Performance analysis of support vector machine for traffic flow prediction

Deshpande, Minal; Bajaj, Preeti

doi:10.1109/icgtspicc.2016.7955283

Cited by 18 publications

(7 citation statements)

References 7 publications

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“…Liu and Wu [39] suggested using random forest (RF) for traffic flow prediction models because of the model's robustness and practicality; their work demonstrated the generalization capabilities of this model. Support vector regressor (SVR) has also been leveraged to model traffic volume and has known superior performance compared to linear models [40].…”

Section: K-nearest Neighbor (Knn)mentioning

confidence: 99%

Automated Machine Learning Pipeline for Traffic Count Prediction

Mahdavian¹,

Shojaei²,

Salem³

et al. 2021

Modelling

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Research indicates that the projection of traffic volumes is a valuable tool for traffic management. However, few studies have examined the application of a universal automated framework for car traffic volume prediction. Within this limited literature, studies using broad data sets and inclusive predictors have been inadequate; such works have not incorporated a comprehensive set of linear and nonlinear algorithms utilizing a robust cross-validation approach. The proposed model pipeline introduced in this study automatically identifies the most appropriate feature-selection method and modeling approach to reduce the mean absolute percentage error. We utilized hyperparameter optimization to generate a universal automated framework, distinct from model optimization techniques that rely on a single case study. The resulting model can be independently customized to any respective project. Automating much of this process minimizes the work and expertise required for traffic count forecasting. To test the applicability of our models, we used Florida historical traffic data from between 2001 and 2017. The results confirmed that nonlinear models outperformed linear models in predicting passenger vehicles’ monthly traffic volumes in this specific case study. By employing the framework developed in this study, transportation planners could identify the critical links on US roads that incur overcapacity issues.

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Section: K-nearest Neighbor (Knn)mentioning

confidence: 99%

Automated Machine Learning Pipeline for Traffic Count Prediction

Mahdavian¹,

Shojaei²,

Salem³

et al. 2021

Modelling

View full text Add to dashboard Cite

show abstract

“…Several studies have applied machine learning methods to predict traffic conditions, such as Neural Network [8,9], SVM [10], Deep Learning [11,12], Bayes Classifier [13][14][15], and Decision Tree [16,17]. In general, the results of machine learning (to predict traffic conditions) are influenced by the features of the dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The methods compared have their own configuration such as Neural Network with 1 hidden layer with 20 neurons [8] and Neural Network with 1 hidden layer and 6 neurons [9]. The other implemented methods are Deep Neural Network with 3 hidden layers contains 40, 50, and 40 neurons [11] and Deep Learning which used 4 hidden layers that each contain 300 neurons [12], Decision Tree [16,17], and Support Vector Machine (SVM) multiclass [10]. The parameters were mapped into a coordinate system (Cartesian) using Principal Component Analysis (PCA) to classify the traffic condition using SVM.…”

Section: A Static Datasetmentioning

confidence: 99%

Predicting Traffic Conditions Using Knowledge-Growing Bayes Classifier

et al. 2020

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Congestion often hinders human mobility. This problem occurs due to the constant increase in vehicles every year. Reliable predictions of traffic conditions would allow drivers to choose their routes to avoid traffic jams while providing the police with traffic management strategies. Therefore, this paper tests the ability of various machine learning methods to predict traffic conditions. The study assesses Neural Networks, Bayes Classifier, Decision Trees, SVM, Deep Neural Network, and Deep Learning. Of these methods, the Decision Tree, Deep Neural Network, and Bayes Classifier show the highest performance in predicting traffic conditions using static data testing. However, in dynamic testing to assess the growth of knowledge, the performance of the Knowledge-Growing Decision Tree tends to decrease as the training data grows. Its performance decreased 3.89 points (88.24% to 84.35%) in accuracy, and 7.55 points (76.25% to 68.70%) for each precision, recall, and F1 Score. Conversely, the Knowledge-Growing Deep Neural Network and Bayes Classifier had a better performance than Decision Tree. The performances of Knowledge-Growing Deep Neural Network increased slightly by 0.35 points (93.38% to 93.73%) for accuracy and 0.69 points (86.77% to 87.64%) in other measurements. Although its performance increased, the processing time takes very long, namely 139452.76 seconds and 318832.80 seconds for sub-scheme (a) and (b), respectively. Meanwhile, the Knowledge-Growing Bayes Classifier offers a greater performance increase of 2.3 points (80.52% to 82.82%) for the accuracy and 4.6 points (65.63% to 61.03%) for the other performance measurements. In addition, it also scored better for processing time, as predictions only take 3 seconds using sub-scheme (a), and 7 seconds when using sub-scheme (b). Therefore, the paper proposes the Knowledge-Growing Bayes Classifier to predict rapidly changing traffic conditions. This method outperform the others. These can be attributed to its ability to 1) adjust to ever-changing the traffic conditions; 2) predict the result as soon as the data are acquired; and 3) make decentralized predictions.

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“…Therefore, data mining and ML algorithms have been gradually introduced into the field of network traffic prediction. Support vector machines (SVMs) have also been applied to the field of network traffic prediction and have achieved better prediction results . Based on the traditional SVM, researchers have further proposed the use of a variety of optimized SVM models for network traffic prediction.…”

Section: Introductionmentioning

confidence: 99%

A network traffic prediction model of smart substation based on IGSA‐WNN

2020

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The network traffic prediction of a smart substation is key in strengthening its system security protection. To improve the performance of its traffic prediction, in this paper, we propose an improved gravitational search algorithm (IGSA), then introduce the IGSA into a wavelet neural network (WNN), iteratively optimize the initial connection weighting, scalability factor, and shift factor, and establish a smart substation network traffic prediction model based on the IGSA‐WNN. A comparative analysis of the experimental results shows that the performance of the IGSA‐WNN‐based prediction model further improves the convergence velocity and prediction accuracy, and that the proposed model solves the deficiency issues of the original WNN, such as slow convergence velocity and ease of falling into a locally optimal solution; thus, it is a better smart substation network traffic prediction model.

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Performance analysis of support vector machine for traffic flow prediction

Cited by 18 publications

References 7 publications

Automated Machine Learning Pipeline for Traffic Count Prediction

Automated Machine Learning Pipeline for Traffic Count Prediction

Predicting Traffic Conditions Using Knowledge-Growing Bayes Classifier

A network traffic prediction model of smart substation based on IGSA‐WNN

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