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%.

翻译：自2012年以来,在布卡拉曼加-哥伦比亚的案例研究中,179名行人死于汽车事故,另有2873名行人受伤。每天至少有1名路人卷入一场悲剧。知道减少事故的原因至关重要,使用系统动力来复制碰撞事件对于防止进一步事故至关重要。这项工作通过降低城市意外事故率和提出新的安全政策来进行模拟以拯救生命。模拟输入是城市某些地区的录像记录。对图像的深度学习分析导致现场不同物体的分解,互动模型确定了行人或车辆行为中普遍存在的主要原因。执行我们模拟所证实的第一个最有效的安全政策将是将事故率降低80%之前的特定地点建立速度加速。