Study on the method of travel time estimation in urban road traffic flow

2011 ◽  
Author(s):  
Mingbo Zhang ◽  
Bin Zeng ◽  
Ziruo Yu
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Road Traffic ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Urban Road

Travel time estimation method for urban road based on traffic stream directions

2016 ◽  
Vol 12 (6) ◽  
pp. 479-503 ◽  
Author(s):  
Dianhai Wang ◽  
Fengjie Fu ◽  
Xiaoqin Luo ◽  
Sheng Jin ◽  
Dongfang Ma
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Urban Road ◽  
Traffic Stream

scholarly journals Developing a Travel Time Estimation Method of Freeway Based on Floating Car Using Random Forests

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Juan Cheng ◽  
Gen Li ◽  
Xianhua Chen
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Random Forests ◽  
Polynomial Regression ◽  
Learning Algorithm ◽  
Estimation Method ◽  
Time Estimation ◽  
Variable Importance ◽  
Estimation Accuracy ◽  
Travel Time Estimation

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 car t-f, traffic state parameter X, density of vehicle Kall, and median travel time of floating car tmenf 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.

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