【正文】 safety research Leidschendam（ 會(huì)議記錄 ），記錄者， . 5 5 POSSIBILITIES AND LIMITATIONS OF ACCIDENT ANALYSIS Keyword： Consequences。一個(gè)在交通意外的過程，結(jié)果是，該實(shí)際發(fā)生是由研究者未落觀測(cè)研究的主要問題。和 2。這種測(cè)試是相當(dāng)麻煩的，因?yàn)槊總€(gè)特定的情況下，每一個(gè)不同的泊松參數(shù)，即，對(duì)所有可能結(jié)果的概率必須計(jì)算應(yīng)用測(cè)試。 例如， 對(duì)比在一年中特定的一天例如下一天，下一個(gè)星期的一天發(fā)生的交通事故 。這適用于事故分析中的交通安全領(lǐng)域。 因?yàn)?事故分析涵蓋了 每一個(gè)活動(dòng)中 的 不同 背景，并根據(jù)不同的信息來源范圍來 補(bǔ)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(dòng)周期 然后再 回答 事故分析 的可能性與限制。將自動(dòng)檢測(cè)和視頻錄制相結(jié)合的研究交通事故的科研論文會(huì)比較容易接受。可能性 摘要： 交通事故的統(tǒng)計(jì)數(shù)字，尤其國(guó)家一級(jí)的數(shù)據(jù)對(duì)監(jiān)控和預(yù)測(cè)事故的發(fā)展，積極或消極檢測(cè)事故的發(fā)展，以及對(duì)定義安全目標(biāo)和評(píng)估工業(yè)安全特別有益。由于 新的視頻設(shè)備和自動(dòng)檢測(cè)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 。在最高一級(jí)事故總數(shù)減少。如果其短時(shí)間內(nèi)能成立，那么它也適用于長(zhǎng)時(shí)間，因?yàn)椴此煞植甲兞康目偤?，即使他們的泊松率是不同的，但也屬于泊松分布。事故黑點(diǎn)分析往往阻礙了這一限制，例如，如果應(yīng)用這種測(cè)試，找出事故是否在特定的位置數(shù)是高于平均水平。 測(cè)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。因此，人們可能會(huì)說，研究對(duì)象是意外。 另一種方 法是看事故特征組合， 然后 找出關(guān)鍵因素。 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 application is not possible. Accident blackspot analysis is often hindered by this limitation, ., 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, . for each different Poisson parameter