Exploring the forecasting approach for road accidents: Analytical measures with hybrid machine learning - INRIA - Institut National de Recherche en Informatique et en Automatique Accéder directement au contenu
Article Dans Une Revue Expert Systems with Applications Année : 2021

Exploring the forecasting approach for road accidents: Analytical measures with hybrid machine learning

Résumé

Urban traffic forecasting models generally follow either a Gaussian Mixture Model (GMM) or a Support Vector Classifier (SVC) to estimate the features of potential road accidents. Although SVC can provide good performances with less data than GMM, it incurs a higher computational cost. This paper proposes a novel framework that combines the descriptive strength of the Gaussian Mixture Model with the high-performance classification capabilities of the Support Vector Classifier. A new approach is presented that uses the mean vectors obtained from the GMM model as input to the SVC. Experimental results show that the approach compares very favorably with baseline statistical methods.
Fichier principal
Vignette du fichier
Exploring the Forecasting Approach for Road Accidents; An Analytical measures with Hybrid ML_v2.pdf (1.34 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03119076 , version 1 (26-01-2021)

Identifiants

Citer

Mamoudou Sangare, Sharut Gupta, Samia Bouzefrane, Soumya Banerjee, Paul Mühlethaler. Exploring the forecasting approach for road accidents: Analytical measures with hybrid machine learning. Expert Systems with Applications, 2021, 167, pp.113855. ⟨10.1016/j.eswa.2020.113855⟩. ⟨hal-03119076⟩
183 Consultations
480 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More