Identifying crash type propensity using real-time traffic data on freeways |
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Authors: | Christoforou Zoi Cohen Simon Karlaftis Matthew G |
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Institution: | a Université Paris-Est, Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), «Le Descartes 2», 2 rue de la Butte Verte, 93 166 Noisy-Le-Grand, Franceb Department of Transportation Planning and Engineering, School of Civil Engineering, National Technical University of Athens, 5, Iroon Polytechniou, 15773 Zografou Campus, Greece |
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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. |
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Keywords: | Traffic accident Crash type Multivariate Probit Freeway |
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