基于物联网传感器的城市交通状态预测

Urban Traffic State Forecast Based on Internet of Things Sensors

随着物联网大规模应用和迅猛发展,针对目前城市交通存在的拥挤状态和不平衡矛盾,基于物联网多传感器轨迹数据,设计适合城市复杂交通网络的地图匹配方法及基于轨迹的交通流量参数估计,研究物联网多传感器轨迹数据的时间序列平稳化和线性化算法,建立了基于物联网传感器的城市交通流量状态时空关联模型,对多源物联网传感器进行感知和反应,在此模型基础上根据需要和单目标跟踪实现城市交通流量的实时分流引导和预测。

The current large-scale state of urban traffic congestion and imbalances are very serious problems now.With the development of Internet and its applications,complex urban traffic network map matching method and the trajectory-based traffic estimation parameter are designed,based on the internet of things multi-sensor object track data.time series smoothing and line algorithm for multi-sensor track data are designed,the urban traffic flow state space-time model based on the internet of things multi-sensor,multi-source material are established in the network sensor perception and response.Finally real-time streaming for urban traffic flow guidance and forecast for single-target tracking need are achieved based on this model.