基于带权重的模式识别算法(WPＲA)的交通流短时预测根据历史交通模式所属时段特征区分不同历史状态值权重系数的大小，但权重值的主观设定降低了方法实际应用的可靠性． 通过分析基于数据驱动的非参数回归交通流预测算法核心原理，针对 WPRA模型权重系数的主观随机性进行预测算法改进，建立了能预测短时交通流的带距离权重的模式识别算法(DWPRA)． 最后，应用实际交通流数据引入均方根误差进行算法验证，验证结果显示相同近邻 K 值情况下，DWPRA 比 WPＲA 均方根误差降低约 4． 8% ～7． 1%，证明了算法的有效性．
In the short-term traffic flow forecasting based on the weighted traffic pattern recognition algorithm(WPRA)，the weights of different historical state values are distinguished according to the time-interval characteristics of historical traffic patterns，but in practical application，the subjective setting of the weight values reduces the reliability of this method． In this paper，by analyzing the core principle of the data-driven non-parametric-regression traffic flow forecasting algorithm and by improving the forecasting algorithm aiming at the uncertainty of the time-interval-characteristic weights of WPRA，a distance-based weighted pattern recognition algorithm (DWPRA) is proposed to forecast the short-term traffic flow． Finally，the root mean square error between real traffic flows and predicted ones is introduced to verify the proposed algorithm． The results show that，at the same neighbor number K，the root mean square error of DWPRA is 4.8% ～7.1% lower than that of WPRA，which proves the effectiveness of DWPRA．
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