Signal Treatments to Reduce the Likelihood of Heavy Vehicle Crashes at Intersections: Microsimulation Modeling Approach

Archer, Jeffery; Young, William

doi:10.1061/(asce)te.1943-5436.0000125

Cited by 11 publications

(3 citation statements)

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“…trj ) containing vehicle trajectories so that it can be directly inputted to SSAM. Archer and Young evaluated the safety impact of possible traffic signal treatments (i.e., amber time extension, dilemma zone green extension, and all‐red extension). VISSIM was used to generate vehicle trajectories, compute surrogate safety measures (i.e., post‐encroachment time (PET)), and estimate potential red‐light violations.…”

Section: State Of the Artmentioning

confidence: 99%

“…These advantages enable traffic safety to be estimated even for untried or new traffic control strategies that do not have the traffic crash data. In particular, the advent of the microscopic traffic simulation models that can generate the microlevel vehicle trajectories including x, y coordinates, speed, and acceleration rate at every time step promotes the use of traffic conflicts as a safety estimator [1][2][3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Classification modeling approach for vehicle dynamics model‐integrated traffic simulation assessing surrogate safety

Park

Yun

2014

J of Advced Transportation

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SUMMARYTo assess safety impacts of untried traffic control strategies, an earlier study developed a vehicle dynamics model-integrated (i.e., VISSIM-CarSim-SSAM) simulation approach and evaluated its performance using surrogate safety measures. Although the study found that the integrated simulation approach was a superior alternative to existing approaches in assessing surrogate safety, the computation time required for the implementation of the integrated simulation approach prevents it from using it in practice. Thus, this study developed and evaluated two types of models that could replace the integrated simulation approach with much faster computation time, feasible for real-time implementation. The two models are as follows: (i) a statistical model (i.e., logit model) and (ii) a nonparametric approach (i.e., artificial neural network). The logit model and the neural network model were developed and trained on the basis of three simulation data sets obtained from the VISSIM-CarSim-SSAM integrated simulation approach, and their performances were compared in terms of the prediction accuracy. These two models were evaluated using six new simulation data sets. The results indicated that the neural network approach showing 97.7% prediction accuracy was superior to the logit model with 85.9% prediction accuracy. In addition, the correlation analysis results between the traffic conflicts obtained from the neural network approach and the actual traffic crash data collected in the field indicated a statistically significant relationship (i.e., 0.68 correlation coefficient) between them. This correlation strength is higher than that of the VISSIM only (i.e., the state of practice) simulation approach. The study results indicated that the neural network approach is not only a time-efficient way to implementing the VISSIM-CarSim-SSAM integrated simulation but also a superior alternative in assessing surrogate safety.

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Section: State Of the Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification modeling approach for vehicle dynamics model‐integrated traffic simulation assessing surrogate safety

Park

Yun

2014

J of Advced Transportation

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“…In addition, as the traffic conflict technique study requires a sizeable observation to obtain vehicle trajectories, the microscopic traffic simulation models have been commonly used to generate vehicle trajectories. The data extraction capability of the microscopic traffic simulation models has provided many advantages in the development of new surrogate safety measures [24][25][26][27][28], the safety impact assessment of traffic alternatives [29][30][31][32][33], and the validation of traffic conflict [34][35][36][37].…”

Section: Research Motivationsmentioning

confidence: 99%

Development of a Surrogate Safety Assessment Framework Incorporating Traffic Simulator, Vehicle Dynamics, Driver Warning, GPS/INU, and V2V/V2I

So¹

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Thank you God, for keeping my body and mind strong and leading me forward one more step further in my life. I wish to convey my sincere appreciation for those who have supported me in successfully completing my doctoral study. I would like to first express my deepest gratitude to my advisor, Dr. Byungkyu (Brian) Park, for his assistance in all my research questions and concerns. This dissertation would not have been possible without his guidance and support. He has always led me to the right path of being a better researcher. I would like to sincerely thank the members of my dissertation committee as well: Dr.

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Simulation of safety: A review of the state of the art in road safety simulation modelling

Young¹,

Sobhani²,

Lenné³

et al. 2014

Accident Analysis & Prevention

142

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Signal Treatments to Reduce the Likelihood of Heavy Vehicle Crashes at Intersections: Microsimulation Modeling Approach

Cited by 11 publications

References 3 publications

Classification modeling approach for vehicle dynamics model‐integrated traffic simulation assessing surrogate safety

Classification modeling approach for vehicle dynamics model‐integrated traffic simulation assessing surrogate safety

Development of a Surrogate Safety Assessment Framework Incorporating Traffic Simulator, Vehicle Dynamics, Driver Warning, GPS/INU, and V2V/V2I

Simulation of safety: A review of the state of the art in road safety simulation modelling

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