Investigating the impacts of crash prediction models on quantifying safety effectiveness of Adaptive Signal Control Systems |
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| Institution: | 1. Glenn Department of Civil Engineering, Clemson University, Clemson, SC 29634, USA;2. Center for Connected Multimodal Mobility, Clemson University, Clemson, SC 29634, USA;3. School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA;1. College of Engineering, Civil Engineering Department University of Wasit, Kut, Iraq;2. Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall, West Lafayette, IN 47907-2051, USA;1. Department of Agricultural & Biological Engineering, The Pennsylvania State University, University Park, PA, USA;2. Department of Agricultural & Biological Engineering, University of Florida, Gainesville, FL, USA;1. Laboratório Nacional de Engenharia Civil, Departamento de Transportes, Núcleo de Planeamento, Tráfego e Segurança Av. do Brasil 101, Lisboa 1700-066, Portugal;2. Department of Civil and Environmental Engineering, Rowan University, Glassboro, NJ 08028, United States;1. Alabama Transportation Institute, The University of Alabama Tuscaloosa, AL, United States;2. Department of Civil, Construction and Environmental Engineering, The University of Alabama Tuscaloosa, AL, United States |
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| Abstract: | Introduction: Adaptive Signal Control System (ASCS) can improve both operational and safety benefits at signalized corridors. Methods: This paper develops a series of models accounting for model forms and possible predictors and implements these models in Empirical Bayes (EB) and Fully Bayesian (FB) frameworks for ASCS safety evaluation studies. Different models are validated in terms of the ability to reduce the potential bias and variance of prediction and improve the safety effectiveness estimation accuracy using real-world crash data from non-ASCS sites. This paper then develops the safety effectiveness of ASCS at six different corridors with a total of 65 signalized intersections with the same type of ASCS, in South Carolina. Results: Validation results show that the FB model that accounts for traffic volume, roadway geometric features, year factor, and spatial effects shows the best performance among all models. The study findings reveal that ASCS reduces crash frequencies in the total crash, fatal and injury crash, and angle crash for most of the intersections. The safety effectiveness of ASCS varies with different intersection features (i.e., AADT at major streets, number of legs at an intersection, the number of through lanes on major streets, the number of access points on minor streets, and the speed limit at major streets). Conclusions: ASCS is associated with crash reductions, and its safety effects vary with different intersection features. Practical Applications: The findings of this research encourage more ASCS deployments and provide insights into selecting ASCS deployment sites for reducing crashes considering the variation of the safety effectiveness of ASCS. |
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| Keywords: | Safety effectiveness Empirical Bayes Fully Bayesian Spatial model Adaptive signal control |
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