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%.
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Acknowledgements
This work is supported by the Government and the Unidades Tecnologicas de Santander (project 879/2017). Thanks to the SC3-UIS Lab, the Colifri association, the CITI Lab at INSA Lyon and the CATAI workgroup - where this project was already discussed and received feedbacks.
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Puentes, M., Novoa, D., Nivia, J.M.D., Hernández, C.J.B., Carrillo, O., Mouël, F.L. (2022). Datacentric Analysis to Reduce Pedestrians Accidents: A Case Study in Colombia. In: Corchado, J.M., Trabelsi, S. (eds) Sustainable Smart Cities and Territories. SSCTIC 2021. Lecture Notes in Networks and Systems, vol 253. Springer, Cham. https://doi.org/10.1007/978-3-030-78901-5_15
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DOI: https://doi.org/10.1007/978-3-030-78901-5_15
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