title: Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway.
abstract: In traffic safety studies, crash frequency modeling of total crashes is the cornerstone before proceeding to more detailed safety evaluation. The relationship between crash occurrence and factors such as traffic flow and roadway geometric characteristics has been extensively explored for a better understanding of crash mechanisms. In this study, a multi-level Bayesian framework has been developed in an effort to identify the crash contributing factors on an urban expressway in the Central Florida area. Two types of traffic data from the Automatic Vehicle Identification system, which are the processed data capped at speed limit and the unprocessed data retaining the original speed were incorporated in the analysis along with road geometric information. The model framework was proposed to account for the hierarchical data structure and the heterogeneity among the traffic and roadway geometric data. Multi-level and random parameters models were constructed and compared with the Negative Binomial model under the Bayesian inference framework. Results showed that the unprocessed traffic data was superior. Both multi-level models and random parameters models outperformed the Negative Binomial model and the models with random parameters achieved the best model fitting. The contributing factors identified imply that on the urban expressway lower speed and higher speed variation could significantly increase the crash likelihood. Other geometric factors were significant including auxiliary lanes and horizontal curvature.
authors.affiliation: Department of Civil, Environmental and Construction Engineering, University of Central Florida, Engineering II-215, Orlando, FL 32816, United States. Electronic address: shiqi@knights.ucf.edu.
authors.forename: Qi
authors.initials: Q
authors.lastname: Shi
authors.affiliation: Department of Civil, Environmental and Construction Engineering, University of Central Florida, Engineering II-215, Orlando, FL 32816, United States. Electronic address: m.aty@ucf.edu.
authors.forename: Mohamed
authors.initials: M
authors.lastname: Abdel-Aty
authors.affiliation: School of Transportation Engineering, Tongji University, 4800 Cao'an Road, 201804 Shanghai, China. Electronic address: yurongjie@tongji.edu.cn.
authors.forename: Rongjie
authors.initials: R
authors.lastname: Yu
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000706:statistics & numerical data*; D001336:Automobiles; D001499:Bayes Theorem; D004779:Environment Design; D005431:Florida; D006801:Humans; D015233:Models, Statistical; D055361:Multilevel Analysis; D012449:Safety
keywords: Automatic Vehicle Identification; Bayesian inference; Multi-level model; Random parameters; Urban expressway safety