Real-time public transport optimization: The future is here with digital twins

The adoption of digital twin technology is expected to reshape public transportation in the coming years, making urban mobility smarter, greener, and more efficient. As cities around the world focus on reducing traffic congestion and lowering carbon emissions, DTs will play a critical role in advancing sustainable urban mobility.

Public transportation is the backbone of urban mobility, connecting millions of people to work, school, and essential services every day. However, transit systems face growing challenges, including traffic congestion, fluctuating passenger demand, operational inefficiencies, and environmental concerns. Cities are rapidly expanding, making the need for smarter, data-driven solutions more critical than ever.

A recent study, "Leveraging Digital Twin Technology for Sustainable and Efficient Public Transportation", by Babin Manandhar, Kayode Dunkel Vance, Danda B. Rawat, and Nadir Yilmaz, published in Applied Sciences (2025, 15, 2942), explores how digital twin (DT) technology is revolutionizing public transit. By creating real-time, virtual replicas of transportation systems, digital twins can simulate, predict, and optimize transit operations, offering a game-changing approach to urban mobility. This study examines the potential of DTs in improving efficiency, enhancing passenger experiences, and supporting sustainability in public transportation networks.

Digital twin technology: A new era for public transit

Digital twin technology is an advanced simulation tool that creates a virtual model of physical systems - in this case, public transportation networks. These digital replicas integrate real-time data from GPS, sensors, traffic reports, and passenger feedback, allowing transit operators to monitor and optimize bus routes, adjust schedules dynamically, and predict potential breakdowns or delays.

Traditional public transit operates on fixed schedules and static routes, making it difficult to adapt to real-time challenges such as sudden traffic congestion, unpredictable weather conditions, or spikes in passenger demand. With digital twins, transit agencies can analyze past data, predict future conditions, and make real-time adjustments to optimize service delivery.

The study highlights how DTs have already transformed industries like manufacturing and smart city planning, but their full potential in public transportation remains underexplored. By modeling entire transit networks - including buses, trains, and passenger flows - DTs can help cities minimize inefficiencies, reduce emissions, and improve the commuter experience.

One example of DT’s effectiveness is dynamic route optimization. Instead of relying on fixed schedules, digital twins use AI-powered analytics to identify congestion hotspots and adjust bus routes and traffic signals accordingly. This can significantly reduce travel time for commuters while improving overall road efficiency.

Improving public transportation with real-time simulations

One of the most powerful applications of digital twin technology in public transportation is real-time simulation and predictive analytics. Cities like Singapore, London, and New York have already begun experimenting with smart transit systems that analyze live traffic conditions, passenger density, and energy consumption to optimize bus and train operations.

The study explains how digital twins process live data from various sources, including road sensors, surveillance cameras, and commuter apps, to predict and prevent potential disruptions. For example, if a sudden traffic jam is detected, the digital twin system can instantly recommend alternative routes, ensuring buses and trains stay on schedule and avoid delays.

Additionally, predictive maintenance is a major benefit of digital twins. Instead of waiting for buses or trains to break down, DT technology can detect early warning signs of mechanical issues and alert maintenance teams before problems escalate. This reduces downtime, lowers operational costs, and ensures a smoother experience for passengers.

The study also highlights the role of digital twins in managing passenger demand. By analyzing historical and real-time ridership data, DTs can help transit agencies allocate resources more effectively, such as increasing the number of buses during peak hours or reducing service in underused areas. This data-driven decision-making improves efficiency while lowering fuel consumption and emissions.

Challenges and solutions in digital twin implementation

While digital twins offer tremendous potential, their implementation in public transportation comes with challenges. The study identifies several key obstacles, including:

1. Scalability and Data Integration: Public transit systems generate massive amounts of data from multiple sources, including GPS tracking, passenger apps, and weather reports. Integrating this data into a single, real-time digital twin model requires robust IT infrastructure and cloud computing resources.

2. Interoperability Issues: Many transit agencies rely on legacy systems that may not be compatible with modern digital twin frameworks. The study suggests using standardized communication protocols and blockchain-based security measures to improve data-sharing across different platforms.

3. High Costs and Infrastructure Investment: Implementing digital twins requires significant upfront investment in hardware, software, and skilled personnel. However, the long-term benefits - including cost savings from optimized fuel use, reduced maintenance costs, and improved commuter satisfaction - make it a worthwhile investment.

4. Cybersecurity and Data Privacy: With digital twins collecting real-time passenger data, there is an increased risk of cyber threats and privacy breaches. The study recommends strong encryption, multi-layered security frameworks, and compliance with data protection regulations like GDPR to safeguard commuter information.

Despite these challenges, the study argues that the benefits of digital twins far outweigh the difficulties, particularly as technology advances and cloud-based computing becomes more accessible.

The future of smart, sustainable public transit

The adoption of digital twin technology is expected to reshape public transportation in the coming years, making urban mobility smarter, greener, and more efficient. As cities around the world focus on reducing traffic congestion and lowering carbon emissions, DTs will play a critical role in advancing sustainable urban mobility.

The study envisions next-generation transit systems where digital twins are fully integrated with AI, IoT, and 5G networks, enabling:

• Real-time coordination between different transportation modes (buses, trains, ride-sharing, and e-scooters) to create a seamless commuter experience.
• Autonomous vehicle management, where AI-powered digital twins monitor and optimize self-driving buses and shuttles in real time.
• Energy-efficient transit networks that prioritize low-carbon transportation options and dynamically adjust operations based on environmental conditions.

Ultimately, digital twins represent a paradigm shift in public transit, moving away from fixed, inflexible schedules toward dynamic, data-driven transportation systems that prioritize efficiency, sustainability, and passenger convenience.

While challenges such as cost, scalability, and cybersecurity remain, the benefits of digital twins - including increased efficiency, optimized resources, and better environmental outcomes - make them a vital tool for the future of smart cities.