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- W2050202488 abstract "Introduction: We examine the effects of various traffic parameters on type of road crash. Method: Multivariate probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de-France region, France. Results: Empirical findings indicate that crash type can almost exclusively be defined by the prevailing traffic conditions shortly before its occurrence. Rear-end crashes involving two vehicles were found to be more probable for relatively low values of both speed and density, rear-end crashes involving more than two vehicles appear to be more probable under congested conditions, while single-vehicle crashes appear to be largely geometry-dependent. Impact on Industry: Results could be integrated in a real-time traffic management application." @default.
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- W2050202488 date "2011-02-01" @default.
- W2050202488 modified "2023-09-27" @default.
- W2050202488 title "Identifying crash type propensity using real-time traffic data on freeways" @default.
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- W2050202488 doi "https://doi.org/10.1016/j.jsr.2011.01.001" @default.
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