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Title: Análisis predictivo de accidentes de tráfico en la ciudad de Madrid
Author: Bejar Gladkowski, Antonio Adam
Tutor: Merino, David  
Others: Martí Pintanel, Javier
Abstract: Road accidents are a real issue that affects everyone, not only drivers, but also pedestrians. Every day, the types of vehicles and the volume of traffic are increasing, causing a greater impact on people's daily lives. For this reason, the development of an analytical and predictive model based on traffic accidents in the city of Madrid has been proposed, with the idea of creating a service that has a positive impact on society and helps to contribute to the reduction of accidents. The development of the project has been based on the analysis of the main factors that influence accidents: the driver's profile, type of vehicle, weather, road/trip and time of day. To this end, data mining techniques, probability analysis and machine learning techniques have been used. The result is presented as a web prototype with a dynamic dashboard and the ability to analyse a street or a route, obtaining the probability of accidents and injuries at points along the route on dynamic maps and supported by graphics. Both the results of the analyses carried out and the web application itself provide a better understanding of accident patterns and profiles, serving as a basis for future projects and can be used for accident prevention.
Keywords: accidents at work
machine learning
public safety
Document type: info:eu-repo/semantics/bachelorThesis
Issue Date: Jan-2023
Publication license:  
Appears in Collections:Bachelor thesis, research projects, etc.

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