scholarly journals Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on Space–Time Analysis and GRU

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 143025-143035 ◽  
Author(s):  
Guowen Dai ◽  
Changxi Ma ◽  
Xuecai Xu
Keyword(s):  
Traffic Flow ◽  
Prediction Method ◽  
Space Time ◽  
Time Analysis ◽  
Short Term ◽  
Urban Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction

scholarly journals Multidimensional Scaling-Based Data Dimension Reduction Method for Application in Short-Term Traffic Flow Prediction for Urban Road Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yi Zhao ◽  
Satish V. Ukkusuri ◽  
Jian Lu
Keyword(s):  
Dimension Reduction ◽  
Traffic Flow ◽  
Reduction Method ◽  
Prediction Models ◽  
Urban Traffic ◽  
Dimension Reduction Method ◽  
Short Term ◽  
Urban Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction

This study develops a multidimensional scaling- (MDS-) based data dimension reduction method. The method is applied to short-term traffic flow prediction in urban road networks. The data dimension reduction method can be divided into three steps. The first is data selection based on qualitative analysis, the second is data grouping using the MDS method, and the last is data dimension reduction based on a correlation coefficient. Backpropagation neural network (BPNN) and multiple linear regression (MLR) models are employed in four kinds of urban traffic environments to test whether the proposed method improves the prediction accuracy of traffic flow. The results show that prediction models using traffic data after dimension reduction outperform the same prediction models using other datasets. The proposed method provides an alternative to existing models for urban traffic prediction.

scholarly journals An AutoEncoder and LSTM-Based Traffic Flow Prediction Method

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2946 ◽  
Author(s):  
Wangyang Wei ◽  
Honghai Wu ◽  
Huadong Ma
Keyword(s):  
Traffic Flow ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Smart Cities ◽  
Prediction Method ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Long Short Term Memory

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques

2015 ◽  
Vol 22 (2) ◽  
pp. 779-786 ◽  
Author(s):  
Meng Meng ◽  
Chun-fu Shao ◽  
Yiik-diew Wong ◽  
Bo-bin Wang ◽  
Hui-xuan Li
Keyword(s):  
Traffic Flow ◽  
Prediction Method ◽  
Short Term ◽  
Two Stage ◽  
Traffic Flow Prediction ◽  
Flow Prediction

scholarly journals Short-term Traffic Flow Prediction Method in Bayesian Networks Based on Quantile Regression

2020 ◽  
Vol 32 (6) ◽  
pp. 821-835
Author(s):  
Jing Luo
Keyword(s):  
Quantile Regression ◽  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Prediction Method ◽  
Time Data ◽  
Short Term ◽  
Internet Of Vehicles ◽  
Traffic Flow Prediction ◽  
Spatiotemporal Correlation ◽  
Flow Prediction

With the popularization of intelligent transportation system and Internet of vehicles, the traffic flow data on the urban road network can be more easily obtained in large quantities. This provides data support for shortterm traffic flow prediction based on real-time data. Of all the challenges and difficulties faced in the research of short-term traffic flow prediction, this paper intends to address two: one is the difficulty of short-term traffic flow prediction caused by spatiotemporal correlation of traffic flow changes between upstream and downstream intersections; the other is the influence of deviation of traffic flow caused by abnormal conditions on short-term traffic flow prediction. This paper proposes a Bayesian network short-term traffic flow prediction method based on quantile regression. By this method the trouble caused by spatiotemporal correlation of traffic flow prediction could be effectively and efficiently solved. At the same time, the prediction of traffic flow change under abnormal conditions has higher accuracy.

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