Traffic accident reconstruction is essential for understanding collision dynamics and supporting forensic, insurance, and legal investigations. This paper presents a unified software platform that integrates heterogeneous data sources, including Event Data Recorder (EDR) telemetry, Light Detection and Ranging (LiDAR), drone photogrammetry, and Cooperative Intelligent Transport Systems (ITS) data, into a single analytical environment. The proposed solution fuses spatial, temporal, and contextual information to enable consistent, evidence-based 3D reconstruction of crash scenarios. A prototype implementation demonstrates the feasibility of multi-source data harmonization and visualization, supporting reproducible and objective analysis. The architecture, data flow, and functional results are described, highlighting the system’s potential contribution to more transparent and data-driven accident investigation. • Integration of heterogeneous data sources (EDR, LiDAR, drone photogrammetry, and ITS data) into a unified analytical platform. • Novel multi-source data-fusion methodology ensuring spatial–temporal synchronization and contextual consistency. • High-fidelity 3D visualization designed for forensic, insurance, and legal applications. • Reduction of human interpretation and dependence on assumptions through objective, sensor-derived data.
Building similarity graph...
Analyzing shared references across papers
Loading...
Bruno M. Correia
Pieter H.H. Aparício
Rogério M.G. Martins
Transportation Engineering
Instituto Politécnico de Leiria
Building similarity graph...
Analyzing shared references across papers
Loading...
Correia et al. (Sat,) studied this question.
www.synapsesocial.com/papers/69a7611fc6e9836116a2ec15 — DOI: https://doi.org/10.1016/j.treng.2026.100425
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: