无线局域网帧数据流量研究的问题和挑战

无线局域网中的信道要比有线局域网信道复杂得多,研究运载其上的流量成为评估和了解无线局域网行为模式的关键。本文比较了有线和无线局域网在体系结构和协议机制上的差异,进而从AP节点处理帧数据流程上分析了无线局域网IP数据流量和帧数据流量上的差异,提出了无线局域网流量研究中存在的问题,探讨了无线局域网帧数据流量的研究内容和方向。     

一种控制BT流量的方法及其对用户的影响

针对BT流量占用大量带宽资源的问题,提出了BT令牌桶流量控制算法。分析了在网络出口控制BT带宽前后,内网用户的下载速率和内网平均用户数的变化。结果表明,控制出口BT带宽后,内网peer之间的数据传输速率会适当增加,平均每个peer的下载速率有所减少,平均内网peer数增加。将具有BT令牌桶流控功能的设备应用于试验网络环境中,测试结果表明,该算法有效,分析结果正确。     

基于ARMA的无线传感器网络流量预测模型的研究

无线传感器网络(WSN)的流量预测研究对于WSN管理具有至关重要的意义。基于WSN的特点,结合自回归滑动平均(ARMA)模型,该文提出了一个面向WSN的流量预测算法。仿真结果表明,该算法具有较高的预测精度;利用本算法对WSN的多路径路由机制进行改进后,可进一步提高WSN的生存期。     

Impulsivity and risky decision making among taxi drivers in Hong Kong: An event-related potential study

Taxi drivers play an important role in providing safe and professional public transport services. However, they tend to be more involved than other professional driver groups in accidents caused by deliberate recklessness. This study used an event-related potential (ERP) experiment to examine risk-taking behavior arising from impulsivity by comparing the underlying neural processes of taxi drivers with and without traffic offence records in Hong Kong. A sample of 15 traffic offenders and 15 nonoffenders, matched by sociodemographic characteristics, was recruited. The results show that the offender group demonstrated significantly less negative-going (less negative) feedback-related negativity but more positive-going (more positive) feedback-related P300 when than with their nonoffending counterparts. These findings show that taxi drivers with traffic offence records were less sensitive to the consequences of behavior and more attuned to the magnitude of potential reward. In addition, behavioral data revealed that they were more willing to make risky decisions. All these characteristics pertain to impulsive personality traits. Based on these findings, we can conclude that the offenders in this sample were more impulsive than their nonoffending counterparts.     

The sensitivity of estimates of regression to the mean

Estimations of the effectiveness of remedial treatments in road safety analysis are frequently bedevilled by the problem of regression to the mean (RTM). The number of accidents x observed at a site in the “before” period is a “noisy” quantity: x is Poisson distributed about an (unknown) true mean m for that site, so that x = m + e. Sites selected for treatment tend to have a positive random error component e, which will on average be zero in the “after” period, even if no treatment is applied.Methods for estimating RTM usually require some assumption about the underlying (prior) between-site distribution of the true means f₀(m): for example, in the empirical Bayes method, a gamma distribution is assumed. The paper considers the impact of different assumptions for this distribution and, indeed, whether any distributional form needs to be assumed. Using Markov Chain Monte Carlo methods, a variety of distributional forms are assumed for f₀(m) and applied to each of a number of real data sets, including that from a major study on the effectiveness of speed cameras. It is shown that, in some cases, the size of the estimated RTM effect can be quite sensitive to the choice of distribution.     

Risk of human fatality in building fires: A decision tool using Bayesian networks

The Netherlands is the most densely populated country of the European Union, which makes space very expensive. This leads to increasing complexity of the cities’ layout and other public spaces, together with a large number of people involved. Authorities would like to know whether new and innovative building designs ensure an appropriate level of safety of people in case of fire, before the accident happens, and to be prepared for the so-called “low probability–high consequences” accidents. Therefore, they need a tool to help them estimate the extent of a fire in a building, given any combination of possible conditions and any unexpected course of events during an emergency. This paper discusses the possibility of using Bayesian belief nets for this task. Using this approach, the people in charge can take decisions at different stages of the design process of a building regarding the location, the structure, the loading of the building, the types of fire protection systems inside the building, as well as the characteristics of the fire brigade that fights the possible fire. In the current study, usefulness of the approach is investigated using a small example. This will show the feasibility of the approach for the Netherlands situation and give authorities involved confidence that building a large comprehensive model would fulfil their needs for a support tool in the planning process. The effort to gather real data therefore was restricted as demonstration of fitness for purpose was the primary objective.     

网络测量及流量采集技术综述

对网络测量进行了定义,描述了网络测量的功能以及网络测量的方法,介绍了RFC中定义的网络性能度量值的概念,最后对网络流量测量中经常用到的流量采集技术进行了分析和比较.     

An assessment of the skid resistance effect on traffic safety under wet-pavement conditions

Pavement-tire friction provides the grip that is required for maintaining vehicle control and for stopping in emergency situations. Statistically significant negative correlations of skid resistance values and wet-pavement accident rates have been found in previous research. Skid resistance measured with SCRIM and crash data from over 1750 km of two-lane rural roads in the Spanish National Road System were analyzed to determine the influence of pavement conditions on safety and to assess the effects of improving pavement friction on safety. Both wet- and dry-pavement crash rates presented a decreasing trend as skid resistance values increased. Thresholds in SCRIM coefficient values associated with significant decreases in wet-pavement crash rates were determined. Pavement friction improvement schemes were found to yield significant reductions in wet-pavement crash rates averaging 68%. The results confirm the importance of maintaining adequate levels of pavement friction to safeguard traffic safety as well as the potential of pavement friction improvement schemes to achieve significant crash reductions.     

Recognising safety critical events: Can automatic video processing improve naturalistic data analyses?

New trends in research on traffic accidents include Naturalistic Driving Studies (NDS). NDS are based on large scale data collection of driver, vehicle, and environment information in real world. NDS data sets have proven to be extremely valuable for the analysis of safety critical events such as crashes and near crashes. However, finding safety critical events in NDS data is often difficult and time consuming. Safety critical events are currently identified using kinematic triggers, for instance searching for deceleration below a certain threshold signifying harsh braking. Due to the low sensitivity and specificity of this filtering procedure, manual review of video data is currently necessary to decide whether the events identified by the triggers are actually safety critical. Such reviewing procedure is based on subjective decisions, is expensive and time consuming, and often tedious for the analysts. Furthermore, since NDS data is exponentially growing over time, this reviewing procedure may not be viable anymore in the very near future.     

Numerical algorithms for dynamic traffic demand estimation between zones in a network

This paper presents numerical methods for dynamic traffic demand estimation between N zones in a network, where the zones are disjoint subsets of nodes of the network. Traffic is assumed to be generated or absorbed only in the zones and nowhere else in the network. Traffic volumes between zones over a fixed period of time are modeled as independent random variables with unknown means which it is desired to estimate. For each zone, the volume of all incoming and outgoing traffic is counted on a regular basis but no information about the origin or destination of the observed traffic is used. Procedures are suggested for a regular update of estimates of the N(N - 1) mean traffic demands between the zones on the basis of an incoming stream of the 2N traffic counts. The procedures are based on an exponential smoothing scheme and are reminiscent of the expectation maximization (EM) algorithm if smoothing is removed. Fast and reliable numerical algorithms, based on the conjugate gradient method, are presented for normal as well as for Poisson traffic demands. The Poisson case is linked with entropy maximization. Computational tests based on simulated data demonstrate both the numerical and statistical efficiency of the procedures.