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In recent years, the rapid growth of mobile communication has led to the continuous expansion of network scale, and the energy consumption of wireless communication has also increased rapidly. Under the high-density networking conditions, frequent communication activities greatly increase the energy consumption. In order to reduce channel competition, optimize resource scheduling and reduce equipment power consumption, the target wake-up time (TWT) mechanism in IEEE 802.11ax system plays an important role through timely scheduling and transmission. To improve channel efficiency for multi-user (MU) transmission and tackle with complicated traffic conditions, this paper presents a traffic awareness-based TWT scheduling scheme (TAT). The traffic is predicted and classified using a spatio-temporal series model for traffic-based TWT parameter generation. Subsequently, the time–frequency scheduling table is developed to adaptively assign Resource Units (RUs) and time slices to active STAs. Simulation results show that TAT can guarantee the Quality of Service (QoS) under dynamic changes in power service and delay requirements while reducing power consumption, effectively meeting the energy-saving demands in the intelligent access scenarios for power terminals.

Traffic prediction is essential for transportation planning, resource allocation, congestion management and enhancing travel experiences. This study optimizes data preprocessing techniques to improve machine learning-based traffic prediction models. Data preprocessing is critical in preparing the data for machine learning models. This study proposes an approach that optimizes data preprocessing techniques, focusing on flow-based analysis and optimization, to enhance traffic prediction models. The proposed approach explores fixed and variable orders of data preprocessing using a genetic algorithm across five diverse datasets. Evaluation metrics such as root mean squared error (RMSE), mean absolute error (MAE) and R-squared error assess model performance. The results indicate that the genetic algorithm’s variable order achieves the best performance for the ArcGIS Hub and Frementon Bridge Cycle datasets, fixed order one preprocessing for the Traffic Prediction dataset and variable order using the genetic algorithm for the PeMS08 dataset. Fixed order 2 preprocessing yields the best performance for the XI AN Traffic dataset. These findings highlight the importance of selecting the appropriate data preprocessing flow order for each dataset, improving traffic prediction accuracy and reliability. The proposed approach advances traffic prediction methodologies, enabling more precise and reliable traffic forecasts for transportation planning and management applications.

Aim: The research aims at developing a traffic prediction and signal controlling model based on deep learning technique in order to provide congestion-free transportation in Intelligent Transport System (ITS).

Need for the Research: Recent technical advancements in the ITS, industrialization, and urbanization increase traffic congestion, which leads to high fuel consumption and health issues. This signifies the need for a dynamic traffic management system to handle the traffic congestion issues that negatively affect the transportation service.

Methods: For promoting congestion-free transportation in the ITS, this research aims to devise a traffic prediction and control system based on deep learning techniques that effectively controls the traffic during peak hours. The proposed mode-search optimization effectively clusters the vehicles based on the necessity. In addition, the mode-search optimization tunes the optimal hyperparameters of the deep Long Short Term Memory classifier, which minimizes the training loss. Further, the traffic signal control system is developed through the mode-search-based deep LSTM classifier for predicting the path of the vehicles by analyzing the attributes, such as velocity, acceleration, jitter, and priority of the vehicles.

Result: The experimental results evaluate the efficacy of the traffic prediction model in terms of quadratic mean of acceleration (QMA), jitter, standard deviation of travel time (SDTT), and throughput, for which the values are found to be 37.43, 0.23, 8.75, and 100 respectively.

Achievements: The proposed method attains the performance improvement of 5% to 42% when compared with the conventional methods.

To improve the quality of service and network performance for the Flash P2P video-on-demand, the prediction Flash P2P network traffic flow is beneficial for the control of the network video traffic. In this paper, a novel prediction algorithm to forecast the traffic rate of Flash P2P video is proposed. This algorithm is based on the combination of the ensemble local mean decomposition (ELMD) and the generalized autoregressive conditional heteroscedasticity (GARCH). The ELMD is used to decompose the original long-related flow into the summation of the short-related flow. Then, the GRACH is utilized to predict the short-related flow. The developed algorithm is tested in a university’s campus network. The predicted results show that our proposed method can further achieve higher accuracy than those obtained by existing algorithms, such as GARCH and Local Mean Decomposition and Generalized AutoRegressive Conditional Heteroskedasticity (LMD-GARCH) while keeping lower computational complexity.

In IP networks, packets forwarding performance can be improved by adding more nodes and dividing the network into smaller segments. Being able to measure and predict traffic flows to direct to a given segment can be crucial in respecting traffic shaping, scheduling and QoS. This paper proposes to model network packets forwarding performance for optimization and prediction purposes by using multi-layer feed-forward neural network model that uses sigmoid functions to activate the hidden nodes. Gradient descent technique has been considered to optimize and enhance the MLP accuracy. Simulations of MPL neurons training stages pointed out a relative improvement of the forwarding process when network posses a larger density of neurons. Numerical results validated our theoretical analysis and confirmed that to enhance the forwarding process, it is necessary to divide the network into small segments by optimizing resources allocation.

This study proposes a data fusion and deep learning (DL) framework that learns high-level traffic features from network-level images to predict large-scale, multi-route, speed and volume of connected vehicles (CVs). We present a scalable and parallel method of processing statewide CVs’ trajectory data that leads to real-time insights on the micro-scale in time and space (two-dimensional (2D) arrays) on graphics processing unit (GPUs) using the Nvidia rapids framework and dask parallel cluster, which provided a 50× speed-up in the data extraction, transform and load (ETL). A UNet model is then applied to perform feature extraction and multi-route speed and volume channels over a multi-step prediction horizon. The accuracy and robustness of the proposed model are evaluated by taking different road types, times of day and image snippets and comparing the model to benchmarks: Convolutional Long–Short-Term Memory (ConvLSTM) and a historical average (HA). The results show that the proposed model outperforms benchmarks with an average improvement of 15% over ConvLSTM and 65% over the HA. Comparing the image snippets from each prediction model to the actual image shows that image textures were highly similar in UNet to the benchmark models used. UNet’s dominance in performing image predictions was also evident in multi-step forecasting, where the increase in errors was relatively minimal over longer prediction horizons.

Traffic prediction is challenging due to the stochastic nonlinear dependencies in spatiotemporal traffic characteristics. We introduce a Graph Convolutional Gated Recurrent Unit Network (GC-GRU-N) to capture the critical spatiotemporal dynamics. Using 15-min aggregated Seattle loop detector data, we recontextualize the prediction challenge across space and time. We benchmark our model against Historical Average, LSTM, and Transformers. While Transformers outperformed other models, our GC-GRU-N came in a close second with notably faster inference time — six times quicker than Transformers. We offer a comprehensive comparison of all models based on training and inference times, MAPE, MAE, and RMSE. Furthermore, we delve into the spatial and temporal characteristics of each model’s performance.

As floating cars’ data become a main traffic information source in fields as intelligent transportation and smart city. people are desirable to use this data processing technology for the real-time traffic prediction. But the difficulty in the system implementation is the big data volume and real-time requirements. Thus the authors designed an high efficient data processing mechanism with combining features of the distributed architecture and NoSQL (not only SQL) database. And in this paper, the authors give a detail description about how to implement such a real-time traffic prediction system with this data processing method. The system named FRFS which the authors have implemented is mainly designed with the geographic information and traffic conditions in Shenzhen China. As a key part of the intelligent navigation platform in the project of Shenzhen smart city, the FRFS has achieved a good market response and the user’s reputation.

Internet traffic analysis is one of the popular research interest area because of its benefits for many applications primarily to the task of internet traffic classification. Many work in this field have progressed in the direction of classification and also for traffic prediction. This paper presents survey on traffic classification and prediction. The paper also mentions the importance of decision trees in the field of traffic classification.