Poisson Log-Linear Regression Model for Rural Signalized Intersection

Abstract:

Safety performance of rural signalized intersections is critical for identifying high-risk sites and predicting the hazardousness. This paper aims to develop a predictive model that will describe the safety of rural signalized intersections based on various input variables. Data are examined from 124 rural signalized intersections over three states, and Poisson log-linear regression model is presented, which connected traffic number and the average traffic volumes, geometric characteristics and signalization characteristics variables together. The model and associated data analysis reveal that average daily traffic, media width, speed limit, degree of horizontal curvature and left-turn lane are the factors that have greatest overall effect on safety. The results show that the Poisson log-linear regression model is able to describe the rural signalized intersection safety accurately. Using this model, effective countermeasures can be applied for improving traffic safety.

• Periodical: Advanced Materials Research (Volumes 594-597)
• Pages: 1391-1394
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.594-597.1391
• Online since: November 2012
• Authors: Zhi Ping Ren
• Keywords: Poisson Regression Model, Rural Signalized Intersection, Safety

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