Abstract
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.
Highlights
Exploring the crash mechanism and contributing factors plays an important role in preventing crash and reducing crash severity to freeway traffic surveillance systems. e occurrence of a crash can generate the turbulence of traffic flow which may lead to further crashes
In this study, compared to other studies based on the Bayesian conditional logit model, the Bayesian conditional logit model was used to quantify the difference in the safety performance of Secondary crash (SC) associated with various traffic flow states divided by three-phase traffic theory. e group variables are separated based on case and control samples
Because the traffic flow in this study is divided into seven states, including free flow (F), synchronized flow (S), wide moving jams (J), the transitional state from free low to synchronized flow (F⟶S), the transitional state from synchronized flow to free flow (S⟶F), the transitional state from synchronized flow to wide moving jams (S⟶J), and the transitional state from wide moving jams to synchronized flow (J⟶S), in this method, the free flow (F) state is considered as the reference level, and the other six traffic flow states are considered as six independent variables
Summary
Exploring the crash mechanism and contributing factors plays an important role in preventing crash and reducing crash severity to freeway traffic surveillance systems. e occurrence of a crash can generate the turbulence of traffic flow which may lead to further crashes. Exploring the crash mechanism and contributing factors plays an important role in preventing crash and reducing crash severity to freeway traffic surveillance systems. E occurrence of a crash can generate the turbulence of traffic flow which may lead to further crashes. Secondary crash (SC) occurs within the spatial and temporal impact ranges of the turbulent traffic conditions caused by the primary crash (PC). With the widespread use of freeway real-time traffic surveillance systems, researchers have started using highresolution dynamic traffic flow data to identify the traffic condition before SC occurrences. SC can be affected by various contributing factors, including traffic flow characteristics, geometric design factors, weather conditions, PC characteristics, etc. E common methods include static threshold method (STM) [8,9,10] and dynamic methods (DM) [11,12,13,14] Many researchers have paid close attention to the identification method of SC. e common methods include static threshold method (STM) [8,9,10] and dynamic methods (DM) [11,12,13,14]
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