统。该系统通过对高架桥下交叉口路面的道路渠划,把各种交通流通过交叉口路面的道路划分为上下两个半幅路面,并使半幅车道数大于进口处的车道数;由信号机控制信号灯以半幅路面路权为单位放行交通流,巧妙安排相位和相位顺序;在整幅放行前的前半幅路权放行时,可以使车流缓慢、安全地分流在较多的放行车道上排成车队形式;待后半幅路权放行时,以多路车队形式集中通过路口。缩短过路时间,提高通行效率,充分利用时间和空间资源。     

有交通条件约束与软时间窗约束的配送配载模型算法研究

由于配送中客户的时间窗限制和车辆在运行过程中的交通条件约束,给配送中心的运营调度工作增加了新的难题,为此建立了在该约束条件下的车辆配送配载模型。通过在路径选择时考虑交通条件约束、在建立目标函数时以阈值的形式引入时间窗约束,对原始模型进行了拓展．并构造了该问题的遗传算法。     

信息融合技术在交通流量预测中的应用

交通流量预测是智能运输系统的一个重要组成内容，但传统的数学方法一直未能取得令人满意的预测效果。信息融合技术是最近十多年来新兴的技术。它通过合理协调多源数据，充分综合有用信息，在较短的时间内，以较小的代价获得对未来交通流量的预测。实验证明，借助信息融合理论建立的聚类分析模型和神经网络模型对未来交通流量的预测比较准确，有实际意义。     

Probit模型及其在交通方式分担中的应用研究

离散选择模型的典型代表Logit模型简洁易用的特点使其在经济、交通等领域得到了广泛的应用，但同时Logit模型的两个重要缺陷限制了其应用范围。本文分析了Logit模型的固有缺陷，阐述了Probit模型的基本原理，研究了Probit模型的性质，并与Logit模型进行了对比研究，最后利用SAS统计软件MDC模块提供的仿真算法进行了实证性分析，研究结果表明，Probit模型克服了Logit模型IIA特性和喜好随机性限制的缺陷，表现出更为丰富的物理意义，对出行选择行为的模拟更接近实际，是一种具有广阔发展的前景的交通方式分担模型。     

集装箱深水港交通需求预测方法研究

作为一个特殊的物流中心，集装箱深水港的交通具有鲜明的特点。从集装箱深水港的特点出发，提出集装箱深水港交通需求分析预测的流程与具体方法。结合大连市大窑湾港区的实际调查数据，对所提出的方法进行了实例说明。     

离散型闭环增益成形算法及其应用

将闭环增益成形算法推广到离散系统中去。分别用Z变换、差分反演法和双线性变换给出一阶、二阶和三阶闭环增益成形算法的离散形式，然后针对船舶航向保持问题，在Matlab环境中进行了仿真研究。仿真结果表明，航向保持的跟踪速度快、没有超调，当模型参数改变时，控制器具有一定的鲁棒性，说明离散型闭环增益成形算法是有效的。     

铁路枢纽折角车流数值变化的动态模拟

以解编车流作业规则和系统资源限制为约束,构造不同目标函数下铁路枢纽编组站作业分工数学模型,给出折角车流理论最大值和最小值及相应编组站作业分工方式及车流条件。基于折角车流变化的复杂性和多样性,将外部车流环境视为一个动态的随机输入,以特定铁路枢纽为背景用计算机模拟技术和遗传算法计算不同条件下折角车流数值解。通过对模拟数据趋势变化比较分析,提出铁路枢纽运输组织不应过分追求折角车流最小等结论。