【正文】 the analysis of the problem, its causes and suggestions for improvement。 原文出處： SWOV institute for road safety research Leidschendam（ 會議記錄 ），記錄者， . 5 5 POSSIBILITIES AND LIMITATIONS OF ACCIDENT ANALYSIS Keyword： Consequences。一般來說，可分為以下幾個方面： 考慮到交通系統(tǒng)，交通量和組成國家，道路使用者，他們的速度，天氣條件下，路面情況，車輛，道路使用者和他們的相互作用的演習(xí)，意外可以或無法預(yù)防。一個在交通意外的過程，結(jié)果是，該實際發(fā)生是由研究者未落觀測研究的主要問題。工資保障運動的一個計算機程序。和 2。從一個給定趨勢的偏差也可以進(jìn)行預(yù)測新的事件。這種測試是相當(dāng)麻煩的，因為每個特定的情況下，每一個不同的泊松參數(shù)，即，對所有可能結(jié)果的概率必須計算應(yīng)用測試。這可能是一個時間上的差異，或不同的地方或不同的條件。 例如， 對比在一年中特定的一天例如下一天，下一個星期的一天發(fā)生的交通事故 。傷亡人數(shù)往往與同一事故 有關(guān)，因此，獨立性假設(shè)不成立。這適用于事故分析中的交通安全領(lǐng)域。他們對事故的看法往往都是一視同仁，因為總的結(jié)果比整個事故中的每個人的因素重要。 因為 事故分析涵蓋了 每一個活動中 的 不同 背景，并根據(jù)不同的信息來源范圍來 補充資料，特別是收集事故 的 數(shù)據(jù)，背景資料等 ， 我們首先要看看在交通安全領(lǐng)域的活動周期 然后再 回答 事故分析 的可能性與限制。深入研究還無法回憶起所有的必要的 用來 測試有關(guān)事故發(fā)生的假設(shè)數(shù)據(jù)。將自動檢測和視頻錄制相結(jié)合的研究交通事故的科研論文會比較容易接受。目的 ?？赡苄? 摘要： 交通事故的統(tǒng)計數(shù)字，尤其國家一級的數(shù)據(jù)對監(jiān)控和預(yù)測事故的發(fā)展，積極或消極檢測事故的發(fā)展，以及對定義安全目標(biāo)和評估工業(yè)安全特別有益。 在缺少 直接觀察 的 事故 中 ， 許多 方法論問題的產(chǎn)生 ， 導(dǎo)致不能直接測試對結(jié)果持 續(xù)討論 。由于 新的視頻設(shè)備和自動檢測事故 設(shè)備 的 不斷 發(fā)展，如 在收集 數(shù)據(jù) 方面不需要很高的成本就能 變得越來越逼真 。做這個決定是重要的。在最高一級事故總數(shù)減少。 事故是罕見的事件，因此不會受到以前事故的影響。如果其短時間內(nèi)能成立，那么它也適用于長時間，因為泊松分布變量的總和，即使他們的泊松率是不同的，但也屬于泊松分布。泊松假設(shè)是研究了很多次， 來獲得 證據(jù)支持。事故黑點分析往往阻礙了這一限制，例如，如果應(yīng)用這種測試，找出事故是否在特定的位置數(shù)是高于平均水平。這種應(yīng)用最好的例子是為一個國家或地區(qū)進(jìn)行超過一年的安全監(jiān)測，用事故的總體數(shù)據(jù)（最終的特定類型，如死亡事故）與前幾年的數(shù)據(jù)相比較。 測試不局限于總體影響，但卡方值就可以分解模型內(nèi)子假說。這對于道路安全分析，那里一段時間，道路使用者的數(shù)量，地點或公里數(shù)的車輛往往是必要的修正有利。因此，人們可能會說，研究對象是意外。 間接觀察和缺乏系統(tǒng)的控制組合使調(diào)查人員很難發(fā)現(xiàn)在什么情況下造成事故的因素。 另一種方 法是看事故特征組合， 然后 找出關(guān)鍵因素。 Accident Analysis。 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 (. , when a number of cars run into each other) the series of accidents may be regarded as one plicated accident with many cars 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 parison of (posite) situations, is whether two outes from an aggregation of situations in time and/or space, have a parable mix of basic situations. . , the parison of the number of accidents on one particular day of the year, as pared 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 outes 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 pute 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 交通事故分析的可能性和局限性 8 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 pared are large enough to show existing differences. In many local cases an