摘要
交通流预测是智能交通系统的关键基础技术之一,直接影响到交通控制与诱导系统的实现。针对当前道路交通流预测方法不能充分揭示交通流内部本质规律的问题,提出一种基于深度学习的预测模型,利用长短期记忆网络(LSTM),对交通流量进行了预测。结果显示,提出的预测模型具有较高的准确性,是一种有效的交通流预测方法。
Traffic flow prediction is one of the key basic technologies of intelligent transportation system,which directly affects the realization of traffic control and induction system.In view of existing prediction methods can not fully reveal the essence of traffic flow,this paper proposes a forecasting model based on depth learning.The traffic flow is predicted by using long short-term memory network.The experiment results show that the proposed prediction model has high accuracy and is an effective method for traffic flow prediction.
作者
庞悦
赵威
张雅楠
许宏科
Pang Yue;Zhao Wei;Zhang Yanan;Xu Hongke(School of Electronics and Control Engineering,Chang’an University,Xi’an 710064,China)
出处
《单片机与嵌入式系统应用》
2019年第3期72-75,共4页
Microcontrollers & Embedded Systems
