A Multivariate Copula-Based Macro-Level Crash Count Model
| Autor: | Naveen Eluru, Tammam Nashad, Salah Uddin Momtaz, Shamsunnahar Yasmin |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Multivariate copula
Estimation Truck 050210 logistics & transportation Multivariate statistics Traffic analysis Computer science Mechanical Engineering 05 social sciences Crash Variable (computer science) Crash frequency 0502 economics and business Statistics Macro level 0501 psychology and cognitive sciences Road traffic 050107 human factors Civil and Structural Engineering |
| Zdroj: | Transportation Research Record: Journal of the Transportation Research Board. 2672:64-75 |
| ISSN: | 2169-4052 0361-1981 |
| DOI: | 10.1177/0361198118801348 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|