An Improved Travel Time Estimation Method without Signal Timing Data Based on an Individual Probe Car

CICTP 2016 ◽  
2016 ◽  
Author(s):  
Huibing Li ◽  
Zhijie Bao ◽  
Geng Yang ◽  
Chao Peng ◽  
Lihua Luo
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Signal Timing ◽  
Travel Time Estimation ◽  
Individual Probe ◽  
Timing Data ◽  
Probe Car

Implementation of a New Travel Time Estimation Method for Demand Forecasting Models

2011 ◽  
Vol 130-134 ◽  
pp. 3410-3415
Author(s):  
Chen Xi Lu ◽  
Fang Zhao ◽  
Mohammed Hadi ◽  
Ren Fa Yang
Keyword(s):  
Travel Time ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Demand Forecasting ◽  
Estimation Method ◽  
Time Estimation ◽  
Signal Timing ◽  
Travel Time Estimation ◽  
Large Network ◽  
Demand Forecasting Model

This paper discusses the implementation of a new travel time estimation method in a regional demand forecasting model. The developed model considers implicitly the influence of signal timing as a function of main street and cross street traffic demands, although signal timing setting is not required as input. The application presented in this paper demonstrates that the developed model is applicable to a large network without the burden of signal timing input requirement. The results indicate that the application of the model can improve the performance of traffic assignment as part of the demand forecasting process. The model is promising to support dynamic traffic assignment (DTA) model applications in the future.

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

A new travel time estimation method for horizontal strata

2005 ◽  
Author(s):  
M. Turhan Taner ◽  
Sven Treitel ◽  
M. Al‐Chalabi
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Travel Time Estimation

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.

scholarly journals Arterial travel time estimation method using SCATS traffic data based on KNN-LSSVR model

2019 ◽  
Vol 11 (5) ◽  
pp. 168781401984192 ◽  
Author(s):  
Qichun Bing ◽  
Dayi Qu ◽  
Xiufeng Chen ◽  
Fuquan Pan ◽  
Jinli Wei
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Traffic Data
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