Joint Modeling of Pedestrian and Bicycle Crashes: Copula-Based Approach. Issue 1 (2016)

• Record Type: Journal Article
• Authors: Nashad, Tammam; Yasmin, Shamsunnahar; Eluru, Naveen; Lee, Jaeyoung; Abdel-Aty, Mohamed A.
• Is Part Of: Transportation research record. Volume 2601:Issue 1(2016)
• Page Start: 119
• Page End: 127
• Publication Date: 2016
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.3141/2601-14
• Languages: English
• ISSNs: 0361-1981

Abstract

This study contributes to the safety literature on active mode transportation safety by using a copula-based model for crash frequency analysis at a macro level. Most studies in the transportation safety area identify a single count variable (such as vehicular, pedestrian, or bicycle crash counts) for a spatial unit at a specific period and study the impact of exogenous variables. Although the traditional count models perform adequately in the presence of a single count variable, these approaches must be modified to examine multiple dependent variables for each study unit. The presented research developed a multivariate model by adopting a copula-based bivariate negative binomial model for pedestrian and bicycle crash frequency analysis. The proposed approach accommodates potential heterogeneity (across zones) in the dependency structure. The formulated models were estimated with pedestrian and bicycle crash count data at the statewide traffic analysis zone level for the state of Florida for 2010 through 2012. The statewide traffic analysis zone level variables considered in the analysis included exposure measures, socioeconomic characteristics, road network characteristics, and land use attributes. A policy analysis was conducted—along with a representation of hot spot identification—to illustrate the applicability of the proposed model for planning purposes. The development of such spatial profiles allows planners to identify high-risk zones for screening and subsequent treatment identification.