Spatiotemporal traffic-flow dependency and short-term traffic forecasting

2008 ◽  
Vol 35 (5) ◽  
pp. 762-771 ◽  
Author(s):  
Yang Yue ◽  
Anthony Gar-On Yeh
Keyword(s):  
Traffic Flow ◽  
Short Term ◽  
Traffic Forecasting ◽  
Flow Dependency

scholarly journals A Hybrid Short-term Traffic Flow Forecasting Method Based on Neural Networks Combined with K-Nearest Neighbor

2018 ◽  
Vol 30 (4) ◽  
pp. 445-456 ◽  
Author(s):  
Zhao Liu ◽  
Jianhua Guo ◽  
Jinde Cao ◽  
Yun Wei ◽  
Wei Huang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Traffic Flow ◽  
Nearest Neighbor ◽  
Network Models ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Neural Network Models ◽  
Traffic Forecasting

It is critical to implement accurate short-term traffic forecasting in traffic management and control applications. This paper proposes a hybrid forecasting method based on neural networks combined with the K-nearest neighbor (K-NN) method for short-term traffic flow forecasting. The procedure of training a neural network model using existing traffic input-output data, i.e., training data, is indispensable for fine-tuning the prediction model. Based on this point, the K-NN method was employed to reconstruct the training data for neural network models while considering the similarity of traffic flow patterns. This was done through collecting the specific state vectors that were closest to the current state vectors from the historical database to enhance the relationship between the inputs and outputs for the neural network models. In this study, we selected four different neural network models, i.e., back-propagation (BP) neural network, radial basis function (RBF) neural network, generalized regression (GR) neural network, and Elman neural network, all of which have been widely applied for short-term traffic forecasting. Using real world traffic data, the  experimental results primarily show that the BP and GR neural networks combined with the K-NN method have better prediction performance, and both are sensitive to the size of the training data. Secondly, the forecast accuracies of the RBF and Elman neural networks combined with the K-NN method both remain fairly stable with the increasing size of the training data. In summary, the proposed hybrid forecasting  approach outperforms the conventional forecasting models, facilitating the implementation of short-term  traffic forecasting in traffic management and control applications.

scholarly journals Short-term Traffic Flow Prediction Using Artificial Intelligence with Periodic Clustering and Elected Set

2020 ◽  
Vol 32 (1) ◽  
pp. 65-78
Author(s):  
Erdem Doğan
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Predictive Performance ◽  
Prediction Errors ◽  
Training Set ◽  
Short Term ◽  
Traffic Forecasting ◽  
Traffic Flow Prediction ◽  
Real Time Traffic ◽  
The Us

Forecasting short-term traffic flow using historical data is a difficult goal to achieve due to the randomness of the event. Due to the lack of a solid approach to short-term traffic prediction, the researchers are still working on novel approaches. This study aims to develop an algorithm that dynamically updates the training set of models in order to make more accurate predictions. For this purpose, an algorithm called Periodic Clustering and Prediction (PCP) has been developed for use in short-term traffic forecasting. In this study, PCP was used to improve Artificial Neural Networks (ANN) predictive performance by improving the training set of ANN to predict short-term traffic flow using selected clusters. A large amount of traffic data collected from the US and UK motorways was used to determine the PCP ability to increase the ANN performance. The robustness of the proposed approach was determined by the performance measures used in the literature and the mean prediction errors of PCP were significantly below other approaches. In addition, the studies showed that the percentage errors of PCP predictions decreased in response to increasing traffic flow values. Considering the obtained positive results, this method can be used in real-time traffic control systems and in different areas needed.

scholarly journals Fuzzy State Transition and Kalman Filter Applied in Short-Term Traffic Flow Forecasting

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Deng Ming-jun ◽  
Qu Shi-ru
Keyword(s):  
Kalman Filter ◽  
Traffic Flow ◽  
Road Traffic ◽  
Short Term ◽  
Traffic Forecasting ◽  
Traffic State ◽  
Forecasting Error ◽  
Trend Forecasting ◽  
State Variation ◽  
Fuzzy State

Traffic flow is widely recognized as an important parameter for road traffic state forecasting. Fuzzy state transform and Kalman filter (KF) have been applied in this field separately. But the studies show that the former method has good performance on the trend forecasting of traffic state variation but always involves several numerical errors. The latter model is good at numerical forecasting but is deficient in the expression of time hysteretically. This paper proposed an approach that combining fuzzy state transform and KF forecasting model. In considering the advantage of the two models, a weight combination model is proposed. The minimum of the sum forecasting error squared is regarded as a goal in optimizing the combined weight dynamically. Real detection data are used to test the efficiency. Results indicate that the method has a good performance in terms of short-term traffic forecasting.

scholarly journals A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Darong Huang ◽  
Zhenping Deng ◽  
Bo Mi
Keyword(s):  
Traffic Flow ◽  
Prediction Method ◽  
Exponential Smoothing ◽  
Weighting Factor ◽  
Prediction Algorithm ◽  
Strong Fluctuation ◽  
Short Term ◽  
Order Difference ◽  
First Order ◽  
Traffic Forecasting

Directing against the shortcoming of low accuracy in short-term traffic flow prediction caused by strong traffic flow fluctuation, a novel method for short-term traffic forecasting based on the combination of improved grey Verhulst prediction algorithm and first-order difference exponential smoothing is proposed. Firstly, we constructed an improved grey Verhulst prediction model by introducing the Markov chain to its traditional version. Then, based on an introduced dynamic weighting factor, the improved grey Verhulst prediction method, and the first-order difference exponential smoothing technique, the new method for short-term traffic forecasting is completed in an efficient way. Finally, experiment and analysis are carried out in the light of actual data gathered from strong fluctuation environment to verify the effectiveness and rationality of our proposed scheme.

An Improved Model for Short-Term Traffic Forecasting Considering Weather Impacts

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Xinchao Chen ◽  
Si Qin ◽  
Jian Zhang ◽  
Huachun Tan ◽  
Yunxia Xu ◽  
...  
Keyword(s):  
Short Term ◽  
Traffic Forecasting ◽  
Improved Model ◽  
Weather Impacts
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