Crash Prediction Models for Rural Motorways
| Autor: | Renato Lamberti, Lucio Colantuoni, Alfonso Montella |
|---|---|
| Přispěvatelé: | Montella, Alfonso, Lamberti, Renato, Colantuoni, L |
| Rok vydání: | 2008 |
| Předmět: |
Generalized linear model
Engineering motorways Injury severity Negative binomial distribution Poison control Sample (statistics) Context (language use) Canosa (Italy) negative binomial distribution Two lane highway Ramps (Interchanges) Transport engineering Consistency (statistics) Statistics Highway design Rural area Civil and Structural Engineering Accident risk forecasting Naples (Italy) business.industry Mechanical Engineering stepwise procedure design consistency Crash prediction models Crash injurie Interchange Variable (computer science) Highway safety Italy Interstate highway Fatalitie crash prediction model business Traffic volume |
| Zdroj: | Transportation Research Record: Journal of the Transportation Research Board. 2083:180-189 |
| ISSN: | 2169-4052 0361-1981 |
| Popis: | In this paper, crash prediction models for estimating the safety of rural motorways are presented. Separate models were developed for total crashes and severe (fatal plus all injury) crashes. Generalized linear modeling techniques were used to fit the models, and a negative binomial distribution error structure was assumed. The study used a sample of 2,245 crashes (728 severe crashes) that occurred from 2001 to 2005 on Motorway A16 between Naples and Canosa in Italy. Many characteristics of the motorway were substandard. The motorway allowed investigation of a wide spectrum of geometric configurations. The models were developed by the stepwise-forward procedure with explanatory variables related to traffic volume and composition, horizontal alignment, vertical alignment, design consistency, sight distance, roadside context, cross section, speed limits, and interchange ramps. The decision to keep a variable in the model was based on two criteria. The first was whether the t-ratio of the variable's estimated coefficient was significant at the 5% level. The second criterion was related to the improvement of goodness-of-fit measures of the model that includes that variable. Goodness-of-fit measures were the parameter Rα2 and Akaike's information criterion. All the parameters have a logical and expected sign. The most important result was that design consistency measures significantly affected road safety, not only on two-lane rural highways, but also on motorways. |
| Databáze: | OpenAIRE |
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