交通工程专业中英文翻译

交通工程专业中英文翻译 河北工程大学交通工程专业 外文翻译 学生姓名: 李 焘 专 业: 交通工程 班 级: 09-02 学 号: 090240225 指导老师: 高 爱 坤 2013年04月 交通事故分析的可能性和局限性 原文出处:SWOV institute for road safety research Leidschendam(会议) 关键字:后果;目的;描述;限制;关注;事故分析;可能性 摘要:交通事故的统计数字，尤其国家一级的数据对监控和预测事故的发展，积极或消极事故的发展，以及对定义安全目标和评估工业安全特别有益。事故分析是应用非常有限的分析，是前瞻性分析和回顾性分析，能够对新开发的交通安全系统和特殊过程的安全措施进行评价。目前迫切需要一个将实时事故分析与研究相结合的行为。将自动检测和视频录制相结合的研究交通事故的科研论文会比较容易接受。这种类型的研究最终会对交通理念有个完善的认识。 1(简介 本文主要是基于个人的经验，研究有关交通安全、安全分析以及事故分析等在研究中的作用。由这些经验推导出的哲学思考就像通过研究和统计得出的实践观点。而这些调查数字已经在其他地方发了。 在缺少直接观察的事故中，许多方法论问的产生，导致不能直接测试对结果持续讨论。通过看事故视频来讨论是富有成效的。事实证明，用来解释事故的大部分有关信息就是事故中缺少的记录。深入研究还无法回忆起所有的必要的用来测试有关事故发生的假设数据，。尤其是车-车相撞发生的车祸，这是在荷兰城市道路交叉口录制的视频，一辆从岔路驶来的汽车与主干路的汽车相撞，下列问题可以问:为什么汽车来自次干路上，突然加速后又几乎停止，撞上了在左侧主路的一辆汽车呢,为什么没有注意到正在驶来的车,是不是因为两车从右边驶来，司机因为前面的交叉为他们提供了可能性而斤斤计较,难道他向左看过，但他认为停在拐角处的绿色货车能让他停下来,当然，交通状况并不复杂。目前这个事故中没有骑自行车或行人在拥挤路口分散他的注意。如果停着的绿色车能够在五分钟内消失，这两辆车可能就不会相撞。在事故发生的相关条件下，几乎不可能观察下一个交通行为，因为交通事故是不可预见的。由于新的视频设备和自动检测事故设备的不断发展，如在收集数据方面不需要很高的成本就能变得越来越逼真。必要的增加数据类型也能更好的解释交通中存在的危险因素。关于事故分析的可能性和限制性的问题是不容易回答的，我们不能确切的分析交通事故。 因为事故分析涵盖了每一个活动中的不同背景，并根据不同的信息来源范围来补充资料，特别是收集事故的数据，背景资料等，我们首先要看看在交通安全领域的活动周期然后再回答事故分析的可能性与限制。这些行为主要是与交通系统的有关，有些则是相关的研究活动。 应该用下面的步骤来加以区分: ——检测交通安全问题; ——描述问题和它的主要特征; ——分析其原因分析和改进建议; ——选择和执行安全措施; ——评价所采取的措施。 虽然这个周期可以由同一人或一群人做出来，而问题在每个阶段(政治/管理或科学)都有不同的背景。我们用事故分析来描述这一阶段。做这个决定是重要的。很多关于分析结果的方法的讨论由于忽视之间的区别而成为徒劳的。政治家或道路管理人员对道路的个别事故不是很留意。他们对事故的看法往往都是一视同仁，因为总的结果比整个事故中的每个人的因素重要。因此，每次事故看做一个个体，之间相互协调就会达成安全的结果。 研究人员研究事故发生时一连串事件中每个人的兴趣。希望从中得到关于每次事故的详细信息并能发现其发生的原因和有关的条件。政治家们希望只是因为细节决定行动。在最高一级事故总数减少。信息的主要来源是国家数据库及其统计学处理系统。对他来说，统计意外数字及其统计的波动来进行事故分析。这适用于事故分析中的交通安全领域。因此，我们将首先描述了事故的这些方面。 2事故的性质和它们的统计特性 事故基本概念是意外，不管是其发生的原因还是引起事故出现的过程。两个简单的假设通常是来描述交通事故的形成过程. -事故发生的概率与以往发生的事故之间是独立 -事故发生在时间上是同性质的 如果这两个假设成立，那么事故是泊松分布。第一个假设与大多数的批判不符。事故是罕见的事件，因此不会受到以前事故的影响。在某些情况下，有一个直接的因果链(例如，大量的车开到一起)这一系列的事故被认为是一个个体事 故但包含许多的车。这个假设并不适用于统计人员伤亡。伤亡人数往往与同一事故有关，因此，独立性假设不成立。第二个假设乍一看似乎不太容易理解。穿越空间或在不同地点发生的的事故同样具有可能性。然而，假设需要很长一段时间并且没有缓缴期。其性质是根据理论的假设。如果其短时间内能成立，那么它也适用于长时间，因为泊松分布变量的总和，即使他们的泊松率是不同的，但也属于泊松分布。对于这些时期的总和泊松率则等于为这些地方的泊松率的总和。假设与一个真正的情况相比较计数，无论是从一两个结果还是总情况来看都有一个基本情况比较符合 例如，对比在一年中特定的一天例如下一天，下一个星期的一天发生的交通事故。如果条件是相同的(同一时间，交通情况相同，同样的天气条件等)，那么由此产生的意外数字是相同的泊松过程的结果。这一假设可以通过估算进行测试的两个观测值的基础上(估计是两个值的平均值)的速度参数。概率理论能够考虑到这两个观察值的平均，用于计算的平等假设的可能性。这是一个相当强大的统计过程。泊松假设是研究了很多次，来获得证据支持。它已经应用于许多情况，数的差异表明在安全性的差异然后确定是否发生意外。这一程序的主要目的是检测在安全分歧。这可能是一个时间上的差异，或不同的地方或不同的条件。这种差异可以指导改进的过程。由于主要关注的是，以减少意外的发生，这种分析可能导致对治疗中最有前途的领域。为这样一个测试应用程序的必要条件是，那意外的数字进行比较是大到足以证明存在的分歧。在许多地方情况下，一个应用程序是不可能的。事故黑点分析往往阻碍了这一限制，例如，如果应用这种测试，找出事故是否在特定的位置数是高于平均水平。该程序的描述，也可以使用，如果发生意外乃根据数的特点找到有前途的安全目标。不仅聚集，而且还与分类泊松假设成立，而意外数字可以相互测试的泊松假设的基础。这种测试是相当麻烦的，因为每个特定的情况下，每一个不同的泊松参数，即，对所有可能结果的概率必须计算应用测试。然后，泊松分布近似为正态分布，均值和方差等于泊松参数。一旦均值和方差的正态分布，给出了所有的测试可以改写了标准零均值和方差的正态分布条件。没有任何更多的必要计算，但测试统计，需要利用表绘制。 ) POSSIBILITIES AND LIMITATIONS OF ACCIDENT ANALYSIS 原文出处:SWOV institute for road safety research Leidschendam(会议记录) Keyword:Consequences; purposes; describe; Limitations; concerned; Accident Analysis; possibilities Abstraet:Accident statistics, especially collected at a national level are particularly useful for the description, monitoring and prognosis of accident developments, the detection of positive and negative safety developments, the definition of safety targets and the (product) evaluation of long term and large scale safety measures. The application of accident analysis is strongly limited for problem analysis, prospective and retrospective safety analysis on newly developed traffic systems or safety measures, as well as for (process) evaluation of special short term and small scale safety measures. There is an urgent need for the analysis of accidents in real time, in combination with background behavioural research. Automatic incident detection, combined with video recording of accidents may soon result in financially acceptable research. This type of research may eventually lead to a better understanding of the concept of risk in traffic and to well-established theories. 1. Introduction. This paper is primarily based on personal experience concerning traffic safety, safety research and the role of accidents analysis in this research. These experiences resulted in rather philosophical opinions as well as more practical viewpoints on research methodology and statistical analysis. A number of these findings are published already elsewhere. From this lack of direct observation of accidents, a number of methodological problems arise, leading to continuous discussions about the interpretation of findings that cannot be tested directly. For a fruitful discussion of these methodological problems it is very informative to look at a real accident on video. It then turns out that most of the relevant information used to explain the accident will be missing in the accident record. In-depth studies also cannot recollect all the data that is necessary in order to test hypotheses about the occurrence of the accident.For a particular car-car accident, that was recorded on video at an urban intersection in the Netherlands, between a car coming from a minor road, colliding with a car on the major road, the following questions could be asked:Why did the driver of the car coming from the minor road, suddenly accelerate after coming almost to a stop and hit the side of the car from the left at the main road? Why was the approaching car not noticed? Was it because the driver was preoccupied with the two cars coming from the right and the gap before them that offered him the possibility to cross? Did he look left before, but was his view possibly blocked by the green van parked at the corner? Certainly the traffic situation was not complicated. At the moment of the accident there were no bicyclists or pedestrians present to distract his attention at the regularly overcrowded intersection. The parked green van disappeared within five minutes, the two other cars that may have been important left without a trace. It is hardly possible to observe traffic behaviour under the most relevant condition of an accident occurring, because accidents are very rare events, given the large number of trips. Given the new video equipment and the recent developments in automatic incident and accident detection, it becomes more and more realistic to collect such data at not too high costs. Additional to this type of data that is most essential for a good understanding of the risk increasing factors in traffic, it also important to look at normal traffic behaviour as a reference base. The question about the possibilities and limitations of accident analysis is not lightly answered. We cannot speak unambiguously about accident analysis. Accident analysis covers a whole range of activities, each originating from a different background and based on different sources of information: national data banks, additional information from other sources, specially collected accident data, behavioural background data etc. To answer the question about the possibilities and limitations, we first have to look at the cycle of activities in the area of traffic safety. Some of these activities are mainly concerned with the safety management of the traffic system, some others are primarily research activities The following steps should be distinguished: - detection of new or remaining safety problems; - description of the problem and its main characteristics; - the analysis of the problem, its causes and suggestions for improvement; -selection and implementation of safety measures; - evaluation of measures taken. Although this cycle can be carried out by the same person or group of persons, the problem has a different (political/managerial or scientific) background at each stage. We will describe the phases in which accident analysis is used. It is important to make this distinction. Many fruitless discussions about the method of analysis result from ignoring this distinction. Politicians, or road managers are not primarily interested in individual accidents. From their perspective accidents are often treated equally, because the total outcome is much more important than the whole chain of events leading to each individual accident. Therefore, each accident counts as one and they add up all together to a final safety result. Researchers are much more interested in the chain of events leading to an individual accident. They want to get detailed information about each accident, to detect its causes and the relevant conditions. The politician wants only those details that direct his actions. At the highest level this is the decrease in the total number of accidents. The main source of information is the national database and its statistical treatment. For him, accident analysis is looking at (subgroups of) accident numbers and their statistical fluctuations. This is the main stream of accident analysis as applied in the area of traffic safety. Therefore, we will first describe these aspects of accidents. 2. The nature of accidents and their statistical characteristics. The basic notion is that accidents, whatever there cause, appear according to a chance process. Two simple assumptions are usually made to describe this process for (traffic) accidents: - the probability of an accident to occur is independent from the occurrence of previous accidents; -the occurrence of accidents is homogeneous in time. If these two assumptions hold, then accidents are Poisson distributed. The first assumption does not meet much criticism. Accidents are rare events and therefore not easily influenced by previous accidents. In some cases where there is a direct causal chain (e.g. , when a number of cars run into each other) the series of accidents may be regarded as one complicated accident with many cars involved.The assumption does not apply to casualties. Casualties are often related to the same accident and therefore the independency assumption does not hold. The second assumption seems less obvious at first sight. The occurrence of accidents through time or on different locations are not equally likely. However, the assumption need not hold over long time periods. It is a rather theoretical assumption in its nature. If it holds for short periods of time, then it also holds for long periods, because the sum of Poisson distributed variables, even if their Poisson rates are different, is also Poisson distributed. The Poisson rate for the sum of these periods is then equal to the sum of the Poisson rates for these parts. The assumption that really counts for a comparison of (composite) situations, is whether two outcomes from an aggregation of situations in time and/or space, have a comparable mix of basic situations. E.g. , the comparison of the number of accidents on one particular day of the year, as compared to another day (the next day, or the same day of the next week etc.). If the conditions are assumed to be the same (same duration, same mix of traffic and situations, same weather conditions etc.) then the resulting numbers of accidents are the outcomes of the same Poisson process. This assumption can be tested by estimating the rate parameter on the basis of the two observed values (the estimate being the average of the two values). Probability theory can be used to compute the likelihood of the equality assumption, given the two observations and their mean. This statistical procedure is rather powerful. The Poisson assumption is investigated many times and turns out to be supported by a vast body of empirical evidence. It has been applied in numerous situations to find out whether differences in observed numbers of accidents suggest real differences in safety. The main purpose of this procedure is to detect differences in safety. This may be a difference over time, or between different places or between different conditions. Such differences may guide the process of improvement. Because the main concern is to reduce the number of accidents, such an analysis may lead to the most promising areas for treatment. A necessary condition for the application of such a test is, that the numbers of accidents to be compared are large enough to show existing differences. In many local cases an application is not possible. Accident black-spot analysis is often hindered by this limitation, e.g., if such a test is applied to find out whether the number of accidents at a particular location is higher than average. The procedure described can also be used if the accidents are classified according to a number of characteristics to find promising safety targets. Not only with aggregation, but also with disaggregation the Poisson assumption holds, and the accident numbers can be tested against each other on the basis of the Poisson assumptions. Such a test is rather cumbersome, because for each particular case, i.e. for each different Poisson parameter, the probabilities for all possible outcomes must be computed to apply the test. In practice, this is not necessary when the numbers are large. Then the Poisson distribution can be approximated by a Normal distribution, with mean and variance equal to the Poisson parameter. Once the mean value and the variance of a Normal distribution are given, all tests can be rephrased in terms of the standard Normal distribution with zero mean and variance one. No computations are necessary any more, but test statistics can be drawn from tables.