TRAJES: um arcabouço para geração e avaliação de modelos de predição de trajetórias veiculares

• Ano de defesa: 2024
• Autor(a) principal: Krohling, Breno Aguiar
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Mestrado em Informática; Centro Tecnológico; UFES; Programa de Pós-Graduação em Informática
• Palavras-chave em Português: Predição de trajetórias; Aprendizado de máquina; Mobilidade urbana; Ciência da Computação
• Link de acesso: http://repositorio.ufes.br/handle/10/18322

Vehicle trajectories prediction enables traffic management optimization and facilitates solutions that require knowledge of where a vehicle, or its driver, is heading. To use such information on a large scale, it is necessary to employ models capable of generalizing complex movement patterns across an entire region or city. To achieve this, an end-to-end framework called TRAJES (Trajectory Estimator) was proposed to generate models from urban vehicle mobility data, using trajectories consisting only of geolocation information. The model generation and selection are based on concrete metrics, such as the actual distance between predicted and real points, and the proposed Hit Race Accuracy metric, which evaluates model performance based on regions of interest throughout the entire city. The framework was employed to create models capable of predicting vehicle positions in both the near and distant future, tested on real-world datasets collected in the cities of Porto and San Francisco. The results demonstrated the ability to generalize effective models for both prediction scenarios, indicating their viability as an intermediate step for external solutions, particularly those requiring knowledge of a vehicle’s future region.