A Multi-Applications Comprehensive Traffic Prediction model for the electric power data network

Based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) algorithm and kernel online sequential extreme learning machine (KOSELM) algorithm, a new hybrid short-term traffic flow prediction model (ICEEMDAN-KOSELM-ARIMA) for signalized intersections is proposed according to the current and historical traffic flow data. First, traffic flow historical time series are decomposed by ICEEMDAN algorithm for the purpose of improving the prediction accuracy. Several intrinsic mode functions could be obtained by the decomposition process. Then, permutation entropy algorithm is employed to analyze the random properties of intrinsic mode function components. According to the different random properties of intrinsic mode functions, different prediction models can be built. On this basis, KOSELM prediction models are established for the intrinsic mode function components with big randomness. And auto-regressive integrated moving average (ARIMA) prediction models are built for the intrinsic mode function components with small randomness. Finally, an actual signalized intersection is selected to verify the effect and performance of the hybrid prediction model proposed in this article. Results show that compared with other models, the new proposed hybrid prediction model can effectively improve prediction accuracy, of which prediction errors are the lowest and fitting effect with actual values is the best.

The intelligent transportation system seeks to reduce traffic and improve the driving experience. They give us a lot of data that we can use to improve services for both the public and transportation officials by feeding it into machine learning systems. Most importantly, Traffic environment refers to everything that might have an impact on how much traffic is moving down the road, including traffic signals, accidents, protests, and even road repairs that might result in a backup. A motorist or rider can make an informed choice if they have previous knowledge that is very close to approximate all the above and many more real-world circumstances that can affect traffic. Additionally, it aids in the development of driverless vehicles. Traffic data have been growing dramatically in recent decades, and we are moving toward big data concepts for transportation. The current approaches for predicting traffic flow use some traffic prediction models, however they are still inadequate to handle practical situations. We thus aimed to focus on the traffic flow forecast problem using the traffic data and prediction models. The proposed model called DRGNN, a dilated recurrent graph neural network framework aims to effectively analyze and predict the traffic pattern by considering the spatial (space) and temporal (time) aspects of the real-time traffic data considering social relationships between internet of vehicles which indeed produced accurate and valuable insights that could help in deploying the model in any suitable real-time traffic monitoring and prediction system.

Traffic flow prediction is one of the point research contents of transportation engineering. Accurate ship traffic flow prediction is the value to ensure the safety of ship navigation and smooth channel. In order to predict the port ship traffic flow more accurately, aiming at the shortcomings of the traditional prediction model, a port ship traffic flow prediction model based on long-term and short-term memory network (LSTM) is proposed. Finally, Qingdao port is taken as an example to predict and compare with ARIMA model. The results show that, compared with ARIMA model, the mean absolute percentage error (MAPE) of LSTM model is as low as 3.476%, which indicates that the prediction accuracy of LSTM model is higher and it can be well applied to the field of ship traffic flow prediction. According to the prediction results, it can provide basic basis for channel planning and design and ship navigation management, maximize the navigation capacity of the channel, and optimize the allocation of port resources.

With the increasing complexity of the network, the current network traffic has strong nonlinearity and burstiness. Therefore, the traditional traffic prediction model is no longer applicable. The neural network model, especially the LSTM, can well fit the nonlinearity of time-series data and preserve the information memory of the past. However, as for the periodicity of long-term span network traffic data, the neural network model does not perform well. Based on this, this paper proposes LTS-TP (Long-Term Span Traffic Prediction model), a network traffic prediction model, to solve the problem. First, the model decomposes the collected network traffic data using the improved STL decomposition algorithm to preserve the seasonal component. Then, the trend component and the remainder component are input into the Seq2Seq model based on the LSTM added with the improved attention mechanism for prediction. Finally, the predicted value of the output is added to the seasonal component, and the final network traffic prediction value is obtained. In the simulation part, this paper uses the MAWI public data set to test the proposed network traffic prediction model and compared performance with other models. The results show that the network traffic prediction model proposed in this paper has a good predictive effect on long-term span network traffic data.

The continuous development of mobile communication technologies has led to a rapid increase in cellular network traffic. Therefore, traffic prediction models have become very important for the design of mobile communication networks, as they are essential for increasing the quality of service (QoS) and ensuring a high level of quality of experience (QoE). Accurate and timely prediction of network traffic volume enables efficient planning of radio resource allocation, improves network energy efficiency, and reduces network congestion and operational costs. However, the task of mobile network traffic prediction is inherently challenging due to the dynamic, multivariate nature of traffic patterns that are influenced by diverse factors such as location, user behavior, and temporal variations. In this article, we propose a novel prediction model based on deep learning techniques. Specifically, we develop a customized temporal fusion transformer (TFT) for accurate time series prediction that effectively captures the complex dependencies in mobile network traffic and ensures resilience to unexpected variations, which is critical for efficient network management and QoE enhancement. The prediction model is evaluated and tested against state-of-the-art prediction models using real-world cellular network data as the training dataset. The experimental results validate the excellence of this customized transformer architecture in capturing the complex temporal dynamics of cellular network traffic by exploiting attention-based mechanisms.

In this paper, a comparison of two network traffic activities prediction models will be presented, namely a backpropagation neural network (BPNN) and a genetic algorithm based backpropagation neural network (GABPNN). A backpropagation neural network (BPNN) prediction model can be used to learn a time-series dataset. However, the performance of the BPNN can be improved by optimizing the BPNN using a genetic algorithm. This paper outlines a network traffic prediction model that is developed using a typical backpropagation neural network (BPNN) coupled with a genetic algorithm (GA). The performance of the GABPNN is measured by using a statistical analysis, namely Mean of Square Error (MSE). The results show that the MSE value of GABPNN obtained was lower with the population size of 400, crossover probability was 0.01, uniform mutation probability was 0.8, and 50 iterations. Therefore, the obtained results of the GABPNN model illustrates that the proposed model GABPNN has improved the prediction accuracy compared to the traditional BPNN model.

Traffic flow prediction plays a vital role in the real-time control of intelligent transportation system. A number of prediction models are established and achieve good results. However, most of these models ignore the intrinsic characteristics of traffic flow data, which can hardly optimize prediction models accordingly. This paper analyzes the characteristics of traffic flow time series and proposes a mode decomposition based hybrid model for traffic prediction. Firstly, the original sequence is decomposed by a mode decomposition algorithm, and the periodic sequence and two-part random sequences are obtained. Then, according to the complexity of decomposed subsequences, a hybrid prediction model with BP, e-SVR and LSTM models is established to predict decomposed subsequences respectively. Finally, the final result is obtained by combining the prediction results of decomposed subsequences. Considering practical application scenario, the proposed model chooses to predict multi-prediction intervals instead of only predicting one single interval, but the predication flow data is still updated in accordance with one interval. The dataset from Caltrans Performance Measurement System is used in experiments. Comparing with typical single predicting models, experiment results show that the proposed hybrid model achieves higher accuracy.

ObjectivesThe aim of this study was the validation of a recently established comprehensive and compact prediction model for 68Ga-PSMA-11-ligand positron-emission tomography (PET) positivity with an independent subsequent patient series.MethodsA total of 292 consecutive patients with early biochemical recurrence after radical prostatectomy and PSA values between 0.2 and 1 ng/ml who underwent 68Ga-PSMA-11-ligand PET/computed tomography (CT) between January 2016 and June 2017 were retrospectively included. The cohort was divided into a very low PSA value (0.2–0.5 ng/ml, n = 151) and a low PSA value (> 0.5–1 ng/ml, n = 141) subgroup. First, pre-test positivity probabilities for each patient were calculated according to the previously published comprehensive prediction model using all clinical variables (PSA value, ISUP grade group, T- and N-stage, patient under androgen deprivation therapy (ADT), previous radiation therapy) and the compact model using just the most predictive factors PSA value, ADT, and grade group. Then, all 68Ga-PSMA-11-ligand PET/CTs were analysed by one experienced nuclear medicine physician, and the results were correlated to the calculated pre-test probabilities.ResultsIn the very low PSA value subgroup, mean pre-test probability for positive findings in 68Ga-PSMA-11-ligand PET/CT was 57% (95% CI 55–60%) according to the compact model and 59% (95% CI 56–61%) according to the comprehensive model. In the low PSA value subgroup, mean pre-test probability was 72% (95% CI 70–74%) in the compact model and 74% (95% CI 72–76%) in the comprehensive model. After image analysis, 59% (89/151) of the patients in the very low PSA value subgroup revealed positive imaging findings. Seventy-nine percent (112/141) of the patients in the low PSA value subgroup presented with positive findings in the 68Ga-PSMA-11-ligand PET/CT. The accuracy (AUC) of the prediction models was 0.71 (95% CI 0.65–0.78) for the compact model and 0.74 (95% CI 0.68–0.80) for the comprehensive model.ConclusionExternal validation of the recently proposed prediction models showed a high concordance of the calculated pre-test probabilities and actual 68Ga-PSMA-11-ligand PET/CT findings in the validation cohort confirming the prediction models’ ability to determine the presence of a positive lesion at 68Ga-PSMA-11-ligand PET. However, the predictive accuracy of the nomogram itself is suboptimal and should be used with caution. Furthermore, the model’s generalizability may be hampered due to the study design (in-house validation). Nevertheless, given the limited health resources and the costs of hybrid imaging techniques, prediction models might be a benefit in patient selection.

Traffic prediction is crucial for transportation management and user convenience. With the rapid development of deep learning techniques, numerous models have emerged for traffic prediction. Recurrent Neural Networks (RNNs) are extensively utilized as representative predictive models in this domain. This paper comprehensively reviews RNN applications in traffic prediction, focusing on their significance and challenges. The review begins by discussing the evolution of traffic prediction methods and summarizing state-of-the-art techniques. It then delves into the unique characteristics of traffic data, outlines common forms of input representations in traffic prediction, and generalizes an abstract description of traffic prediction problems. Then, the paper systematically categorizes models based on RNN structures designed for traffic prediction. Moreover, it provides a comprehensive overview of seven sub-categories of applications of deep learning models based on RNN in traffic prediction. Finally, the review compares RNNs with other state-of-the-art methods and highlights the challenges RNNs face in traffic prediction. This review is expected to offer significant reference value for comprehensively understanding the various applications of RNNs and common state-of-the-art models in traffic prediction. By discussing the strengths and weaknesses of these models and proposing strategies to address the challenges faced by RNNs, it aims to provide scholars with insights for designing better traffic prediction models.

This paper proposes a unified spatio-temporal model on the basis of STARIMA (Space-Time AutoRegressive Integrated Moving Average) for short-term road traffic prediction. The contributions of this paper are as follows. First, we develop a physically intuitive approach to traffic prediction that captures the time-varying spatio-temporal correlation between traffic at different measurement points. The spatio-temporal correlation is affected by the road network topology, time-varying speed, and time-varying trip distribution. Distinctly different from previous black-box approaches to road traffic modeling and prediction, parameters of the proposed approach have physically intuitive meanings which make them readily amenable to suit changing road and traffic conditions. Second, unlike some existing techniques which capture the variation of spatio-temporal correlation by a complete redesign and calibration of the model, the proposed approach uses a unified model which incorporates the physical factors potentially affecting the variation of spatio-temporal correlation into a series of parameters. These parameters are relatively easy to control and adjust when road and traffic conditions change, thereby greatly reducing the computational complexity. Experiments using two set of real traffic traces demonstrate that the proposed approach has superior accuracy compared with the widely used ARIMA (AutoRegressive Integrated Moving Average) and is only marginally inferior to that obtained by constructing multiple STARIMA models for different time of the day, however with a much reduced computational and implementation complexity.

The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic flow prediction is essential for mitigating congestion, improving road safety, optimizing traffic signal control, and enhancing overall transportation efficiency. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of traffic management, offering sophisticated algorithms capable of modeling, analyzing, and predicting complex traffic patterns with high accuracy. The application of AI in traffic flow prediction leverages vast amounts of real-time and historical data to generate precise forecasts, supporting data-driven decision-making by urban planners and traffic control authorities. The prediction of traffic flow involves analyzing time-series data that exhibit nonlinear, dynamic, and often stochastic behavior. Traditional statistical models, such as autoregressive integrated moving average (ARIMA), have proven to be limited in handling the high dimensionality and variability inherent in traffic systems. In contrast, AI algorithms possess the capacity to learn and adapt from complex data inputs without the need for explicit programming, making them particularly suitable for traffic-related applications. AI algorithms used in traffic flow prediction can be broadly categorized into machine learning (ML) and deep learning (DL) approaches. Machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees, and random forests have demonstrated effectiveness in short-term traffic prediction tasks. These algorithms are capable of identifying hidden patterns in traffic data and adjusting to changes in traffic behavior over time. Ensemble methods, which combine the strengths of multiple learning models, further enhance prediction accuracy and robustness. Deep learning algorithms, a subfield of AI inspired by the human brain’s neural architecture, have shown exceptional performance in capturing spatial-temporal dependencies in traffic data. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks and gated recurrent units (GRUs), are widely used for their ability to process sequential data and retain information over extended time intervals. Convolutional neural networks (CNNs) are employed to extract spatial features from traffic sensor data or road network imagery. Hybrid models that integrate CNNs with RNNs have achieved high levels of predictive precision by simultaneously learning spatial and temporal correlations. In addition to supervised learning methods, unsupervised and reinforcement learning techniques are also applied in traffic flow prediction. Clustering algorithms, such as k-means and DBSCAN, assist in identifying traffic patterns, while reinforcement learning models optimize adaptive traffic signal control systems by learning optimal actions through environmental interaction. This study explores the different types of AI algorithms used in traffic flow prediction, examining their theoretical foundations, structural differences, and practical applications. It aims to evaluate the comparative advantages of various algorithms in addressing the challenges of real-time traffic prediction in increasingly complex transportation networks.

Vehicular Clouds introduces a new paradigm that addresses and potentially enhances underutilization of on-board computing resources through aggregation to solve several computational tasks in Intelligent Transportation System. The most challenging issue in Vehicle Cloud is the task allocation among the dynamically changing amount of available resources. For further research towards this issue, a realistic road traffic system models which could generate traffic flow with high accuracy must be designed. In this paper, we conduct a study on short-term traffic flow predictions for our envisioned road traffic prediction system. Five prediction models, including double exponential smoothing (DES), seasonal autoregressive moving average (SARIMA), K-nearest neighbor (KNN), back-propagation neural network (BP-NN) and support vector regression (SVR), are implemented. Then, three different error metrics are used to evaluate the performance of these models. Finally, the results shows that SARIMA and BP neural network are two precise and stationary prediction models and thus are the best candidates to be embedded in an road traffic load prediction system.

Wireless mobile radio systems have to serve users with different quality of service (QoS) requirements. Scheduling algorithms like weighted proportional fair (WPF) have been proposed considering channel gains to improve the system capacity in terms of sector throughput as well as user priorities to fulfill QoS requirements. These scheduling algorithms allow assigning different priority factors to users and services so that different QoS requirements can be fulfilled. This paper provides an analysis of the influence of users with different priorities on the sector and user throughput. It is shown analytically and by system level simulations that the average user throughput can be adjusted by choosing a specific priority factor. Furthermore, it is shown that the sector throughput decreases if WPF scheduling for users with different priorities and full buffer model are considered compared to a scenario with users having equal priority. If QoS requirements for realistic traffic models, e.g. FTP, have to be fulfilled, for each high priority user low priority users have to be removed from the system in order not to exceed an acceptable number of unsatisfied users. It is shown that the decrease in sector throughput with a realistic traffic model and QoS requirements is higher than for the full buffer model.

End-to-end network slicing in unmanned aerial vehicle (UAV) networks has attracted significant attention for delivering differentiated services tailored to the quality of service (QoS) requirements of user terminals (UTs). In a sliced UAV network, the dual mobility of both UTs and UAVs, combined with the three-layer association of UT-UAV-network slice (NS), makes handoff a critical and challenging problem for maintaining the communication performance of UTs. This paper proposes a sequential handoff process where a central controller makes handoff decisions for each UT sequentially according to the UT queue. This process is modeled as a Markov decision process (MDP). In the MDP, the state space includes NS available resources, UTs' QoS requirements, and UT queue information. Additionally, the reward function accounts for UTs' QoS and handoff overhead. The double deep Q-network (DDQN) technique is then employed to design a handoff algorithm. This algorithm achieves the optimization goal of ensuring UTs' QoS requirements while avoiding frequent handoffs by maximizing the total reward of the MDP. Additionally, a state-dependent exploration technique with adaptive intensity adjustment, called noisy network (NoisyNet), is utilized to drive the DDQN for effective exploration, thereby enhancing the performance of the handoff algorithm. Finally, the runtime complexity of the proposed NoisyNet-DDQN-based sequential handoff (NoisyNet-DDQN-SH) algorithm is analyzed. The simulation results show that the NoisyNet-DDQN-SH algorithm converges well. Moreover, it exhibits strong robustness. Additionally, the NoisyNet technique effectively enhances the algorithm's performance, making the NoisyNet-DDQN-SH algorithm superior to the vanilla DDQN (with an ϵ-greedy strategy), greedy, maximum signal-to-interference-plus-noise ratio (MaxSINR), and random algorithms.

In recent years, traffic flow prediction has attracted increasing interest from both academia and industry, and existing data-driven learning models for traffic flow prediction have achieved excellent success. However, this requires a large number of datasets for efficient model training, while it is difficult to acquire all the data from one agent, and thus data collaboration among different agents becomes an attracting trend. Moreover, with the increase in the number of agents, how to perform accurate multi-agent traffic forecasting while protecting privacy is an important issue. To address this challenge, we introduce a privacy-preserving federated learning framework. In this paper, we propose a novel Dynamic Spatio-Temporal traffic flow prediction model based on graph convolutional network (DST-GCN), which incorporates both dynamic spatial and temporal dependence of intersection traffic. In addition, we provide an improved federated learning framework with opportunistic client selection (FLoS). In the proposed FLoS protocol, we employ a FedAVG algorithm for secure parameter aggregation and design an optimal client selection algorithm to reduce the communication overhead during the transfer of model updates. Experiments based on real-world datasets demonstrate that our proposed DST-GCN traffic prediction model outperforms state-of-the-art baseline models. And our proposed FLoS can achieve superior results while reducing communication consumption. © 2022 IEEE.