A novel network flow prediction method based on UWB in power communication networks

2021 ◽  
Author(s):  
Hao Huang ◽  
Yaoyu Ma ◽  
Mengxian Chen ◽  
Enjie Zhang ◽  
Linghong Jiang ◽  
...  
Keyword(s):  
Communication Networks ◽  
Network Flow ◽  
Prediction Method ◽  
Flow Prediction

A clustering based traffic flow prediction method with dynamic spatiotemporal correlation analysis

2021 ◽  
Author(s):  
Unsok Ryu ◽  
Jian Wang ◽  
Unjin Pak ◽  
Sonil Kwak ◽  
Kwangchol Ri ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Traffic Flow ◽  
Prediction Method ◽  
Traffic Flow Prediction ◽  
Spatiotemporal Correlation ◽  
Flow Prediction

scholarly journals Tensor Decomposition for Spatial—Temporal Traffic Flow Prediction with Sparse Data

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6046
Author(s):  
Funing Yang ◽  
Guoliang Liu ◽  
Liping Huang ◽  
Cheng Siong Chin
Keyword(s):  
Traffic Flow ◽  
Prediction Method ◽  
Sparse Data ◽  
Time Of Day ◽  
Traffic Prediction ◽  
Traffic Surveillance ◽  
Data Sparsity ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Sparsity Problem

Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order tensor, which incorporates the location, the time of day, the day of the week, and the week of the month. Based on the constructed traffic flow tensor, we either propose a model to estimate the correlation in each dimension of the tensor. Furthermore, we utilize the gradient descent strategy to design a traffic flow prediction algorithm that is capable of tackling the data sparsity problem from the spatial and temporal perspectives of the traffic pattern. To validate the proposed traffic prediction method, case studies using real-work datasets are constructed, and the results demonstrate that the prediction accuracy of our proposed method outperforms the baselines. The accuracy decreases the least with the percentage of missing data increasing, including the situation of data being missing on neighboring roads in one or continuous multi-days. This certifies that the proposed prediction method can be utilized for sparse data-based transportation traffic surveillance.

scholarly journals Research on bus elastic departure interval based on Wavelet Neural Network

2021 ◽  
Vol 261 ◽  
pp. 03052
Author(s):  
Zhe Lv ◽  
Jiayu Zou ◽  
Zhongyu Zhao
Keyword(s):  
Neural Network ◽  
Prediction Method ◽  
Wavelet Neural Network ◽  
Economic Costs ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Intelligent Optimization Algorithm ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Important Basis

In recent years, more and more people choose to travel by bus to save time and economic costs, but the problem of inaccurate bus arrival has become increasingly prominent. The reason is the lack of scientific planning of departure time. This paper takes the passenger flow as an important basis for departure interval, proposes a passenger flow prediction method based on wavelet neural network, and uses intelligent optimization algorithm to study the bus elastic departure interval. In this paper, the wavelet neural network prediction model and the elastic departure interval optimization model are established, and then the model is solved by substituting the data, and finally the theoretical optimal departure interval is obtained.

Research on the Application of Time and Space Prediction Method in Traffic Flow Prediction

2021 ◽  
Author(s):  
Chunyan Diao ◽  
Dafang Zhang ◽  
Juan Chen ◽  
Yuting Tang ◽  
Lijun Xiao
Keyword(s):  
Traffic Flow ◽  
Prediction Method ◽  
Time And Space ◽  
Traffic Flow Prediction ◽  
Flow Prediction
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