CNN-LSTM Base Station Traffic Prediction Based On Dual
The proposed CNN-LSTM model leverages a dual channel attention mechanism to bolster key feature information for long-term traffic data predictions. Specifically, a temporal
Base Station Traffic Prediction Using Wavelet Transform and Bi
Therefore, to improve the learning efficiency and prediction accuracy, this paper presents a new method that first uses decomposition and reconstruction of wavelet transform to preprocess
Traffic Data Viewer
The Traffic Data Viewer (TDV) is an interactive map that allows users to access traffic data information. Using the TDV, the Annual Average Daily Traffic (AADT) and additional traffic
NYC DOT
The Vision Zero View Map is an interactive tool that shows detailed information on traffic injuries and fatal crashes in New York City. Vision Zero View also displays the City''s initiatives on how
NYC DOT
View real-time traffic and transit events, as well as cameras in the New York City area, via 511NY, New York State''s official traffic and travel information source. 511NY''s Real-Time Traffic map
Long term 5G base station traffic prediction method based on
In this research experiment, we collected historical traffic data from 67 5 G base stations in a certain region to train and evaluate a traffic prediction model for multiple base
NYC DOT
For each trip that runs in the subway, the dataset contains the list of stations the trip called at and the times it stopped. The dataset is
Subway Data NYC
For each trip that runs in the subway, the dataset contains the list of stations the trip called at and the times it stopped. The dataset is updated every morning at around 7am with
Deep learning-based prediction of base station traffic
In order to meet this challenge, it is necessary to accurately perceive the application-level network traffic at multiple levels, such as edge network, MAN and backbone network.
Model for Base Station Traffic Prediction Using the FECAM
Based on the accurate traffic prediction results of the base station, operators can adjust network parameters and resource allocation to ensure stable connections and low
Estimating Base Station Traffic and Throughput Using Machine
This study explores the use of machine learning algorithms to predict traffic and downlink throughput at base stations based on hourly Key Performance Indicator (KPI) data.