A noise-immune Kalman filter for short-term traffic flow forecasting

Cai, Lingru; Zhang, Zhanchang; Yang, Junjie; Yu, Yidan; Zhou, Teng; Qin, Jing

doi:10.1016/j.physa.2019.122601

Cited by 96 publications

(63 citation statements)

References 39 publications

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“…Our framework is beneficial to college student, which helps them to improve the physical fitness. In future, we plan to extend this method to the applications of other domains, such as basketball game prediction or time series analysis [13][14][15][16][17][18].…”

Section: Resultsmentioning

confidence: 99%

An enhanced learning framework for the classification of college student physical fitness

Wang¹,

Xie²,

Yan³

et al. 2019

Proceedings of the 2nd International Conference on Humanities Education and Social Sciences (ICHESS 2019)

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Physical test (PF) is demonstrated by a variety of factors including body weight status, cardiorespiratory fitness, musculoskeletal fitness (muscular strength and endurance) and flexibility, which are related to college student's physical condition. The test helps them require an understanding of behavioral attributes and causative mechanisms that promote their physical fitness. Judging the level of college student's physical fitness quickly is very necessary. In this paper, we present an enhanced learning framework for the classification of college student physical fitness. To achieve this, we first design a selection strategy for the students by considering the features that have the greatest impact on. Then, we present an enhanced learning framework equipped with our custom loss function and boosting strategy to classify the level of students' physical fitness. Extensive experiments have demonstrated the outpeformance of our framework.

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Section: Resultsmentioning

confidence: 99%

An enhanced learning framework for the classification of college student physical fitness

Wang¹,

Xie²,

Yan³

et al. 2019

Proceedings of the 2nd International Conference on Humanities Education and Social Sciences (ICHESS 2019)

Self Cite

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“…The results show that our method helps the athletes to achieve personal breakthroughs and create their own success. In future, we plan to extend this method to the applications of other domains, such as time series analysis [7,[20][21][22][23].…”

Section: Resultsmentioning

confidence: 99%

Towards the athletic psychological adjustment: an empirical study based on the Inverted-U-Type hypothesis

Jia-jun¹,

Xie²,

Yan³

et al. 2019

Proceedings of the 2nd International Conference on Humanities Education and Social Sciences (ICHESS 2019)

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The technical and emotional control of the javelin game plays an important role for the athlete's performance. However, previous researches show that many psychological methods do not consider the psychological state of the athletes, and take the corresponding measures for psychological adjustment. In this paper, we propose an improved Inverted-U-Type hypothesis method for athlete's psychological adjustment during the training time before competition. According to the athletes' psychological state, adjusting the factors, we carry the weak and strong interventions to adjust psychological and physiological state of the athletes to an optimal level. The results show that the adjustment of the athletes' psychological by our method can effectively eliminate the impact of internal and external factors to improve the stability, which is also help for the athletes' psychological training of other sports.

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“…The model-driven methods mainly include the autoregressive integrated moving average (ARIMA) model [9]- [11], seasonal ARIMA (SARIMA) model [12], [13], Markov chain (MC) [14]- [17], Bayesian network (BN) [18]- [20], and Kalman filter (KF) [21]- [23]. These methods cannot perform normally without several preconditions, e.g.…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic Flow Forecast Through Time Series Analysis Based on Deep Learning

Jiao

Huang

2020

IEEE Access

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Traffic congestion is a thorny issue to many large and medium-sized cities, posing a serious threat to sustainable urban development. Recently, intelligent traffic system (ITS) has emerged as an effective tool to mitigate urban congestion. The key to the ITS lies in the accurate forecast of traffic flow. However, the existing forecast methods of traffic flow cannot adapt to the stochasticity and sheer length of traffic flow time series. To solve the problem, this paper relies on deep learning (DL) to forecast traffic flow through time series analysis. The authors developed a traffic flow forecast model based on the long shortterm memory (LSTM) network. The proposed model was compared with two classic forecast models, namely, the autoregressive integrated moving average (ARIMA) model and the backpropagation neural network (BPNN) model, through long-term traffic flow forecast experiments, using an actual traffic flow time series from OpenITS. The experimental results show that the proposed LSTM network outperformed the classic models in prediction accuracy. Our research discloses the dynamic evolution law of traffic flow, and facilitates the decision-making of traffic management. INDEX TERMS Traffic flow forecast, time series analysis, deep learning (DL), long short-term memory (LSTM).

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A noise-immune Kalman filter for short-term traffic flow forecasting

Cited by 96 publications

References 39 publications

An enhanced learning framework for the classification of college student physical fitness

An enhanced learning framework for the classification of college student physical fitness

Towards the athletic psychological adjustment: an empirical study based on the Inverted-U-Type hypothesis

Traffic Flow Forecast Through Time Series Analysis Based on Deep Learning

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