车辆作用下桥梁冲击系数分析

应用达朗贝尔原理推导了1/4车辆模型和桥梁的振动平衡方程;采用三角级数法生成各级桥面不平度序列;用数值方法分析了桥面平整度、车速及车辆参数对桥梁冲击系数的影响。结果表明:桥面不平度对桥梁冲击系数影响很大;冲击系数随着速度的增加而发生波动;合理的车辆参数可以减小对桥梁的冲击作用;车辆固有频率同样是影响冲击系数的一个不容忽视的因素。     

Part 2. Beyond single-outcome models: Decompositions of aggregate and disaggregate road safety risk

The second part of the state-of-the-art focuses on the development of the founders' double streams explaining single-outcome indicators (probability of accidents and fatalities, respectively) by fixed form regression, as outlined in the Part 1. Following Page (1997, pp. 67–122, 2001) and others, we use as turning point of the evolution of both aggregate and discrete approaches the DRAG-1 model of 1984, itself based on aggregate data, which introduced four key innovations in principle applicable to both streams.     

Effect of risk preference on the valuation of time charter contracts with extension options

ABSTRACT

The literature on valuation of time charter contracts and real options in shipping generally relies on the complete markets hypothesis and the risk-neutrality of agents. However, these assumptions fail completely in some shipping market segments. This study proposes a numerical approach—based on discounting the certainty equivalent payoff at the risk-free rate—which incorporates the agent’s risk preferences through an exponential utility function. The method comprises an iterative Monte Carlo nested simulation with the real probability measure. This method is applied to a case of Suezmax tankers. The stochastic evolution of the time charter rates is modelled as a geometric mean-reverting process. The case study supports the applicability of the proposed method and evidences that the effect of risk preference may be significant, mainly for more risk-averse agents. Although the method involves intensive computation, it has the benefits of theoretical ease and flexibility, which could encourage utilisation by practitioners.     

聚类分析在商用车销售客户细分中的应用

选取了某商用车公司的部分销售数据,在SQL Server 2008中对数据进行了预处理,利用经典的K-means算法对其进行了聚类分析,按客户购买车的种类和地区进行细分,细分结果对于该公司制定有针对性的销售方案有着一定的参考价值。     

利用GNSS时序分析辨识大跨桥梁结构荷载响应研究

使用小波分析、功率谱密度等方法,对武汉市阳逻大桥GNSS监测数据进行分析,识别并分离大桥不同位置结构车辆移动荷载响应、车辆振动荷载响应和噪声,首先去除噪声,然后利用信号不同频率特性,分离静态信号和动态信号,从而得到更为清晰的大跨度桥动力响应特性。     

Examining the Political and Practical Reality of Bus-based Real Time Passenger Information

Abstract

Enhancing the bus experience through improved information provision is a key element of UK Government transport policy. Real time passenger information (RTPI) is perceived to reassure waiting passengers, to benefit the bus operator through increased revenue and the local authority, by promoting social inclusion and achieving a modal shift. RTPI also provides an important tool for operators by allowing them to monitor services and refine their schedules.

The aim of this paper is to understand the reasons for implementing RTPI in the bus sector, and to determine the key issues impacting on the likely success of such a policy. A case study approach investigates the experiences of two provincial towns in the UK. The paper suggests that, whilst it is unclear whether RTPI has resulted in an increase in bus patronage, it is considered to be most effective when combined as part of a package of measures. It is intended that the findings from the two case studies will reveal lessons of relevance to authorities contemplating the introduction of RTPI.     

某大桥地震作用下的时程响应分析

文章以某桥主桥2×100m的梁-斜拉组合体系为研究对象,采用ANSYS有限元软件对该桥进行了抗震性能研究,并基于地震时程分析法,评估了该大桥在地震荷载作用下的应力和位移响应情况,找到了该桥的薄弱环节,为该桥的设计提出有益的建议。     

路面裂缝图像自动识别算法综述

路面裂缝自动检测对于路面养护管理、路面性能评价与预测、路面材料和结构设计具有重要的实用价值,但快速、准确、全面且稳定地识别路面裂缝一直是个难题.为此,对路面裂缝自动检测研究现状进行综述,包括以图像增强和去噪为目的的预处理方法,基于阈值分割、边缘检测和种子生长的空间域识别算法,以小波变换为代表的频域识别算法,基于有监督学习的识别算法及其他裂缝识别方法;指出既有裂缝识别算法存在易受光照和油污等因素的影响、裂缝识别图像连续性差和识别速度和精度较低等不足.最后,提出综合考虑边界和区域特征消除纹理和噪声干扰、基于局部和全局信息设计优化识别算法和基于三维图像进行裂缝识别等研究展望,为裂缝自动识别算法的改进提供参考.     

Probabilistic Modeling of Erroneous Human Response to In-Vehicle Route Guidance Systems: A First Look

An integral component of (in-vehicle) navigation systems is the determination of optimal routes to the desired destination. An implicit assumption in the underlying algorithms is that people do not make mistakes when following the prescribed routes. This is, however, not always consistent with reality, especially when driving in unfamiliar environments. This article presents a first look at the possibility of mistakes when driving. This possibility is formalized in a Markov decision process. It is demonstrated that quite paradoxical situations can occur when accounting for mistakes. As the most interesting—but perhaps extreme—example, we have shown that under certain conditions, it is no longer optimal to recommend drivers to take the shortest route. Instead, a longer route (in certain cases even the longest!) becomes optimal. Numerical results are provided throughout the article to reveal the fundamental properties of this problem.     

A Variable Time-Discretization Strategies-Based,Time-Dependent Shortest Path Algorithm for Dynamic Traffic Assignment

Within the simulation-based dynamic traffic assignment (SBDTA) model, the time-dependent shortest path (TDSP) algorithm plays a crucial role in the path-set update procedure by solving for the current optimal auxiliary solution (shortest path). Common types of TDSP algorithms require temporal discretization of link/node time/cost data, and the discretization could affect the solution quality of TDSP and of the overall SBDTA as well. This article introduces two variable time-discretization strategies applicable to TDSP algorithms. The strategies are aimed at determining the optimal time discretization for time-dependent links/nodes travel time data. The first proposed strategy produces a specific discretization interval for each link. The second proposed strategy generates time-varying intervals for the same link over the analysis period. The proposed strategies are implemented in a link-based time-dependent A* algorithm in a SBDTA model DynusT and tested with two numerical experiments on two traffic networks. The results show that the proposed discretization methods achieve the research goal—to flexibly and scalably balance the memory usage and run time for SBDTA without degrading the convergence. This property is rather important when dealing with a large real-world network with a long analysis period.