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.     

带延迟的双险种复合Poisson风险模型

由于保险公司风险经营规模的不断扩大,考虑到单险种风险模型的局限性,研究了带延迟的双险种复合Poisson风险模型,给出了该模型破产概率的表达式及有限时间生存概率等重要指标.     

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.     

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.     

舰艇编队无线自组网建模分析研究

路由协议是自组网的关键技术.路由协议的设计和性能评估都要涉及到一些基本的网络参数.本文结合舰艇编队无线通信网络实际,对编队无线自组网进行了合理假设,运用马氏链建模的方法对这一问题进行了研究,推导了任意节点相邻概率和邻居数的计算公式,实现了路由协议性能研究中涉及到的一些基本网络参数的定量处理.最后结合海军战斗群的典型应用进行了实例计算.文章基于马氏链建模所得到的结论,为军用无线自组网的理论分析提供了依据,对编队无线自组网的工程实践具有一定的指导意义.     

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.     

Stability of transportation networks under adaptive routing policies

Growing concerns regarding urban congestion, and the recent explosion of mobile devices able to provide real-time information to traffic users have motivated increasing reliance on real-time route guidance for the online management of traffic networks. However, while the theory of traffic equilibria is very well-known, fewer results exist on the stability of such equilibria, especially in the context of adaptive routing policy. In this work, we consider the problem of characterizing the stability properties of traffic equilibria in the context of online adaptive route choice induced by GPS-based decision making. We first extend the recent framework of “Markovian Traffic Equilibria” (MTE), in which users update their route choice at each intersection of the road network based on traffic conditions, to the case of non-equilibrium conditions, while preserving consistency with known existence and uniqueness results on MTE. We then exhibit sufficient conditions on the network topology and the latency functions for those MTEs to be stable in the sense of Lyapunov for a single destination problem. For various more restricted classes of network topologies motivated by the observed properties of travel patterns in the Singapore network, under certain assumptions we prove local exponential stability of the MTE, and derive analytical results on the sensitivity of the characteristic time of convergence to network and traffic parameters. The results proposed in this work are illustrated and validated on synthetic toy problems as well as on the Singapore road network with real demand and traffic data.     

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。