Uncertain demand,modal competition and optimal price-capacity adjustments in air transportation

A new microeconomic model for the operation of an airline facing modal competition with uncertain total demand is developed to analyze optimal price capacity combinations. The novelty is the treatment of the capacity restriction, which is not viewed as affecting negatively individual preferences (e.g. probability of a full flight), but does influence aggregate utility. A mode choice model is used to represent unrestricted individual preferences assuming full availability (phone call demand); air capacity is treated as a variable that acts on the actual choice set. Restricted choices and total demand stochasticity are integrated in welfare calculations (users' benefits and profits). Numerical examples are given and results are analyzed in terms of load factors fare levels, and sensitivity to the stochasticity of requests.This research was partially funded by FONDECYT, Chile, Direction Génerale de l'Aviation Civile, France, the Andes Foundation and the Fulbright Commission.     

Predicting queue variability to enable analysis of overload risk

ABSTRACT

Predicting the risk of traffic demands and delays exceeding critical limits at road junctions, airports, hospitals, etc., requires knowing how both mean and variance of queue size vary over time. Microscopic simulation can explore variability but is computationally demanding and gives only sample results. A computationally efficient approximation to the mean is used in many modelling tools, but only empirical extensions for variance in particular situations have been available. The paper derives theoretical formulae for time-dependent and equilibrium variance, believed to be novel and to apply generally to queues covered by the Pollaczek–Khinchin mean formula, and offering possible structural insights. These are applied in an extended approximation giving mutually consistent mean and variance estimates with improved accuracy. Tests on oversaturated peak demand cases are compared with Markov probabilistic simulation, demonstrating accuracy (R^2?>?0.99) for typical random, priority-like (M/M/1) and traffic-signal-like (M/D/1) queues. Implications for risk analysis, planning and policy are considered.     

船模阻力数值水池试验不确定度评估

文章针对水面船模阻力数值水池试验,开展了不确定度分析与评估研究。不确定度分析中,验证方法和流程基于正交设计和方差分析方法,确认方法和流程基于统计推断理论。以水面船标模DTMB5415为对象,进行了船模阻力数值水池试验不确定度分析评估的实例计算,给出了对数值试验结果有重要影响的试验因素和交互作用以及各类不确定度分量的大小,并提出了降低船模阻力数值试验不确定度的建议。     

A new schedule-based transit assignment model with travel strategies and supply uncertainties

This paper proposes a new scheduled-based transit assignment model. Unlike other schedule-based models in the literature, we consider supply uncertainties and assume that users adopt strategies to travel from their origins to their destinations. We present an analytical formulation to ensure that on-board passengers continuing to the next stop have priority and waiting passengers are loaded on a first-come-first-serve basis. We propose an analytical model that captures the stochastic nature of the transit schedules and in-vehicle travel times due to road conditions, incidents, or adverse weather. We adopt a mean variance approach that can consider the covariance of travel time between links in a space–time graph but still lead to a robust transit network loading procedure when optimal strategies are adopted. The proposed model is formulated as a user equilibrium problem and solved by an MSA-type algorithm. Numerical results are reported to show the effects of supply uncertainties on the travel strategies and departure times of passengers.     

The Impact of Travel Time Information on Travelers’ Learning under Uncertainty

In this work, laboratory experiment was conducted in order to evaluate the effect of feedback on decision-making under uncertainty, with and without provided information about travel times. We discuss the prediction of travelers’ response to uncertainty in two route–choice situations. In the first situation travelers are faced with a route–choice problem in which travel times are uncertain but some external information about routes’ travel times is provided. The second situation takes place in a more uncertain environment in which external information about travel times is not provided, and the travelers’ only source of information is their own experience. Experimental results are in conflict with the paradigm about traveler information systems: As a consequence of information, the propensity of travelers to minimize expected travel time is not necessarily increased. Providing travelers with static information about expected travel times reveals an increase in the heterogeneity of travelers’ choices and reduces the maximization rate.     

城市轨道交通站点客流不确定性研究

为提高城市轨道交通站点客流预测的可靠性,在分析客流不确定性影响因素的基础上,基于ARIMA-GARCH模型,依据南京地铁珠江路站点客流数据对客流不确定性进行建模和预测,并从预测置信区间和无效覆盖率两方面与传统的时间序列进行对比分析,研究结果表明,ARIMA-GARCH能够较好地拟合客流波动情况,为城市轨道交通运营与管理提供理论依据。     

基于深度强化学习的车辆跟驰控制

针对自适应巡航控制系统在控制主车跟驰行驶中受前车运动状态的不确定性影响问题，在分析车辆运动特点的基础上，提出一种能够考虑前车运动随机性的跟驰控制策略。搭建驾驶人实车驾驶数据采集平台，招募驾驶人进行实车跟驰道路试验，建立驾驶人真实驾驶数据库。假设车辆未来时刻的加速度决策主要受前方目标车辆运动影响，建立基于双前车跟驰结构的主车纵向控制架构。将驾驶数据库中的驾驶数据分别视作前车和前前车运动变化历程，利用高斯过程算法建立了前车纵向加速度变化随机过程模型，实现对前方目标车运动状态分布的概率性建模。将车辆跟驰问题构建为一定奖励函数下的马尔可夫决策过程，引入深度强化学习研究主车跟驰控制问题。利用近端策略优化算法建立车辆跟驰控制策略，通过与前车运动随机过程模型进行交互式迭代学习，得到具有运动不确定性跟驰环境下的主车纵向控制策略，实现对车辆纵向控制的最优决策。最后基于真实驾驶数据，对控制策略进行测试。研究结果表明：该策略建立了车辆纵向控制与主车和双前车状态之间的映射关系，在迭代学习过程中对前车运动的随机性进行考虑，跟驰控制中不需要对前车运动进行额外的概率预测，能够以较低的计算量实现主车稳定跟随前车行驶。     

扭矩标准装置优化设计研究

文章介绍了通过优化设计研制成功的高准确度2kN·m扭矩标准装置的结构、特点和关键技术.该装置采用传统的静重平衡式结构,对力臂杠杆、刀口支承和砝码等进行了优化,并采用了自动加载、对中调整、对偶平衡以及PLC控制程序等技术,使该装置的校准可信度和准确度都有所提高.     

基于序贯代理模型的转子系统频响特性区间分析方法

在转子系统的频率响应分析中,不确定性广泛存在,并且对分析结果有较大的影响。本文提出了一种面向含区间不确定性转子系统频响分析的序贯代理模型方法。基于区间数学以及转子动力学理论,本文首先建立了含区间参数转子系统频响的控制方程。基于传统的静态代理模型提出了面向转子系统区间分析的序贯代理模型方法。该方法首先通过叫少量的样本点构造粗糙代理模型,再提出了两个指标函数进行下一阶段的选点。重复该过程,直到满足收敛条件,最终可以得到转子系统频率响应的精细代理模型,进而得到区间边界。和传统的区间分析方法相比,该方法精度高,适合非线性问题。     

不完全信息下的CGF主体决策行为及模型研究

着重分析了决策的风险特性,建立了CGF主体的决策行为模型。分析加入感知行为后对决策行为复杂性的影响;提出了一种不确定型决策方法用于处理不完全感知信息对决策行为的影响;最后用示例说明了该方法的有效性。