Af Wåhlberg A E
Department of Psychology, Uppsala University, PO Box 1225, 751 42 Uppsala, Sweden.
J Safety Res. 2007;38(1):9-15. doi: 10.1016/j.jsr.2006.10.002. Epub 2007 Feb 5.
Driver celeration (speed change) behavior of bus drivers has previously been found to predict their traffic incident involvement, but it has also been ascertained that the level of celeration is influenced by the number of passengers carried as well as other traffic density variables. This means that the individual level of celeration is not as well estimated as could be the case. Another hypothesized influence of the number of passengers is that of differential quality of measurements, where high passenger density circumstances are supposed to yield better estimates of the individual driver component of celeration behavior.
Comparisons were made between different variants of the celeration as predictor of traffic incidents of bus drivers. The number of bus passengers was held constant, and cases identified by their number of passengers per kilometer during measurement were excluded (in 12 samples of repeated measurements).
After holding passengers constant, the correlations between celeration behavior and incident record increased very slightly. Also, the selective prediction of incident record of those drivers who had had many passengers when measured increased the correlations even more.
The influence of traffic density variables like the number of passengers have little direct influence on the predictive power of celeration behavior, despite the impact upon absolute celeration level. Selective prediction on the other hand increased correlations substantially. This unusual effect was probably due to how the individual propensity for high or low celeration driving was affected by the number of stops made and general traffic density; differences between drivers in this respect were probably enhanced by the denser traffic, thus creating a better estimate of the theoretical celeration behavior parameter C. The new concept of selective prediction was discussed in terms of making estimates of the systematic differences in quality of the individual driver data.
此前已发现公交司机的驾驶加速度（速度变化）行为可预测其交通事故的参与情况，但也已确定加速度水平受载客量以及其他交通密度变量的影响。这意味着个体的加速度水平未能得到尽可能准确的估计。关于载客量的另一个假设影响是测量质量的差异，即在高乘客密度情况下，应该能够更好地估计加速度行为中个体司机的因素。
对公交司机交通事故预测指标加速度的不同变体进行了比较。保持公交乘客数量不变，并排除了在测量期间按每公里乘客数量确定的案例（在12个重复测量样本中）。
在保持乘客数量不变后，加速度行为与事故记录之间的相关性略有增加。此外，对测量时载有大量乘客的司机的事故记录进行选择性预测，相关性增加得更多。
尽管载客量等交通密度变量对绝对加速度水平有影响，但对加速度行为的预测能力几乎没有直接影响。另一方面，选择性预测大幅提高了相关性。这种不寻常的效果可能是由于停车次数和总体交通密度对个体高或低加速度驾驶倾向的影响方式；在这方面，司机之间的差异可能因更密集的交通而加剧，从而能更好地估计理论加速度行为参数C。从对个体司机数据质量的系统差异进行估计的角度讨论了选择性预测的新概念。
