Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication

Abstract

Real-time and reliable short-term traffic state prediction is one of the most critical technologies in intelligent transportation systems (ITS). However, the traffic state is generally perceived by single sensor in existing studies, which is difficult to satisfy the requirement of real-time prediction in complex traffic networks. In this paper, a short-term traffic prediction model based on complex neural network is proposed under the environment of vehicle-to-everything (V2X) communication systems. Firstly, a traffic perception system of multi-source sensors based on V2X communication is proposed and designed. A mobile edge computing (MEC)-assisted architecture is then introduced in a V2X network to facilitate perceptual and computational abilities of the system. Moreover, the graph convolutional network (GCN), the gated recurrent unit (GRU), and the soft-attention mechanism are combined to extract spatiotemporal features of traffic state and integrate them for future prediction. Finally, an intelligent roadside test platform is demonstrated for perception and computation of real-time traffic state. The comparison experiments show that the proposed method can significantly improve the prediction accuracy by comparing with the existing neural network models, which consider one of the spatiotemporal features. In particular, for comparison results of the traffic state prediction and the error value of root mean squared error (RMSE) is reduced by 39.53%, which is the greatest reduction in error occurrences by comparing with the GCN and GRU models in 5, 10, 15 and 30 minutes respectively.

Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios

“…Better prediction performance is obtained by optimizing the LSTM network parameters. With the widespread application of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies [ 22 , 23 , 24 ], the accuracy of trajectory prediction under urban scenarios has been improved greatly by combining the advantages of sensor fusion technologies. Zyner et al [ 25 ] proposed a trajectory prediction method based on multimodal probabilistic solutions, which combined recurrent neural networks (RNNs) with mixture density networks (MDNs) to predict vehicle trajectories with high prediction accuracy.…”

Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

“…The high-precision and robust information of vehicular positioning can not only work for the navigation, but also provide the data support for the perception, decision-making, and path planning modules in CEVs [4][5][6]. However, it is hard to meet the requirements of high-precision perception though any single sensor in real complex traffic environments [7][8][9].…”