Toward data-driven Digital Twins for regional transportation networks: A scalable traffic forecasting study
Open Access
- 29 June 2026
- journal article
- Published by Elsevier in Future Generation Computer Systems
- Vol. 185
Abstract
No abstract availableFunding Information
- HORIZON EUROPE Framework Programme
This publication has 29 references indexed in Scilit:
- A scalable and modular open-source stack for computing continuum digital twinsFuture Generation Computer Systems, 2026
- Digital twins in public bus transport: A systematic literature review of architectures, intelligence, and interactionFuture Generation Computer Systems, 2026
- On-device AI and digital twins: A synergistic approach to intelligent cyber-physical systemsFuture Generation Computer Systems, 2025
- Dynamic multi-graph spatio-temporal learning for citywide traffic flow prediction in transportation systemsChaos, Solitons & Fractals, 2025
- One-step vs horizon-step training strategies for multi-step traffic flow forecasting with direct particle swarm optimization grid search support vector regression and long short-term memoryExpert Systems with Applications, 2024
- An Overview Based on the Overall Architecture of Traffic ForecastingData Science and Engineering, 2024
- Towards a service-oriented cyber–physical systems of systems for smart city mobility applicationsFuture Generation Computer Systems, 2018
- A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competitionExpert Systems with Applications, 2012
- Extremely randomized treesMachine Learning, 2006
- Multilayer perceptrons for classification and regressionNeurocomputing, 1991