A stochastic model of traffic flow: Theoretical foundations

In a variety of applications of traffic flow, including traffic simulation, real-time estimation and prediction, one requires a probabilistic model of traffic flow. The usual approach to constructing such models involves the addition of random noise terms to deterministic equations, which could lead to negative traffic densities and mean dynamics that are inconsistent with the original deterministic dynamics. This paper offers a new stochastic model of traffic flow that addresses these issues. The source of randomness in the proposed model is the uncertainty inherent in driver gap choice, which is represented by random state dependent vehicle time headways. A wide range of time headway distributions is allowed. From the random time headways, counting processes are defined, which represent cumulative flows across cell boundaries in a discrete space and continuous time conservation framework. We show that our construction implicitly ensures non-negativity of traffic densities and that the fluid limit of the stochastic model is consistent with cell transmission model (CTM) based deterministic dynamics.     

Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data

Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced speed data also brings very important challenges, such as the highly variable measurement noise in the data due to a variety of driving behaviors and sample sizes. When not properly accounted for, this noise can severely compromise any application that relies on accurate traffic data. In this article, we propose the use of heteroscedastic Gaussian processes (HGP) to model the time-varying uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a HGP conditioned on sample size and traffic regime (SSRC-HGP), which makes use of sample size information (probe vehicles per minute) as well as previous observed speeds, in order to more accurately model the uncertainty in observed speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we empirically show that the proposed heteroscedastic models produce significantly better predictive distributions when compared to current state-of-the-art methods for both speed imputation and short-term forecasting tasks.     

经验过程的一般加权大数定律

研究了经验过程中加权系数具有某些较弱性质的加权和收敛问题.利用经验过程中已有的几个概率不等式与一般加权和的对称化不等式,得到了经验过程中的独立同分布随机元序列的这类加权和的强、弱大数定律成立的充分条件(E‖f(X0)‖1/αG<∞).同时,对经验过程中的Cesaro大数定律和欧拉弱大数定律进行了推广.     

胶州湾地貌特征及海床演变分析

为判明胶州湾海床冲淤演变趋势 ,在地貌调查及有关资料整理的基础上 ,对胶州湾的形成和演变进行了分析 ,从沉积特征与泥沙来源上对海床稳定性的影响进行了探讨 ,认为海床处于稳定状态 ,并将延续这种演变趋势。     

A competing Markov model for cracking prediction on civil structures

Cracks on the surface of civil structures (e.g. pavement sections, concrete structures) progress in several formations and under different deterioration mechanisms. In monitoring practice, it is often that cracking type with its worst damage level is selected as a representative condition state, while other cracking types and their damage levels are neglected in records, remaining as hidden information. Therefore, the practice in monitoring has a potential to conceal with a bias selection process, which possibly result in not optimal intervention strategies. In overcoming these problems, our paper presents a non-homogeneous Markov hazard model, with competing hazard rates. Cracking condition states are classified in three types (longitudinal crack, horizontal crack, and alligator crack), with three respective damage levels. The dynamic selection of cracking condition states are undergone a competing process of cracking types and damage levels. We apply a numerical solution using Bayesian estimation and Markov Chain Monte Carlo method to solve the problem of high-order integration of complete likelihood function. An empirical study on a data-set of Japanese pavement system is presented to demonstrate the applicability and contribution of the model.     

集值渐近准非扩张映象多步迭代的收敛性

在严格凸Banach空间中,用集值映象点值化方法,证明了集值渐近准非扩张映象带误差的三步迭代列收敛于耦合不动点的充要条件.     

自校正极点配置解耦控制器

本文提出的多变量自校正控制器，完成了对控制系统的闭环及极点配置和动态解耦，且能使系统对阶跃给定值实现稳态无差跟踪。本算法适用于最小相位系统、非最小相位系统以及各道时延不等的系统。数字仿睦证实了本算法的良好性能。     

微观过程中的共振振动激发

提出微观过程中共振振动激发的概念，结合无阈能反应的理论，对反应（１）和（２）的理论计算结果作了进一步的解释和说明。     

公路工程材料费用的全过程控制

材料费用的控制是控制工程成本的关键。通过对影响材料消耗的过程和影响材料费用的要素进行分析,提出控制材料费用必须从材料消耗量和材料价格两方面着手。并应在影响材料费用的设计、施工及运营三个阶段进行全过程控制,才能取得最好效果;在材料控制的全过程中,不能忽视设计阶段对降低材料费用的重要作用;对材料费用的控制效果还应进行实事求是的评价,以总结经验教训,为以后的材料费用控制工作提供借鉴。     

舰船电力推进系统故障仿真软件关键技术研究

提出了采用计算机仿真产生专家系统所需样本,并设计出电力推进系统故障仿真软件体系结构,阐述了软件开发中的模型管理、数据共享、模型控制等关键技术,从而在降低横型程序和故障诊断软件之间的耦合度的同时,实现模型程序和仿真软件之间快速的交互信息。