A Bayesian Ridge Regression Analysis Of Congestion'S Impact On Urban Expressway Safety
Keywords
Bayesian inference; Congestion; Ridge regression; Urban expressway safety
Abstract
With the rapid growth of traffic in urban areas, concerns about congestion and traffic safety have been heightened. This study leveraged both Automatic Vehicle Identification (AVI) system and Microwave Vehicle Detection System (MVDS) installed on an expressway in Central Florida to explore how congestion impacts the crash occurrence in urban areas. Multiple congestion measures from the two systems were developed. To ensure more precise estimates of the congestion's effects, the traffic data were aggregated into peak and non-peak hours. Multicollinearity among traffic parameters was examined. The results showed the presence of multicollinearity especially during peak hours. As a response, ridge regression was introduced to cope with this issue. Poisson models with uncorrelated random effects, correlated random effects, and both correlated random effects and random parameters were constructed within the Bayesian framework. It was proven that correlated random effects could significantly enhance model performance. The random parameters model has similar goodness-of-fit compared with the model with only correlated random effects. However, by accounting for the unobserved heterogeneity, more variables were found to be significantly related to crash frequency. The models indicated that congestion increased crash frequency during peak hours while during non-peak hours it was not a major crash contributing factor. Using the random parameter model, the three congestion measures were compared. It was found that all congestion indicators had similar effects while Congestion Index (CI) derived from MVDS data was a better congestion indicator for safety analysis. Also, analyses showed that the segments with higher congestion intensity could not only increase property damage only (PDO) crashes, but also more severe crashes. In addition, the issues regarding the necessity to incorporate specific congestion indicator for congestion's effects on safety and to take care of the multicollinearity between explanatory variables were also discussed. By including a specific congestion indicator, the model performance significantly improved. When comparing models with and without ridge regression, the magnitude of the coefficients was altered in the existence of multicollinearity. These conclusions suggest that the use of appropriate congestion measure and consideration of multicolilnearity among the variables would improve the models and our understanding about the effects of congestion on traffic safety.
Publication Date
3-1-2016
Publication Title
Accident Analysis and Prevention
Volume
88
Number of Pages
124-137
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1016/j.aap.2015.12.001
Copyright Status
Unknown
Socpus ID
84952802176 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84952802176
STARS Citation
Shi, Qi; Abdel-Aty, Mohamed; and Lee, Jaeyoung, "A Bayesian Ridge Regression Analysis Of Congestion'S Impact On Urban Expressway Safety" (2016). Scopus Export 2015-2019. 2626.
https://stars.library.ucf.edu/scopus2015/2626