Junction Traffic Prediction, Using Adjacent Junction Traffic Data, Based on Neural Networks

2018 ◽  
pp. 49-57
Author(s):  
Teresa Pamuła ◽  
Wiesław Pamuła
Keyword(s):  
Neural Networks ◽  
Traffic Prediction ◽  
Traffic Data

scholarly journals A Convolution Component-Based Method with Attention Mechanism for Travel-Time Prediction

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2063 ◽  
Author(s):  
Xiangdong Ran ◽  
Zhiguang Shan ◽  
Yufei Fang ◽  
Chuang Lin
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Predictive Accuracy ◽  
Attention Mechanism ◽  
Traffic Prediction ◽  
Time Interval ◽  
Learning Approaches ◽  
Traffic Data ◽  
Travel Time Prediction ◽  
Time Intervals

Deep learning approaches have been recently applied to traffic prediction because of their ability to extract features of traffic data. While convolutional neural networks may improve the predictive accuracy by transiting traffic data to images and extracting features in the images, the convolutional results can be improved by using the global-level representation that is a direct way to extract features. The time intervals are not considered as aspects of convolutional neural networks for traffic prediction. The attention mechanism may adaptively select a sequence of regions and only process the selected regions to better extract features when aspects are considered. In this paper, we propose the attention mechanism over the convolutional result for traffic prediction. The proposed method is based on multiple links. The time interval is considered as the aspect of attention mechanism. Based on the dataset provided by Highways England, the experimental results show that the proposed method can achieve better accuracy than the baseline methods.

scholarly journals Short-term Traffic Prediction with Deep Neural Networks: A Survey

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Kyungeun Lee ◽  
Moonjung Eo ◽  
Euna Jung ◽  
Yoonjin Yoon ◽  
Wonjong Rhee
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Traffic Prediction ◽  
Short Term

Optimization of Dynamic Neural Network Performance for Short-Term Traffic Prediction

2003 ◽  
Vol 1836 (1) ◽  
pp. 45-56 ◽  
Author(s):  
Sherif Ishak ◽  
Prashanth Kotha ◽  
Ciprian Alecsandru
Keyword(s):  
Neural Networks ◽  
Network Performance ◽  
Information Dissemination ◽  
Target Location ◽  
Prediction Performance ◽  
Time Factor ◽  
Traffic Prediction ◽  
Short Term ◽  
Traffic Condition ◽  
Dynamic Neural Networks

An approach is presented for optimizing short-term traffic-prediction performance by using multiple topologies of dynamic neural networks and various network-related and traffic-related settings. The conducted study emphasized the potential benefit of optimizing the prediction performance by deploying multimodel approaches under parameters and traffic-condition settings. Emphasis was placed on the application of temporal-processing topologies in short-term speed predictions in the range of 5-min to 20-min horizons. Three network topologies were used: Jordan–Elman networks, partially recurrent networks, and time-lagged feedforward networks. The input patterns were constructed from data collected at the target location and at upstream and downstream locations. However, various combinations were also considered. To encourage the networks to associate with historical information on recurrent conditions, a time factor was attached to the input patterns to introduce time-recognition capabilities, in addition to information encoded in the recent past data. The optimal prediction settings (type of topology and input settings) were determined so that performance was maximized under different traffic conditions at the target and adjacent locations. The optimized performance of the dynamic neural networks was compared to that of a statistical nonlinear time series approach, which was outperformed in most cases. The study showed that no single topology consistently outperformed the others for all prediction horizons considered. However, the results showed that the significance of introducing the time factor was more pronounced under longer prediction horizons. A comparative evaluation of performance of optimal and nonoptimal settings showed substantial improvement in most cases. The applied procedure can also be used to identify the prediction reliability of information-dissemination systems.

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