Quantifying uncertainty in short-term traffic prediction and its application to optimal staffing plan development

In this paper, we aim to quantify uncertainty in short-term traffic volume prediction by enhancing a hybrid machine learning model based on Particle Swarm Optimization (PSO) and Extreme Learning Machine (ELM) neural network. Different from the previous studies, the PSO-ELM models require no statistical inference nor distribution assumption of the model parameters, but rather focus on generating the prediction intervals (PIs) that can minimize a multi-objective function which considers two criteria, reliability and interval sharpness. The improved PSO-ELM models are developed for an hourly border crossing traffic dataset and compared to: (1) the original PSO-ELMs; (2) two state of the art models proposed by Zhang et al. (2014) and Guo et al. (2014) separately; and (3) the traditional ARMA and Kalman filter models. The results show that the improved PSO-ELM can always keep the mean PI length the lowest, and guarantee that the PI coverage probability is higher than the corresponding PI nominal confidence, regardless of the confidence level assumed. The study also probes the reasons that led to a few points being not covered by the PIs of PSO-ELMs. Finally, the study proposes a comprehensive optimization framework to make staffing plans for border crossing authority based on bounds of PIs and point predictions. The results show that for holidays, the staffing plans based on PI upper bounds generated much lower total system costs, and that those plans derived from PI upper bounds of the improved PSO-ELM models, are capable of producing the lowest average waiting times at the border. For a weekday or a typical Monday, the workforce plans based on point predictions from Zhang et al. (2014) and Guo et al. (2014) models generated the smallest system costs with low border crossing delays. Moreover, for both holiday and normal Monday scenarios, if the border crossing authority lacked the required staff to implement the plans based on PI upper bounds or point predictions, the staffing plans based on PI lower bounds from the improved PSO-ELMs performed the best, with an acceptable level of service and total system costs close to the point prediction plans.     

自由飞行环境中飞机对导弹的规避方法*

在当今空战情况下,导弹对于飞机的威胁愈发增长。为避免发生飞行冲突,需要飞机驾驶员适当改变航行诸元,但是由于导弹的速度在一般情况下远远大于飞机速度,且发现时双方距离较近,留给驾驶员的反应与操作时间较少。文章采用速度矢量三角形分析法,在二维平面上分析了飞机对于导弹的规避方法,提出了综合调整航向和航速冲突的方法。     

天津地铁6号线双线平行盾构隧道施工间隔对地表土体变形影响研究

研究双线盾构隧道在不同施工间隔下施工时地表的变形规律,对控制地表整体变形及不均匀变形十分重要。依托天津地铁6号线双线盾构隧道下穿天津西站站场实际工程实例,以铁路线设施的关键变形控制指标为评判依据,研究盾构左右线不同施工间隔下的地表变形分布特性,对比分析间隔距离与地表沉降和不均匀沉降的关系,为双线盾构隧道工程选择合适的施工间隔提供依据,以保证工程安全及地表铁路设施的正常运行。结果表明,不同施工间隔的影响主要表现为掘进过程对地表土体变形的扰动程度及扰动范围的明显差异:对于地表沉降变形而言,施工间隔越小,掌子面处地表土体沉降越快,且左线完全先行时,地表土体的纵向变形范围约为20 m,相较两洞同时施工时变形范围减小约25 m;对于地表不均匀变形而言,左线完全先行施工条件下,地表轨向变形、水平变形、轨距变形最大分别约为1、0.6、0.2 mm,相较两洞同时施工时分别减小0.8、0.2、0.15 mm。因此,对于双线盾构隧道而言,两洞同时施工时最不利于地表变形的控制,而一条隧道完全先行掘进的方案最有利于地表变形的控制。     

Study on Probability Plot Correlation Coefficient of the Log-Weibull Distribution

The log-Weibull distribution is a variant of the three-parameter Weibull distribution. The probability plot of a distribution model is desired since it can help to decide on whether the model is appropriate for fitting a given dataset and can provide the initial estimate of the model parameters. The decision on the appropriateness of a distribution is somehow subjective. This paper presents a probability plot of the log-Weibull distribution(LWPP). The distribution of the probability plot correlation coefficient is studied. From this distribution, a lower confidence limit is determined for determining whether the probability plot correlation coefficient derived from a given data set is large enough. The appropriateness and usefulness of this study are illustrated by two real-world examples.     

基于附着系数曲线封闭面积的路面识别研究

以滑移率区间[0,0.16]上附着系数曲线的封闭面积作为参数指标,在Burckhardt轮胎—路面模型的基础上设计了7种典型路面的识别区间,据此在制动时完成路面识别。使用制动单轮模型进行了仿真试验,结果表明:该方法只需一次判定即可完成识别,识别快速准确,能充分利用不同路面的附着条件,提高车辆的制动效能。     

基于Kriging代理模型的高速列车悬挂参数的区间优化

针对高速列车悬挂设计参数难以选定的问题,提出一种基于Kriging代理模型的高速列车悬挂参数区间优化的方法.采用拉丁超立方试验设计方法确定仿真数据样本得到动力学仿真数据,利用偏最小二乘方法确定影响脱轨系数的主要悬挂参数,进而建立基于设计参数与仿真数据Kriging代理模型,以此模型实现悬挂参数区间优化.最后以CRH某型动车组悬挂参数为例进行了验证性优化分析,结果表明该方法正确可行.     

多个重现期雨水管渠系统的设计

西安铁路北客站是陕西最大的地标性建筑,也是亚洲当前最大的现代化大型客站。站场排雨设计重现期P=10 a,站前南、北广场重现期采用3 a,市政道路下穿铁路箱涵通道设计重现期采用5 a,其他一般地区设计重现期P=1 a,由此引出多个重现期雨水管渠系统的设计问题。在探讨、明晰多个重现期雨水管渠流量采用各重现期延续、低重现期乘压力系数的计算方法基础上,提出地势对排水安全的决定作用及克服区域低地势缺陷需要采取的措施。     

指挥信息系统仿真试验可信度的度量

由于指挥信息系统是一个复杂的大系统,常规试验方法通常难以全面考核,基于仿真的试验已经成为一种主要的试验形式。系统仿真可信度研究是合理利用系统仿真结果的前提。分析了影响试验评估结果可信度的因素,给出了可信度评估的模糊层次分析方法,并结合某指挥控制系统的仿真试验,对仿真试验结果的可信度进行了具体的分析。     

A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction

This paper presents a Bayesian inference-based dynamic linear model (DLM) to predict online short-term travel time on a freeway stretch. The proposed method considers the predicted freeway travel time as the sum of the median of historical travel times, time-varying random variations in travel time, and a model evolution error, where the median is employed to recognize the primary travel time pattern while the variation captures unexpected supply (i.e. capacity) reduction and demand fluctuations. Bayesian forecasting is a learning process that revises sequentially the state of a priori knowledge of travel time based on newly available information. The prediction result is a posterior travel time distribution that can be employed to generate a single-value (typically but not necessarily the mean) travel time as well as a confidence interval representing the uncertainty of travel time prediction. To better track travel time fluctuations during non-recurrent congestion due to unforeseen events (e.g., incidents, accidents, or bad weather), the DLM is integrated into an adaptive control framework that can automatically learn and adjust the system evolution noise level. The experiment results based on the real loop detector data of an I-66 segment in Northern Virginia suggest that the proposed method is able to provide accurate and reliable travel time prediction under both recurrent and non-recurrent traffic conditions.     

对空情报雷达探测威力试验飞行设计

在分析雷达威力探测试验条件和方法的基础上,对威力试验的航线及飞行架次进行了设计,研究了发现概率区间与飞行试验的观测次数关系及误差分析,以及发现概率区间与发现概率曲线关系;仿真实验表明,在距离区间内选择合适的观测次数和发现概率,对对空情报雷达威力试验有着直接影响。