摘要：为了研究快速公交（BRT）系统公交站台停靠时间的可靠预测技术，对 BRT 车辆在站台停靠的物理过程进行分析．该过程既具有纵向时间相关性，又受到其他交通子系统的非线性作用，因此将 BRT 车辆停站时间拆解成线性部分和非线性部分．分别采用差分自回归移动平均（ARI-MA）模型和支持向量机（SVM）方法对两部分进行预测，并将预测结果叠加，构成一种快速公交停站时间的组合预测方法．以常州 BRT 2号线2个快速公交站的停站时间数据及其相关数据为样本进行建模，建模结果表明该组合预测方法行之有效．相较于单一的 ARIMA 模型和 SVM 模型，组合模型停站时间预测值的平均相对百分误差、均方误差均明显降低，误差1 s 内命中百分率提高，且在训练数据足够时，组合模型的平均相对百分误差、均方误差分别为0.62％和4.05 s2，误差1 s 内命中百分率达到96.79％．%To explore a reliable dwell time prediction technology through experiments,the physical process of bus rapid transit (BRT)when it stays at the stops is analyzed.Both the longitudinal cor-relation and nonlinear effects from other traffic subsystems are included in this process.Therefore, the dwell time can be divided into the linear and nonlinear parts.Accordingly,autoregressive inte-grated moving average(ARIMA)model and support vector machine (SVM)are adopted to predict these two parts,and the final prediction results are produced by combining the two parts.Thus,the hybrid dwell time prediction method for BRT is established.The dwell time and the relative data gained at two stops in BRT Line 2 in Changzhou are modeled.The results indicate that the hybrid prediction method is effective.Compared with the single ARIMA and SVM models,the hybrid pre-diction method has a sharp decline of the mean absolute error (MAPE)and the mean square error (MSE).Also,the target percent whose prediction error is less than 1 s significantly increases.Fur-thermore,the MAPE,MSE and the target percent can reach 0.62%,4.05 s2 and 96.79%,respec-tively,when training data is enough.
