Skip to Main content Skip to Navigation
Conference papers

Datacentric analysis to reduce pedestrians accidents: A case study in Colombia

Abstract : Since 2012, in a case-study in Bucaramanga-Colombia, 179 pedestrians died in car accidents, and another 2873 pedestrians were injured. Each day, at least one passerby is involved in a tragedy. Knowing the causes to decrease accidents is crucial, and using system-dynamics to reproduce the collisions' events is critical to prevent further accidents. This work implements simulations to save lives by reducing the city's accidental rate and suggesting new safety policies to implement. Simulation's inputs are video recordings in some areas of the city. Deep Learning analysis of the images results in the segmentation of the different objects in the scene, and an interaction model identifies the primary reasons which prevail in the pedestrians or vehicles' behaviours. The first and most efficient safety policy to implement-validated by our simulations-would be to build speed bumps in specific places before the crossings reducing the accident rate by 80%.
Complete list of metadata

https://hal.inria.fr/hal-03188256
Contributor : Frédéric Le Mouël <>
Submitted on : Thursday, April 1, 2021 - 5:33:29 PM
Last modification on : Sunday, April 4, 2021 - 3:22:42 AM

Files

Identifiers

  • HAL Id : hal-03188256, version 1
  • ARXIV : 2104.00912

Collections

Citation

Michael Puentes, Diana Novoa, John Delgado Nivia, Carlos Barrios Hernández, Oscar Carrillo, et al.. Datacentric analysis to reduce pedestrians accidents: A case study in Colombia. International Conference on Sustainable Smart Cities and Territories (SSCt2021), Apr 2021, Doha, Qatar. ⟨hal-03188256⟩

Share

Metrics

Record views

13

Files downloads

200