scholarly journals Sensor Data Fusion Using Mutual Information Algorithm

2015 ◽  
Vol 37 ◽  
pp. 146 ◽  
Author(s):  
Mahdieh Shabanian ◽  
Seyed Hadi Hosseini
Keyword(s):  
Mutual Information ◽  
Data Fusion ◽  
Traffic Flow ◽  
Hybrid Algorithm ◽  
Climatic Conditions ◽  
Sensor Data ◽  
Traffic Flow Prediction ◽  
Traffic Conditions ◽  
Flow Prediction ◽  
On The Road

Traffic flow prediction is one of the congestion avoidance methods in highways. According to previous studies, no comprehensive model has been proposed for traffic flow prediction which can prevent congestion in many different traffic conditions. Using data fusion to reduce prediction error is an interesting idea to solve this problem. In this paper, a new hybrid algorithm based on mutual information for traffic flow prediction will be proposed and compared with various types of previous hybrid algorithms and predictors. The Mutual Information (MI) algorithm is used to calculate the interdependency of data, so we expect this new hybrid algorithm to have high precision in comparison with others. Simulations will be implemented based on real data in MATLAB environment as a performance demonstration of new hybrid algorithm. Due to variety of traffic flow, performance investigations of our new hybrid algorithm will be done in presence of polluted traffic data in different climatic conditions such as rain/snow fall or other traffic conditions like congestions and accidents on the road, indicating robustness of this algorithm to different types of noisy data

scholarly journals The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fan Hou ◽  
Yue Zhang ◽  
Xinli Fu ◽  
Lele Jiao ◽  
Wen Zheng
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Road Network ◽  
Short Term Memory ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Point Of Interest ◽  
Flow Prediction ◽  
Long Short Term Memory ◽  
On The Road

Aiming at the traffic flow prediction problem of the traffic network, this paper proposes a multistep traffic flow prediction model based on attention-based spatial-temporal-graph neural network-long short-term memory neural network (AST-GCN-LSTM). The model can capture the complex spatial dependence of road nodes on the road network and use LSGC (local spectrogram convolution) to capture spatial correlation features from the K-order local neighbors of the road segment nodes in the road network. It is more accurate to extract the information of neighbor nodes by replacing the single-hop neighborhood matrix with K-order local neighborhoods to expand the receptive field of graph convolution. The high-order neighborhood of road nodes is also fully considered instead of only extracting features from first-order neighbor nodes. In addition, an external attribute enhancement unit is designed to extract external factors (weather, point of interest, time, etc.) that affect traffic flow in order to improve the accuracy of the model’s traffic flow prediction. The experimental results show that when considering the static, dynamic, and static and dynamic combination, the model has excellent performance: RMSE (4.0406, 4.0362, 4.0234), MAE (2.7184, 2.7044, 2.7030), accuracy (0.7132, 0.7190, 0.7223).

Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions

2009 ◽  
Vol 36 (3) ◽  
pp. 6164-6173 ◽  
Author(s):  
Manoel Castro-Neto ◽  
Young-Seon Jeong ◽  
Myong-Kee Jeong ◽  
Lee D. Han
Keyword(s):  
Traffic Flow ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Traffic Conditions ◽  
Flow Prediction

scholarly journals Hybrid LSTM Neural Network for Short-Term Traffic Flow Prediction

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 105 ◽  
Author(s):  
Yuelei Xiao ◽  
Yang Yin
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
The Other ◽  
Maximum Relative Error ◽  
Traffic Network ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Traffic Conditions ◽  
Road Section ◽  
Flow Prediction

The existing short-term traffic flow prediction models fail to provide precise prediction results and consider the impact of different traffic conditions on the prediction results in an actual traffic network. To solve these problems, a hybrid Long Short–Term Memory (LSTM) neural network is proposed, based on the LSTM model. Then, the structure and parameters of the hybrid LSTM neural network are optimized experimentally for different traffic conditions, and the final model is compared with the other typical models. It is found that the prediction error of the hybrid LSTM model is obviously less than those of the other models, but the running time of the hybrid LSTM model is only slightly longer than that of the LSTM model. Based on the hybrid LSTM model, the vehicle flows of each road section and intersection in the actual traffic network are further predicted. The results show that the maximum relative error between the actual and predictive vehicle flows of each road section is 1.03%, and the maximum relative error between the actual and predictive vehicle flows of each road intersection is 1.18%. Hence, the hybrid LSTM model is closer to the accuracy and real-time requirements of short-term traffic flow prediction, and suitable for different traffic conditions in the actual traffic network.

scholarly journals Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 137 ◽  
Author(s):  
Daeho Kim ◽  
Okran Jeong
Keyword(s):  
Reinforcement Learning ◽  
Traffic Flow ◽  
Learning Algorithm ◽  
Traffic Signal ◽  
Traffic Information ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Flow Prediction ◽  
Traffic Conditions ◽  
Flow Prediction

As traffic congestion in cities becomes serious, intelligent traffic signal control has been actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning algorithm, is applied to various domains from fully-observable game environment to traffic signal control. Due to the effective performance of DQN, deep reinforcement learning has improved speeds and various DQN extensions have been introduced. However, most traffic signal control researches were performed at a single intersection, and because of the use of virtual simulators, there are limitations that do not take into account variables that affect actual traffic conditions. In this paper, we propose a cooperative traffic signal control with traffic flow prediction (TFP-CTSC) for a multi-intersection. A traffic flow prediction model predicts future traffic state and considers the variables that affect actual traffic conditions. In addition, for cooperative traffic signal control in multi-intersection, each intersection is modeled as an agent, and each agent is trained to take best action by receiving traffic states from the road environment. To deal with multi-intersection efficiently, agents share their traffic information with other adjacent intersections. In the experiment, TFP-CTSC is compared with existing traffic signal control algorithms in a 4 × 4 intersection environment. We verify our traffic flow prediction and cooperative method.

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