基于网络层析成像技术的道路交通流预测算法

为了更有效地预测城市路网交通流量，本文提出了一种城市道路交通预测模型.该模型基于网络层析成像(Network Tomography, NT)技术建立生成树，采用期望最大 (Expectation Maximization, EM) 算法得到路网子网车流概率分布，再结合路网子网中流量守恒原则，对待预测路段流量进行推测.实验结果表明，该模型优于现常用的人工智能模型，对城市交通流量预测更为有效，且提高了预测精度.

Traffic flow forecasting based on network tomography

To forecast the urban traffic flow more effectively, this paper proposes a Network Tomography based traffic flow prediction model. The model builds a spanning tree based on Network Tomography, estimates traffic probability distribution in road network subnet by Expectation Maximization (EM) algorithm, forecasts the traffic flow according to the flow conservation in road network. Experimental results show that the new model has higher estimation accuracy compared to the Artificial intelligence model.