An adaptive hybrid model for short-term urban traffic flow prediction

2019 ◽  
Vol 527 ◽  
pp. 121065 ◽  
Author(s):  
Qinzhong Hou ◽  
Junqiang Leng ◽  
Guosheng Ma ◽  
Weiyi Liu ◽  
Yuxing Cheng
Keyword(s):  
Traffic Flow ◽  
Hybrid Model ◽  
Urban Traffic ◽  
Short Term ◽  
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.

Short-Term Traffic Flow Prediction Based on Hybrid Model

2020 ◽  
pp. 135-143
Author(s):  
Yong Hu ◽  
Meng Yu ◽  
Guanxiang Yin ◽  
Fei Du ◽  
Meng Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Hybrid Model ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction

scholarly journals Short-Term Traffic Flow Prediction of Expressway: A Hybrid Method Based on Singular Spectrum Analysis Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chunyan Shuai ◽  
Zhengyang Pan ◽  
Lun Gao ◽  
HongWu Zuo
Keyword(s):  
Traffic Flow ◽  
Hybrid Model ◽  
Spectrum Analysis ◽  
Short Term Memory ◽  
Singular Spectrum Analysis ◽  
Support Vector ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Singular Spectrum ◽  
Flow Prediction

Real-time expressway traffic flow prediction is always an important research field of intelligent transportation, which is conducive to inducing and managing traffic flow in case of congestion. According to the characteristics of the traffic flow, this paper proposes a hybrid model, SSA-LSTM-SVR, to improve forecasting accuracy of the short-term traffic flow. Singular Spectrum Analysis (SSA) decomposes the traffic flow into one principle component and three random components, and then in terms of different characteristics of these components, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) are applied to make prediction of different components, respectively. By fusing respective forecast results, SSA-LSTM-SVR obtains the final short-term predictive value. Experiments on the traffic flows of Guizhou expressway in January 2016 show that the proposed SSA-LSTM-SVR model has lower predictive errors and a higher accuracy and fitting goodness than other baselines. This illustrates that a hybrid model for traffic flow prediction based on components decomposition is more effective than a single model, since it can capture the main regularity and random variations of traffic flow.

The Urban Road Short-Term Traffic Flow Prediction Research

2013 ◽  
Vol 423-426 ◽  
pp. 2954-2956 ◽  
Author(s):  
Zhen Hai Qin
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Evaluation Model ◽  
Social Resources ◽  
Urban Traffic ◽  
Short Term ◽  
Urban Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Short Period

To predict the future traffic flow status more accurately is of great significance to alleviate urban traffic congestion for a short period of time and avoid the waste of social resources. At first, this paper summarizes the characteristics of urban expressway traffic flow. Then establishes BP neural network short-term traffic flow evaluation model based on MATLAB, and finally through the instance, verify the validity of the model.

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