认知结构方法在交通信息融合中的应用

认知结构不确定推理框架采用4值(最值、置信度、信任值、似真值)表达不确定认知情况,并采用认知结构运算来进行不确定信息处理。该方法可以用来处理交通不确定信息的表达、融合和决策,进一步还可以对交通仿真中面临的多种不确定信息进行有效处理和应用。文中探讨了采用认知结构进行交通信息融合的方法,并对认知结构方法进行了深入研究,引入了2个公设,将认知结构的运算由定义转化为定理。     

考虑行程时间不确定性的服务设施时空可达性度量

在我国拥挤的多模式交通网络中,出行者的行程时间具有较大的不确定性.行程时间不确定性是影响人们出行行为选择的重要因素,对于某些重要活动(如就医等),人们对行程时间的可靠度有较高要求.本文以南京市玄武区综合医院的时空可达性为例,采用引力模型和行程时间预算模型深入探索多模式交通网络中服务设施的时空可达性,提出了考虑行程时间不确定性的时空可达性度量方法,针对不同交通模式分析了出行者的风险规避行为,为城市多模式交通规划和土地利用布局提供了有效依据及评价指标.研究表明,对于不同的交通模式,在不同可靠性下服务设施的时空可达性具有显著差异.     

光学平行装置精度测量及数据处理

通过对光学平行装置精度测量及数据的分析处理,确定了均方根、合成不确定度和扩展不确定度,为光学平行装置测量精度的校准提供了依据。     

基于蒙特卡洛法的实船功率性能试验不确定度分析

针对实船功率性能测量与分析涉及因素多,难以通过传统GUM法对修正得到的理想状态功率与航速结果进行不确定度评价的问题,提出基于蒙特卡洛法的实船功率性能不确定度分析方法,分析实船功率性能试验中的主要不确定度源,依据ISO15016数据处理方法建立测量模型,以大连海事大学教学实习船"育鲲"轮为对象分析实船功率性能试验与不确定...     

铁路桥梁浅基础设计变量的不确定性分析

设计变量不确定性的计算分析是可靠性理论修订规范的基础工作。本文对铁路桥梁浅基础可靠度设计中各种变量的不确定性 进行了分析计算，推导了其作用荷载和地基土抗力的变异系数的计算表达式，可供可靠度理论规范改革时参考和作用。     

Capacity investment model for airport facilities under demand uncertainty

This paper addresses strategic airport facility planning under demand uncertainty. Existing studies are improved by (1) allowing capacity contraction and (2) adopting more flexible delay functions. A mixed‐integer nonlinear program, which incorporates scale economies in construction, time value of money, nonlinear congestion effect, and other factors, is proposed for optimizing the capacity expansion/contraction decisions over time for multiple airport components. The stochastic problem is converted into its deterministic equivalent because the number of demand scenarios considered is finite. A discrete approximation technique is used to remove the nonlinearities. Numerical studies are presented to demonstrate the capability of the proposed model and the computational efficiency of the solution method. The “Flaw of Averages” due to faulty decisions based on the average future condition is illustrated, and trade‐offs among various costs are discussed in the numerical analyses. Copyright © 2016 John Wiley & Sons, Ltd.     

Estimating probability distributions of dynamic queues

Queues are often associated with uncertainty or unreliability, which can arise from chance or climatic events, phase changes in system behaviour, or inherent randomness. Knowing the probability distribution of the number of customers in a queue is important for estimating the risk of stress or disruption to routine services and upstream blocking, potentially leading to exceeding critical limits, gridlock or incidents. The present paper focuses on time-varying queues produced by transient oversaturation during demand peaks where there is randomness in arrivals and service. The objective is to present practical methods for estimating a probability distribution from knowledge of the mean, variance and utilisation (degree of saturation) of a queue available from computationally efficient, if approximate, time-dependent calculation. This is made possible by a novel expression for time-dependent queue variance. The queue processes considered are those commonly used to represent isolated priority (M/M/1) and signal-like (M/D/1) systems, plus some statistical variations within the common Pollaczek-Khinchin framework. Results are verified by comparison with Markov simulation based on recurrence relations.     

Drivers’ parking location choice under uncertain parking availability and search times: A stated preference experiment

To assess parking pricing policies and parking information and reservation systems, it is essential to understand how drivers choose their parking location. A key aspect is how drivers’ behave towards uncertainties towards associated search times and finding a vacant parking spot. This study presents the results from a stated preference experiment on the choice behaviour of drivers, in light of these uncertainties. The attribute set was selected based on a literature review, and appended with the probabilities of finding a vacant parking spot upon arrival and after 8 min (and initially also after 4 min, but later dropped to reduce the survey complexity). Efficient Designs were used to create the survey design, where two rounds of pilot studies were conducted to estimate prior coefficients. Data was successfully collected from 397 respondents. Various random utility maximisation (RUM) choice models were estimated, including multinomial logit, nested logit, and mixed logit, as well as models accounting for panel effects. These model analyses show how drivers appear to accept spending time on searching for a vacant parking spot, where parking availability after 8 min ranks second most important factor in determining drivers’ parking decisions, whilst parking availability upon arrival ranks fourth. Furthermore, the inclusion of heterogeneity in preferences and inter-driver differences is found to increase the predictive power of the parking location choice model. The study concludes with an outlook of how these insights into drivers’ parking behaviour can be incorporated into traffic assignment models and used to support parking systems.     

Uncertainty modeling for bus selection and allocation in a private transportation system

ABSTRACT

This paper describes the development of a probabilistic formulation that provides global optimum selection and allocation of a fleet of buses in a private transportation system of an organization where a third party is hired to provide transportation for its employees and their dependents. In this private transportation system, a fleet of buses is to be selected and allocated to serve employees and their independents on different prescheduled trips along different routes from the organization’s headquarters and residential compound where round-trip times of scheduled trips are subject to uncertainty due to random delays. We propose a probabilistic approach based on 0-1 integer programming for the selection and allocation to determine the optimal number and size of buses assigned to a set of prescheduled trips in a particular time interval. Examples and a case study are presented to illustrate the applicability and suitability of the proposed approach.     

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区域公交调度是未来城市公共交通的发展趋势,主要解决如何合理统一安排最初分布于不同车场的车辆完成所有线路固定时刻表对应班次任务,从而减少车队规模和降低营运成本.考虑现实中许多突发事件干扰车辆按时完成班次,借助双层规划模型,本文探讨区域公交车辆调度和购车计划之间的有机联系,在满足多车型、车场容量限制、燃料限制等现实因素基础上,设计求解上下层模型的遗传算法,引入满意解的概念,将下层规划产生的一组满意解供上层规划比选,进而生成最佳公交车调度方案,以及与之对应的购车计划.最后给出了一个实例,验证模型和算法的正确性和有效性.