Road traffic state prediction based on a graph embedding recurrent neural network under the SCATS

2019 ◽  
Vol 29 (10) ◽  
pp. 103125 ◽  
Author(s):  
Dongwei Xu ◽  
Hongwei Dai ◽  
Yongdong Wang ◽  
Peng Peng ◽  
Qi Xuan ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Road Traffic ◽  
Graph Embedding ◽  
Traffic State ◽  
State Prediction

scholarly journals A Dilated Recurrent Neural Network-Based Model for Graph Embedding

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 32085-32092
Author(s):  
Xiao Han ◽  
Chunhong Zhang ◽  
Yang Ji ◽  
Zheng Hu
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Graph Embedding

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

2021 ◽  
Vol 11 (23) ◽  
pp. 11530
Author(s):  
Pangwei Wang ◽  
Xiao Liu ◽  
Yunfeng Wang ◽  
Tianren Wang ◽  
Juan Zhang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Short Term ◽  
Spatiotemporal Features ◽  
Traffic State ◽  
State Prediction ◽  
Comparison Results ◽  
Perception System

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.

A new car-following model considering recurrent neural network

2019 ◽  
Vol 33 (26) ◽  
pp. 1950304 ◽  
Author(s):  
Chen Hua
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Relative Velocity ◽  
Road Traffic ◽  
Safe Distance ◽  
Velocity Difference ◽  
Car Following ◽  
Depth Analysis ◽  
Car Following Model ◽  
The Relationship

A new car-following model is proposed based on recurrent neural network (RNN) to effectively describe the state change and road traffic congestion while the vehicle is moving. The model firstly gives a full velocity difference car-following model according to the driver’s reaction sensitivity and relative velocity, and then takes the vehicle position and velocity as the input parameters to optimize the safe distance between the front and rear vehicles in the car-following model based on RNN model. Finally, the effectiveness of the above model is validated by building a simulation experiment platform, and an in-depth analysis is conducted on the relationship among influencing factors, e.g., relative velocity, reaction sensitivity, headway, etc. The results reveal that, compared with traditional car-following models, the model can quickly analyze the relationship between initial position and velocity of the vehicle in a shorter time and thus obtain a smaller safe distance. In the case of small velocity difference between the front and rear vehicles, the running velocity of the front and rear vehicles is relatively stable, which is conducive to maintaining the headway.

Real-time road traffic state prediction based on ARIMA and Kalman filter

2017 ◽  
Vol 18 (2) ◽  
pp. 287-302 ◽  
Author(s):  
Dong-wei Xu ◽  
Yong-dong Wang ◽  
Li-min Jia ◽  
Yong Qin ◽  
Hong-hui Dong
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Road Traffic ◽  
Traffic State ◽  
State Prediction

Real-time road traffic state prediction based on kernel-KNN

2018 ◽  
Vol 16 (1) ◽  
pp. 104-118 ◽  
Author(s):  
Dongwei Xu ◽  
Yongdong Wang ◽  
Peng Peng ◽  
Shen Beilun ◽  
Zhang Deng ◽  
...  
Keyword(s):  
Real Time ◽  
Road Traffic ◽  
Traffic State ◽  
State Prediction
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