Path-constrained traffic assignment: A trip chain analysis under range anxiety

This paper proposes and analyzes a distance-constrained traffic assignment problem with trip chains embedded in equilibrium network flows. The purpose of studying this problem is to develop an appropriate modeling tool for characterizing traffic flow patterns in emerging transportation networks that serve a massive adoption of plug-in electric vehicles. This need arises from the facts that electric vehicles suffer from the “range anxiety” issue caused by the unavailability or insufficiency of public electricity-charging infrastructures and the far-below-expectation battery capacity. It is suggested that if range anxiety makes any impact on travel behaviors, it more likely occurs on the trip chain level rather than the trip level, where a trip chain here is defined as a series of trips between two possible charging opportunities (Tamor et al., 2013). The focus of this paper is thus given to the development of the modeling and solution methods for the proposed traffic assignment problem. In this modeling paradigm, given that trip chains are the basic modeling unit for individual decision making, any traveler’s combined travel route and activity location choices under the distance limit results in a distance-constrained, node-sequenced shortest path problem. A cascading labeling algorithm is developed for this shortest path problem and embedded into a linear approximation framework for equilibrium network solutions. The numerical result derived from an illustrative example clearly shows the mechanism and magnitude of the distance limit and trip chain settings in reshaping network flows from the simple case characterized merely by user equilibrium.     

Evaluating sustainability of supply chains by two-stage range directional measure in the presence of negative data

Assessing sustainability of supply chains is a critical and increasingly complex problem. In recent years sustainability has received more attention in supply chain management (SCM) literature with triple bottom lines including social, environmental, and economic factors. Conventional data envelopment analysis (DEA) models consider decision making units (DMUs) as black boxes that consume a set of inputs to produce a set of outputs and do not take into consideration internal interactions of DMUs. Two-stage DEA models deal with such DMUs. However, existing two-stage DEA models are applicable only in technologies characterized by positive inputs/outputs. This paper aims to build and present a new two-stage DEA model considering negative input-intermediate-output data. Some numerical examples along with some theorems and properties are given to show capability of proposed method. The proposed ideas are used in a case study where 29 Iranian supply chains producing equipment of expendable medical devices are evaluated in terms of sustainability.     

Airline energy efficiency measures using the Virtual Frontier Network RAM with weak disposability

In this paper, airline energy efficiency is divided into three stages: the operations stage, the services stage and the sales stage. Greenhouse gas emissions are treated as an undesirable output of the services stage. This new three-stage strategic operating framework is a modification of existing models. A new model, Virtual Frontier Network Range Adjusted Measure with weak disposability, is proposed to evaluate the efficiencies of 22 international airlines, from 2008 to 2012. The results show that the new model can establish more reasonable rankings and confirm new benchmarking airlines and that inclusion in the European Union’s Emissions Trading Scheme has had little influence on the improvement of airline energy efficiency.     

Investigating micro-simulation error in activity-based travel demand forecasting: a case study of the FEATHERS framework

Activity-based models of travel demand have received considerable attention in transportation planning and forecasting in recent years. However, in most cases they use a micro-simulation approach, thereby inevitably including a stochastic error that is caused by the statistical distributions of random components. As a consequence, running a transport micro-simulation model several times with the same input will generate different outputs, which baffles practitioners in applying such a model and in interpreting the results. A common approach is therefore to run the model multiple times and to use the average value of the results. The question then becomes: what is the minimum number of model runs required to reach a stable result? In this paper, systematic experiments are carried out using Forecasting Evolutionary Activity-Travel of Households and their Environmental RepercussionS (FEATHERS), an activity-based micro-simulation modelling framework currently implemented for the Flanders region of Belgium. Six levels of geographic detail are taken into account. Three travel indices – average daily activities per person, average daily trips per person and average daily distance travelled per person, as well as their corresponding segmentations – are calculated by running the model 100 times. The results show that the more disaggregated the level, the larger the number of model runs is needed to ensure confidence. Furthermore, based on the time-dependent origin-destination table derived from the model output, traffic assignment is performed by loading it onto the Flemish road network, and the total vehicle kilometres travelled in the whole Flanders are subsequently computed. The stable results at the Flanders level provides model users with confidence that application of FEATHERS at an aggregated level requires only limited model runs.     

软件可靠性估计模型的研究

通过对JM模型的研究,给出了一种新的软件可靠性模型,并给出了模型参数的点估计和置信区间.     

汽车最优维护周期的确定与优化研究

笔者以计划预防为方针 ,根据预定的置信度 ,通过建立和求解数学模型 ,确定汽车维护周期及置信区间 ,并将优化概念应用于维护周期的确定及分析研究 .通过技术经济法、回归 -概率法和经济 -概率法 ,根据不同目标的要求进行修正 ,以适应不同情况的需要 ,从而达到汽车维护周期最优化的目的     

太阳能游览船能量平衡及续航里程研究

以某太阳能游览船为对象,分析船机桨能量平衡关系,建立船舶运动能量计算模型,计算匀速航速下的能量消耗,根据电池输出能量与船舶航行消耗能量相等原则,预测游览船续驶里程。通过实际运行数据表明,该模型能够较为准确的计算消耗功率,有效地预测游船的续驶里程。     

不同气象数据精度对驼峰峰高设计的影响

气象数据是驼峰峰高设计的重要资料,驼峰设计规范使用月均数据作为驼峰设计计算温度、风速的依据.本文采用日均数据对全路49 个主要编组站驼峰设计计算温度、风速分析发现,日均数据下南、北方地区驼峰设计计算温度分别较月均数据低 1.34 ℃、0.94 ℃,计算风速分别较月均数据高1.29 m/s、1.15 m/s,增幅达30%以上.本文从理论上分析了差异产生的原因,并以三间房编组站为例进行了实证.进一步从置信概率的角度分析了全路主要编组站不同数据精度下驼峰设计计算温度、风速,基于日均数据的驼峰设计温度、风速与预期差值最大仅1.83%,基于月均数据的风速与预期差值最小达 18.74%.在此基础上,研究了不同数据精度对峰高设计的影响,揭示出采用月均数据存在峰高设计偏低的问题,并提出至少采用日均数据的驼峰设计气象资料选用及设计规范修改建议.     

基于大规模浮动车数据的城市道路网复杂度分析

针对现有浮动车技术应用研究中缺乏道路网络复杂度的定量分析,引入置信点的概念,提出了一种基于大规模浮动车数据的城市路网复杂度分析方法.利用该方法统计了路网中各路段的置信点分布情况和置信点的平均匹配距离,建立了城市路网复杂度模型.结合浮动车数据的预处理,提出了城市路网复杂度的分析流程.以广州市为例,分析了广州市全局路网和4类典型城市道路代表路段的复杂度.对于地图匹配算法实证研究中的指定路径,具体分析了其各组成路段的复杂度及其主要影响因素.     

基于大规模浮动车数据的交叉口转向规则自动提取算法

针对传统电子地图在生成时只包含路网拓扑关系,不具备路口转向规则的自动生成及更新能力,提出了利用大规模浮动车数据自动生成交叉口转向规则的算法.该方法建立转向规则数学表达和存储模型,并依托大规模浮动车数据分析和处理,引入置信点概念,提出基于首尾置信点控制的转向规则自动提取算法;以广州市为例,选取1d的浮动车GPS数据,对提出的算法进行效用评价,实验结果表明提出的算法准确率达90.4％,可准确实现多数交叉口转向规则的自动提取.