Short-term Speed Research Articles

Does Short-Term Speed Endurance Soccer Training Improve Physical Performance?

Objectives. This study is aimed at analyzing the impact of short-term Speed Endurance Soccer Training (SEST) training on the physical performance of young soccer players. Materials and methods. The true experiment method was used in this study. The study population comprised a total of 122 individuals. Furthermore, by using random sampling techniques, a sample of 30 persons was selected with an average age of 15.2 ± 0.81 years, height 168.1 ± 4.82 cm, weight 57.85 ± 11.93 kg, and BMI 20.34 ± 3.37. Physical performance instruments included the Running Based Anaerobic Sprint Test (RAST), Yo-Yo intermittent test level 1, 20-meter sprint, and standing broad jump test. The study used the paired t-test to evaluate the effect of each group and the independent samples t-test to compare the improvement in the experimental (SEST) and control groups. Statistical significance was set at p < 0.05. Results. The paired t-test showed significant differences between the pretest and post-test in the SEST group in anaerobic (p = .000, Δ% = 8.4), aerobic (p = .000, Δ% = 7.48), and speed (p = .000, Δ% = 3.39) capacities. Meanwhile, there were no considerable differences in the variables for the control group. According to the independent t-test results, a significant difference in effect between the SEST group and the CTRL group was revealed. The SEST group demonstrated marked differences in anaerobic, aerobic and speed variables with a p-value = 0.000. Conclusions. The study reveals that short-term Speed Endurance Soccer Training contributes to enhancing the physical performance of soccer players. However, caution should be exercised regarding the potential risk of hamstring injury in individuals with unprepared muscles.

Approximate Global Energy Management Based on Macro–Micro Mixed Traffic Model for Plug-in Hybrid Electric Vehicles

A multi-scale physical process management system is presented in this paper, taking the plug-in hybrid electric vehicle system as the physical interface connecting the macro traffic system to the micro energy conversion process, with the ultimate goal of global energy management in the full temporal–spatial domain for autonomous plug-in hybrid electric vehicles. This novel method adopts a macro traffic flow model at a large time scale, in which only the initial conditions and the traffic information of key road sections are required, and a car following model at the micro scale. Furthermore, local replanning of energy management is carried out by adjusting the power threshold and the efficiency weight through the type of reinforcement learning that is closest to human learning, once a short term speed disturbance is induced by unknown disturbances in the macro traffic flow. Due to the nonlinear relationship between speed fluctuation and power fluctuation, it is necessary to map the vehicle speed and acceleration characteristics to the power characteristics, instead of directly utilizing the traffic model characterized by the speed and acceleration characteristics. The results show that novel multi-scale physical management can achieve a smaller deviation from the global optimal solution and enhanced robustness of global energy management. Additionally, close coupling between the dynamic characteristics of vehicle components and speed fluctuation ensures correct tracking of the optimized target value.

Short-Term Speed Variability as an Index of Pacing Stochasticity in Athletic Running Events.

We aimed to compare differences in performance and pacing variability indices between 5000 m heats and finals during major championships in men and women. Data with 100 m time resolution were used to compare overall pacing variability (standard deviation of 100 m section times, SD; and coefficient of variation, CV%) and short-term pacing variability (root mean square of successive differences between 100 m section times, RMSSD). The changes in performance and pacing indices differed between races and competitions. For instance, the men's final in Beijing 2008 was quicker than the heat (p < 0.01) while the CV% was reduced (p = 0.03) and RMSSD increased (p < 0.01). For women, the heats and the final exhibited a similar mean time in London 2017 (p = 0.33) but with CV% (p < 0.001) and RMSSD (p < 0.001) showing opposite trends. Individual analyses of men's and women's champions revealed highly individual variability metrics. The use of RMSSD can complement overall variability indices for better characterization of pacing stochasticity.

Multi-objective real-time energy management for series–parallel hybrid electric vehicles considering battery life

The real-time control of energy management strategy (EMS) is becoming increasingly challenging as the complexity of the model and control strategy increases. To address this issue while ensuring the accuracy of the EMS, a multi-objective real-time EMS based on model predictive control (MPC) that considers battery life is proposed in this paper. Firstly, to balance the accuracy and efficiency of the prediction module, a kernel extreme learning machine based on the whale optimization algorithm is proposed as a short-term speed prediction model. Secondly, an adaptive state of charge (SOC) trajectory planning method is established to plan MPC reference trajectory. Next, to optimize fuel efficiency, electrical energy consumption, and battery aging in real-time, a multi-objective real-time MPC (MOR-MPC) algorithm is proposed. Finally, the effectiveness, real-time performance, and robustness of the proposed strategy are verified. Simulation results demonstrate that the total cost of the strategy is reduced by 6.15% compared to the equivalent consumption minimization strategy (ECMS), with 98.17% of dynamic programming (DP) performance achieved. Real-time performance is improved by 97.89% compared to DP-MPC. Hardware-in-the-loop (HIL) testing is also carried out to evaluate the proposed strategy.

Strategically managing learning during perceptual decision making.

Making optimal decisions in the face of noise requires balancing short-term speed and accuracy. But a theory of optimality should account for the fact that short-term speed can influence long-term accuracy through learning. Here, we demonstrate that long-term learning is an important dynamical dimension of the speed-accuracy trade-off. We study learning trajectories in rats and formally characterize these dynamics in a theory expressed as both a recurrent neural network and an analytical extension of the drift-diffusion model that learns over time. The model reveals that choosing suboptimal response times to learn faster sacrifices immediate reward, but can lead to greater total reward. We empirically verify predictions of the theory, including a relationship between stimulus exposure and learning speed, and a modulation of reaction time by future learning prospects. We find that rats' strategies approximately maximize total reward over the full learning epoch, suggesting cognitive control over the learning process.

Impact of High Resolution Radar-Obtained Weather Data on Spatio-Temporal Prediction of Freeway Speed

Inclement weather and environmental factors impact traffic operations resulting in travel delays and a reduction in travel time reliability. Precipitation is an example of an environmental factor that affects travel conditions, including traffic speed. While Intelligent Transportation Systems services aim to proactively mitigate congestion on roadways, these services are often not sensitive to weather conditions. This paper investigates the application of high-resolution weather data in improving the performance of proactive transportation management models and proposes short-term speed prediction models that fuse real-time high-resolution weather surveillance radar data with traffic stream data to conduct spatial and temporal prediction of the speed of roadway segments. Extreme gradient boosting weather-aware speed prediction models were developed for a 7-km segment of Interstate 270 in St. Louis, MO, USA. The performance of the weather-aware models was compared with the performance of weather-insensitive speed prediction models that did not take precipitation into account. The results indicated that in the majority of instances, the weather-aware models outperformed the weather-insensitive models. The extreme gradient boosting models were compared with the K-nearest neighbors algorithm and feed-forward neural network models. The extreme gradient boosting model consistently outperformed the other two methods. In addition to speed prediction models, van Aerde speed-flow traffic stream models were developed for rain and no-rain conditions to study the impact of precipitation on the traffic stream across the corridor. Results indicated that the impact of precipitation is not identical across the corridor, which was mirrored in the results obtained from weather-aware speed prediction models.

Integrated energy management of hybrid power supply based on short-term speed prediction

The energy management strategy and output limitation of energy storage system affect the actual regenerative braking recovery. In order to optimize the performance and energy efficiency of vehicle energy storage system in the process of braking energy recovery, an integrated energy management strategy based on short-term speed prediction is proposed in this paper. Firstly, support vector machine classification method is used for off-line training and on-line recognition of driving patterns, and then input-output hidden Markov model and Gaussian mixture model are fused to establish speed prediction models under urban, suburban, and high-speed conditions for real-time prediction of speed sequences. Then, the power is distributed by the dynamic programming algorithm according to the short-term speed prediction results, and the distribution coefficient is corrected through the integrated strategy of braking force distribution and power distribution. Finally, the simulation results under continuously changing driving conditions show that the pattern recognition with changeable span is conducive to improving its accuracy during the transition period, and the proposed integrated energy management strategy is helpful to optimizing the performance of the hybrid energy storage system. The renewable energy utilization efficiency index indicates the efficiency of the integrated energy management strategy.

Modeling the impact of the COVID-19 pandemic on speeding at rural roadway facilities in Maine using short-term speed and traffic count data

The COVID-19 pandemic caused a significant change in traffic operations and safety. For instance, various U.S. states reported an increase in the rate of fatal and severe injury crashes over this duration. In April and May of 2020, comprehensive stay-at-home orders were issued across the country, including in Maine. These orders resulted in drastic reductions in traffic volume. Additionally, there is anecdotal evidence that speed enforcement had been reduced during pandemic. Drivers responded to these changes by increasing their speed. More importantly, data show that speeding continues to occur, even one year after the onset of the pandemic. This study develops statistical models to quantify the impact of the pandemic on speeding in Maine. We developed models for three rural facility types (i.e., major collectors, minor arterials, and principal arterials) using a mixed effect Binomial regression model and short duration speed and traffic count data collected at continuous count stations in Maine. Our results show that the odds of speeding by more than 15 mph increased by 34% for rural major collectors, 32% for rural minor arterials, and 51% for rural principal arterials (non-Interstates) during the stay-at-home order in April and May of 2020 compared to the same months in 2019. In addition, the odds of speeding by more than 15 mph, in April and May of 2021, one year after the order, were still 27% higher on rural major collectors and 17% higher on rural principal arterials compared to the same months in 2019.

The effect of externally attached archival data loggers on the short-term dispersal behaviour and migration speed of European eel (Anguilla anguilla L.)

BackgroundExternally attached archival data logging tags are increasingly used to unravel migration routes of fish species at sea. Due to the relatively large size of the tags, their application on seaward migrating anguillid eels often forms a challenge in terms of feasibility and impact on the eel’s swimming performance. In this study, we investigated the impact of externally attached pop-up data storage tags (PDSTs) on the departure direction, time spent at the release location and ground speed of European eels (Anguilla anguilla).ResultsWe tagged 66 eels with internal acoustic transmitters of which half of the eels were additionally tagged with externally attached PDSTs. A network of acoustic receivers allowed us to analyse if the dispersal behaviour (i.e. residence time and departure direction) from the release site differed between eels tagged with and without the PDSTs. In addition, we tracked the eels for ca. 83 km in the marine environment and determined their migration speed. The results showed no differences between eels tagged with or without external PDSTs in respect of the external tagging effect on residence time (n = 60), departure direction at the release site (n = 60) or on the migration speed (n = 20).ConclusionsWe conclude that the impact of the PDSTs is minimal on these metrics for at least the first part of the marine migration. While these field-based findings suggest that anguillid eels may be largely unaffected by the applied PDSTs, we recognize that more research is needed in both the field and the laboratory to study the impact of PDSTs and externally attached tags in general on fish swimming performance and energy expenditure. This can help interpret the results from the field, but also aid developing more hydrodynamic tag shapes or improved attachment methods.

A two-term energy management strategy of hybrid electric vehicles for power distribution and gear selection with intelligent state-of-charge reference

This paper presents a two-term energy management strategy (EMS) to obtain optimal power distribution with proper gear selection under intelligent state of charge (SOC) reference for parallel hybrid electric vehicles (HEV). A long-term SOC planning level utilizes dynamic programming (DP) to calculate SOC trajectories under many repetitive routes. Then, the characteristics of driving route and relatively SOC value from DP algorithm are respectively as input and output data to train artificial neural network to generate intelligent SOC reference planning model. In the short-term online optimization level, deep neural network model is built to forecast velocity sequence over each predictive horizon. According to route characteristics, this SOC reference model could be real-time gained for model predictive control (MPC) scheme as terminal SOC value in each prediction horizon. Moreover, based on the SOC constraint and predictive velocity, MPC is employed to achieve energy management online by DP optimization solver in combination with adjacently searching gear skill. Numerical simulations show that MPC with intelligent SOC reference planning and adjacently searching gear methods has yielded the desirable performance of the fuel economy compared with the fixed SOC constraint MPC. More importantly, inaccurately short-term speed prediction in real cycles indicating the favorable robustness of the proposed methods, which the adaptability is urgent for practical application.

Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction

This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks. The unidirectional LSTM (Uni-LSTM) model provides high performance through its ability to recognize longer sequences of traffic time series data. In this work, Uni-LSTM is extended to bidirectional LSTM (BiLSTM) networks which train the input data twice through forward and backward directions. The paper presents a comparative evaluation of the two models for short-term speed and traffic flow prediction using a common dataset of field observations collected from multiple freeways in Australia. The results showed BiLSTM performed better for variable prediction horizons for both speed and flow. Stacked and mixed Uni-LSTM and BiLSTM models were also investigated for 15-minute prediction horizons resulting in improved accuracy when using 4-layer BiLSTM networks. The optimized 4-layer BiLSTM model was then calibrated and validated for multiple prediction horizons using data from three different freeways. The validation results showed a high degree of prediction accuracy exceeding 90% for speeds up to 60-minute prediction horizons. For flow, the model achieved accuracies above 90% for 5- and 10-minute prediction horizons and more than 80% accuracy for 15- and 30-minute prediction horizons. These findings extend the set of AI models available for road operators and provide them with confidence in applying robust models that have been tested and evaluated on different freeways in Australia.

Effect of Inpatient and Outpatient Pneumonia on Mobility Disability, Gait Speed, and Physical Activity in Older Adults.

Pathophysiological changes caused by pneumonia may influence physical functioning in older adults. This study was a secondary analysis of the Lifestyle Interventions and Independence for Elders (LIFE) Study. The LIFE Study included 1635 individuals over an average follow-up of 2.6 years at eight clinical sites during 2010–2013. Adults ≥70 years-old with mobility limitations (Short Physical Performance Battery score ≤9) were randomized to a physical activity (exercise) intervention or health education control arm. This analysis evaluated the association between pneumonia events and major mobility disability (MMD), gait speed, and physical activity levels. Pneumonia events, classified as inpatient or outpatient, were assessed by self-report during longitudinal follow-up. MMD was measured by the inability to complete a 400-m walk test, or other proxies, as a binary outcome and separately analyzed as “short-term” and “long-term” MMD. Short-term MMD was defined as MMD occurring in the assessment period immediately following (between 1-day to 6-months after) a pneumonia event and long-term was in the following assessment period (6 to 12 months after the event). Short- and long-term gait speed was similarly recorded during the walk test in meters per second (m/s) and measured on a linear scale. Physical activity levels were captured via accelerometry and shown visually. Mixed-effects repeated measures regression adjusted for intervention assignment, baseline demographics, comorbid conditions, and frailty. Among the 1635 participants, n = 174 (10.7%) had a pneumonia event of which 80 (46% of events) were hospitalized. Those with pneumonia during follow-up had higher baseline medication use, prior hospitalizations, and higher prevalence of lung disorders but similar baseline functioning. Pneumonia hospitalization was associated with a 4-fold increase [OR = 4.1 (3.2–5.0)] and outpatient events were associated with a 2-fold increase [OR = 2.6 (2.1–3.1)] in the odds of short-term MMD. Pneumonia hospitalizations, but not outpatient events, were associated with a nearly 10% decrement in short-term gait speed. Pneumonia events were not associated with either long-term MMD or gait speed outcomes. Physical activity levels decreased from baseline immediately following the pneumonia episode (10–30% reductions) and returned to baseline after 6 months. These results emphasize the importance of managing pneumonia risk factors to prevent disease in order to maintain physical independence and activity in older adults.

Feasibility and Psychometric Integrity of Mobile Phone-Based Intensive Measurement of Cognition in Older Adults

ABSTRACT Background:There is a pressing need for assessment approaches that can be deployed remotely to measure cognitive outcomes in clinical trials and longitudinal aging cohorts. We evaluated the utility of a mobile phone-based intensive measurement study for this purpose. Method:A small cohort of healthy older adults (N = 17, mean age = 73) completed five assessment “bursts” over 12 months, with each measurement burst involving two assessments daily for five consecutive days. Each assessment included brief tests of visual short-term memory and information processing speed, as well as surveys measuring state factors that can affect cognition. Results:At study endpoint we had 94% retention, 97% compliance, and high participant satisfaction. Mobile cognitive test scores demonstrated good reliability, moderate correlations with in-person baseline neuropsychological testing, and significant associations with participant age and education level. Conclusions: Mobile phone-based intensive measurement designs represent a promising assessment approach for measuring cognition longitudinally in older adults.

Dual-attention network with multitask learning for multistep short-term speed prediction on expressways

In this study, a dual-attention network (DAN) with multitask learning is proposed to solve the short-term prediction problems of traffic speed. The proposed DAN includes a road-type attention module (RAM), which performs accurate short-term speed prediction using road-type attention scores, a low-speed attention module (LAM), which is trained on weighted samples and fits low speed, and a decision support module, which outputs either RAM or LAM by estimating the level of the predicted speed. DAN can improve the transfer in the feature and speed prediction task layers by learning-associated and time-dependent tasks. The Shanghai expressway dataset is used to test and compare the proposed method and 15 other techniques. The results show that DAN with a multitask loss function obtains the smallest mean squared error (MSE) and mean absolute percentage error (MAPE) in most cases. LAM efficiently improves the predictive accuracy of low-speed samples, whereas RAM performs better in terms of the overall error reduction. DAN achieves the largest R-squared of 0.93 with a small reduction in R-squared by 0.12% from the training data to the test data, thereby illustrating its excellent generalization. DAN outperforms the other models by at least 13.5% in terms of the MSE and by 5.07% in terms of the MAPE on different road types. Adding LAM effectively improves the MAPE by at least 21.4% over RAM without increasing the error of the other speed levels. In terms of the MSE, RAM outperforms DAN by 12.6% in the best case. This study proved that the short-term speed prediction based on DAN has the ability to improve the accuracy on low-speed level and the generalization on different road types.

Improved Short-Term Speed Prediction Using Spatiotemporal-Vision-Based Deep Neural Network for Intelligent Fuel Cell Vehicles

In this article, an improved short-term speed prediction method is proposed to predict short-term future speed and analyze future energy consumption of intelligent fuel cell vehicles. The short-term future speed is predicted by the proposed Inflated 3-D Inception long short-term memory (LSTM) network, which takes the spatiotemporal-vision information and vehicle motion states. Specifically, the spatiotemporal-vision-based deep neural network utilizes image sequences captured by a front-facing camera as environmental information and historical speed series as motion information to improve the prediction accuracy. Then, a case study of the proposed speed prediction method, with rule-based energy management strategy to calculate future energy consumption, is presented. The simulation results show that short-term speed prediction based on the Inflated 3-D Inception LSTM network can achieve high accuracy of speed prediction in various traffic densities, as well as low prediction errors of future energy consumption including the hydrogen consumption and state-of-charge attenuation.

Predictive Equivalent Consumption Minimization Strategy With Segmented Traffic Information

This paper proposes a predictive equivalent consumption minimization strategy (P-ECMS) for a plug-in hybrid electric vehicle (PHEV), assuming the availability of two levels of traffic information. The two levels of traffic information include 1) segmented traffic information available from mobile mapping applications, and 2) detailed velocity information, possibly obtained by short-term speed forecasting. Battery state-of-charge (SOC) reference waypoints are obtained by a simplified speed profile constructed from that segmented traffic information. The proposed P-ECMS adjusts its co-state or equivalence factor based on the difference between the future SOC obtained from short-horizon prediction and a future reference SOC. The benefits of the proposed P-ECMS are evaluated through vehicle simulations on a specified trip, compared against an adaptive ECMS (A-ECMS) with the same control parameters minus prediction. Simulation results on the trip demonstrate that the proposed P-ECMS can achieve a 9.7% reduction in the corresponding fuel consumption on average, compared to the A-ECMS across different combinations of initial co-state and control gain. Moreover, its performance is robust to variations in the initial co-state, the co-state update rate, and the prediction horizon, as well as to inaccuracies in the velocity prediction. Compared to the A-ECMS, the standard deviation in fuel consumption with the P-ECMS can be reduced by 96% from 84.1 to 3.4 grams.

GraphSAGE-Based Traffic Speed Forecasting for Segment Network With Sparse Data

Forecasting of traffic conditions plays a significant role in smart traffic management systems. With the prevalent use of massive vehicle trajectory data, agencies inevitably encounter missing data issues that hinder traffic flow forecasting in an urban road network. This paper studies the urban network-wide short-term forecasting of traffic speed with consideration to missing link speed data via (i) a data recovery algorithm to impute missing speed data for the segment network with nonlinear spatial and temporal correlations; and (ii) forecasting of spatially heterogeneous traffic speed within the road network using the GraphSAGE model. The influences of partially missing data and recovered data on the traffic speed forecasting are investigated. A case study of the urban area in Hangzhou, China, is presented, and it is found that the proposed recovery algorithm has the best performance in terms of traffic speed information reconstruction compared to benchmark methods. The case study also shows that using the recovered data acquires higher accuracy and efficiency in the short-term speed forecasting, compared to the case of using the original data without recovery. The proposed methods tackle missing traffic data issues and forecasting problems in the presence of missing data in an urban road network.

A Brief Talk on Using Computer Aided Technology to Analyze the Feasibility of Introducing Ball Game in the Physical Education Courses of Applied Universities

On October 25, 2016, the Central Committee of the Communist Party of China and the State Council issued the “Outline for the “Healthy China 2030” Plan”, which stated that the implementation of the Youth Sports Promotion Program, the cultivation of young people’s sports hobbies, and the basic realization of adolescents’ mastery of more than one sport. This paper uses computer-aided technology to conduct an empirical study on the influence of Dutch sports on the physical quality of college students through literature research, experimental methods and other research methods. The results show that: Ball sports can effectively improve the body’s physical quality, especially the ability of women to run fast in short-term speed, aerobic work ability and ventilator function.

Naturalistic Data-Driven Predictive Energy Management for Plug-In Hybrid Electric Vehicles

A predictive energy management strategy considering travel route information is proposed to explore the energy-saving potential of plug-in hybrid electric vehicles. The extreme learning machine is used as a short-term speed predictor, and the battery temperature is added as an optimization term to the cost function. By comparing the training data sets, it is found that using the real-world historical speed information for training can achieve higher prediction accuracy than using typical standard driving cycles. The speed predictor trained based on the data considering travel route information can further improve the prediction accuracy. The impact of battery temperature on the total cost is also analyzed. By adjusting the temperature weighting coefficient of the battery, a balance between economy and battery aging can be achieved. In addition, it is found that the ambient temperature also affects vehicular energy consumption. Finally, the proposed method is compared with PMP, MPC, and CD-CS methods, showing its effectiveness and practicability.

Eco-Driving of Connected and Automated Vehicle With Preceding Driver Behavior Prediction

Abstract The development of vehicle connectivity and autonomy in the ground transportation sector is not only able to enhance traffic safety and driving comfort as well as fuel economy. This study presents a receding-horizon optimization-based control strategy integrated with the preceding vehicle speed prediction model to achieve an eco-driving strategy for connected and automated vehicles (CAVs). In the real traffic scenario where the CAV follows the preceding vehicle on the road, a gated recurrent unit (GRU) network is used to predict the behavior of the preceding vehicle by utilizing the historical inter-vehicle information collected through on-board sensors. Then, a nonlinear model predictive control (NMPC) algorithm is adopted for CAV to minimize the accumulated fuel consumption within the preview horizon. The NMPC approach solves the fuel-optimal speed profile of the CAV, considering a predicted short-term speed preview of the preceding vehicle. With the awareness of the preview speed conditions, the fuel consumption of the CAV is reduced by avoiding unnecessary braking and acceleration, especially during transient traffic conditions. The Pareto front framework is used to examine a trade-off between the vehicle speed prediction accuracy, computational burden, and the fuel consumption of the CAV in the proposed GRU-NMPC design. To analyze the effectiveness of the GRU-NMPC design, adaptive cruise control with constant time headway policy (ACC-CTH) is adopted as a benchmark control design. Comparison results show significant fuel economy improvement of the proposed design and expose possible fuel benefits from vehicle autonomy and sensor fusion technology.