Exploring the forecasting approach for road accidents: Analytical measures with hybrid machine learning - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Expert Systems with Applications Year : 2021

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

Abstract

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
Origin Files produced by the author(s)

Dates and versions

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

Identifiers

Cite

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⟩
191 View
496 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More