关键词: 城市交通, 速度预测, 深度学习, 交通流, 时空特征

Abstract:

With the increasing application of internet of things, cloud computing and big data in the field of intelligent transportation system, traditional traffic prediction methods which take the road sections as research object cannot satisfy the development of intelligent connected technique. To forecast the traffic state of lanes, a novel combined deep learning (CDL) model is proposed to predict the travel speed of the lanes of expressways. First, the CDL model introduces an entropy- based grey relation analysis to extract the variables of spatial characteristics. Then, the CDL model uses long short- term memory neural network to capture the temporal characteristics of the extracted spatial variables. Finally, the gated recurrent unit neural network is utilized to predict the travel speed of target lane section in the next time intervals. Validated by the ground-truth microwave data of lane sections on the 2nd ring road of Beijing, the proposed model can well capture the trend of speed change during different time periods of the different lanes and realize the single-step and multi-step prediction of lane speed effectively. The prediction results illustrate that the CDL model outperforms many traditional methods in terms of accuracy and stability.

Key words: urban traffic, speed prediction, deep learning, traffic flow, spatio-temporal characteristics