PREDICTION PERFORMANCE OF AIRPORT TRAFFIC USING BILSTM AND CNN-BI-LSTM MODELS

The COVID-19 pandemic has had a significant and enduring impact on the aviation industry, necessitating the accurate prediction of airport traffic. This study compares the predictive accuracy of biLSTM (Bidirectional Long Short-Term Memory) and CNN-biLSTM (Convolutional Neural Network-Bidirectional Long Short-Term Memory) models using various optimization techniques such as RMSProp, Stochastic Gradient Descent (SGD), Adam, Nadam, and Adamax. The evaluation is based on Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) indices. In the United States, the biLSTM model utilizing the Nadam optimizer achieved an MAPE score of 9.76%. On the other hand, the CNN-biLSTM model utilizing the Nadam optimizer demonstrated a slightly improved MAPE score of 9.62%. For Australia, the biLSTM model using the Nadam optimizer obtained an MAPE score of 31.52%. However, the CNN-biLSTM model employing the RMSprop optimizer had a marginally higher MAPE score of 33.33%. In Chile, the biLSTM model using the Adam optimizer obtained an MAPE score of 44.04%. Conversely, the CNN-biLSTM model using the RMSprop optimizer had a slightly higher MAPE score of 44.09%. Lastly, in Canada, the biLSTM model using the Nadam optimizer achieved a comparatively low MAPE score of 14.99%. Similarly, the CNN-biLSTM model utilizing the Adam optimizer demonstrated a slightly better MAPE score of 14.75%. These results highlight that the choice of optimization technique, model architecture, and balanced dataset can significantly influence the prediction accuracy of airport traffic

Short-Term Traffic Prediction Based on Genetic Algorithm Improved Neural Network

This paper takes the time series of short-term traffic flow as research object. The delay time and embedding dimension are calculated by C-C algorithm, and the chaotic characteristics of the time series are verified by small data sets method.Then based on the neural network prediction model and the chaotic phase space reconstruction theory, the network topology is determined, and the prediction is conducted by the wavelet neural network and RBF neural network using Lan-Hai expressway experimental data. The results show that the prediction effect of RBF neural network is better. Due to the poor stability of the network caused by the initial parameters randomness, the genetic algorithm is used to optimize the initial parameters. The results show that the prediction error of the optimized wavelet neural network or RBF neural network is reduced by more than 10%, and prediction accuracy of the latter is better

Modeling Dynamic Multifractal Efficiency of US Electricity Market

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Forecasting wind speeds at tall tower heights within Missouri

Forecasting of wind speeds is necessary for the planning and operations of the wind power generating plants. This research investigates the short term forecasting of wind speeds at tall tower heights for stations within Missouri: Columbia, Neosho and Blanchard. The first objective was to characterize the chaotic nature of this parameter using mono and multi fractal analysis using the Rescale Range Analysis (R/S Analysis) and the Multifractal Detrended Fluctuation Analysis respectively (MF-DFA). It was determined that the system was fractal and there were no trends indicative of increasing fractality and complexity with increasing height. The second objective was the qualitative and quantitative chaotic characterization of the wind speeds using phase-space portraits and the Largest Lyapunov Exponent (LLE) respectively. The methods confirm the results of the fractal analyses. A simple non-linear prediction algorithm, Empirical Dynamical Modeling (EDM) was then used to forecast the wind speeds using a moving window. It was determined that the EDM was comparable to persistence. It beats this benchmark model in the very short term range of one time step or 10 minutes. The third objective was to cluster the data using Self-Organizing Maps (SOMs), having identified the optimum number of clusters as 4 using the Elbow and Silhouette Methods, among others. Three continuous intervals belonging to a particular cluster, which represented approximately 50 percent and over of the input vectors or rows from the data frame were identified. These intervals were then used as inputs into a Long Short-Term Memory Network (LSTM) with variables, pressure and wind speeds, as well as a lagged series LSTM with embedding dimension, d, and time delay (tau). These were compared to the Moving window Auto Regressive Integrated Moving Average (ARIMA) and to persistence. It was determined that the lagged series LSTM improved on the LSTM with wind speed and pressure series inputs, and all models beat persistence. The lagged LSTM beats the Moving ARIMA for at least 2 of the forecasting times of 60 and 120 minutes for all intervals.Includes bibliographical references

Predicting Internet of Things Data Traffic Through LSTM and Autoregressive Spectrum Analysis

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM's performance deterioration with the increase of sequence length. A sliding window enables predicting the long-term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.Peer reviewe

Predicting Internet of Things Data Traffic Through LSTM and Autoregressive Spectrum Analysis

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM's performance deterioration with the increase of sequence length. A sliding window enables predicting the long-term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.Peer reviewe

Future Transportation

Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others

Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks

Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT) and mobile networks. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low-pass component. Otherwise, for the remaining high-pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods

Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems’ persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)