The invention discloses a highway congestion dissipation time prediction method considering traffic event information, and the method comprises the steps: carrying out the decision tree model learning through employing the detected abnormal event detailed information data, and constructing a decision tree model which can be used for predicting the duration time of an event; constructing a traffic capacity estimation model by using the event report information; on this basis, a congestion dissipation time prediction model based on a fuzzy traffic wave model is constructed, the output of the two parts and the fuzzy traffic wave model are integrated, and finally the congestion dissipation time prediction model under the event condition is obtained. The method can effectively overcome the limitation that specific conditions of abnormal events are not fully considered when an existing congestion dissipation time prediction method is used for prediction, and meanwhile, the fuzzy traffic wave model can be used for fully considering the situation that the speed distribution difference in the vehicle queuing process is not fully considered when a traditional traffic wave model is used; and thus, higher prediction precision and generalization ability for the congestion dissipation time are realized.
本发明公开了一种考虑交通事件信息的高速公路拥堵消散时间预测方法,通过利用检测到的异常事件详细信息数据来进行决策树模型学习,进而构建出可用于预测事件持续时间的决策树模型;同时利用事件上报信息构建出通行能力估计模型;在此基础上构建出基于模糊交通波模型的拥堵消散时间预测模型,综合上述两部分的输出和模糊交通波模型,最后得出事件情况下的拥堵消散时间预测模型。本发明方法可以有效克服现有拥堵消散时间预测方法预测时对异常事件具体情况的考虑不充分的局限性,同时利用模糊交通波模型可以充分考虑到利用传统交通波模型时未充分考虑车辆排队过程中的速度分布差异性,进而实现对拥堵消散时间更高的预测精度和泛化能力。
Expressway congestion dissipation time prediction method considering traffic event information
一种考虑交通事件信息的高速公路拥堵消散时间预测方法
2024-04-26
Patent
Electronic Resource
Chinese
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