Study on Three-lane Cellular Automata Traffic Model and Simulation System in Three-phase Traffic Theory

2015 ◽  
Vol 12 (18) ◽  
pp. 6967-6978 ◽  
Author(s):  
Yu Wang
Keyword(s):  
Cellular Automata ◽  
Simulation System ◽  
Traffic Model ◽  
Model And Simulation ◽  
Three Phase ◽  
Traffic Theory

scholarly journals Cellular automata approach to three-phase traffic theory

2002 ◽  
Vol 35 (47) ◽  
pp. 9971-10013 ◽  
Author(s):  
Boris S Kerner ◽  
Sergey L Klenov ◽  
Dietrich E Wolf
Keyword(s):  
Cellular Automata ◽  
Three Phase ◽  
Traffic Theory

Using empirical traffic trajectory data for crash risk evaluation under three-phase traffic theory framework

2021 ◽  
Vol 157 ◽  
pp. 106191
Author(s):  
Tong Liu ◽  
Zhibin Li ◽  
Pan Liu ◽  
Chengcheng Xu ◽  
David A. Noyce
Keyword(s):  
Risk Evaluation ◽  
Crash Risk ◽  
Trajectory Data ◽  
Three Phase ◽  
Traffic Theory ◽  
Theory Framework

scholarly journals Analysis of Freeway Secondary Crashes in Different Traffic Flow States by Three-Phase Traffic Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Bo Yang ◽  
Yao Wu ◽  
Weihua Zhang
Keyword(s):  
Traffic Flow ◽  
Safety Performance ◽  
Crash Risk ◽  
Contributing Factors ◽  
Crash Data ◽  
Conditional Logit ◽  
Three Phase ◽  
Secondary Crashes ◽  
Traffic Theory ◽  
Flow States

The objective of this study is to analyse the relationship between secondary crash risk and traffic flow states and explore the contributing factors of secondary crashes in different traffic flow states. Crash data and traffic data were collected on the I-880 freeway in California from 2006 to 2011. The traffic flow states are categorised by three-phase traffic theory. The Bayesian conditional logit model has been established to analyse the statistical relationship between the secondary crash probability and various traffic flow states. The results showed that free flow (F) state has the best safety performance of secondary crash and synchronized flow (S) state has the worst safety performance of secondary crashes. The traditional logistic regression model has been used to analyse the contributing factors of secondary crashes in different traffic flow states. The results indicated that the contributing factors in different traffic flow states are significantly different.

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