The invention relates to a method for intelligently, accurately and automatically identifying and pre-judging an expressway bottleneck area. Expressway bottleneck area data, expressway bottleneck areadata and rail transit bottleneck station data with less sample size are used; according to a spatial element learning method, a universal expressway bottleneck area identification model with relatively high precision is obtained through training in combination with a neural network model, the model can continuously and actively perform reasoning on an area and time in which a bottleneck area maybe formed on an expressway section in real time, and once the model detects that the bottleneck area may be formed, early warning can be performed in time. By means of the artificial intelligence method, the expressway bottleneck area can be intelligently and automatically recognized, other similar bottleneck area data can be effectively combined, the model precision of small sample learning of the urban expressway bottleneck area is improved, and thus early warning is conducted in time in advance; and the method has important value and significance in relieving urban traffic congestion.
本发明涉及一种智能精确自动识别预判快速路瓶颈区方法,通过较少样本量的快速路瓶颈区数据、高速公路瓶颈区数据、轨道交通瓶颈站点数据,根据空间元学习方法,结合神经网络模型,训练得到一个通用的、精度较高的快速路瓶颈区识别模型,该模型可以实时、不间断地主动对快速路段可能会形成瓶颈区的区域和时间进行推理,一旦模型检测到可能会形成瓶颈区,会及时进行预警。通过本发明中的人工智能方法,不仅能够智能自动识别快速路瓶颈区,还能够有效结合其他类似瓶颈区数据,提高城市快速路瓶颈区小样本学习的模型精度,从而及时提前进行预警,对缓解城市交通拥堵有重要价值和意义。
Method for intelligently, accurately and automatically identifying and pre-judging expressway bottleneck area
一种智能精确自动识别预判快速路瓶颈区方法
2020-09-29
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06K Erkennen von Daten , RECOGNITION OF DATA / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08B SIGNALLING OR CALLING SYSTEMS , Signalisier- oder Rufsysteme |
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