Successful Implementation of AI & IoT-based Solutions in Metro Systems

Introduction 

In the last decade, urban infrastructure growth has been incredible worldwide, especially in developing countries like India. The heightened urban infrastructure also increased the population in emerging urban centres. The rapid growth of urbanisation has placed tremendous pressure on metro systems. This necessitated innovative solutions to ensure efficient, safe, and reliable public transportation. The integration of technologies such as Artificial Intelligence (AI) and Internet of Trains (IoT) into metro infrastructure has not only increased efficiency and frequency but also reduced human errors through the automation of critical systems such as rolling stock. This paper explores the role of IoT and AI in transportation infrastructure, particularly Metro Systems. With cities striving to build smarter transit networks, AI and IoT have become indispensable in creating intelligent metro systems that can adapt to the demands of modern urban mobility.

Internet of Trains (IoT)

The concept of the Internet of Trains emerged from the Internet of Things. It refers to the integration of intelligent onboard devices and cloud-based applications that facilitate improved communication, control, and data exchange within railway networks. This digital interaction allows for real-time monitoring and management of train operations, infrastructure, and passenger services.

Key Components 

• Connected Devices: Trains are equipped with various sensors and devices that collect data on performance, location, and environmental conditions.
• Cloud Computing: Data from trains is transmitted to cloud-based platforms for analysis, which enables real-time decision-making.
• Communication Networks: High-speed communication systems (such as LTE and 5G) facilitate seamless data exchange between trains, control centres, and infrastructure.

The Need for AI & IoT in Metro Systems

Metro systems are lifelines to an urban area, but traditional systems always seem to struggle to manage increasing passenger numbers, inefficient operations, and maintenance-related issues. 

Challenges in Traditional Metro Systems: 

1. Overcrowding

Overcrowding is the most common problem of the metro, especially during rush hour. Tozai, Japan’s metro line, has a congestion rate of 199%. A 100% congestion rate would mean that there is not a single free seat in the train; 199% is almost double this capacity. New Delhi is to emerge as the world’s largest agglomeration with a mind-boggling population of 37.2 million people by 2028. In such a scenario where expanding the infrastructure is tough and costlier, the integration of advanced technology like IoT may make the metro systems even more efficient by increasing the efficiency & safety of their operations.

2. Unpredictable Breakdowns

Mechanical or electrical failures in trains or infrastructure lead to service interruptions. Frequent breakdowns can strain operational resources and require additional maintenance efforts. These issues can be addressed by implementing IoT technologies as they provide real-time monitoring of assets which helps to identify the issues before the fault escalates.

3. Higher Operational Costs

The frequent breakdowns increase the operational costs of metro systems. Unexpected breakdowns necessitate immediate repairs, which can be costly. The cost of maintaining an inventory of spare parts also increases as systems strive to minimise downtime.

Key Applications AI & IoT in Metro Systems

1. Predictive Maintenance: IoT sensors are applied to critical components, which are tracks, wheels, and signalling systems to obtain real-time data on their condition. AI algorithms analyze such data for anomalies and predictive potential in issues, hence allowing maintenance on time. Machine learning analyzes historical data and real-time data, identifying patterns in maintenance activities and when specific components will fail. This approach has reduced the amount of downtime associated with these systems, averted some unexpected failures, and maximized resource usage. Integrating IoT with AI enables metro systems to be reliable, efficient, and at low maintenance cost, making them operate more smoothly and safely.

Example: Metro systems in Hong Kong use predictive maintenance to avoid unexpected breakdowns, and reduce delays and operational disruptions. Similarly, Delhi Metro’s Phase 4 is set for the integration of artificial intelligence (AI) into its operations and project management.

2. Smart Ticketing and Fare Collection: AI makes it possible to have dynamic fare pricing, while IoT allows for contactless payments via mobile apps, smart cards, or digital wallets. This provides a seamless experience for passengers and reduces the time spent in queues.

Example: London Underground has an Oyster card system that integrates IoT for efficient and smooth fare collection.

3. Traffic and Crowd Management: Traffic and Crowd Management: Using data from IoT-enabled cameras and sensors, the AI-powered tools analyze the flow of passengers. Therefore, operators can change train frequencies and reroute traffic in real time to prevent bottlenecks during peak hours.

Example: Hong Kong’s Mass Transit Railway or MTR uses AI to forecast congestion levels and move more trains proactively into that area.

4. Energy Efficiency: AI-driven systems analyze train schedules and passenger density to optimize energy consumption. IoT-enabled devices manage station utilities such as lighting, air conditioning, and escalators based on real-time usage. 

AI-Enabled Metro Systems

1. Enhanced Passenger Travel Experience

Artificial Intelligence (AI) has the potential to upgrade passenger experiences. It can offer smarter, safer, and more accessible train journeys. Here’s how AI-driven technologies transform train travel:

• Accessibility Support: AI-powered computer vision systems can enhance the boarding process for passengers with mobility challenges. For instance, object detection algorithms can identify wheelchair users and guide them to designated accessible sections of the train. 
• Empty Train Check: At the final station of a metro or train network, AI-driven computer vision systems can perform an “empty train check” to ensure no passengers remain onboard. These systems can scan each compartment and promptly alert station staff if any individuals are still inside, thus improving operational efficiency and passenger safety.
• Abandoned Luggage Management: Abandoned luggage poses a potential security threat and inconvenience to passengers. AI-based computer vision systems can identify unattended items, analyze their location, and flag them to security teams. 

2. AI for Last-Mile Connectivity

Artificial Intelligence (AI) offers practical solutions for improving last-mile connectivity, helping passengers transition smoothly from metro stations to their destinations. By integrating AI into bike-sharing stations, ride-hailing services, and shuttle systems, transport networks can make these services more efficient and responsive.

1. Optimising Resource Availability

AI systems can analyze real-time data to predict demand for last-mile services, ensuring that resources like bikes, shuttles, or ride-hailing vehicles are available when needed.

2. Applications of YOLOv8 in Last-Mile Solutions

AI models like Ultralytics YOLOv8 contribute to last-mile solutions through real-time object detection and tracking. YOLOv8’s capabilities include:

• Passenger movement tracking: Monitor the flow of passengers to assess demand for last-mile services.
• Congestion analysis: Identify crowd patterns through video feeds from station cameras.
• Resource planning: Count passengers exiting a station to predict requirements for nearby services.

3. Implementing AI for Track Inspection

AI enhances track inspection and maintenance by streamlining monitoring processes. Systems like the Duos Technologies Railcar Inspection Portal (RIP) use AI to capture and analyse 360-degree images of train cars at regular operating speeds. These systems identify potential maintenance issues and provide timely notifications to railway personnel for prompt intervention.

4. Energy Management

AI plays a crucial role in optimising energy usage in metro system.  For instance, Metro de Madrid (Spain) employs an AI-based system to improve ventilation efficiency. The system manages 891 ventilation fans, which consume up to 80 gigawatt-hours of energy annually, using an optimisation algorithm inspired by the foraging behaviour of bee colonies. This approach has enabled Metro de Madrid to reduce energy costs for ventilation by 25% and cut CO2 emissions by approximately 1,800 tons annually. 

Concerns Associated with AI-Driven Systems

AI has brought several benefits to metro systems. It enhances efficiency by ensuring that trains run on schedule. It minimises delays, and optimise operations for smoother services. AI also improves safety by providing continuous monitoring, while predictive maintenance helps identify potential issues before they lead to accidents or malfunctions. However AI-driven systems face some challenges which are as follows:

• Data Privacy: Collecting passenger data raises privacy concerns. Proper measures are needed to protect personal information.
• Integration with Existing Infrastructure: AI systems may require changes tp the current metro infrastructure, which can be complex and costly.
• High Implementation Costs: The initial cost of installing AI technology can be high, which may be a barrier for some metro systems.
• Need for Skilled Professionals: Implementing and maintaining AI systems requires skilled professionals, which may be a challenge to find and retain.

Case Studies of Successful AI and IoT Applications in Metro Rail Systems

1. China’s AI-Driven Train Operations: China’s high-speed rail network uses AI to manage train scheduling and operations which contributes to improving efficiency and punctuality across the extensive network.
2. India’s Smart Railway Stations: Indian Railways is integrating IoT and AI at major stations to enhance passenger services, including automated ticketing and real-time platform information.
3. Japan’s AI-Powered Safety Systems: Japan employs AI-driven drones and robots for safety inspections, which enables more accurate monitoring of track conditions and reduces human error.
4. US Freight Railways Adopting IoT: In the United States, freight rail companies use IoT to track cargo, monitor train health, and optimise fuel usage, which results in cost savings and improved operational efficiency.

Conclusion

The integration of Artificial Intelligence and the Internet of Trains introduces practical solutions to challenges faced by metro systems, such as overcrowding, maintenance issues, and energy efficiency. Predictive maintenance, automated ticketing, energy management, and crowd control are some of the key applications that improve operational efficiency and passenger services.

While these developments have been instrumental, there are still challenges: data privacy, the cost of implementation, and the need for highly skilled professionals to manage the systems. Case studies from China, and Japan are examples of how these technologies can be effectively implemented.

As urbanisation continues to grow, adopting AI and IoT in metro systems can help improve efficiency and reliability, but careful planning and addressing associated concerns will be crucial for sustainable progress in public transportation.