01657478

Component

https://doi.org/10.1177/0361198118801348

https://doi.org/10.1177/0361198118801348

The current study contributes to safety literature both methodologically and empirically by developing a macro-level multivariate copula-based crash frequency model for crash counts. The multivariate model accommodates for the impact of observed and unobserved effects on zonal level crash counts of different road user groups including car, light truck, van, other motorized vehicle (including truck, bus and other vehicles), and non-motorists (including pedestrians and cyclists). The proposed model is estimated using Statewide Traffic Analysis Zone (STAZ) level road traffic crash data for the state of Florida. A host of variable groups including land-use characteristics, roadway attributes, traffic characteristics, socio-economic characteristics and demographic characteristics are considered. The model estimation results illustrate the applicability of the proposed framework for multivariate crash counts. Model estimation results are further augmented by evaluation of predictive performance and policy analysis.

18-01587

English

pp 64-75

2018

Transportation Research Record: Journal of the Transportation Research Board

Print

References (41) ; Tables (3)

Crash data; Crash rates; Crash risk forecasting; Multivariate analysis; Policy analysis; Traffic analysis zones; Traffic crashes; Traffic safety; Vehicle mix

Copula-based models

Florida

Data and Information Technology; Highways; Motor Carriers; Pedestrians and Bicyclists; Planning and Forecasting; Safety and Human Factors; Vehicles and Equipment

TRIS, TRB, ATRI

Jan 8 2018 10:24AM

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