Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 9 Issue 5
Short-term Traffic Flow Prediction of Urban Roads Based on Random Forest
Luofeng Jiang
10.7753/IJCATR0905.1001
keywords : Prediction; Random forest; Regression; Traffic flows;Model
In the intelligent traffic system, accurately grasp the intrinsic changes in the traffic flow law, and timely scientific prediction of the future traffic flow for a number of moments, will have a very important significance to traffic guidance work, traffic management work, traffic planning work, etc. In this paper, we propose a short-time traffic flow prediction model based on random forest regression(RFR) algorithm, and improve the performance of the model by adjusting the hyperparameters of the model. The performance of the random forest model was also compared with the support vector regression (SVR) model and the decision tree regression(DTR) model, and the RFR model ultimately yielded the best predictions.
@artical{l952020ijcatr09051001,
Title = "Short-term Traffic Flow Prediction of Urban Roads Based on Random Forest",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "9",
Issue ="5",
Pages ="169 - 171",
Year = "2020",
Authors ="Luofeng Jiang"}
The paper proposes the prediction of short-term traffic flow using random forest
The performance of the model is improved by adjusting the hyperparameters
Use R^2, MAE, and RMSE as model performance metrics
Evaluation of model performance by comparison with other prediction models.