The importance of flow composition in real-time crash prediction
Author
dc.contributor.author
Basso Sotz, Franco
Author
dc.contributor.author
Basso Sotz, Leonardo
Author
dc.contributor.author
Pezoa, Raúl
Admission date
dc.date.accessioned
2020-05-13T12:49:38Z
Available date
dc.date.available
2020-05-13T12:49:38Z
Publication date
dc.date.issued
2020
Cita de ítem
dc.identifier.citation
Accident Analysis and Prevention 137 (2020) 105436
es_ES
Identifier
dc.identifier.other
10.1016/j.aap.2020.105436
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/174685
Abstract
dc.description.abstract
Previous real-time crash prediction models have scarcely used data disaggregated by vehicle type such as light, heavy and motorcycles. Thus, little effort has been made to quantify the impact of flow composition variables as crash precursors. We analyze the advantages of having access to this data by analyzing two scenarios, namely, with aggregated and disaggregated data. For each case, we build Logistics Regressions and Support Vector Machines models to predict accidents in a major urban expressway in Santiago, Chile. Our results show that having access to disaggregated data by vehicle type increases the prediction power up to 30 % providing, at the same time, much better intuition about the actual traffic conditions that may lead to accidents. These results may be useful when evaluating technology investments and developments in urban freeways.
es_ES
Patrocinador
dc.description.sponsorship
Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)
CONICYT PIA/BASAL
AFB180003
Fondecyt1191010