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摘要:
精准的短时交通状态预测是实施有效的交通管理与控制的重要依据。而可预知性特殊事件（PSEs）短时间内在其举办地点周边产生异常的交通出行需求，又因为事件发生数量少、数据样本收集困难等不利因素，往往造成预测精度难以保证。为此，通过实测数据分析了PSEs下短时交通演化特性，在此基础上，采用改进的K近邻（KNN）算法框架，提出一种短时交通状态的KNN（PSE-KNN）预测模型，并通过基于深度强化学习的实时超参数优化方法将其构建成自适应PSE-KNN（APSE-KNN）模型，最后以北京市演唱会场景为例对所提模型的效果进行了验证。结果表明：所提模型在多步预测实验中，相对于其他7种对比预测模型，平均减少残差值12.43%、降低绝对值百分比误差29.90%。证明所提模型有优异的快速调整能力，其更适应于PSEs场景下短时交通状态预测任务。
Abstract:Accurate short-term traffic state prediction is an important basis for effective traffic management and control. The planned special events (PSEs) generate abnormal traffic demand around the venue in a short time. However, due to the limited number of the special events and the difficulty in data sample collection, the prediction accuracy is hard to guarantee.Therefore, the short-term traffic evolution characteristics under PSEs are analyzed by measured data. On this basis, a short-term traffic state prediction model is established by using the framework of improved K-nearest neighbor (KNN) algorithm. Therefore, the evolution characteristics of short-term traffic under PSEs are analyzed through real event data, and a short-term traffic state KNN (PSE-KNN) prediction model was proposed. Moreover, through real-time super parameter optimization method based on Deep reinforcement learning, we constructed into an adaptive PSE-KNN (APSE-KNN) model. Finally, the effect of the model is verified by taking the concert scene in Beijing as an example. The results show that in the multi-step prediction experiment, compared with the other seven comparative prediction models, the proposed prediction model reduces the mean residual error by 12.43 % and the mean absolute percentage error by 29.90 % on average. These results prove that this model has excellent rapid adjustment ability and is more suitable for short-term traffic state prediction task under PSEs.
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图 1 PSEs下的交通状态演化特性分析实例
Figure 1. Example of traffic state evolution characteristics analysis under PSEs
图 3 演唱会影响下的短时交通状态预测残差
Figure 3. Residual error of short-term traffic state prediction under influence of concert
图 4 演唱会影响下的短时交通状态预测eMAPE误差
Figure 4. eMAPE error of short-term traffic state prediction under influence of concert
图 5 演唱会结束时本文APSE-KNN与PSE-KNN模型预测效果对比
Figure 5. Comparison of APSE-KNN and PSE-KNN prediction effect at end of concert
表 1 PSE-KNN模型在多步预测任务中标定的最优参数
Table 1. Optimal parameters calibrated by PSE-KNN model in multi-step prediction task
$f$ $K$ $\alpha $ 1 97 0.9 2 57 0.9 3 57 0.8 4 54 0.8 5 54 0.7 
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