Speed Prediction Research Articles

Development of a stress-free algorithm for controlling active running platforms

The problem of increasing the accuracy of predicting human actions is an urgent task for various human-machine systems. The study examines the solution to the problem of predicting human speed using neural network algorithms, computer vision technologies, and machine learning. The formalization and software implementation of a neural network speed prediction algorithm are presented. To solve the problems of determining the current speed and predicting the upcoming positions of the human body depending on the dynamics of its movement, a comparison of various machine learning models was carried out. The RandomForestRegressor algorithm showed the best position prediction accuracy. The best determination of the current speed was demonstrated by dense multilayer neural networks. The experiment revealed that when predicting a person's position at an interval of 0.6 seconds, his speed is determined with an accuracy of more than 90%. The results obtained can be used to implement neural network algorithms for controlling human-machine systems.

Research on Numerical Weather Prediction Wind Speed Correction Based on Stacking Ensemble Learning Algorithm

The transportation of wind fields plays a crucial role in the dispersion and distribution of air pollutants, and accurate wind speed prediction is essential for assessing pollutant concentrations. In this study, we constructed a Stacking ensemble learning model using three models, namely Random Forest, LightGBM, and XGBoost, as base learners, and the LASSO regression model as the meta-learner to optimize wind speed data forecasting for Zhengzhou City’s urban area using WRF. Firstly, based on Pearson correlation coefficients, we selected meteorological variables that have a significant impact on near-surface wind speed and derived historical lagged features. Bayesian TPE was utilized for hyperparameter tuning and model building. Finally, the performance of the trained model was evaluated by comparing it with ground observation data. The results showed that compared to the WRF model, Random Forest, LightGBM, and XGBoost effectively reduced forecast errors and significantly improved wind speed predictions. Both LightGBM and XGBoost demonstrated similar performance in the correction models across the 11 stations and outperformed Random Forest. The Stacking method integrated the advantages of the base learners and exhibited improvement capabilities over individual models, highlighting the potential of machine learning in enhancing localized and accurate weather forecasting.

Multistep Wind Speed Prediction Based on the Fractional‐Order Longicorn Swarm Algorithm and the Two‐Stage Modal Decomposition Strategy

Least squares support vector machine (LSSVM) and variational mode decomposition are common research methods for wind speed time series prediction. Addressing the challenge of selecting relevant parameters of LSSVM and VMD, a fractional‐order Beetle swarm optimization algorithm is proposed to optimize the relevant parameters. To weaken the negative impact of wind speed volatility on the accuracy of the prediction model, a two‐stage modal decomposition strategy based on extreme‐point symmetric mode decomposition, FO‐BSO algorithm, and VMD is proposed. ESMD is used to decompose the original wind speed time series. The diversity entropy is introduced as the criterion, and the FO‐BSO‐VMD secondary decomposition is applied for the high‐entropy modal components to further weaken the volatility of wind speed. Simulation results show that compared with the original LSSVM model, the ESMD‐FO‐BSO‐VMD‐LSSVM model presented in this study exhibits an average 44.10% increase in the fitting degree of wind speed prediction over three steps, which verifies the superior performance of the model in short‐term multistep wind speed prediction.

The Effect of Data Decomposition on Prediction Performance in Wind Speed Prediction with Artificial Neural Network

This study investigates the effect of data decomposition to improve the performance of artificial neural networks (ANNs), widely used in wind speed forecasting in the wind energy sector. Artificial neural networks are essential tools for planning and optimizing the daily generation of wind power plants. However, prediction errors can lead to significant problems in power generation and energy grid management. The results show that data decomposition substantially affects the wind speed forecasting performance of neural networks. These findings are essential for researchers and industry professionals interested in developing more accurate forecasting models for power generation planning and management in the wind energy sector. By integrating artificial neural networks and data disaggregation methods, the study stands out as an essential step forward to improve the accuracy of wind speed forecasts and optimize the efficiency of wind energy facilities.

Fully Convolutional Neural Network for Vehicle Speed and Emergency-Brake Prediction.

Ego-vehicle state prediction represents a complex and challenging problem for self-driving and autonomous vehicles. Sensorial information and on-board cameras are used in perception-based solutions in order to understand the state of the vehicle and the surrounding traffic conditions. Monocular camera-based methods are becoming increasingly popular for driver assistance, with precise predictions of vehicle speed and emergency braking being important for road safety enhancement, especially in the prevention of speed-related accidents. In this research paper, we introduce the implementation of a convolutional neural network (CNN) model tailored for the prediction of vehicle velocity, braking events, and emergency braking, employing sequential image sequences and velocity data as inputs. The CNN model is trained on a dataset featuring sequences of 20 consecutive images and corresponding velocity values, all obtained from a moving vehicle navigating through road-traffic scenarios. The model's primary objective is to predict the current vehicle speed, braking actions, and the occurrence of an emergency-brake situation using the information encoded in the preceding 20 frames. We subject our proposed model to an evaluation on a dataset using regression and classification metrics, and comparative analysis with existing published work based on recurrent neural networks (RNNs). Through our efforts to improve the prediction accuracy for velocity, braking behavior, and emergency-brake events, we make a substantial contribution to improving road safety and offer valuable insights for the development of perception-based techniques in the field of autonomous vehicles.

Deep learning-based forecasting modeling of micro gas turbine performance projection: An experimental approach

Performance forecasting of aeroengines is crucial for achieving better operational efficiency, ensuring safety, reducing costs, and minimizing environmental impact in the aviation industry. It enables engineers and researchers to make informed decisions, leading to advancements in technology and the overall evolution of aviation. This study is focused on the effects of flight conditions on the performance of a turbojet, which in consequence affects the environmental aspect of operation. By investigating the relationships between aeroengine efficiency and performance indicators such as thrust, shaft speed, and exhaust gas temperature (EGT), and flight characteristics expressed in terms of environmental and operational conditions, the study seeks to elucidate these connections. The article's significance lies in its successful application of Long Short-Term Memory (LSTM) networks to predict thrust, shaft speed, and EGT variations in turbojet engines under varying flight conditions. Experimental data from a turbojet test bench is processed with deep learning, specifically LSTM recurrent neural networks that are developed based on Matrix Laboratory (MATLAB). The model inputs are free stream air speed, compressor inlet pressure, combustor inlet temperature, combustor inlet pressure, turbine inlet temperature, turbine inlet pressure, nozzle inlet pressure and fuel flow, and the outputs are thrust, shaft speed and EGT. Predicted thrust closely aligns with actual thrust values, though with minor discrepancies. Shaft speed predictions exhibit a similar trend, while EGT predictions showcase a comparable pattern with slight variations. Despite the prediction errors, a thorough evaluation of median values, box plots, and probability density functions confirms that the models effectively capture available information, though discrepancies may arise from measurement inaccuracies and initial engine conditions. These results show that it is possible to accurately predict turbojet performance using LSTM recurrent neural network. This research paves the way for enhanced aeroengine performance prediction, particularly in scenarios requiring off-board or high-performance applications.

Using meta-learning to establish a highly transferable driving speed prediction model from the visual road environment

Driving speed prediction is of great significance for the realization of autonomous driving and intelligent transportation systems. The effectiveness of the existing driving speed prediction models often relies on a large number of training data, and their performance will drop sharply with the change in the usage environment. To solve these problems, this study adopted a Meta-learning algorithm to propose a highly transferable driving speed prediction model based on the visual road environment. The data used in this study were the visual road environment pictures and corresponding driving speed from three different types of scenarios (i.e., rural roads, urban roads, and highways) in a naturalistic driving experiment and a public KITTI dataset. The analysis of a Random forest model showed that the influence degree of visual road environment elements on driving speed was quite distinct in different scenarios. Then, a Meta-learning-based driving speed prediction model was presented to address two issues, including the small sample size and transferability. To examine the performance of this new model, a driving speed prediction model based on a convolutional neural network (CNN) was also used for comparison. The results showed that the prediction accuracy of Meta-learning was significantly improved compared to CNN in the face of the small sample size problem and when transferred to new scenarios. The findings in this study can contribute to the optimization of the driving speed prediction model and the improvement of the visual road environment design.

A Point-Interval Forecasting Method for Wind Speed Using Improved Wild Horse Optimization Algorithm and Ensemble Learning

With the implementation of the green development strategy and the “double carbon goal”, as an important energy for sustainable development, wind power has been widely researched and vigorously developed across the world. Wind speed prediction has a major impact on the grid dispatching of wind power connection. Most current studies only focus on the deterministic prediction of wind speed. However, the traditional deterministic forecast only provides the single wind speed prediction results and cannot meet the diverse demands of dispatchers. To bridge the gap, a wind speed point-interval forecasting method is proposed that utilizes empirical wavelet transform, an improved wild horse optimization algorithm, a multi-predictor, and improved kernel density estimation. This method decomposes the wind speed sequence into stationary subsequences through empirical wavelet transform, and then optimizes three basic learners with completely different learning mechanisms to form an ensemble model using the modified wild horse optimization algorithm. Finally, the uncertainty is analysed using an improved kernel density estimation. The datasets of three sites from America’s national renewable energy laboratory are used for comparison experiments with other models, and the predictions are discussed from different angles. The simulation results demonstrate that the model can produce high-precision deterministic results and high-quality probabilistic results. The reference information the model provides can be extremely valuable for scheduling operators.

Desain dan Simulasi Model Predictive Control pada Sistem Pembagian Daya untuk Kendaraan Listrik Hibrida Fuel Cell – Baterai

This research examines the performance of implementing Model Predictive Control (MPC) in the energy management system of fuel cell – battery hybrid electric vehicles. Three parameter variations were carried out on the MPC controller, namely variations in the horizon value, objective function, and weighting in the objective function. In testing variations in the horizon value of the designed MPC controller, it shows that the higher the horizon value used, the SOC and the final SOC are faster and closer to the optimal SOC determined by compensating for higher hydrogen consumption. Testing of objective function variations shows that the objective function implemented on the MPC controller influences the system response characteristics. It was found in objective function testing that optimal use of fuel cell power produces fuel cell power output with a working efficiency range of 57% - 60% when compared to other objective functions that work in the efficiency range of 49.2% - 57%. Finally, in testing variations in weighting values, it was found that the higher the weighting of an expression in the objective function, the more the optimizer will penalize the expression so that the solver will minimize the expression in the optimization process. Therefore, the MPC controller parameter values ​​need to be paid attention to so that the response characteristics are in accordance with the design. which are desired. It should be noted that this research does not use a speed prediction model so it is assumed that the speed is known without any uncertainty.

Wind gust forecasting by post-processing the WRF model outputs using ANN

Strong and highly variable winds and gusts are major hazards to infrastructure, properties, and life. Consequently, accurate prediction and timely detection of wind gust intensity have always been a focus of interest for earth scientists and weather forecasters.In this study, The WRF (Weather Research and Forecasting) post-process diagnostic of wind gusts (WPD method) was utilized to predict non-convective wind gust speeds using the direct outputs of the WRF model. To improve the prediction accuracy of this method, the results were post-processed using an artificial neural network (ANN). Multiple different ANN algorithms were examined to achieve the most accurate predictions possible. The results were evaluated using observational data extracted from 32 synoptic stations across Iran during the time period from 2014 to 2018.The results indicate that employing a multilayer perceptron ANN with a hybrid structure, consisting of one input layer comprising five parameters (10 m wind speed, sea level pressure, temperature, relative humidity, and predicted wind gust speed obtained from the WPD method), one hidden layer with a sigmoid activation function and 12 neurons, one output layer with a linear activation function and using the BR (Bayesian Regularization) training algorithm, significantly improve the accuracy of the WPD wind gust speed prediction method. The RMSE for wind gust speed prediction has decreased from 3.68 m/s (WPD method) to 1.88 m/s for the validation dataset. Additionally, there were considerable improvements of 50 %, 74 %, and 17 % in the MAE, MSE, and R2, respectively.

A Machine Learning Scheme for Speed Prediction in Intelligent Transportation Systems Using a Bi-LSTM Based Model

Congestion has become a big issue in major cities throughout the world. Numerous transportation activities have been affected, and travel times have increased as a result of many travelers spending lengthy hours on the road. To overcome these challenges, the Intelligent Transportation System (ITS), which provides efficient traffic service and management, has sparked widespread attention. Collection and analysis of traffic data has been made possible by the algorithms implemented by the ITS. Huge volumes of data are produced by the vast wide range of sensors used in the Internet of Things (IoT), enabling the collection of a variety of traffic information. Development of short-term traffic speed prediction, has been made possible using deep learning models such as Long Short Term Memory (LSTM) and Bidirectional LSTM. Numerous variables, including the weather, the state of the roads, and traffic congestion, can have long-term dependencies and influence traffic speed. The bidirectional architecture of Bi-LSTMs enables them to handle long-term dependencies in sequences and efficiently capture both past and future context in a sequence, which is crucial for producing accurate forecasts of traffic speed. In this paper, the upstream and downstream flow of traffic speed on various pathways has been investigated using a traffic path planning algorithm based on Bi-LSTM models. The algorithm considers the factors affecting the flow of traffic at different seasons and time of the day and tries to predict the average speed associated to that path several timeslots ahead. The experimental results demonstrated that the Bi-LSTM model has the benefit of predicting speed for various timeslots while retaining a high level of accuracy.

Prediction and Early Warning of Extreme Winds for High-Speed Railway Bridge Construction Using Machine-Learning Methods

Measuring the impact of extreme winds is important in high-speed railway bridge construction to avoid the risk of engineering accidents. This study presents an early-warning framework for high-speed railway (HSR) bridge construction under extreme wind conditions, using a long-span continuous beam bridge constructed in a typhoon-prone area as a case study. Specifically, on-site wind environment measurements during a typhoon are utilized, and parameters such as fluctuating wind turbulence intensity, gust factor, turbulence integral scale, and power spectral density are employed to characterize the wind environment. Multi-step prediction of gust wind speed during the bridge construction is performed using the hybrid Back Propagation-Genetic Algorithm (BP-GA). Hierarchical warnings and control actions are proposed and implemented based on the prediction results. The analysis results of wind parameters revealed a discrepancy between the measured and specified typhoon turbulence intensities, with the windward turbulence intensity being lower than specified. The longitudinal average gust factor exceeded the transverse value. The prediction curve based on the BP-GA algorithm closely resembles the actual curve, meeting accuracy requirements. Notably, the one-step prediction provided the most accurate results. Based on the predicted wind speed trend for the upcoming half-hour, appropriate hierarchical warnings and control measures were conducted.

A methodology for setting credible speed limits based on numerical analyses and driving simulator experiments

Speed management is an integral part of the Safe System approach and tackling unsafe speeds is the first action to fix a transport system that fails to protect people. There is a consensus that where traffic speeds are a safety issue, lowering the speed limit is considered “reasonable and safe” for conditions. Nevertheless, not only should a speed limit be reasonable and safe, but it should also be credible. Otherwise, that posted speed limit is likely to be ignored. In many instances, speed limits are not credible and highway agencies still need guidance on appropriate procedures to set credible speed limits. The main objective of this study is to propose and test a novel methodology to set credible speed limits, based on the integration of the results achieved by numerical analyses and driving simulator experiments. The proposed methodology is innovative since it takes into consideration both the design characteristics of the road infrastructure according to a specific procedure as well as the drivers’ operating speeds, which are evaluated using the results of both speed prediction models and driving simulator experiments. The methodology was tested to set new speed limits on the A16 Naples–Canosa motorway, section Baiano–Candela, in southern Italy, where a posted speed limit of 80 km/h is installed in both travel directions and a new speed limit of 100 km/h is proposed, based on the results of the experiments developed within the methodology. Since the speed limit selection is associated with the expected crash frequency, the final selection of the speed limit should take into account also a safety impact assessment, considering both the expected change in the speed distribution as well as the effects of the safety countermeasures implemented in association to the speed limit change. In this study, the proposed safety countermeasures are the activation of four sections with point-to-point speed control and targeted measures at 45 curves, consisting of (1) high friction surface treatments, (2) correction of superelevation deficiencies, (3) installation of curve warning signs, chevrons, and sequential flashing beacons, and (4) shoulder rumble strips. The safety impact assessment shows that the increase in the speed limit combined with the implementation of the proposed safety countermeasures allows a crash reduction of 23%. The estimated benefit/cost ratio of the safety countermeasures is 4.66.

Ultra-short-term wind speed prediction based on empirical wavelet transform and combined model

Ultra-short-term wind speed prediction based on empirical wavelet transform and combined model

Ocean Wind Speed Prediction Based on the Fusion of Spatial Clustering and an Improved Residual Graph Attention Network

Accurately predicting wind speed is crucial for the generation efficiency of offshore wind energy. This paper proposes an ultra-short-term wind speed prediction method using a graph neural network with a multi-head attention mechanism. The methodology aims to effectively explore the spatio-temporal correlations present in offshore wind speed data to enhance the accuracy of wind speed predictions. Initially, the offshore buoys are organized into a graphical network. Subsequently, in order to cluster the nodes with comparable spatio-temporal features, it clusters the nearby nodes around the target node. Then, a multi-head attention mechanism is incorporated to prioritize the interconnections among distinct regions. In the construction of the graph neural network, a star topology structure is formed by connecting additional nodes to the target node at the center. The effectiveness of this methodology is validated and compared to other time series-based approaches through comparative testing. Metrics such as Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R yielded values of 0.364, 0.239, 0.489, and 0.985, respectively. The empirical findings indicate that graph neural networks utilizing a multi-head attention mechanism exhibit notable benefits in the prediction of offshore wind speed, particularly when confronted with intricate marine meteorological circumstances.

A Case Study on the Effect of Geometric Design Consistency on Road Crashes on Narayanghat-Muglin Road Section

Design consistency is overlooked while designing roads, resulting in unsafe road facilities. The safety performance of a highway can be greatly enhanced by detecting and addressing any inconsistencies present on the road. Over time, the application of geometric standards shifted from new road construction to upgrading, which could result in limited design options. Due to the emerging importance of design consistency in geometric design, this research has been initiated to evaluate horizontal curve consistency based on an established operating speed prediction model.
 Horizontal curve data of the Narayanghat–Mugling Road section has been collected from the Division Road Office, Bharatpur, and accident data has been obtained from the District Traffic Police, Chitwan. Out of 242 horizontal curves in the Narayanghat-Muglin road section, the design consistency of 223 horizontal curves was evaluated, while 19 horizontal curves with posted speed limits were not considered because these curves are not within the range of design criteria set out in Asian Highways Design Standards (1993).
 The average, maximum, and minimum 85th percentile speed was found to be 78, 101, and 32 kmph respectively. Out of 223 horizontal curves evaluated for individual consistency based on operating speed, 75% of the curves were categorized as “poor”, 16% as “fair”, and 9% as “good”. It was found that under successive element consistency evaluation based on operating speed, 22% of horizontal curves were categorized as “poor”, 23% as “fair”, and 55% as “good”. When studied under vehicle stability consistency evaluation criteria, 88% of the curves were classified as “fair” and 12% as “good”. It was found that the predicted accidents caused by geometric design inconsistency and vehicle stability are 5.12% of total accidents per year.
 From the safety standpoint, if the condition |V85-Vd|>20 is met, critical discrepancies between the design speed and operating speed arise, resulting in unsafe operations. Therefore, it is generally advisable to consider redesigning those road sections.

A Hybrid Model of Variational Mode Decomposition and Long Short-Term Memory for Next-Hour Wind Speed Forecasting in a Hot Desert Climate

Advancements in technology, policies, and cost reductions have led to rapid growth in wind power (WP) production. One of the major challenges in wind energy production is the instability of WP generation due to weather changes. Efficient power grid management requires accurate power output forecasting. New wind energy forecasting methods based on deep learning (DL) are delivering competitive performance versus traditional methods, like numerical weather prediction (NWP), statistical models and machine learning (ML) models. This is truer for short-term prediction. Since there is a relationship between methods, climates and forecasting complexity, forecasting methods do not always perform the same depending on the climate and terrain of the data source. This paper presents a novel model that combines the variational mode decomposition (VMD) method with a long short-term memory (LSTM) model for next-hour wind speed (WS) prediction in a hot desert climate, such as the climate in Saudi Arabia. The proposed model performance is compared to two other hybrid models, six DL models and four ML models using different feature sets. Also, the proposed model is tested on data from different climates, Caracas and Toronto. The proposed model showed a forecast skill (FS) between 61% and 74% based on mean absolute error (MAE), 64% and 72% based on root mean square error (RMSE), and 59% and 68% based on mean absolute percentage error (MAPE) for locations in Saudi Arabia.

Lane‐level short‐term travel speed prediction for urban expressways: An attentive spatio‐temporal deep learning approach

AbstractNumerous efforts have been made to address the section‐level travel speed prediction problem. However, section‐level predictions can hardly be used for fine‐grained applications, such as lane management and lane‐level navigation. The main reason for this is that significant speed heterogeneity exists among the lanes within one section. Thus, this study proposes a three‐dimensional (3D) dual attention convolution‐based deep learning model for predicting the lane‐level travel speed. 3D convolutions are designed to learn high‐dimensional spatiotemporal traffic flow features, that is, the relationships between different sections, lanes, and periods. Dual attention modules are used to focus on the traffic flow propagation patterns and to explain the model's mechanisms. To evaluate the proposed model, an indicator is introduced to assess the spatio‐temporal learning ability, based on targeting the lane‐level case. Evaluation experiments are conducted based on loop detector data in Shanghai, China. The results show that high accuracy is obtained by the proposed model, with a 2.9 km/h mean absolute error, thereby outperforming several existing methods. Finally, an in‐depth analysis is provided regarding the attention coefficients and interpretation of real‐world lane‐level traffic flow propagation patterns, so as to gain insights into the model's mechanism when capturing dynamic lane‐level traffic flow.

Real-time surgical tool detection with multi-scale positional encoding and contrastive learning.

Real-time detection of surgical tools in laparoscopic data plays a vital role in understanding surgical procedures, evaluating the performance of trainees, facilitating learning, and ultimately supporting the autonomy of robotic systems. Existing detection methods for surgical data need to improve processing speed and high prediction accuracy. Most methods rely on anchors or region proposals, limiting their adaptability to variations in tool appearance and leading to sub-optimal detection results. Moreover, using non-anchor-based detectors to alleviate this problem has been partially explored without remarkable results. An anchor-free architecture based on a transformer that allows real-time tool detection is introduced. The proposal is to utilize multi-scale features within the feature extraction layer and at the transformer-based detection architecture through positional encoding that can refine and capture context-aware and structural information of different-sized tools. Furthermore, a supervised contrastive loss is introduced to optimize representations of object embeddings, resulting in improved feed-forward network performances for classifying localized bounding boxes. The strategy demonstrates superiority to state-of-the-art (SOTA) methods. Compared to the most accurate existing SOTA (DSSS) method, the approach has an improvement of nearly 4% on mAP and a reduction in the inference time by 113%. It also showed a 7% higher mAP than the baselinemodel.

Tuning data preprocessing techniques for improved wind speed prediction

Accurate wind speed forecasting is crucial for efficiently integrating of wind power into the electrical grid and ensuring a stable power supply. However, wind speed is inherently noisy and unpredictable, making it challenging to forecast accurately. This study investigates the effects of hyperparameters of Discrete Wavelet Transform (DWT) and Singular Spectrum Analysis (SSA) on the accuracy of wind speed forecasting using various prediction models. Our proposed method focuses on the optimisation of hyperparameters within the existing models, suggesting that significant untapped potential remains. Our study examines a wide range of wavelet function orders for DWT and varying trend ratio parameter for SSA, and evaluates their impact on the prediction accuracy using real data from thirteen locations in Jordan. Particularly, our investigation reveals that high-order Daubechies wavelets in DWT outperform low-order wavelets. The study also illustrates that optimal hyperparameters must be modified when changing the prediction model and the combination of DWT and SSA enhances prediction performance when the trend ratio is set to 90%. Our results demonstrate that fine-tuning data preprocessing techniques is essential for accurate wind speed prediction since hyperparameter tuning results in greater improvements in prediction accuracy than sophisticated prediction models alone. Our findings underscore the importance of leveraging data preprocessing techniques and hyperparameter tuning for accurate wind speed forecasting.