【正文】 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, the probabilities for all possible outes must be puted 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 putations are necessary any more, but test statistics can be drawn from tables. 3. The use of accident statistics for traffic safety policy. The testing procedure described has its merits for those types of analysis that are based on the assumptions mentioned. The best example of such an application is the monitoring of safet