A Pragmatic Approach for Refined Feature Selection for the Prediction of Road Accident Severity

Ramani, R.; Selvaraj, Shanthi

doi:10.24846/v23i1y201405

Cited by 3 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4 aimed to build the genetic programming model for real-time crash prediction on freeways and evaluated the application of the model. Ramani and Selvaraj 5 optimized the aggregated feature selection with voting algorithm. An optimal number of significant features with majority votes were selected.…”

Section: Introductionmentioning

confidence: 99%

Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization

Wang

et al. 2017

Journal of Algorithms & Computational Technology

View full text Add to dashboard Cite

Road traffic safety is essential, therefore in order to predict traffic fatalities effectively and promote the harmonious development of transportation, a traffic fatalities prediction model based on support vector machine is established in this paper. The selection of parameters greatly affects the prediction accuracy of support vector machine. Introducing particle swarm optimization can find the optimal parameters and improve the prediction accuracy of support vector machine by parameter optimization. However, standard particle swarm optimization is easy to trap into the local optimum, so that the best parameter solutions cannot be found. Therefore, the mutation operation of the genetic algorithm is introduced into particle swarm optimization, particle swarm with mutation optimization is generated. It expands the search space and makes parameter selection more accurate. This paper predicts fatalities of traffic accident using small samples and nonlinear data. The results show that compared with particle swarm with mutation optimization back propagation neural network prediction model, particle swarm optimization-support vector machine model, support vector machine, back propagation neural network, K Nearest Neighbor (K-NN), and Bayesian network, the prediction model of traffic fatalities based on particle swarm with mutation optimization-support vector machine has higher prediction precision and smaller errors. It is feasible and effective to use particle swarm with mutation optimization to optimize the parameters of support vector machine, and this model can predict the accident more accurately.

show abstract

Section: Introductionmentioning

confidence: 99%

Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization

Wang

et al. 2017

Journal of Algorithms & Computational Technology

View full text Add to dashboard Cite

show abstract

Indian Road Conditions and Accident Risk Predictions using Deep Learning Approach – A Review

Partheeban

Ranganathan

Elamparithi

et al. 2020

2020 3rd International Conference on Intelligent Sustainable Systems (ICISS)

View full text Add to dashboard Cite

Traffic fatalities prediction based on support vector machine

Yang

Wang

et al. 2016

AoT

View full text Add to dashboard Cite

To effectively predict traffic fatalities and promote the friendly development of transportation, a prediction model of traffic fatalities is established based on support vector machine (SVM). As the prediction accuracy of SVM largely depends on the selection of parameters, Particle Swarm Optimization (PSO) is introduced to find the optimal parameters. In this paper, small sample and nonlinear data are used to predict fatalities of traffic accident. Traffic accident statistics data of China from 1981 to 2012 are chosen as experimental data. The input variables for predicting accident are highway mileage, vehicle number and population size while the output variables are traffic fatality. To verify the validity of the proposed prediction method, the back-propagation neural network (BPNN) prediction model and SVM prediction model are also used to predict the traffic fatalities. The results show that compared with BPNN prediction model and SVM model, the prediction model of traffic fatalities based on PSO-SVM has higher prediction precision and smaller errors. The model can be more effective to forecast the traffic fatalities. And the method using particle swarm optimization algorithm for parameter optimization of SVM is feasible and effective. In addition, this method avoids overcomes the problem of “over learning” in neural network training progress

show abstract

A Pragmatic Approach for Refined Feature Selection for the Prediction of Road Accident Severity

Cited by 3 publications

References 15 publications

Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization

Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization

Indian Road Conditions and Accident Risk Predictions using Deep Learning Approach – A Review

Traffic fatalities prediction based on support vector machine

Contact Info

Product

Resources

About