The invention discloses a highway vehicle speed prediction model training method based on a toll station flow. In and out flows of large and small vehicles between different toll stations are taken asa characteristic, then a distance weight is added, and finally an LVQ algorithm is used for matching optimization so that time and space factors can be comprehensively considered, and a prediction speed can also be accelerated. According to the model training method, speeds of the large and small vehicles can be monitored without complex and expensive detection instruments; and the speeds of thelarge and small vehicles can be predicted only by counting station charge data, an overall traffic flow is roughly judged, speed limiting or flow limiting can be indicated for a certain section of highway through a traffic sign, an effect of controlling an overall traffic road condition is achieved, and traffic accidents can be effectively reduced.
本发明公开了一种基于收费站流量的高速公路车速预测模型训练方法,通过不同收费站点之间大小车的进出流量作为特征,然后加入距离权重,最后使用LVQ算法进行匹配优化,这样不仅能综合考虑时间和空间因素,也能加快预测速度。本发明模型训练方法无需通过复杂和昂贵的检测仪器来监测大小车车速,只需要统计站点收费数据,就可以预测大小车车速,对于整体的交通流有一个大致的判断,可以通过交通指示牌对于某一段高速公路进行指示限速或者限流,达到控制整体交通路况的作用,能有效减少交通事故的发生。
Highway vehicle speed prediction model training method based on toll station flow
一种基于收费站流量的高速公路车速预测模型训练方法
2020-04-24
Patent
Electronic Resource
Chinese
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