The paper presents a digital twin mobility profiling (DTMP) framework for intelligent transportation systems. It uses the concept of a ‘digital twin’, a virtual representation of a network, to simulate its behaviour. This framework is designed to learn node profiles on a mobility network. The process of extracting an entity’s mobility patterns from data is referred to as mobility profiling. It may aid our understanding of urban transportation, provide accurate predictions, and increase the efficiency of decision-making. The effectiveness of DTMP was tested with real-world datasets.
Publication date: 7 Feb 2024
Project Page: Not specified
Paper: https://arxiv.org/pdf/2402.03750
