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

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.

scholarly journals Real-time Detection of Road Traffic Incidents

2012 ◽  
Vol 22 (4) ◽  
pp. 273-283 ◽  
Author(s):  
Pero Škorput ◽  
Sadko Mandžuka ◽  
Niko Jelušić
Keyword(s):  
Kalman Filter ◽  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Support System ◽  
Management System ◽  
Road Traffic ◽  
Incident Management ◽  
Incident Detection

The paper analyses the real-time detection of incidents in road traffic. A general model is presented of an integral road traffic incident management system. The paper presents the major incident detection methods. The detection procedure on open highway sections has been dealt with in particular. Adequate mathematical model has been defined, as the base for the realisation of the estimators of the traffic flow condition variables. The proposed method is the Extended Kalman Filter. The final part of the paper deals with an example for the realisation of the Incident Management Decision Support System (IMDSS). KEY WORDS: intelligent transport system, incident management system, traffic model in the status space, theory of estimation, extended Kalman filter, automatic incident detection, decision support system

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
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