Developing a Travel Time Estimation Method of Freeway Based on Floating Car Using Random Forests. (3rd January 2019)

• Record Type: Journal Article
• Authors: Cheng, Juan; Li, Gen; Chen, Xianhua
• Other Names: Tizghadam Ali Guest Editor.
• Is Part Of: Journal of advanced transportation. Volume 2019(2019)
• Publication Date: 2019-01-03
• Subjects: Transportation -- Periodicals; 388.05
• DOI: 10.1155/2019/8582761
• Languages: English
• ISSNs: 0197-6729

Abstract

Abstract : Travel time of traffic flow is the basis of traffic guidance. To improve the estimation accuracy, a travel time estimation model based on Random Forests is proposed. 7 influence variables are viewed as candidates in this paper. Data obtained from VISSIM simulation are used to verify the model. Different from other machine learning algorithm as black boxes, Random Forests can provide interpretable results through variable importance. The result of variable importance shows that mean travel time of floating cart - f, traffic state parameterX, density of vehicleK a l l, and median travel time of floating cart m e n f are important variables affecting travel time of traffic flow; meanwhile other variables also have a certain influence on travel time. Compared with the BP (Back Propagation) neural network model and the quadratic polynomial regression model, the proposed Random Forests model is more accurate, and the variables contained in the model are more abundant.