Predicting passenger flows in rail transport systems plays an important role for traffic management centers to make fast decisions during service disruptions. This paper presents an innovative cross-disciplinary approach based on digital twins, deep learning, and traffic simulation to predict the total number of passengers in each train stations and evaluate the impact of service disruptions across stations. First, we present a four-layer system architecture for building a digital twin which ingests real-time data streams, including train movements and timetable scheduling. Second, we deploy several deep learning models to predict the total number of passengers in each station using mobile data. The results showcase significant accuracy for recurrent versus non-recurrent traffic conditions even under severe large disruptions such as the COVID-19 travel restrictions. Our case study of the Sydney rail network demonstrates that the proposed digital twin powered by deep learning can provide more granular real-time insights into the impact on passengers, allowing rail operation centers to better mitigate service disruptions.
The convergence of wearable technologies and digital twin (DT) systems is transforming rehabilitation engineering, enabling continuous monitoring, personalized therapeutic interventions, and predictive modeling of patient recovery pathways. This review examines the growing role o…
The global burden of diabetes mellitus (DM) continues to escalate, posing significant challenges to healthcare systems worldwide. This study compares machine learning (ML) and deep learning (DL) methods, their hybrids, and ensemble strategies for predicting the health outcomes of…
As user requirements become increasingly complex, the demand for product personalization is growing, but traditional hardware-centric production relies on fixed procedures that lack the flexibility to support diverse requirements. Although bespoke manufacturing has been introduce…
In recent years, digital twin technology has demonstrated remarkable potential in intelligent transportation systems, leveraging its capabilities of high-precision virtual mapping and real-time dynamic simulation of physical entities. By integrating multi-source data, it construc…
Aim: Machine learning (ML) and deep learning (DL) predictive models have been employed widely in clinical settings. Their potential support and aid to the clinician of providing an objective measure that can be shared among different centers enables the possibility of building mo…
With the advancement of smart education and carbon neutrality goals, optimizing Indoor Environmental Quality (IEQ) while minimizing energy consumption is critical. Traditional PID or rule-based strategies struggle with the strong non-linearity and time delays of photothermal coup…