Short-Time Prediction of Traffic Flow Based on PSO Optimized SVM

Duan, Min

doi:10.1109/icitbs.2018.00018

Cited by 37 publications

(14 citation statements)

References 4 publications

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“…The method for converting the predicted point value into those of the upper and lower bounds was learned from the following equation. (37) where Y i is the value of the upper and lower bounds, y i is the predicted point value, and 1 and 2 were width factors.…”

Section: A Data and Model Descriptionmentioning

confidence: 99%

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A Hybrid Deep Interval Prediction Model for Wind Speed Forecasting

Chen

et al. 2021

IEEE Access

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How to predict wind speed with high accuracy is a fundamental issue for the generation of wind power and energy management of power systems. Moreover, the nonlinear and non-stationary characteristics of wind speed make the task extremely challenging. To resolve this issue, a novel hybrid interval prediction model based on long short-term memory (LSTM) networks and variational mode decomposition (VMD) algorithm is developed in the frame of lower upper bound estimation in this study. Firstly, VMD is applied to decompose sequences of raw wind speed into several different modal components containing different information. Then, a LSTM-based model for each component is built to predict the corresponding wind speed information. In the LSTM structure, lower and upper bounds of the prediction intervals (PIs) are constructed and two customized cost functions based on the probability of convergence and width of the PIs are proposed to optimize the model with gradient descent algorithm. Additionally, particle swarm optimization (PSO) is applied to optimize the hyperparameters in the two cost functions of each LSTM. Finally, experiments with different scales of datasets were conducted to show the feasibility and effectiveness of the proposed method by comparing it with traditional methods. The results demonstrate that the proposed model yielded significantly superior performance with a clear enhancement in the PI quality.INDEX TERMS Interval prediction, wind speed forecasting, long short-term memory networks, variational mode decomposition, lower upper bound estimation, particle swarm optimization, customized cost function.

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Section: A Data and Model Descriptionmentioning

confidence: 99%

“…For instance, the performance of the SVM and KELM rely on the choice of hyperparameters highly. So these models usually are optimized by a heuristic optimization algorithm, like PSO-KELM [36], PSO-SVM [37]. Reference [35] applied GSA to synchronously optimize hyperparameters in a hybrid model.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Interval Prediction Model for Wind Speed Forecasting

Chen

et al. 2021

IEEE Access

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“…The particle swarm algorithm was first proposed by Kennedy and Eberhart in 1995 in [12]. In 2018, Duan et al introduced particle swarm optimization to solve the parameter selection problem of support vector machine prediction model [13]. The optimal prediction model is obtained by choosing the appropriate learning parameters using the swarm algorithm.…”

Section: Parameter Optimization For Ocsvmmentioning

confidence: 99%

Power System Anomaly Detection Based on OCSVM Optimized by Improved Particle Swarm Optimization

Wang

Song

et al. 2019

IEEE Access

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This paper tries to solve anomaly detection, a very important issue in ensuring the safe and stable operation of power system. As the proportion of abnormal data in the operation of power system is very small, a one-class support vector machine (OCSVM) is adopted in this paper, which is suitable for classification of unbalanced data. However, the performance of OCSVM is sensitive to its parameters, and an unsuitable choice will decrease the classification accuracy and generalization ability of it. In this paper, particle swarm optimization (PSO) is used to optimize the parameters of OCSVM. The original PSO algorithm converges slowly and easily falls into local optimum. To overcome this issue, this paper proposes an improved PSO algorithm for parameters optimization, in which adaptive speed weighting and adaptive population splitting are introduced to improve the convergence speed of the algorithm and help the algorithm jump out of the local optimal position. Experiments on standard benchmarks and real power system experimental data sets demonstrate the effectiveness of the proposed algorithm.

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“…Several machine learning algorithms have also been proposed for traffic forecasting and these algorithms have shown promising improvement over statistical techniques. For instance, different researchers developed a number of approaches using the Artificial Neural Network (ANN) (Khotanzad & Sadek (2003); Csikós et al (2015); Sharma et al (2018)), Support Vector Machine (SVM) (Yang & Lu (2010); Duan (2018)), k-Nearest Neighbor (kNN) (Yu et al (2016); Cai et al (2016)), Bayesian network (Sun et al (2006); Castillo et al (2008)), XGBoost (Dong et al (2018); Zhang et al (2019a)) and random forest (Zarei et al (2013); Liu & Wu (2018)) for traffic prediction problem. Although the machine learning models have shown performance improvement over statistical techniques, these techniques rely highly on manually selected features and these features vary from problem to problem.…”

Section: Introductionmentioning

confidence: 99%

Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies

Modi,

Bhattacharya,

Basak

2021

Preprint

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Traffic management in a city has become a major problem due to the increasing number of vehicles on roads. Intelligent Transportation System (ITS) can help the city traffic managers to tackle the problem by providing accurate traffic forecasts. For this, ITS requires a reliable traffic prediction algorithm that can provide accurate traffic prediction at multiple time steps based on past and current traffic data. In recent years, a number of different methods for traffic prediction have been proposed which have proved their effectiveness in terms of accuracy. However, most of these methods have either considered spatial information or temporal information only and overlooked the effect of other. In this paper, to address the above problem a deep learning based approach has been developed using both the spatial and temporal dependencies. To consider spatio-temporal dependencies, nearby road sensors at a particular instant are selected based on the attributes like traffic similarity and distance. Two pre-trained deep auto-encoders were cross-connected using the concept of latent space mapping and the resultant model was trained using the traffic data from the selected nearby sensors as input. The proposed deep learning based approach was trained using the real-world traffic data collected from loop detector sensors installed on different highways of Los Angeles and Bay Area. The traffic data is freely available from the web portal of the California Department of Transportation Performance Measurement System (PeMS). The effectiveness of the proposed approach was verified by comparing it with a number of machine/deep learning approaches. It has been found that the proposed approach provides accurate traffic prediction results even for 60-min ahead prediction with least error than other techniques.

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Short-Time Prediction of Traffic Flow Based on PSO Optimized SVM

Cited by 37 publications

References 4 publications

A Hybrid Deep Interval Prediction Model for Wind Speed Forecasting

A Hybrid Deep Interval Prediction Model for Wind Speed Forecasting

Power System Anomaly Detection Based on OCSVM Optimized by Improved Particle Swarm Optimization

Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies

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