Hybrid dual Kalman filtering model for short‐term traffic flow forecasting

Zhou, Teng; Jiang, Dazhi; Lin, Zhizhe; Han, Guoqiang; Xu, Xuemiao; Qin, Jing

doi:10.1049/iet-its.2018.5385

Cited by 91 publications

(49 citation statements)

References 43 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)

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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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“…Different methods and theories were proposed for traffic flow forecasting [3], which can be usually classified into parametric methods and non-parametric ones [4]. Parametric methods include moving average [5], exponential smoothing (ES) [6], auto-regressive integrated moving average (ARIMA) models [7], [8], Kalman filtering methods [9]- [11], multivariate time series models [12], [13], and spectral analysis [14]. Support vector machine regression (SVR) [15], [16], non-parameter regression models [17], artificial neural network (ANN) [18], fuzzy logic system methods [19], [20], deep feature fusion model [21] and deep belief network [22] belong to the non-parametric ones.…”

Section: Introductionmentioning

confidence: 99%

PSO-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting

Cai

Yang

et al. 2020

IEEE Access

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Accurate and reliable traffic flow forecasting is of importance for urban planning and mitigation of traffic congestion, and it is also the basis for the deployment of intelligent traffic management systems. However, constructing a reasonable and robust forecasting model is a challenging task due to the uncertainties and nonlinear characteristics of traffic flow. Aiming at the nonlinear relationship affecting traffic flow forecasting effect, a PSO-ELM model based on particle swarm optimization is proposed for short-term traffic flow forecasting, which takes the advantages of particle swarm optimization to search global optimal solution and extreme learning machine to fast deal with the nonlinear relationship. The proposed model improves the accuracy of traffic flow forecasting. The traffic flow data from highways A1, A2, A4, A8 connecting to Amsterdam's ring road are employed for the case study. The RMSEs of PSO-ELM model are respectively 252.61, 173.75, 200.24, 146.05, while the MAPEs of PSO-ELM model are respectively 11.86%, 10.10%, 10.74%, 11.60%. The experimental results show that the performance of the proposal is significantly better than the performance of state-of-the-art models. INDEX TERMS Short-term traffic flow forecasting, extreme learning machine, particle swarm optimization, time-series model.

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Hybrid dual Kalman filtering model for short‐term traffic flow forecasting

Cited by 91 publications

References 43 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

PSO-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting

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